RESEARCH ARCHIVE

'Freire combines a compassion for the wretched of the earth with an intellectual and practical confidence and personal humility...Most of all he has a vision of man.' Times Higher Educational Supplement Paulo Freire (1921-97) was an educationalist based in Brazil and became the most influential writer and thinker on education in the late twentieth century. His seminal work Pedagogy of the Oppressed has sold almost 1 million copies. Education for Critical Consciousness is the main statement of Freire's revolutionary method of education. It takes the life situation of the learner as its starting point and the raising of consciousness and the overcoming of obstacles as its goals. For Freire, man's striving for his own humanity requires the changing of structures which dehumanise both the oppressor and the oppressed, rather than therapy.

Large intervening non-coding RNAs (lincRNAs) are pervasively transcribed in the genome yet their potential involvement in human disease is not well understood. Recent studies of dosage compensation, imprinting, and homeotic gene expression suggest that individual lincRNAs can function as the interface between DNA and specific chromatin remodelling activities. Here we show that lincRNAs in the HOX loci become systematically dysregulated during breast cancer progression. The lincRNA termed HOTAIR is increased in expression in primary breast tumours and metastases, and HOTAIR expression level in primary tumours is a powerful predictor of eventual metastasis and death. Enforced expression of HOTAIR in epithelial cancer cells induced genome-wide re-targeting of Polycomb repressive complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2. Conversely, loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings indicate that lincRNAs have active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy.

BackgroundConsciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?Presentation of the hypothesisThis paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.Testing the hypothesisThe information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness.Implications of the hypothesisThe theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.

Reports of environmental and human health impacts of per‐ and polyfluoroalkyl substances (PFAS) have greatly increased in the peer‐reviewed literature. The goals of the present review are to assess the state of the science regarding toxicological effects of PFAS and to develop strategies for advancing knowledge on the health effects of this large family of chemicals. Currently, much of the toxicity data available for PFAS are for a handful of chemicals, primarily legacy PFAS such as perfluorooctanoic acid and perfluorooctane sulfonate. Epidemiological studies have revealed associations between exposure to specific PFAS and a variety of health effects, including altered immune and thyroid function, liver disease, lipid and insulin dysregulation, kidney disease, adverse reproductive and developmental outcomes, and cancer. Concordance with experimental animal data exists for many of these effects. However, information on modes of action and adverse outcome pathways must be expanded, and profound differences in PFAS toxicokinetic properties must be considered in understanding differences in responses between the sexes and among species and life stages. With many health effects noted for a relatively few example compounds and hundreds of other PFAS in commerce lacking toxicity data, more contemporary and high‐throughput approaches such as read‐across, molecular dynamics, and protein modeling are proposed to accelerate the development of toxicity information on emerging and legacy PFAS, individually and as mixtures. In addition, an appropriate degree of precaution, given what is already known from the PFAS examples noted, may be needed to protect human health. Environ Toxicol Chem 2021;40:606–630. © 2020 SETAC

Here we summarize our present approach to the problem of consciousness. After an introduction outlining our general strategy, we describe what is meant by the term 'framework' and set it out under ten headings. This framework offers a coherent scheme for explaining the neural correlates of (visual) consciousness in terms of competing cellular assemblies. Most of the ideas we favor have been suggested before, but their combination is original. We also outline some general experimental approaches to the problem and, finally, acknowledge some relevant aspects of the brain that have been left out of the proposed framework.

There have been a number of advances in the search for the neural correlates of consciousness — the minimum neural mechanisms sufficient for any one specific conscious percept. In this Review, we describe recent findings showing that the anatomical neural correlates of consciousness are primarily localized to a posterior cortical hot zone that includes sensory areas, rather than to a fronto-parietal network involved in task monitoring and reporting. We also discuss some candidate neurophysiological markers of consciousness that have proved illusory, and measures of differentiation and integration of neural activity that offer more promising quantitative indices of consciousness.

A novel member of the tumor necrosis factor (TNF) cytokine family, designated TRANCE, was cloned during a search for apoptosis-regulatory genes using a somatic cell genetic approach in T cell hybridomas. The TRANCE gene encodes a type II membrane protein of 316 amino acids with a predicted molecular mass of 35 kDa. Its extracellular domain is most closely related to TRAIL, FasL, and TNF. TRANCE is an immediate early gene up-regulated by TCR stimulation and is controlled by calcineurin-regulated transcription factors. TRANCE is most highly expressed in thymus and lymph nodes but not in nonlymphoid tissues and is abundantly expressed in T cells but not in B cells. Cross-hybridization of the mouse cDNA to a human thymus library yielded the human homolog, which encodes a protein 83% identical to the mouse ectodomain. HumanTRANCE was mapped to chromosome 13q14 while mouseTRANCE was located to the portion of mouse chromosome 14 syntenic with human chromosome 13q14. A recombinant soluble form of TRANCE composed of the entire ectodomain induced c-Jun N-terminal kinase (JNK) activation in T cells but not in splenic B cells or in bone marrow-derived dendritic cells. These results suggest a role for this TNF-related ligand in the regulation of the T cell-dependent immune response.

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific – it is what it is by how it differs from alternative experiences; integration says that it is unified – irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as “differences that make a difference” within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information ΦMax its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true “zombies” – unconscious feed-forward systems that are functionally equivalent to conscious complexes.

The science of consciousness has made great strides by focusing on the behavioural and neuronal correlates of experience. However, while such correlates are important for progress to occur, they are not enough if we are to understand even basic facts, for example, why the cerebral cortex gives rise to consciousness but the cerebellum does not, though it has even more neurons and appears to be just as complicated. Moreover, correlates are of little help in many instances where we would like to know if consciousness is present: patients with a few remaining islands of functioning cortex, preterm infants, non-mammalian species and machines that are rapidly outperforming people at driving, recognizing faces and objects, and answering difficult questions. To address these issues, we need not only more data but also a theory of consciousness—one that says what experience is and what type of physical systems can have it. Integrated information theory (IIT) does so by starting from experience itself via five phenomenological axioms: intrinsic existence, composition, information, integration and exclusion. From these it derives five postulates about the properties required of physical mechanisms to support consciousness. The theory provides a principled account of both the quantity and the quality of an individual experience (a quale), and a calculus to evaluate whether or not a particular physical system is conscious and of what. Moreover, IIT can explain a range of clinical and laboratory findings, makes a number of testable predictions and extrapolates to a number of problematic conditions. The theory holds that consciousness is a fundamental property possessed by physical systems having specific causal properties. It predicts that consciousness is graded, is common among biological organisms and can occur in some very simple systems. Conversely, it predicts that feed-forward networks, even complex ones, are not conscious, nor are aggregates such as groups of individuals or heaps of sand. Also, in sharp contrast to widespread functionalist beliefs, IIT implies that digital computers, even if their behaviour were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing.

Intertidal mangrove forests are a dynamic ecosystem experiencing rapid changes in extent and habitat quality over geological history, today and into the future. Climate and sea level have drastically altered mangrove distribution since their appearance in the geological record ∼75 million years ago (Mya), through to the Holocene. In contrast, contemporary mangrove dynamics are driven primarily by anthropogenic threats, including pollution, overextraction, and conversion to aquaculture and agriculture. Deforestation rates have declined in the past decade, but the future of mangroves is uncertain; new deforestation frontiers are opening, particularly in Southeast Asia and West Africa, despite international conservation policies and ambitious global targets for rehabilitation. In addition, geological and climatic processes such as sea-level rise that were important over geological history will continue to influence global mangrove distribution in the future. Recommendations are given to reframe mangrove conservation, with a view to improving the state of mangroves in the future.

TRANCE (tumor necrosis factor–related activation-induced cytokine) is a recently described member of the tumor necrosis factor superfamily that stimulates dendritic cell survival and has also been found to induce osteoclastic differentiation from hemopoietic precursors. However, its effects on mature osteoclasts have not been defined. It has long been recognized that stimulation of osteoclasts by agents such as parathyroid hormone (PTH) occurs through a hormonal interaction with osteoblastic cells, which are thereby induced to activate osteoclasts. To determine whether TRANCE accounts for this activity, we tested its effects on mature osteoclasts. TRANCE rapidly induced a dramatic change in osteoclast motility and spreading and inhibited apoptosis. In populations of osteoclasts that were unresponsive to PTH, TRANCE caused activation of bone resorption equivalent to that induced by PTH in the presence of osteoblastic cells. Moreover, osteoblast-mediated stimulation of bone resorption was abrogated by soluble TRANCE receptor and by the soluble decoy receptor osteoprotegerin (OPG), and stimulation of isolated osteoclasts by TRANCE was neutralized by OPG. Thus, TRANCE expression by osteoblasts appears to be both necessary and sufficient for hormone-mediated activation of mature osteoclasts, and TRANCE-R is likely to be a receptor for signal transduction for activation of the osteoclast and its survival.

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.

Substantial progress has been made over the past two decades in detecting, predicting and promoting recovery of consciousness in patients with disorders of consciousness (DoC) caused by severe brain injuries. Advanced neuroimaging and electrophysiological techniques have revealed new insights into the biological mechanisms underlying recovery of consciousness and have enabled the identification of preserved brain networks in patients who seem unresponsive, thus raising hope for more accurate diagnosis and prognosis. Emerging evidence suggests that covert consciousness, or cognitive motor dissociation (CMD), is present in up to 15–20% of patients with DoC and that detection of CMD in the intensive care unit can predict functional recovery at 1 year post injury. Although fundamental questions remain about which patients with DoC have the potential for recovery, novel pharmacological and electrophysiological therapies have shown the potential to reactivate injured neural networks and promote re-emergence of consciousness. In this Review, we focus on mechanisms of recovery from DoC in the acute and subacute-to-chronic stages, and we discuss recent progress in detecting and predicting recovery of consciousness. We also describe the developments in pharmacological and electrophysiological therapies that are creating new opportunities to improve the lives of patients with DoC. In this Review, the authors discuss recent progress in the detection and prediction of recovery of consciousness in patients with disorders of consciousness caused by severe brain injuries. They describe the ongoing development of pharmacological and electrophysiological therapies designed to enhance recovery. A common pathophysiological mechanism underlying disorders of consciousness (DoC) is the withdrawal of excitatory synaptic activity across the cerebrum produced by deafferentation or disfacilitation of neocortical, thalamic and striatal neurons. Recovery from coma involves various mechanisms, culminating in the restoration of excitatory neurotransmission across long-range corticocortical, thalamocortical and thalamostriatal connections. The re-emergence of consciousness is associated with a shift in patterns of neuronal activity across the corticothalamic system that can be measured with EEG, PET or resting-state functional MRI. Task-based functional MRI and EEG can reveal cognitive motor dissociation in up to 15–20% of patients who seem unresponsive on behavioural examination, and emerging evidence suggests that early detection of cognitive motor dissociation in the intensive care unit predicts 1-year functional outcomes. Amantadine is the only therapy that has been associated with the acceleration of recovery of consciousness in a randomized controlled trial of patients with subacute traumatic DoC, but multiple pharmacological and neuromodulatory therapies are now being tested. Emerging advances in diagnostic and prognostic techniques provide new opportunities to detect consciousness, monitor its recovery, elucidate its neuronal substrate and identify the therapeutic potential of promoting re-emergence of consciousness in a subset of patients with DoC. A common pathophysiological mechanism underlying disorders of consciousness (DoC) is the withdrawal of excitatory synaptic activity across the cerebrum produced by deafferentation or disfacilitation of neocortical, thalamic and striatal neurons. Recovery from coma involves various mechanisms, culminating in the restoration of excitatory neurotransmission across long-range corticocortical, thalamocortical and thalamostriatal connections. The re-emergence of consciousness is associated with a shift in patterns of neuronal activity across the corticothalamic system that can be measured with EEG, PET or resting-state functional MRI. Task-based functional MRI and EEG can reveal cognitive motor dissociation in up to 15–20% of patients who seem unresponsive on behavioural examination, and emerging evidence suggests that early detection of cognitive motor dissociation in the intensive care unit predicts 1-year functional outcomes. Amantadine is the only therapy that has been associated with the acceleration of recovery of consciousness in a randomized controlled trial of patients with subacute traumatic DoC, but multiple pharmacological and neuromodulatory therapies are now being tested. Emerging advances in diagnostic and prognostic techniques provide new opportunities to detect consciousness, monitor its recovery, elucidate its neuronal substrate and identify the therapeutic potential of promoting re-emergence of consciousness in a subset of patients with DoC.

Patients with acquired brain injury and acute or prolonged disorders of consciousness (DoC) are challenging. Evidence to support diagnostic decisions on coma and other DoC is limited but accumulating. This guideline provides the state‐of‐the‐art evidence regarding the diagnosis of DoC, summarizing data from bedside examination techniques, functional neuroimaging and electroencephalography (EEG).

Tumor necrosis factor (TNF)-related activation-induced cytokine (TRANCE), a member of the TNF family expressed on activated T-cells, bone marrow stromal cells, and osteoblasts, regulates the function of dendritic cells (DC) and osteoclasts. The TRANCE receptor (TRANCE-R), recently identified as receptor activator of NF-κβ (RANK), activates NF-κB, a transcription factor critical in the differentiation and activation of those cells. In this report we identify the TNF receptor-associated factor (TRAF) family of signal transducers as important components of TRANCE-R-mediated NF-κB activation. Coimmunoprecipitation experiments suggested potential interactions between the cytoplasmic tail of TRANCE-R with TRAF1, TRAF2, TRAF3, TRAF5, and TRAF6. Dominant negative forms of TRAF2, TRAF5, and TRAF6 and an endogenous inhibitor of TRAF2, TRAF-interacting protein (TRIP), substantially inhibited TRANCE-R-mediated NF-κB activation, suggesting a role of TRAFs in regulating DC and osteoclast function. Overexpression of combinations of TRAF dominant negative proteins revealed competition between TRAF proteins for the TRANCE-R and the possibility of a TRAF-independent NF-κB pathway. Analysis of TRANCE-R deletion mutants suggested that the TRAF2 and TRAF5 interaction sites were restricted to the C-terminal 93 amino acids (C-region). TRAF6 also complexed to the C-region in addition to several regions N-terminal to the TRAF2 and TRAF5 association sites. Furthermore, transfection experiments with TRANCE-R deletion mutants revealed that multiple regions of the TRANCE-R can mediate NF-κB activation.

The first of Erich Neumann's works to be translated into English, this eloquent book draws on a full range of world mythology to show that individual consciousness undergoes the same archetypal stages of development as has human consciousness as a whole. Neumann, one of Jung's most creative students and a renowned practitioner of analytical psychology in his own right, shows how the stages begin and end with the symbol of the Uroboros, or tail-eating serpent. The intermediate stages are projected in the universal myths of the World Creation, Great Mother, Separation of the World Parents, Birth of the Hero, Slaying of the Dragon, Rescue of the Captive, and Transformation and Deification of the Hero. Throughout the sequence the Hero is the evolving ego consciousness.

The role of the frontal cortex in consciousness remains a matter of debate. In this Perspective, we will critically review the clinical and neuroimaging evidence for the involvement of the front versus the back of the cortex in specifying conscious contents and discuss promising research avenues. Dual Perspectives Companion Paper: Should a Few Null Findings Falsify Prefrontal Theories of Conscious Perception?, by Brian Odegaard, Robert T. Knight, and Hakwan Lau

Dynamic patterns of brain activity at rest distinguish conscious and unconscious states in humans. Adopting the framework of brain dynamics as a cornerstone of human consciousness, we determined whether dynamic signal coordination provides specific and generalizable patterns pertaining to conscious and unconscious states after brain damage. A dynamic pattern of coordinated and anticoordinated functional magnetic resonance imaging signals characterized healthy individuals and minimally conscious patients. The brains of unresponsive patients showed primarily a pattern of low interareal phase coherence mainly mediated by structural connectivity, and had smaller chances to transition between patterns. The complex pattern was further corroborated in patients with covert cognition, who could perform neuroimaging mental imagery tasks, validating this pattern’s implication in consciousness. Anesthesia increased the probability of the less complex pattern to equal levels, validating its implication in unconsciousness. Our results establish that consciousness rests on the brain’s ability to sustain rich brain dynamics and pave the way for determining specific and generalizable fingerprints of conscious and unconscious states.

Objective To update the 1995 American Academy of Neurology (AAN) practice parameter on persistent vegetative state and the 2002 case definition for the minimally conscious state (MCS) by reviewing the literature on the diagnosis, natural history, prognosis, and treatment of disorders of consciousness lasting at least 28 days. Methods Articles were classified per the AAN evidence-based classification system. Evidence synthesis occurred through a modified Grading of Recommendations Assessment, Development and Evaluation process. Recommendations were based on evidence, related evidence, care principles, and inferences according to the AAN 2011 process manual, as amended. Results No diagnostic assessment procedure had moderate or strong evidence for use. It is possible that a positive EMG response to command, EEG reactivity to sensory stimuli, laser-evoked potentials, and the Perturbational Complexity Index can distinguish MCS from vegetative state/unresponsive wakefulness syndrome (VS/UWS). The natural history of recovery from prolonged VS/UWS is better in traumatic than nontraumatic cases. MCS is generally associated with a better prognosis than VS (conclusions of low to moderate confidence in adult populations), and traumatic injury is generally associated with a better prognosis than nontraumatic injury (conclusions of low to moderate confidence in adult and pediatric populations). Findings concerning other prognostic features are stratified by etiology of injury (traumatic vs nontraumatic) and diagnosis (VS/UWS vs MCS) with low to moderate degrees of confidence. Therapeutic evidence is sparse. Amantadine probably hastens functional recovery in patients with MCS or VS/UWS secondary to severe traumatic brain injury over 4 weeks of treatment. Recommendations are presented separately.

TNF-related activation-induced cytokine (TRANCE) is a member of the TNF family recently identified in activated T cells. We report here that TRANCE mRNA is constitutively expressed in memory, but not naive, T cells and in single-positive thymocytes. Upon TCR/CD3 stimulation, TRANCE mRNA and surface protein expression are rapidly up-regulated in CD4+ and CD8+ T cells, which can be further enhanced on CD4+ T cells by CD28-mediated costimulation. However, TRANCE induction is significantly suppressed when cells are stimulated in the presence of IL-4, but is not modified in the presence of IFN-alpha, IFN-gamma, TGF-beta, TNF-alpha, or IL-2. High levels of TRANCE receptor expression are found on mature dendritic cells (DCs). In this study we show that activated T and B cells also express TRANCE receptor, but only at low levels. TRANCE, however, does not exert any significant effect on the proliferation, activation, or survival of those cells. In DCs, TRANCE induces the expression of proinflammatory cytokines (IL-6, IL-1) and T cell growth and differentiation factors (IL-12, IL-15) in addition to enhancing DC survival. Moreover, TRANCE cooperates with CD40 ligand or TNF-alpha to further increase the viability of DCs, suggesting that several TNF-related molecules on activated T cells may cooperatively regulate the function and survival of DCs to enhance T cell-mediated immune responses.

Functional MRI and electrophysiology studies suggest that consciousness depends on large-scale thalamocortical and corticocortical interactions. However, it is unclear how neurons in different cortical layers and circuits contribute. We simultaneously recorded from central lateral thalamus (CL) and across layers of the frontoparietal cortex in awake, sleeping, and anesthetized macaques. We found that neurons in thalamus and deep cortical layers are most sensitive to changes in consciousness level, consistent across different anesthetic agents and sleep. Deep-layer activity is sustained by interactions with CL. Consciousness also depends on deep-layer neurons providing feedback to superficial layers (not to deep layers), suggesting that long-range feedback and intracolumnar signaling are important. To show causality, we stimulated CL in anesthetized macaques and effectively restored arousal and wake-like neural processing. This effect was location and frequency specific. Our findings suggest layer-specific thalamocortical correlates of consciousness and inform how targeted deep brain stimulation can alleviate disorders of consciousness.

Proper lymph node (LN) development requires tumor necrosis factor–related activation-induced cytokine (TRANCE) expression. Here we demonstrate that the defective LN development in TRANCE−/− mice correlates with a significant reduction in lymphotoxin (LT)αβ+α4β7 +CD45+CD4+CD3− cells and their failure to form clusters in rudimentary mesenteric LNs. Transgenic TRANCE overexpression in TRANCE−/− mice results in selective restoration of this cell population into clusters, and results in full LN development. Transgenic TRANCE-mediated restoration of LN development requires LTαβ expression on CD45+ CD4+CD3− cells, as LNs could not be induced in LTα−/− mice. LTα−/− mice also showed defects in the fate of CD45+CD4+CD3− cells similar to TRANCE−/− mice. Thus, we propose that both TRANCE and LTαβ regulate the colonization and cluster formation by CD45+ CD4+CD3− cells in developing LNs, the degree of which appears to correlate with the state of LN organogenesis.

In recent years, the scientific study of meditation and psychedelic drugs has seen remarkable developments. The increased focus on meditation in cognitive neuroscience has led to a cross-cultural classification of standard meditation styles validated by functional and structural neuroanatomical data. Meanwhile, the renaissance of psychedelic research has shed light on the neurophysiology of altered states of consciousness induced by classical psychedelics, such as psilocybin and LSD, whose effects are mainly mediated by agonism of serotonin receptors. Few attempts have been made at bridging these two domains of inquiry, despite intriguing evidence of overlap between the phenomenology and neurophysiology of meditation practice and psychedelic states. In particular, many contemplative traditions explicitly aim at dissolving the sense of self by eliciting altered states of consciousness through meditation, while classical psychedelics are known to produce significant disruptions of self-consciousness, a phenomenon known as drug-induced ego dissolution. In this article, we discuss available evidence regarding convergences and differences between phenomenological and neurophysiological data on meditation practice and psychedelic drug-induced states, with a particular emphasis on alterations of self-experience. While both meditation and psychedelics may disrupt self-consciousness and underlying neural processes, we emphasize that neither meditation nor psychedelic states can be conceived as simple, uniform categories. Moreover, we suggest that there are important phenomenological differences even between conscious states described as experiences of self-loss. As a result, we propose that self-consciousness may be best construed as a multidimensional construct, and that “self-loss,” far from being an unequivocal phenomenon, can take several forms. Indeed, various aspects of self-consciousness, including narrative aspects linked to autobiographical memory, self-related thoughts and mental time travel, and embodied aspects rooted in multisensory processes, may be differently affected by psychedelics and meditation practices. Finally, we consider long-term outcomes of experiences of self-loss induced by meditation and psychedelics on individual traits and prosocial behavior. We call for caution regarding the problematic conflation of temporary states of self-loss with “selflessness” as a behavioral or social trait, although there is preliminary evidence that correlations between short-term experiences of self-loss and long-term trait alterations may exist.

In this paper, the concept of sustainability consciousness, an individual's experience and awareness of sustainable development, is introduced. Based on UNESCO's definition of sustainable development, a sustainability consciousness questionnaire (SCQ) is theoretically and empirically developed. Using the data of 638 respondents from Sweden aged 18–19 years old, the scale is developed in two versions. The long version (SCQ‐L) can be used to measure individuals' environmental, social and economic knowingness, attitudes and behaviour (nine valid and reliable subscales), in addition to the second order constructs of sustainability knowingness, sustainability attitudes and sustainability behaviour, as well as the third order construct, sustainability consciousness. For the short version (SCQ‐S) 27 items were identified that can be used to measure the second and third order constructs. Both versions of the scale present excellent psychometric quality. Possible applications of the questionnaire instruments in the context of evaluation of sustainability policies, practices and stakeholder engagement are discussed.

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.

A new prior is proposed for learning representations of high-level concepts of the kind we manipulate with language. This prior can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by cognitive neuroscience theories of consciousness, seen as a bottleneck through which just a few elements, after having been selected by attention from a broader pool, are then broadcast and condition further processing, both in perception and decision-making. The set of recently selected elements one becomes aware of is seen as forming a low-dimensional conscious state. This conscious state is combining the few concepts constituting a conscious thought, i.e., what one is immediately conscious of at a particular moment. We claim that this architectural and information-processing constraint corresponds to assumptions about the joint distribution between high-level concepts. To the extent that these assumptions are generally true (and the form of natural language seems consistent with them), they can form a useful prior for representation learning. A low-dimensional thought or conscious state is analogous to a sentence: it involves only a few variables and yet can make a statement with very high probability of being true. This is consistent with a joint distribution (over high-level concepts) which has the form of a sparse factor graph, i.e., where the dependencies captured by each factor of the factor graph involve only very few variables while creating a strong dip in the overall energy function. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in a form similar to facts and rules, albeit capturing uncertainty as well as efficient search mechanisms implemented by attention mechanisms.

Critical consciousness refers to an individual's awareness of oppressive systemic forces in society, a sense of efficacy to work against oppression, and engagement in individual or collective action against oppression. In the past few decades, interest in critical consciousness as a resource that may promote thriving in marginalized people has grown tremendously. This article critically examines the results of a systematic review of 67 studies of critical consciousness in children and adolescents, published between 1998 and 2019. Across these studies, major themes included the role of socialization experiences, relationships, and context in the development of critical consciousness. In addition, critical consciousness was associated with a number of adaptive developmental outcomes, including career-related, civic, social-emotional, and academic outcomes-especially for marginalized youth. However, our analysis highlights several critical gaps in the literature. We highlight the need for further delineation of the impacts of parent and peer socialization on critical consciousness in specific developmental periods and for studying critical consciousness at multiple levels of the ecological system. We further note the dearth of rigorous experimental or quasi-experimental studies in the area of interventions to promote critical consciousness. In addition, we note that developmental questions-questions about the nature and function of critical consciousness over time-are largely unanswered in the literature, including questions about how critical consciousness manifests and develops during childhood. Leveraging the findings of our systematic review, we outline key next steps for this rapidly growing area of research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Significance Lysergic acid diethylamide (LSD) is a psychedelic drug that reliably induces an altered state of consciousness. Interest in psychedelic compounds is growing due to their remarkable potential for understanding altered neural states and potential clinical applications. However, there are major knowledge gaps regarding LSD’s neuropharmacology. Using cutting-edge neuroimaging methods we investigated directed connectivity between cortico–striato–thalamo-cortical (CSTC) regions after administration of LSD together with the specific role of the serotonin 2A receptor. Our results provide evidence that LSD alters directed connectivity within CSTC pathways in humans, suggesting that a disintegration of information processing within these loops is underlying the psychedelic state. These results inform the neurobiology of altered states of consciousness with critical implications for rational development of novel treatments. Psychedelics exert unique effects on human consciousness. The thalamic filter model suggests that core effects of psychedelics may result from gating deficits, based on a disintegration of information processing within cortico–striato–thalamo-cortical (CSTC) feedback loops. To test this hypothesis, we characterized changes in directed (effective) connectivity between selected CTSC regions after acute administration of lysergic acid diethylamide (LSD), and after pretreatment with Ketanserin (a selective serotonin 2A receptor antagonist) plus LSD in a double-blind, randomized, placebo-controlled, cross-over study in 25 healthy participants. We used spectral dynamic causal modeling (DCM) for resting-state fMRI data. Fully connected DCM models were specified for each treatment condition to investigate the connectivity between the following areas: thalamus, ventral striatum, posterior cingulate cortex, and temporal cortex. Our results confirm major predictions proposed in the CSTC model and provide evidence that LSD alters effective connectivity within CSTC pathways that have been implicated in the gating of sensory and sensorimotor information to the cortex. In particular, LSD increased effective connectivity from the thalamus to the posterior cingulate cortex in a way that depended on serotonin 2A receptor activation, and decreased effective connectivity from the ventral striatum to the thalamus independently of serotonin 2A receptor activation. Together, these results advance our mechanistic understanding of the action of psychedelics in health and disease. This is important for the development of new pharmacological therapeutics and also increases our understanding of the mechanisms underlying the potential clinical efficacy of psychedelics.

In February 2023, Antarctic sea ice set a record minimum; there have now been three record-breaking low sea ice summers in seven years. Following the summer minimum, circumpolar Antarctic sea ice coverage remained exceptionally low during the autumn and winter advance, leading to the largest negative areal extent anomalies observed over the satellite era. Here, we show the confluence of Southern Ocean subsurface warming and record minima and suggest that ocean warming has played a role in pushing Antarctic sea ice into a new low-extent state. In addition, this new state exhibits different seasonal persistence characteristics, suggesting that the underlying processes controlling Antarctic sea ice coverage may have altered.

Prominent theories of consciousness emphasise different aspects of neurobiology, such as the integration and diversity of information processing within the brain. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from awake volunteers, propofol-anaesthetised volunteers, and patients with disorders of consciousness, in order to identify consciousness-specific patterns of brain function. We demonstrate that cortical networks are especially affected by loss of consciousness during temporal states of high integration, exhibiting reduced functional diversity and compromised informational capacity, whereas thalamo-cortical functional disconnections emerge during states of higher segregation. Spatially, posterior regions of the brain’s default mode network exhibit reductions in both functional diversity and integration with the rest of the brain during unconsciousness. These results show that human consciousness relies on spatio-temporal interactions between brain integration and functional diversity, whose breakdown may represent a generalisable biomarker of loss of consciousness, with potential relevance for clinical practice. How do diversity (entropy) and integration of activity across brain regions interact to support consciousness? Here the authors show that anaesthetised individuals and patients with disorders of consciousness exhibit overlapping reductions in both diversity and integration in the brain’s default mode network.

ABSTRACT Environmental certifications are considered an extremely useful tool to promote sustainable tourism; however, little is known about customer perceptions of these schemes and how these certifications might relate to customer behavior when there are customer segments with different levels of environmental consciousness. This study develops a model that investigates the relationship between green practices, green image, environmental consciousness and the behavioral intentions of customers in a certified hotel context. The study examines the direct and moderating role of environmental consciousness in the formation of behavioral intentions based on green initiatives. To test the proposed model empirically, 502 personal surveys of hotel customers were conducted in Spain using a structured questionnaire. The findings show that customer perceptions of green practices have a directly positive effect on a hotel's green image. At the same time, this green image has directly positive effects on customer behavioral intentions toward certified hotels. The authors also found that the higher the environmental consciousness of consumers, the greater their intention to stay, to spread positive word-of-mouth and pay a premium for environmentally certified hotels. Finally, consumer environmental consciousness also exerts a moderating effect on the causal relationship between green image and behavioral intentions.

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties"of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.

As purchase power and consumption knowledge increase, consumers gradually demand safer and healthier products. In addition, consumers focus greater attention on organic food. This study investigates the relationship between environmental consciousness and organic food purchase intention with the mediating roles of perceived food quality and price sensitivity. The objective is to shed new light on our understanding of consumers’ perceptions and behavioral intentions toward organic food. Using sample data of 518 consumers in different food retailers in China, empirical results show that environmental consciousness has a positive impact on organic food purchase intention. Perceived food quality has a mediating effect on the link between environmental consciousness and organic food purchase intention. Price sensitivity moderates the relationship between perceived food quality and organic food purchase intention. Furthermore, price sensitivity moderates the indirect effect of environmental consciousness on organic food purchase intention through perceived food quality.

Abstract Aims Takotsubo syndrome (TTS) is characterized by acute left ventricular dysfunction often triggered by emotional or physical stress. Severe activation of the sympathetic nervous system with catecholamine release caused by a dysfunctional limbic system has been proposed as a potential mechanism. We hypothesize that brain regions responsible for autonomic integration and/or limbic processing might be involved in the development of TTS. Here, we investigated alterations in resting state functional connectivity in TTS patients compared with healthy controls. Methods and results Using brain functional magnetic resonance imaging (fMRI), resting state functional connectivity has been assessed in 15 subjects with TTS and 39 healthy controls. Network-based statistical analyses were conducted to identify subnetworks with altered resting state functional connectivity. Sympathetic and parasympathetic networks have been constructed in addition to the default mode network and whole-brain network. We found parasympathetic- and sympathetic-associated subnetworks both showing reduced resting state functional connectivity in TTS patients compared with controls. Important brain regions constituting parasympathetic- and sympathetic-associated subnetworks included the amygdala, hippocampus, and insula as well as cingulate, parietal, temporal, and cerebellar regions. Additionally, the default mode network as well as limbic regions in the whole-brain analysis demonstrated reduced resting state functional connectivity in TTS, including the hippocampus, parahippocampal, and medial prefrontal regions. Conclusion For the first time, we demonstrate hypoconnectivity of central brain regions associated with autonomic functions and regulation of the limbic system in patients with TTS. These findings suggest that autonomic-limbic integration might play an important role in the pathophysiology and contribute to the understanding of TTS.

Two-way dual language (TWDL) bilingual education programs share three core goals: academic achievement, bilingualism and biliteracy, and sociocultural competence. This article proposes a fourth core goal: critical consciousness. Although TWDL programs are designed to integrate students from diverse language, culture, and race backgrounds, equity is unfortunately still a challenge in TWDL classrooms and schools. We argue that centering critical consciousness—or fostering among teachers, parents, and children an awareness of the structural oppression that surrounds us and a readiness to take action to correct it—can support increased equity and social justice in TWDL education. We elaborate four elements of critical consciousness: interrogating power, critical listening, historicizing schools, and embracing discomfort. We illustrate these elements with examples from TWDL research and practice. In addition, we describe how critical consciousness impacts and radicalizes the other three core goals, in turn supporting the development of more successful, equitable, and socially just TWDL schools.

Neuroimaging suggests that human consciousness relies on frequent access to default mode and dorsal attention brain networks. The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a “temporal circuit” characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in consciousness.

Is self-consciousness necessary for consciousness? The answer is yes. So there you have it—the answer is yes. This was my response to a question I was asked to address in a recent AEON piece (https://aeon.co/essays/consciousness-is-not-a-thing-but-a-process-of-inference). What follows is based upon the notes for that essay, with a special focus on self-organization, self-evidencing and self-modeling. I will try to substantiate my (polemic) answer from the perspective of a physicist. In brief, the argument goes as follows: if we want to talk about creatures, like ourselves, then we have to identify the characteristic behaviors they must exhibit. This is fairly easy to do by noting that living systems return to a set of attracting states time and time again. Mathematically, this implies the existence of a Lyapunov function that turns out to be model evidence (i.e., self-evidence) in Bayesian statistics or surprise (i.e., self-information) in information theory. This means that all biological processes can be construed as performing some form of inference, from evolution through to conscious processing. If this is the case, at what point do we invoke consciousness? The proposal on offer here is that the mind comes into being when self-evidencing has a temporal thickness or counterfactual depth, which grounds inferences about the consequences of my action. On this view, consciousness is nothing more than inference about my future; namely, the self-evidencing consequences of what I could do.

Championing open science, an adversarial collaboration aims to unravel the footprints of consciousness The history of science includes numerous challenging problems, including the “hard problem” (1) of consciousness: Why does an assembly of neurons—no matter how complex, such as the human brain—give rise to perceptions and feelings that are consciously experienced, such as the sweetness of chocolate or the tenderness of a loving caress on one's cheek? Beyond satisfying this millennia-old existential curiosity, understanding consciousness bears substantial medical and ethical implications, from evaluating whether someone is conscious after brain injury to determining whether nonhuman animals, fetuses, cell organoids, or even advanced machines (2) are conscious. A comprehensive and agreed-upon theory of consciousness is necessary to answer the question of which systems—biologically evolved or artificially designed—experience anything and to define the ethical boundaries of our actions toward them. The research projects described here will hopefully point the way and indicate whether some of today's major theories hold water or not.

A common observation in EEG research is that consciousness vanishes with the appearance of delta (1 - 4 Hz) waves, particularly when those waves are high amplitude. High amplitude delta oscillations are very frequently observed in states of diminished consciousness, including slow wave sleep, anaesthesia, generalised epileptic seizures, and disorders of consciousness such as coma and vegetative state. This strong correlation between loss of consciousness and high amplitude delta oscillations is thought to stem from the widespread cortical deactivation that occurs during the "down states" or troughs of these slow oscillations. Recently, however, many studies have reported the presence of prominent delta activity during conscious states, which casts doubt on the hypothesis that high amplitude delta oscillations are an indicator of unconsciousness. These studies include work in Angelman syndrome, epilepsy, behavioural responsiveness during propofol anaesthesia, postoperative delirium, and states of dissociation from the environment such as dreaming and powerful psychedelic states. The foregoing studies complement an older, yet largely unacknowledged, body of literature that has documented awake, conscious patients with high amplitude delta oscillations in clinical reports from Rett syndrome, Lennox-Gastaut syndrome, schizophrenia, mitochondrial diseases, hepatic encephalopathy, and nonconvulsive status epilepticus. At the same time, a largely parallel body of recent work has reported convincing evidence that the complexity or entropy of EEG and magnetoencephalogram or MEG signals strongly relates to an individual's level of consciousness. Having reviewed this literature, we discuss plausible mechanisms that would resolve the seeming contradiction between high amplitude delta oscillations and consciousness. We also consider implications concerning theories of consciousness, such as integrated information theory and the entropic brain hypothesis. Finally, we conclude that false inferences of unconscious states can be best avoided by examining measures of electrophysiological complexity in addition to spectral power.

The immune systems of free-living mammals such as humans and wild mice display a heightened degree of activation compared with laboratory mice maintained under artificial conditions. Here, we demonstrate that releasing inbred laboratory mice into an outdoor enclosure to mimic life in a natural environment alters the state of immunity. In addition to enhancing the differentiation of T cell populations previously associated with pathogen exposure, we found that outdoor release of mice led to an increase in circulating granulocytes. However, rewilded mice were not infected by pathogens previously implicated in immune activation. Rather, changes to the immune system were associated with an altered composition of the microbiota, and fungi isolated from rewilded mice were sufficient to increase granulocytes. These findings establish an experimental procedure to investigate the impact of the natural environment on immune development and identify a role for sustained fungal colonization in determining granulocyte numbers. One sentence summary Controlled release of laboratory mice into a natural environment changes the composition of the bacterial and fungal microbiota leading to an altered immune system.

There is no agreement on whether any invertebrates are conscious and no agreement on a methodology that could settle the issue. How can the debate move forward? I distinguish three broad types of approach: theory-heavy, theory-neutral and theory-light. Theory-heavy and theory-neutral approaches face serious problems, motivating a middle path: the theory-light approach. At the core of the theory-light approach is a minimal commitment about the relation between phenomenal consciousness and cognition that is compatible with many specific theories of consciousness: the hypothesis that phenomenally conscious perception of a stimulus facilitates, relative to unconscious perception, a cluster of cognitive abilities in relation to that stimulus. This “facilitation hypothesis” can productively guide inquiry into invertebrate consciousness. What is needed? At this stage, not more theory, and not more undirected data gathering. What is needed is a systematic search for consciousness-linked cognitive abilities, their relationships to each other, and their sensitivity to masking.

Consciousness shared Humans have tended to believe that we are the only species to possess certain traits, behaviors, or abilities, especially with regard to cognition. Occasionally, we extend such traits to primates or other mammals—species with which we share fundamental brain similarities. Over time, more and more of these supposed pillars of human exceptionalism have fallen. Nieder et al. now argue that the relationship between consciousness and a standard cerebral cortex is another fallen pillar (see the Perspective by Herculano-Houzel). Specifically, carrion crows show a neuronal response in the palliative end brain during the performance of a task that correlates with their perception of a stimulus. Such activity might be a broad marker for consciousness. Science, this issue p. 1626; see also p. 1567 A marker for consciousness is identified in a crow’s brain. Subjective experiences that can be consciously accessed and reported are associated with the cerebral cortex. Whether sensory consciousness can also arise from differently organized brains that lack a layered cerebral cortex, such as the bird brain, remains unknown. We show that single-neuron responses in the pallial endbrain of crows performing a visual detection task correlate with the birds’ perception about stimulus presence or absence and argue that this is an empirical marker of avian consciousness. Neuronal activity follows a temporal two-stage process in which the first activity component mainly reflects physical stimulus intensity, whereas the later component predicts the crows’ perceptual reports. These results suggest that the neural foundations that allow sensory consciousness arose either before the emergence of mammals or independently in at least the avian lineage and do not necessarily require a cerebral cortex.

Abstract Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

ABSTRACT Consciousness is now a well-established field of empirical research. A large body of experimental results has been accumulated and is steadily growing. In parallel, many Theories of Consciousness (ToCs) have been proposed. These theories are diverse in nature, ranging from computational to neurophysiological and quantum theoretical approaches. This contrasts with other fields of natural science, which host a smaller number of competing theories. We suggest that one reason for this abundance of extremely different theories may be the lack of stringent criteria specifying how empirical data constrains ToCs. First, we argue that consciousness is a well-defined topic from an empirical point of view and motivate a purely empirical stance on the quest for consciousness. Second, we present a checklist of criteria that, we propose, empirical ToCs need to cope with. Third, we review 13 of the most influential ToCs and subject them to the criteria. Our analysis helps to situate these different ToCs in the theoretical landscapeand sheds light on their strengths and weaknesses from a strictly empirical point of view.

Legal consciousness is a vibrant research field attracting growing numbers of scholars worldwide. Yet differing assumptions about aims and methods have generated vigorous debate, typically resulting from a failure to recognize that three different clusters of scholars—identified here as the Identity, Hegemony, and Mobilization schools—are pursuing different goals and deploying the concept of legal consciousness in different ways. Scholarship associated with these three schools demonstrates that legal consciousness is actually a flexible paradigm with multiple applications rather than a monolithic approach. Furthermore, a new generation of scholars has energized the field in recent years, focusing on marginalized peoples and non-Western settings. Through their findings, and as a result of broader trends across the social sciences, relational legal consciousness has taken on greater importance. Legal consciousness research should be imagined on a continuum ranging from individualistic conceptualizations of thought and action to interactive, co-constitutive approaches.

Many functional neuroimaging studies have reported differential patterns of spontaneous brain activity in posttraumatic stress disorder (PTSD), but the findings are inconsistent and have not so far been quantitatively reviewed. The present study set out to determine consistent, specific regional brain activity alterations in PTSD, using the Effect Size Signed Differential Mapping technique to conduct a quantitative meta-analysis of resting-state functional neuroimaging studies of PTSD that used either a non-trauma (NTC) or a trauma-exposed (TEC) comparison control group. Fifteen functional neuroimaging studies were included, comparing 286 PTSDs, 203 TECs and 155 NTCs. Compared with NTC, PTSD patients showed hyperactivity in the right anterior insula and bilateral cerebellum, and hypoactivity in the dorsal medial prefrontal cortex (mPFC); compared with TEC, PTSD showed hyperactivity in the ventral mPFC. The pooled meta-analysis showed hypoactivity in the posterior insula, superior temporal, and Heschl’s gyrus in PTSD. Additionally, subgroup meta-analysis (non-medicated subjects vs. NTC) identified abnormal activation in the prefrontal-limbic system. In meta-regression analyses, mean illness duration was positively associated with activity in the right cerebellum (PTSD vs. NTC), and illness severity was negatively associated with activity in the right lingual gyrus (PTSD vs. TEC).

— Scholarship on critical consciousness frames how people who are more marginalized deeply analyze, feel empowered to change, and take collective action to redress perceived inequities. These three dimensions cor-respond to critical reflection, motivation, and action, respectively. In this article, we aim to recenter action in scholarship on critical consciousness, given the disproportionate attention that has been paid to reflection. To achieve this aim, we review empirical associations between critical action and positive developmental consequences among more marginalized youth, highlight promising practices to foster critical action, and identify questions and key areas for inquiry. We hope this article motivates a recentering of critical action in scholarship, policy, and practice on critical consciousness.

Recent functional magnetic resonance imaging (fMRI) studies on autism spectrum condition (ASC) have identified dysfunctions in specific brain networks involved in social and non-social cognition that persist into adulthood. Although increasing numbers of fMRI studies have revealed atypical functional connectivity in the adult ASC brain, such functional alterations at the network level have not yet been fully characterized within the recently developed graph-theoretical framework. Here, we applied a graph-theoretical analysis to resting-state fMRI data acquired from 46 adults with ASC and 46 age- and gender-matched controls, to investigate the topological properties and organization of autistic brain network. Analyses of global metrics revealed that, relative to the controls, participants with ASC exhibited significant decreases in clustering coefficient and characteristic path length, indicating a shift towards randomized organization. Furthermore, analyses of local metrics revealed a significantly altered organization of the hub nodes in ASC, as shown by analyses of hub disruption indices using multiple local metrics and by a loss of “hubness” in several nodes (e.g., the bilateral superior temporal sulcus, right dorsolateral prefrontal cortex, and precuneus) that are critical for social and non-social cognitive functions. In particular, local metrics of the anterior cingulate cortex consistently showed significant negative correlations with the Autism-Spectrum Quotient score. Our results demonstrate altered patterns of global and local topological properties that may underlie impaired social and non-social cognition in ASC.

The aims of this study were to 1) compare resting state functional connectivity (rs-fc) of the periaqueductal gray (PAG), a key region in the descending pain modulatory system (DPMS) between migraine without aura (MwoA) patients and healthy controls (HC) and 2) investigate how an effective treatment can influence the PAG rs-fc in MwoA patients. One hundred MwoA patients and forty-six matched HC were recruited. Patients were randomized to verum acupuncture, sham acupuncture and waiting list groups. Resting state fMRI data were collected and seed based functional connectivity analysis was applied. Compared with HC, MwoA patients showed reduced rs-fc between the PAG and rostral anterior cingulate cortex/medial prefrontal cortex (rACC/mPFC), key regions in the DPMS and other pain related brain regions. The reduced rs-fc between the PAG and rACC/mPFC was associated with increased migraine headache intensity at the baseline. After treatments, rs-fc between the PAG and the rACC in MwoA patients significantly increased. The changes of rs-fc among the PAG, rACC and ventral striatum were significantly associated with headache intensity improvement. Impairment of the DPMS is involved in the neural pathophysiology of migraines. Impaired DPMS in migraine patients can be normalized after effective treatment.

What is the role of consciousness in our mental lives? This book argues that consciousness plays an essential role in explaining how we can acquire knowledge and epistemically justified belief about ourselves and our surroundings. On this view, our mental lives cannot be preserved in unconscious creatures—zombies—who behave just as we do. Only conscious creatures have epistemic justification to form beliefs about the world. Zombies cannot know anything about the world, since they have no epistemic justification to believe anything. On this view, all epistemic justification depends ultimately on consciousness. This book builds a sustained argument for the epistemic role of phenomenal consciousness, which draws on a range of considerations in epistemology and the philosophy of mind. The book is divided into two parts, which approach the theory of epistemic justification from opposite directions. Part I argues from the bottom up by drawing on considerations in the philosophy of mind about the role of consciousness in mental representation, perception, cognition, and introspection. Part II argues from the top down by arguing from general principles in epistemology about the nature of epistemic justification. These mutually reinforcing arguments form the basis for a unified theory of the epistemic role of phenomenal consciousness, one that bridges the gap between epistemology and the philosophy of mind.

Follow up what we will offer in this article about disorders of consciousness. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.

One fundamental feature of consciousness is that the contents of consciousness depend on the state of consciousness. Here, we propose an answer to why this is so: both the state and the contents of consciousness depend on the activity of cortical layer 5 pyramidal (L5p) neurons. These neurons affect both cortical and thalamic processing, hence coupling the cortico-cortical and thalamo-cortical loops with each other. Functionally this coupling corresponds to the coupling between the state and the contents of consciousness. Together the cortico-cortical and thalamo-cortical loops form a thalamo-cortical broadcasting system, where the L5p cells are the central elements. This perspective makes one quite specific prediction: cortical processing that does not include L5p neurons will be unconscious. More generally, the present perspective suggests that L5p neurons have a central role in the mechanisms underlying consciousness.

The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers ( www.psymri.com ). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 ± 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 ± 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.

Loss of consciousness is associated with the disruption of long-range thalamocortical and corticocortical brain communication. We tested the hypothesis that deep brain stimulation (DBS) of central thalamus might restore both arousal and awareness following consciousness loss. We applied anesthesia to suppress consciousness in nonhuman primates. During anesthesia, central thalamic stimulation induced arousal in an on-off manner and increased functional magnetic resonance imaging activity in prefrontal, parietal, and cingulate cortices. Moreover, DBS restored a broad dynamic repertoire of spontaneous resting-state activity, previously described as a signature of consciousness. None of these effects were obtained during the stimulation of a control site in the ventrolateral thalamus. Last, DBS restored a broad hierarchical response to auditory violations that was disrupted under anesthesia. Thus, DBS restored the two dimensions of consciousness, arousal and conscious access, following consciousness loss, paving the way to its therapeutical translation in patients with disorders of consciousness.

Abstract A key aspect of consciousness is that it represents bound or integrated information, prompting an increasing conviction that the physical substrate of consciousness must be capable of encoding integrated information in the brain. However, as Ralph Landauer insisted, ‘information is physical’ so integrated information must be physically integrated. I argue here that nearly all examples of so-called ‘integrated information’, including neuronal information processing and conventional computing, are only temporally integrated in the sense that outputs are correlated with multiple inputs: the information integration is implemented in time, rather than space, and thereby cannot correspond to physically integrated information. I point out that only energy fields are capable of integrating information in space. I describe the conscious electromagnetic information (cemi) field theory which has proposed that consciousness is physically integrated, and causally active, information encoded in the brain’s global electromagnetic (EM) field. I here extend the theory to argue that consciousness implements algorithms in space, rather than time, within the brain’s EM field. I describe how the cemi field theory accounts for most observed features of consciousness and describe recent experimental support for the theory. I also describe several untested predictions of the theory and discuss its implications for the design of artificial consciousness. The cemi field theory proposes a scientific dualism that is rooted in the difference between matter and energy, rather than matter and spirit.

This sequel to Lycan's Consciousness (1987) continues the elaboration of his general functionalist theory of consciousness, answers the critics of his earlier work, and expands the range of discussion to deal with the many new issues and arguments that have arisen in the intervening years -- an extraordinarily fertile period for the philosophical investigation of consciousness.Lycan not only uses the numerous arguments against materialism, and functionalist theories of mind in particular, to gain a more detailed positive view of the structure of the mind, he also targets the set of really hard problems at the center of the theory of consciousness: subjectivity, qualia, and the felt aspect of experience. The key to his own enlarged and fairly argued position, which he calls the "hegemony of representation," is that there is no more to mind or consciousness than can be accounted for in terms of intentionality, functional organization, and in particular, second-order representation of one's own mental states.A Bradford Book

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness. The authors propose an explainable consciousness indicator using deep learning to quantify arousal and awareness under sleep, anesthesia, and in patients with disorders of consciousness.

ABSTRACT Here we examine how people’s understanding of consciousness may have been shaped by an implicit theory of mind. This social cognition approach may help to make sense of an apparent divide between the physically incoherent consciousness we think we have and the complex, rich, but mechanistic consciousness we may actually have. We suggest this approach helps reconcile some of the current cognitive neuroscience theories of consciousness. We argue that a single, coherent explanation of consciousness is available and has been for some time, encompassing the views of many researchers, but is not yet recognized. It is obscured partly by terminological differences, and partly because researchers view isolated pieces of it as rival theories. It may be time to recognize that a deeper, coherent pool of ideas, a kind of standard model, is available to explain multiple layers of consciousness and how they relate to specific networks within the brain.

Psychopathy is a personality disorder characterized by callous antisocial behavior and criminal recidivism. Here we examine whether psychopathy is associated with alterations in functional connectivity in three large-scale cortical networks. Using fMRI in 142 adult male prison inmates, we computed resting-state functional connectivity using seeds from the default mode network, frontoparietal network, and cingulo-opercular network. To determine the specificity of our findings to these cortical networks, we also calculated functional connectivity using seeds from two comparison primary sensory networks: visual and auditory networks. Regression analyses related network connectivity to overall psychopathy scores and to subscores for the “factors” and “facets” of psychopathy: Factor 1, interpersonal/affective traits; Factor 2, lifestyle/antisocial traits; Facet 1, interpersonal; Facet 2, affective; Facet 3, lifestyle; Facet 4, antisocial. Overall psychopathy severity was associated with reduced functional connectivity between lateral parietal cortex and dorsal anterior cingulate cortex. The two factor scores exhibited contrasting relationships with functional connectivity: Factor 1 scores were associated with reduced functional connectivity in the three cortical networks, whereas Factor 2 scores were associated with heightened connectivity in the same networks. This dissociation was evident particularly in the functional connectivity between anterior insula and dorsal anterior cingulate cortex. The facet scores also demonstrated distinct patterns of connectivity. We found no associations between psychopathy scores and functional connectivity within visual or auditory networks. These findings provide novel evidence on the neural correlates of psychopathy and suggest that connectivity between cortical association hubs, such as the dorsal anterior cingulate cortex, may be a neurobiological marker of the disorder.

Abstract The association between neural oscillations and functional integration is widely recognized in the study of human cognition. Large-scale synchronization of neural activity has also been proposed as the neural basis of consciousness. Intriguingly, a growing number of studies in social cognitive neuroscience reveal that phase synchronization similarly appears across brains during meaningful social interaction. Moreover, this inter-brain synchronization has been associated with subjective reports of social connectedness, engagement, and cooperativeness, as well as experiences of social cohesion and ‘self-other merging’. These findings challenge the standard view of human consciousness as essentially first-person singular and private. We therefore revisit the recent controversy over the possibility of extended consciousness and argue that evidence of inter-brain synchronization in the fastest frequency bands overcomes the hitherto most convincing sceptical position. If this proposal is on the right track, our understanding of human consciousness would be profoundly transformed, and we propose a method to test this proposal experimentally.

Depression has been associated with changes in both functional and effective connectivity of large scale brain networks, including the default mode network, executive network, and salience network. However, studies of effective connectivity by means of spectral dynamic causal modeling (spDCM) are still rare and the interaction between the different resting state networks has not been investigated in detail. Thus, we aimed at exploring differences in effective connectivity among eight right hemisphere brain areas—anterior insula, inferior frontal gyrus, middle frontal gyrus (MFG), frontal eye field, anterior cingulate cortex, superior parietal lobe, amygdala, and hippocampus, between a group of healthy controls (N = 20) and medicated depressed patients (N = 20). We found that patients not only had significantly reduced strength of the connection from the anterior insula to the MFG (i.e., dorsolateral prefrontal cortex) but also a significant connection between the amygdala and the anterior insula. Moreover, depression severity correlated with connectivity of the hippocampal node. In conclusion, the results from this resting state spDCM study support and enrich previous data on the role of the right anterior insula in the pathophysiology of depression. Furthermore, our findings add to the growing evidence of an association between depression severity and disturbances of the hippocampal function in terms of impaired connectivity with other brain regions.

“Altered State of Consciousness” (ASC) has been defined as a changed overall pattern of conscious experience, or as the subjective feeling and explicit recognition that one's own subjective experience has changed. We argue that these traditional definitions fail to draw a clear line between altered and normal states of consciousness (NSC). We outline a new definition of ASC and argue that the proper way to understand the concept of ASC is to regard it as a representational notion: the alteration that has happened is not an alteration of consciousness (or subjective experience) per se, but an alteration in the informational or representational relationships between consciousness and the world. An altered state of consciousness is defined as a state in which the neurocognitive background mechanisms of consciousness have an increased tendency to produce misrepresentations such as hallucinations, delusions, and memory distortions. Paradigm examples of such generally misrepresentational, temporary, and reversible states are dreaming, psychotic episodes, psychedelic drug experiences, some epileptic seizures, and hypnosis in highly hypnotizable subjects. The representational definition of ASC should be applied in the theoretical and empirical studies of ASCs to unify and clarify the conceptual basis of ASC research.

Structural and functional abnormalities of the cerebellum in schizophrenia have been reported. Most previous studies investigating resting-state functional connectivity (rsFC) have relied on a priori restrictions on seed regions or specific networks, which may bias observations. In this study, we aimed to elicit the connectivity alterations of the cerebellum in schizophrenia in a hypothesis-free approach. Ninety-five schizophrenia patients and 93 sex- and age-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI). A voxel-wise data-driven method, resting-state functional connectivity density (rsFCD), was used to investigate cerebellar connectivity changes in schizophrenia patients. Regions with altered rsFCD were chosen as seeds to perform seed-based resting-state functional connectivity (rsFC) analyses. We found that schizophrenia patients exhibited decreased rsFCD in the right hemispheric VI; moreover, this cerebellar region showed increased rsFC with the prefrontal cortex and subcortical nuclei and decreased rsFC with the visual cortex and sensorimotor cortex. In addition, some rsFC changes were associated with positive symptoms. These findings suggest that abnormalities of the cerebellar hub and cerebellar-subcortical-cortical loop may be the underlying mechanisms of schizophrenia.

Despite increasing interest in pathological and non-pathological dissociation, few researchers have focused on the spiritual experiences involving dissociative states such as mediumship, in which an individual (the medium) claims to be in communication with, or under the control of, the mind of a deceased person. Our preliminary study investigated psychography – in which allegedly “the spirit writes through the medium's hand” – for potential associations with specific alterations in cerebral activity. We examined ten healthy psychographers – five less expert mediums and five with substantial experience, ranging from 15 to 47 years of automatic writing and 2 to 18 psychographies per month – using single photon emission computed tomography to scan activity as subjects were writing, in both dissociative trance and non-trance states. The complexity of the original written content they produced was analyzed for each individual and for the sample as a whole. The experienced psychographers showed lower levels of activity in the left culmen, left hippocampus, left inferior occipital gyrus, left anterior cingulate, right superior temporal gyrus and right precentral gyrus during psychography compared to their normal (non-trance) writing. The average complexity scores for psychographed content were higher than those for control writing, for both the whole sample and for experienced mediums. The fact that subjects produced complex content in a trance dissociative state suggests they were not merely relaxed, and relaxation seems an unlikely explanation for the underactivation of brain areas specifically related to the cognitive processing being carried out. This finding deserves further investigation both in terms of replication and explanatory hypotheses.

The temporal aspects of experience raise three related questions, central to our understanding of temporal consciousness: how do sensory experiences carry information about, or make us aware of, some of the temporal features of perceived events (if at all)—in what format, by what mechanisms?; do the temporal properties of conscious experiences—including the arrangement of their temporal parts—play any role in how these experiences present or represent the temporal properties of perceived events?; how does such temporal representation manifest itself in the phenomenology of the relevant experiences? Most theories of temporal consciousness can be divided in terms of how they treat these questions. This chapter begins with a brief sketch of the main theories currently on the market and some of their background assumptions; it then moves to a—also brief—critical review of some of the arguments at the centre of the dispute.

Abstract There have been a number of electrophysiological and neuroimaging studies in “altered” states of consciousness including dissociative conditions, hypnosis, and meditation; however, the nature and clinical significance of trance states remain elusive. Alterations of consciousness that accompany trance can shed light on the brain networks contributing to the experience of autobiographical self; the subjective demarcation of “self” from others and reality at large; and normative vs. pathological domains of self-experience. Shamanic trance is a volitional, self-induced state of consciousness that historically served the purposes of social cohesion and healing interventions in diverse tribal settings. We present the first neurophysiological study of a normal subject, who has received extensive training in the Mongolian shamanic tradition and is capable of self-inducing a trance state without external sensory stimulation. Quantitative EEG mapping and LORETA (low resolution electromagnetic tomography) source imaging indicate that shamanic state of consciousness (SSC) involves a shift from the normally dominant left analytical to the right experiential mode of self-experience, and from the normally dominant anterior prefrontal to the posterior somatosensory mode. These findings have implications for the psychobiology of the normative conscious mode of awareness and neurophysiological processes contributing to dissociative, psychotic, and transpersonal domains of self-experience. They may be used as a foundation to bridge Western and traditional healing techniques.

Depressive symptoms are common in Parkinson’s disease (PD), but the neurophysiological mechanisms of depression in PD are poorly understood. The current study attempted to examine disrupted spontaneous local brain activities and functional connectivities that underlie the depression in PD. We recruited a total of 20 depressed PD patients (DPD), 40 non-depressed PD patients (NDPD) and 43 matched healthy controls (HC). All the subjects underwent neuropsychological tests and resting-state fMRI scanning. The between-group differences in the amplitude of low frequency fluctuations (ALFF) of BOLD signals were examined using post-hoc tests after the analysis of covariance. Compared with the NDPD and HC, the DPD group showed significantly increased ALFF in the left median cingulated cortex (MCC). The functional connectivity (FC) between left MCC and all the other voxels in the brain were then calculated. Compared with the HC and NDPD group, the DPD patients showed stronger FC between the left MCC and some of the major nodes of the default mode network (DMN), including the post cingulated cortex/precuneus, medial prefrontal cortex, inferior frontal gyrus, and cerebellum. Correlation analysis revealed that both the ALFF values in the left MCC and the FC between the left MCC and the nodes of DMN were significantly correlated with the Hamilton Depression Rating Scale score. Moreover, higher local activities in the left MCC were associated with increased functional connections between the MCC and the nodes of DMN in PD. These abnormal activities and connectivities of the limbic-cortical circuit may indicate impaired high-order cortical control or uncontrol of negative mood in DPD, which suggested a possible neural mechanism of the depression in PD.

The aim of this study was to investigate alterations in functional connectivity (FC) within and interactions between resting‐state networks involved in salience, executive control, and interoception in participants with obesity (OB).

Abstract Background The role of the cerebellum in obsessive-compulsive disorder (OCD) has drawn increasing attention. However, the functional connectivity between the cerebellum and the cerebral cortex has not been investigated in OCD, nor has the relationship between such functional connectivity and clinical symptoms. Methods A total of 27 patients with OCD and 21 healthy controls (HCs) matched on age, sex and education underwent magnetic resonance imaging (MRI). Seed-based connectivity analyses were performed to examine differences in cerebellar-cerebral connectivity in patients with OCD compared with HCs. Associations between functional connectivity and clinical features in OCD were analyzed. Results Compared with HCs, OCD patients showed significantly decreased cerebellar-cerebral functional connectivity in executive control and emotion processing networks. Within the OCD group, decreased functional connectivity in an executive network spanning the right cerebellar Crus I and the inferior parietal lobule was positively correlated with symptom severity, and decreased connectivity in an emotion processing network spanning the left cerebellar lobule VI and the lingual gyrus was negatively correlated with illness duration. Conclusions Altered functional connectivity between the cerebellum and cerebral networks involved in cognitive-affective processing in patients with OCD provides further evidence for the involvement of the cerebellum in the pathophysiology of OCD, and is consistent with impairment in executive control and emotion regulation in this condition.

Abstract There are plenty of issues to be solved in order for researchers to agree on a neural model of consciousness. Here we emphasize an often under-represented aspect in the debate: time consciousness. Consciousness and the present moment both extend in time. Experience flows through a succession of moments and progresses from future predictions, to present experiences, to past memories. However, a brief review finds that many dominant theories of consciousness only refer to brief, static, and discrete “functional moments” of time. Very few refer to more extended, dynamic, and continuous time, which is associated with conscious experience (cf. the “experienced moment”). This confusion between short and discrete versus long and continuous is, we argue, one of the core issues in theories of consciousness. Given the lack of work dedicated to time consciousness, its study could test novel predictions of rival theories of consciousness. It may be that different theories of consciousness are compatible/complementary if the different aspects of time are taken into account. Or, if it turns out that no existing theory can fully accommodate time consciousness, then perhaps it has something new to add. Regardless of outcome, the crucial step is to make subjective time a central object of study.

Abstract Technological advances raise new puzzles and challenges for cognitive science and the study of how humans think about and interact with artificial intelligence (AI). For example, the advent of large language models and their human-like linguistic abilities has raised substantial debate regarding whether or not AI could be conscious. Here, we consider the question of whether AI could have subjective experiences such as feelings and sensations (‘phenomenal consciousness’). While experts from many fields have weighed in on this issue in academic and public discourse, it remains unknown whether and how the general population attributes phenomenal consciousness to AI. We surveyed a sample of US residents (n = 300) and found that a majority of participants were willing to attribute some possibility of phenomenal consciousness to large language models. These attributions were robust, as they predicted attributions of mental states typically associated with phenomenality—but also flexible, as they were sensitive to individual differences such as usage frequency. Overall, these results show how folk intuitions about AI consciousness can diverge from expert intuitions—with potential implications for the legal and ethical status of AI.

Abstract We report the results of an academic survey into the theoretical and methodological foundations, common assumptions, and the current state of the field of consciousness research. The survey consisted of 22 questions and was distributed on two different occasions of the annual meeting of the Association of the Scientific Study of Consciousness (2018 and 2019). We examined responses from 166 consciousness researchers with different backgrounds (e.g. philosophy, neuroscience, psychology, and computer science) and at various stages of their careers (e.g. junior/senior faculty and graduate/undergraduate students). The results reveal that there remains considerable discussion and debate between the surveyed researchers about the definition of consciousness and the way it should be studied. To highlight a few observations, a majority of respondents believe that machines could have consciousness, that consciousness is a gradual phenomenon in the animal kingdom, and that unconscious processing is extensive, encompassing both low-level and high-level cognitive functions. Further, we show which theories of consciousness are currently considered most promising by respondents and how supposedly different theories cluster together, which dependent measures are considered best to index the presence or absence of consciousness, and which neural measures are thought to be the most likely signatures of consciousness. These findings provide us with a snapshot of the current views of researchers in the field and may therefore help prioritize research and theoretical approaches to foster progress.

Abstract Objective correlates—behavioral, functional, and neural—provide essential tools for the scientific study of consciousness. But reliance on these correlates should not lead to the ‘fallacy of misplaced objectivity’: the assumption that only objective properties should and can be accounted for objectively through science. Instead, what needs to be explained scientifically is what experience is intrinsically—its subjective properties—not just what we can do with it extrinsically. And it must be explained; otherwise the way experience feels would turn out to be magical rather than physical. We argue that it is possible to account for subjective properties objectively once we move beyond cognitive functions and realize what experience is and how it is structured. Drawing on integrated information theory, we show how an objective science of the subjective can account, in strictly physical terms, for both the essential properties of every experience and the specific properties that make particular experiences feel the way they do.

Freezing of gait (FOG) is a common and debilitating symptom in Parkinson’s disease (PD). The current study investigated alterations of resting-state spontaneous brain activity in PD patients with FOG. A total of 29 patients with FOG, 28 patients without FOG and 31 controls were included. All subjects underwent resting-state functional MRI, and the amplitude of low-frequency fluctuation (ALFF) was calculated to measure the spontaneous brain activity. Between-group differences and correlations with FOG severity (both subjective and objective measures) were analyzed. Compared to those without FOG, patients with FOG showed increased ALFF in right anterior cingulate cortex (ACC) and left inferior parietal lobule (IPL), as well as decreased ALFF in right superior frontal gyrus (SFG), bilateral cerebellum and left thalamus. Correlation analyses demonstrated that ALFF within the right SFG, right ACC and bilateral pallidum were positively correlated with FOG; while ALFF within the thalamus, putamen, cerebellum and sensorimotor regions were negatively correlated. Our results indicate that FOG is associated with dysfunction within frontal-parietal regions, along with increased inhibitory outputs from basal ganglia. Additionally, altered activity of cerebellum implicates its role in the pathophysiology of FOG. These findings provide further insight into the underlying neural mechanisms of FOG in PD patients.

Abstract The theoretical landscape of scientific studies of consciousness has flourished. Today, even multiple versions of the same theory are sometimes available. To advance the field, these theories should be directly compared to determine which are better at predicting and explaining empirical data. Systematic inquiries of this sort are seen in many subfields in cognitive psychology and neuroscience, e.g. in working memory. Nonetheless, when we surveyed publications on consciousness research, we found that most focused on a single theory. When ‘comparisons’ happened, they were often verbal and non-systematic. This fact in itself could be a contributing reason for the lack of convergence between theories in consciousness research. In this paper, we focus on how to compare theories of consciousness to ensure that the comparisons are meaningful, e.g. whether their predictions are parallel or contrasting. We evaluate how theories are typically compared in consciousness research and related subdisciplines in cognitive psychology and neuroscience, and we provide an example of our approach. We then examine the different reasons why direct comparisons between theories are rarely seen. One possible explanation is the unique nature of the consciousness phenomenon. We conclude that the field should embrace this uniqueness, and we set out the features that a theory of consciousness should account for.

Abstract This article discusses a hypothesis recently put forward by Kanai et al., according to which information generation constitutes a functional basis of, and a sufficient condition for, consciousness. Information generation involves the ability to compress and subsequently decompress information, potentially after a temporal delay and adapted to current purposes. I will argue that information generation should not be regarded as a sufficient condition for consciousness, but could serve as what I will call a “minimal unifying model of consciousness.” A minimal unifying model (MUM) specifies at least one necessary feature of consciousness, characterizes it in a determinable way, and shows that it is entailed by (many) existing theories of consciousness. Information generation fulfills these requirements. A MUM of consciousness is useful, because it unifies existing theories of consciousness by highlighting their common assumptions, while enabling further developments from which empirical predictions can be derived. Unlike existing theories (which probably contain at least some false assumptions), a MUM is thus likely to be an adequate model of consciousness, albeit at a relatively general level. Assumptions embodied in such a model are less informative than assumptions made by more specific theories and hence function more in the way of guiding principles. Still, they enable further refinements, in line with new empirical results and broader theoretical and evolutionary considerations. This also allows developing the model in ways that facilitate more specific claims and predictions.

Highly anxious individuals often show excessive emotional arousal, somatic arousal, and characteristics of mental illness. Previous researches have extensively investigated the emotional and cognitive biases of individuals with high anxiety, but overlooked the spontaneous brain activity and functional connections associated with somatic arousal. In this study, we investigated the relationship between state anxiety and the spontaneous brain activity of the somatosensory cortex in a non-clinical healthy population with state anxiety. Furthermore, we also explored the functional connections of the somatosensory cortex. We found that state anxiety was positively correlated with the amplitude of low-frequency fluctuations (ALFFs) of somatic related brain regions, including the right postcentral gyrus (somatosensory cortex) and the right precentral gyrus (somatic motor cortex). Furthermore, we found that state anxiety was positively correlated with the connections between the postcentral gyrus and the left cerebellum gyrus, whereas state anxiety was negatively correlated with the connectivity between the postcentral gyrus and brain regions including the left inferior frontal cortex and left medial superior frontal cortex. These results revealed the association between the anxious individuals’ body-loop and state anxiety in a healthy population, which revealed the importance of somatic brain regions in anxiety symptoms and provided a new perspective on anxiety for further study.

Abstract Objectives: Research into the neural basis of social anxiety disorder (SAD) suggests alterations in prefrontal networks, which may in turn disrupt regulation of the limbic system. Better understanding of the disturbed interface between these networks may improve current pathogenic models of this disorder. Methods: Applying group independent component analysis (ICA) to recordings of fMRI resting-state, connectivity in the executive control network was studied in 18 patients with SAD and 15 age- and sex-matched healthy controls. Results: Results revealed a dissociation within the left executive control network, with SAD patients showing decreased connectivity of the orbitofrontal gyrus and increased connectivity of the middle frontal gyrus compared to healthy controls. In a subsequent seed-based functional connectivity analysis, patients with SAD displayed increased connectivity between the left orbitofrontal gyrus and the left amygdala. Conclusions: Findings suggest that hypo-connectivity in the executive control network and hyper-connectivity between the orbitofrontal cortex and the amygdala may reflect a disturbance in the balance between top-down and bottom-up control processes, potentially contributing to the development of SAD.

The common marmoset (Callithrix jacchus) is a New World primate that is becoming increasingly popular as a preclinical model. To assess functional connectivity (FC) across the marmoset brain, resting-state functional MRI (RS-fMRI) is often performed under isoflurane anesthesia to avoid the effects of motion, physiological stress, and training requirements. In marmosets, however, it remains unclear how isoflurane anesthesia affects patterns of FC. Here, we investigated the effects of isoflurane on FC when delivered with either medical air or 100% pure oxygen, two canonical methods of inhalant isoflurane anesthesia delivery. The results demonstrated that when delivered with either medical air or 100% oxygen, isoflurane globally decreased FC across resting-state networks that were identified in awake marmosets. Generally, although isoflurane globally decreased FC in resting-state networks, the spatial structure of the networks was preserved. Outside of the context of RS networks, we indexed pair-wise functional connectivity between regions across the brain and found that isoflurane substantially altered interhemispheric and thalamic FC. Taken together, these findings indicate that RS-fMRI under isoflurane anesthesia is useful to evaluate the global structure of functional networks, but may obfuscate important nodes of some network components when compared to data acquired in fully awake marmosets.

Orgasm is one of the most intense pleasures attainable to an organism, yet its underlying mechanisms remain poorly understood. On the basis of existing literatures, this article introduces a novel mechanistic model of sexual stimulation and orgasm. In doing so, it characterizes the neurophenomenology of sexual trance and climax, describes parallels in dynamics between orgasms and seizures, speculates on possible evolutionary origins of sex differences in orgasmic responding, and proposes avenues for future experimentation. Here, a model is introduced wherein sexual stimulation induces entrainment of coupling mechanical and neuronal oscillatory systems, thus creating synchronized functional networks within which multiple positive feedback processes intersect synergistically to contribute to sexual experience. These processes generate states of deepening sensory absorption and trance, potentially culminating in climax if critical thresholds are surpassed. The centrality of rhythmic stimulation (and its modulation by salience) for surpassing these thresholds suggests ways in which differential orgasmic responding between individuals—or with different partners—may serve as a mechanism for ensuring adaptive mate choice. Because the production of rhythmic stimulation combines honest indicators of fitness with cues relating to potential for investment, differential orgasmic response may serve to influence the probability of continued sexual encounters with specific mates.

ABSTRACT This article examines neoliberal leverage, states and ‘deep marketization’ in relation to resource extraction in Indonesia. The concept of ‘altered’ state developmentalism within a world-historical analysis of the semi-peripheral zone of the world-economy is introduced to address how much and in what ways resource extraction persists and even continues to expands. While most theories of development assume either that natural resource extraction will gradually fade or that the ‘curse’ of natural resources requires institutional reform, Indonesia’s ‘extractive regime’ persistently and under different political systems relies on extraction of land, forest, and mineral resources. Altered state developmentalism occurs, from this perspective, within a world-historical cycle of resource nationalism (CRN) as US hegemony declines. In contrast to the A first CRN’s put more emphasis on nationalizing ownership, e for progressive redistribution aims and industrial transformation. In contrast to the first CRN's emphasis on nationalizing ownership, empirical evidence from the oil, mining, and oil palm sectors illustrate how the advance of neoliberalism is tempered in the second CRN by altered developmentalism that aspires for growth and poverty reduction in a more market-oriented direction pushed by neoliberal leverage. While accommodating some pressures for land rights against the expansion of extractive frontiers, the overall thrust expands the conditions for yet more deep marketization.

Abstract Characterizing consciousness in and of itself is notoriously difficult. Here, we propose an alternative approach to characterize, and eventually define, consciousness through exhaustive descriptions of consciousness’ relationships to all other consciousness. This approach is founded in category theory. Indeed, category theory can prove that two objects A and B in a category can be equivalent if and only if all the relationships that A holds with others in the category are the same as those of B; this proof is called the Yoneda lemma. To introduce the Yoneda lemma, we gradually introduce key concepts of category theory to consciousness researchers. Along the way, we propose several possible definitions of categories of consciousness, both in terms of level and contents, through the usage of simple examples. We propose to use the categorical structure of consciousness as a gold standard to formalize empirical research (e.g. color qualia structure at fovea and periphery) and, especially, the empirical testing of theories of consciousness.

Altered afferent input and central neural modulation are thought to contribute to fibromyalgia symptoms, and these processes converge within the spinal cord. We undertook this study to investigate the hypothesis that, using resting‐state functional magnetic resonance imaging (rs‐fMRI) of the cervical spinal cord, we would observe altered frequency‐dependent activity in fibromyalgia.

What makes a network complex, in addition to its size, is the interconnected interactions between elements, disruption of which inevitably results in dysfunction. Likewise, the brain networks’ complexity arises from interactions beyond pair connections, as it is simplistic to assume that in complex networks state of a link is independently determined only according to its two constituting nodes. This is particularly of note in genetically complex brain impairments, such as the autism spectrum disorder (ASD), which has a surprising heterogeneity in manifestations with no clear-cut neuropathology. Accordingly, structural balance theory (SBT) affirms that in real-world signed networks, a link is remarkably influenced by each of its two nodes’ interactions with the third node within a triadic interrelationship. Thus, it is plausible to ask whether ASD is associated with altered structural balance resulting from atypical triadic interactions. In other words, it is the abnormal interplay of positive and negative interactions that matters in ASD, besides and beyond hypo (hyper) pair connectivity. To address this question, we explore triadic interactions based on SBT in the weighted signed resting-state functional magnetic resonance imaging networks of participants with ASD relative to healthy controls (CON). We demonstrate that balanced triads are overrepresented in the ASD and CON networks while unbalanced triads are underrepresented, providing first-time empirical evidence for the strong notion of structural balance on the brain networks. We further analyze the frequency and energy distributions of different triads and suggest an alternative description for the reduced functional integration and segregation in the ASD brain networks. Moreover, results reveal that the scale of change in the whole-brain networks’ energy is more narrow in the ASD networks during development. Last but not least, we observe that energy of the salience network and the default mode network are lower in ASD, which may be a reflection of the difficulty in dynamic switching and flexible behaviors. Altogether, these results provide insight into the atypical structural balance of the ASD brain (sub) networks. It also highlights the potential value of SBT as a new perspective in functional connectivity studies, especially in the case of neurodevelopmental disorders.

Abstract Consciousness is an unusual phenomenon to study scientifically. It is defined as a subjective, first-person phenomenon, and science is an objective, third-person endeavor. This misalignment between the means—science—and the end—explaining consciousness—gave rise to what has become a productive workaround: the search for ‘neural correlates of consciousness’ (NCCs). Science can sidestep trying to explain consciousness and instead focus on characterizing the kind(s) of neural activity that are reliably correlated with consciousness. However, while we have learned a lot about consciousness in the bargain, the NCC approach was not originally intended as the foundation for a true explanation of consciousness. Indeed, it was proposed precisely to sidestep the, arguably futile, attempt to find one. So how can an account, couched in terms of neural correlates, do the work that a theory is supposed to do: explain consciousness? The answer is that it cannot, and in fact most modern accounts of consciousness do not pretend to. Thus, here, we challenge whether or not any modern accounts of consciousness are in fact theories at all. Instead we argue that they are (competing) laws of consciousness. They describe what they cannot explain, just as Newton described gravity long before a true explanation was ever offered. We lay out our argument using a variety of modern accounts as examples and go on to argue that at least one modern account of consciousness, attention schema theory, goes beyond describing consciousness-related brain activity and qualifies as an explanatory theory.

In the Ganzfeld technique, the visual and auditory perceptual fields are homogenized. After a short exposure to completely unstructured sensory input, participants transit into an altered state of consciousness. Visual homogenization is typically accomplished by a combination of goggles and bright light; auditory homogenization is accomplished by the presentation of unstructured auditory noise via headphones. The induced state is phenomenologically similar to a transition state between wakefulness and sleep, characterized by alterations in attentiveness, perception, and awareness, as well as by a compressed sense of time. Due to these replicable features of the Ganzfeld-induced state, it can be used within empirical research on the neuronal underpinnings of altered states phenomena. After a historic overview, here, we present data from a study on the stability of the subjectively experienced effects induced under different auditory homogenization conditions. In a fully randomized within-subject design (n = 24), we tested for the effects of three different auditory noise conditions: (1) violet, (2) white, and (3) brown noise. The combination of a standardized psychometric assessment, ratings on subjective time perception, as well as open reports prove the Ganzfeld-induced effects as being stable and effects within each participant as highly replicable, and therefore well suited for experimental purposes. Finally, the subjective experiences elucidated by the Ganzfeld technique are discussed within the framework of predictive coding and how changes in the interaction of top-down and bottom-up brain mechanisms could lead to the observed phenomenology.

In this article, I present the concept of “birthing consciousness,” a psychophysical altered state of women that can occur during natural and undisturbed birth. I demonstrate that this altered state of consciousness (ASC) has phenomenological and cognitive features of hypofrontality; thus, birthing consciousness probably shares a similar brain mechanism to that postulated by the transient-hypofrontality theory (THT). I argue that until recently (with the advent of modern medical intervention), in evolutionary terms, women lacking the proclivity for this specific brain mechanism had a lower chance of reproducing successfully. Hence, I suggest a general and preliminary hypothesis concerning THT: Birthing consciousness is one example of an adaptive pain-induced ASC associated with transient hypofrontality.

Different pharmacologic agents have been used to investigate the neuronal underpinnings of alterations in consciousness states, such as psychedelic substances. Special attention has been drawn to the role of thalamic filtering of cortical input. Here, we investigate the neuronal mechanisms underlying an altered state of consciousness (ASC) induced by a non-pharmacological procedure. During fMRI scanning, N = 19 human participants were exposed to multimodal Ganzfeld stimulation, a technique of perceptual deprivation where participants are exposed to intense, unstructured, homogenous visual and auditory stimulation. Compared to pre- and post-resting-state scans, the Ganzfeld data displayed a progressive decoupling of the thalamus from the cortex. Furthermore, the Ganzfeld-induced ASC was characterized by increased eigenvector centrality in core regions of the default mode network (DMN). Together, these findings can be interpreted as an imbalance of sensory bottom-up signaling and internally-generated top-down signaling. This imbalance is antithetical to psychedelic-induced ASCs, where increased thalamo-cortical coupling and reduced DMN activity were observed.

Background: Numerous world cultures believe channeling provides genuine information, and channeling rituals in various forms are regularly conducted in both religious and non-religious contexts. Little is known about the physiological correlates of the subjective experience of channeling. Methods: We conducted a prospective within-subject design study with 13 healthy adult trance channels. Participants alternated between 5-minute blocks of channeling and no-channeling three times while electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and respiration were collected on two separate days. Voice recordings of the same story read in channeling and no-channeling states were also analyzed. Results: The pre-laboratory survey data about demographics, perception of the source, purpose and utility of channeled information reflected previous reports. Most participants were aware of their experience (rather than in a full trance) and had varying levels of perceived incorporation (i.e. control of their body). Voice analysis showed an increase in voice arousal and power (dB/Hz) differences in the 125 Hz bins between 0 and 625 Hz, and 3625 and 3875 Hz when reading during the channeling state versus control. Despite subjective perceptions of distinctly different states, no substantive differences were seen in EEG frequency power, ECG measures, GSR and respiration. Conclusions: Voice parameters were different between channeling and no-channeling states using rigorous controlled methods, but other physiology measure collected were not. Considering the subjective and phenomenological differences observed, future studies should include other measures such as EEG connectivity analyses, fMRI and biomarkers.

Abstract Previous research using stepwise regression analyses found self-reported hypnotic depth (srHD) to be a function of suggestibility, trance state effects, and expectancy. This study sought to replicate and expand that research using a general state measure of hypnotic responsivity, the Phenomenology of Consciousness Inventory: Hypnotic Assessment Procedure (PCI-HAP). Ninety-five participants completed an Italian translation of the PCI-HAP, with srHD scores predicted from the PCI-HAP assessment items. The regression analysis replicated the previous research results. Additionally, stepwise regression analyses were able to predict the srHD score equally well using only the PCI dimension scores. These results not only replicated prior research but suggest how this methodology to assess hypnotic responsivity, when combined with more traditional neurophysiological and cognitive-behavioral methodologies, may allow for a more comprehensive understanding of that enigma called hypnosis.

An empirical investigation of how local harmonic structures (e.g., chord progressions) contribute to the experience and enjoyment of uplifting trance (UT) music is presented. The connection between rhythmic and percussive elements and resulting trance-like states has been highlighted by musicologists, but no research, to our knowledge, has explored whether repeated harmonic elements influence affective responses in listeners of trance music. Two alternative hypotheses are discussed, the first highlighting the direct relationship between repetition/complexity and enjoyment, and the second based on the theoretical inverted-U relationship described by the Wundt curve. We investigate the connection between harmonic structure and subjective enjoyment through interdisciplinary behavioral and computational methods: First we discuss an experiment in which listeners provided enjoyment ratings for computer-generated UT anthems with varying levels of harmonic repetition and complexity. The anthems were generated using a statistical model trained on a corpus of 100 uplifting trance anthems created for this purpose, and harmonic structure was constrained by imposing particular repetition structures (semiotic patterns defining the order of chords in the sequence) on a professional UT music production template. Second, the relationship between harmonic structure and enjoyment is further explored using two computational approaches, one based on average Information Content, and another that measures average tonal tension between chords. The results of the listening experiment indicate that harmonic repetition does in fact contribute to the enjoyment of uplifting trance music. More compelling evidence was found for the second hypothesis discussed above, however some maximally repetitive structures were also preferred. Both computational models provide evidence for a Wundt-type relationship between complexity and enjoyment. By systematically manipulating the structure of chord progressions, we have discovered specific harmonic contexts in which repetitive or complex structure contribute to the enjoyment of uplifting trance music.

While historians of spiritualism have been eager to focus on its political and social implications, less attention has been given to the fact that spirit communication was also a matter of visual spectacle. This article aims to analyse spiritualist séances as a form of spectacular entertainment. Relying on a wide array of spiritualist sources, it argues that séances were meant not only as moments of religious and scientific inquiry, but also as a brilliant amusement where theatrical effects embellished an exciting shared experience. The intermingling of religion and entertainment can thus be seen as one of the defining characteristics of the spiritualist experience. After sketching the history of the presence of spiritualist mediums on the stage and discussing the involvement of professionalism in mediumship, the article will then focus on the trance as a specific performance strategy. It will examine how the trance combined issues of automatism, theatricality and absorption, and contributed to the coexistence in spirit séances of spectacular features and claims of authenticity.

This is a personal account of a recent criminal trial in the United Kingdom that the author was involved in as an expert witness, involving a young Zimbabwean woman who attacked her mother with a knife, when she (as she, her mother, and relatives claimed) was possessed by an evil spirit as the result of another family member’s witchcraft. Evidence for her abnormal state of consciousness was corroborated by police evidence that described her as “in a trance” on the night in question, and despite a wide range of medical and psychiatric assessments, no clear neurological, medical, psychiatric, or sleep-disorder causes for her “possession” were ever established. The article describes the difficulties encountered in producing anthropological evidence for the criminal court that sought to go beyond the limitations of conventional forms of “cultural defense” to argue for the limits of knowledge and the “possibility of other possibilities.” With a nod to Harry West’s notion of “ethnographic sorcery,” this unusual court case illustrates how anthropological expert evidence can be constrained by courts constructing their own kinds of certainty, and yet still have efficacy in unintended ways.

Trance processes are a form of altered states of consciousness (ASC) widely reported across cultures. Entering these states is often linked to auditory stimuli such as singing, chanting, or rhythmic drumming. While scientific research into this phenomenon is relatively nascent, there is emerging interest in investigating the neural correlates of altered states of consciousness such as trance. This report aims to add to this field of ASC through exploring how the perception of an experienced Sangoma--traditional South African healer--entering a trance process correlates to BOLD signal modulation with auditory stimuli. Functional MRI data were analyzed using a General Linear model comparing music versus no music condition multiplied by the percept of experiencing trance (High or Low). Positive BOLD activation was shown in auditory cortex in both hemispheres during a trance process. Other brain regions tightly correlated to trance perception were right parietal, right frontal, and area prostriata (P<0.05, Bonferroni corrected). Orbitofrontal cortex (part of the Default Mode Network) was negatively activated and most correlated with music when trance was high, showing the largest differential between high and low trance perception. This is the first study to directly correlate BOLD signal variations in an expert subject’s percept of trance onset and intensity, providing insight into the neural signature and dynamics of this unique form of ASC. Future studies should examine in greater detail perception of trance processes in expert subjects, adding other neuroimaging modalities to further investigate how these brain regions are modulated by trance expertise.

This article explores a religious community in Algeria where, with the ignition and structure of ritual music, a wide spectrum of trance processes are explicitly cultivated so that pain and suffering can be engaged, moved, and expressed through trance dancing. The way that trance is described in d̄ıwān indicates that it is understood as emerging primarily from the realms of feelings, particularly the dialectical role of painful feelings. Furthermore, varieties of trance are named and indexed either by the sorts of affects they involve or as types of actions that trance feels like. Thus, rather than trance being something of the “mind” here, it is an affective experience, ordered by how it feels. This article takes a close-up, sensory ethnographic approach to flesh out the rich, detailed taxonomy of feeling intensities that are used to describe how trance feels, examining what tranced suffering does both socially and personally. [affect, body, suffering, Sufism, trance]

We read with great interest the editorial by Krüger (2020) in which she provides a review of some of the cultural aspects of dissociation and trauma. We especially appreciate her call for the study of the “entire spectrum of dissociation” (p. 1) from normal to pathological. We have been working along similar lines (e.g., Maraldi et al., 2017; Maraldi et al., 2019) and would like to take the opportunity offered by Krüger to explore further the literature on possession experiences, which she mentions only briefly in her paper. Cardeña et al. (2009, p. 173) define possession trance as “a temporary alteration of consciousness, identity, and/or behavior, attributed to possession by a spiritual force or another person”. When involuntary, unwanted and incompatible with cultural or religious norms and practices, such experiences might be classified as pathological. The diagnosis of “possession trance disorder” was first proposed in the DSM-IV (APA, 1994) as a condition for further study, but in the DSM-5 (APA, 2013) pathological experiences of possession were subsumed under the dissociative identity disorder (DID) category. Pathological possession is now thought in terms of a “possession-form presentation” of DID. But the link between DID and possession trance has been contested by researchers from different countries (e.g., Delmonte et al., 2015; Maraldi et al., 2017; Van Duijl et al., 2013). Also, it is still unclear to what extent pathological possession differs from other pathological phenomena, such as possession delusions in psychosis and some of the so-called cultural syndromes (e.g., ataque de nervios, LewisFernández, 1994), which are considered by some authors as instances of dissociative phenomena (Hegeman, 2013; Ross et al., 2018). On the other hand, there are many nonpathological dissociative experiences about which we know very little, including spiritual experiences reported during religious or ritualistic practices (Alvarado, 2005; Queiroz, 2017). The DSM

Abstract Trance states include various practices characterized by a modulation of consciousness, but with their own specific characteristics and induction techniques. They have been very seldom scientifically studied, and their phenomenological similarities and differences are poorly documented. This paper will focus on two types of Western trances developed after the leaders were trained in traditional shamanic communities: the auto-induced cognitive trance (AICT) and the Mahorikatan® trance (MT). Twenty-five AICT and 26 MT participants who were able to self-induce the trance state completed questionnaires about their trance practice (e.g. context of the first trance episode, frequency of practice, and consequences on personal life) and the phenomenological characteristics (i.e. emotional, physical, and cognitive) of the trance episodes they experienced. These characteristics were compared to explore similarities and differences between the two trance states. AICT and MT are characterized by the expression of different emotions, modification of various perceptions, a feeling of unicity (i.e. being completely oneself), and an expansion of consciousness. AICT participants commonly reported body movements, vocalizations, as well as increased creativity, visions of entities and/or places, and feeling of interaction with the environment. MT participants commonly reported a feeling of body dissolution. Most participants in both groups reported positive effects of their trance practice on their personal life. These results helped characterize AICT and MT, as well as their similarities and differences. Further studies should continue to explore the characteristics of such trance states, as well as their potential clinical applications.

ABSTRACT The Bwiti is one out of several religions in Africa and through it, it is believed, that the members can be connected to the world of the ancestors. There is also the ritual of healing in Bwiti. In this ritual, participants are invited to take iboga. They fall into a trance and after this phase, which in principle lasts three days; those who were sick recover the health in many cases. We try to find out in the article, if the change of the state of consciousness like trance can heal. In the end we discover that it is possible. In Bwiti, the change of the paradigm is the most important principle.

One of the techniques used to induce trance state in shamanic ceremonies is monotonous drumming. According to previous EEG research, certain patterns of drumming cause an increase in power of alpha ...

BACKGROUND: One of the works of Javanese culture is the kuda kepang. None of the historical records explains the origin of this dance. Kuda kepang or also called kuda lumping or jathilan. In the kuda kepang dance, the trance process takes place through the calling of the spirit or called endang through the songs that are played. In the International Classification of Disease Edition (ICD-10), it is called trance and possession disorders and in the Diagnostic and Statistical Manual of Mental Disorders five editions (DSM-5) it is included into Other Specified Dissociative Disorder. CASE REPORT: This is a case of the direct interview with the kuda kepang player, SI (30 years old), and HO (54 years old) both were the Javanese tribe as the owner of the kuda kepang art group called “Kesenian Tunas Baru” in Sukaraya Village, Kutalimbaru District. He was one of the music players in the arts. CONCLUSION: The traditional performance of kuda kepang is a change and innovation that characterises the traditional art world where kuda kepang performances are truly sacred events and now become extraordinary and can be performed. In this case, it appears when the magical side is in the trance condition; this trance can be performed.

Hypnosis has often, and primarily, been portrayed as a mystical means that controls and exploits vulnerable and defenseless people. Sources accused of perpetuating hypnosis myths and misconceptions have included numerous media productions and stage demonstrations at state fairs and festivals. Ironically, one largely unexamined potential culprit disseminating misinformation about hypnosis is the field of clinical hypnosis itself. This article not only questions the legitimacy of the term “hypnotic induction” and its derivatives but also explores the potential impact these terms have on the perpetuation of hypnosis myths and misconceptions. Through an examination of a selective history of hypnotic induction, the customary language of hypnosis, and information promoted by professional hypnosis societies, some of the contributing terminology is identified. Alternative terms that more appropriately embody the manifestation of trance are offered and discussed.

Since palaeolithic times, shamans involved animal depictions in cave ceremonies and adopted animals as helping spirits during their trance journeys. This study aims at explaining the rituals with new evidence: the shamans were acting rationally to deal with an inverted otherworld, still rooted in many traditional beliefs, and seen as an impressive natural phenomenon in superior mirages. After experimenting with unhealthy upside down positions, shamans have adopted more convenient ways to “invert” their consciousness by generating oxygen deficiency and trance for entering the inverted world. San rock art from Brandberg, Namibia, is used as a model to learn more about the logic behind shamanistic trance travel, for example aimed at attracting rain. When travelling to an inverted otherworld San shamans used, besides trance-inverting their consciousness, symbols of inversion (inverted sex, inverted body posture and body painting) and were hiding their faces. Negative hand stencils, dotted (mirage) animals and hidden faces were correspondingly used in palaeolithic caves during trance ceremonies. The animals painted on cave walls were aimed at meeting their counterparts in the other world, a condition for the termination of earthly life, to enable their successful hunting. Animals seen to behave “supernaturally” in mirages and mirage phenomena themselves became helping spirits for the voyage. The mirage of earthly objects into the sky itself served as role model for shamanic travel. Voluptuous Venus figures were carved for the inverted world. They show an inversion of reality. The conclusion is that early spiritual and religious concepts developed rationally, like other strategies for survival, in attempts to deal with an occasionally but worldwide seen natural phenomenon which suggested an inverted otherworld.

Stephen Gilligan has a well-established, well-deserved reputation as one of Milton Erickson’s most adept students. Generative Trance demonstrates Gilligan’s exceptional ability to carry Erickson’s pioneering ideas forward, and further define possibilities within the territory of unconscious hypnotic work. In the preface, Gilligan explains his view of using trance to “elevate consciousness to a higher level of creativity” (page ix). The meaning of this concept is illustrated with his own autobiographical tale of transformation.

This article conceptualizes community cultural wealth as a critical race theory (CRT) challenge to traditional interpretations of cultural capital. CRT shifts the research lens away from a deficit view of Communities of Color as places full of cultural poverty disadvantages, and instead focuses on and learns from the array of cultural knowledge, skills, abilities and contacts possessed by socially marginalized groups that often go unrecognized and unacknowledged. Various forms of capital nurtured through cultural wealth include aspirational, navigational, social, linguistic, familial and resistant capital. These forms of capital draw on the knowledges Students of Color bring with them from their homes and communities into the classroom. This CRT approach to education involves a commitment to develop schools that acknowledge the multiple strengths of Communities of Color in order to serve a larger purpose of struggle toward social and racial justice.

We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained for 2D image synthesis have been turned into generative video models by inserting temporal layers and finetuning them on small, high-quality video datasets. However, training methods in the literature vary widely, and the field has yet to agree on a unified strategy for curating video data. In this paper, we identify and evaluate three different stages for successful training of video LDMs: text-to-image pretraining, video pretraining, and high-quality video finetuning. Furthermore, we demonstrate the necessity of a well-curated pretraining dataset for generating high-quality videos and present a systematic curation process to train a strong base model, including captioning and filtering strategies. We then explore the impact of finetuning our base model on high-quality data and train a text-to-video model that is competitive with closed-source video generation. We also show that our base model provides a powerful motion representation for downstream tasks such as image-to-video generation and adaptability to camera motion-specific LoRA modules. Finally, we demonstrate that our model provides a strong multi-view 3D-prior and can serve as a base to finetune a multi-view diffusion model that jointly generates multiple views of objects in a feedforward fashion, outperforming image-based methods at a fraction of their compute budget. We release code and model weights at https://github.com/Stability-AI/generative-models .

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and finetuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution $512 \times 1024$, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pretrained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to $1280 \times 2048$. We show that the temporal layers trained in this way generalize to different finetuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Project page: https://nv-tlabs.github.io/VideoLDM/

We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a novel video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generator and discriminator architectures, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our approach to future video prediction, outperforming several state-of-the-art competing systems.

BackgroundClustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism; that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes.ResultsCRT was compared to CRISPR detection tools, Patscan and Pilercr. In terms of correctness, CRT was shown to be very reliable, demonstrating significant improvements over Patscan for measures precision, recall and quality. When compared to Pilercr, CRT showed improved performance for recall and quality. In terms of speed, CRT proved to be a huge improvement over Patscan. Both CRT and Pilercr were comparable in speed, however CRT was faster for genomes containing large numbers of repeats.ConclusionIn this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, showed some important improvements over current techniques for CRISPR identification. CRT's approach to detecting repetitive sequences is straightforward. It uses a simple sequential scan of a DNA sequence and detects repeats directly without any major conversion or preprocessing of the input. This leads to a program that is easy to describe and understand; yet it is very accurate, fast and memory efficient, being O(n) in space and O(nm/l) in time.

We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex. Traditional optical-flow-based solutions often fail where flow estimation is challenging, while newer neural-network-based methods that hallucinate pixel values directly often produce blurry results. We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow. Our method requires no human supervision, and any video can be used as training data by dropping, and then learning to predict, existing frames. The technique is efficient, and can be applied at any video resolution. We demonstrate that our method produces results that both quantitatively and qualitatively improve upon the state-of-the-art.

Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on image animation benchmarks, achieving state-of-the-art results.

We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. Our model learns to synthesize a talking-head video using a source image containing the target person’s appearance and a driving video that dictates the motion in the output. Our motion is encoded based on a novel keypoint representation, where the identity-specific and motion-related information is decomposed unsupervisedly. Extensive experimental validation shows that our model outperforms competing methods on benchmark datasets. Moreover, our compact keypoint representation enables a video conferencing system that achieves the same visual quality as the commercial H.264 standard while only using one-tenth of the bandwidth. Besides, we show our keypoint representation allows the user to rotate the head during synthesis, which is useful for simulating face-to-face video conferencing experiences.

We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appearance representation. We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e.g. a ‘bullet-time’ video effect. NR-NeRF disentangles the dynamic scene into a canonical volume and its deformation. Scene deformation is implemented as ray bending, where straight rays are deformed non-rigidly. We also propose a novel rigidity network to better constrain rigid regions of the scene, leading to more stable results. The ray bending and rigidity network are trained without explicit supervision. Our formulation enables dense correspondence estimation across views and time, and compelling video editing applications such as motion exaggeration. Our code will be open sourced.

We present an algorithm for generating novel views at arbitrary viewpoints and any input time step given a monocular video of a dynamic scene. Our work builds upon recent advances in neural implicit representation and uses continuous and differentiable functions for modeling the time-varying structure and the appearance of the scene. We jointly train a time-invariant static NeRF and a time-varying dynamic NeRF, and learn how to blend the results in an unsupervised manner. However, learning this implicit function from a single video is highly ill-posed (with infinitely many solutions that match the input video). To resolve the ambiguity, we introduce regularization losses to encourage a more physically plausible solution. We show extensive quantitative and qualitative results of dynamic view synthesis from casually captured videos.

Vision and language are the two foundational senses for humans, and they build up our cognitive ability and intelligence. While significant breakthroughs have been made in AI language ability, artificial visual intelligence, especially the ability to generate and simulate the world we see, is far lagging behind. To facilitate the development and accessibility of artificial visual intelligence, we created Open-Sora, an open-source video generation model designed to produce high-fidelity video content. Open-Sora supports a wide spectrum of visual generation tasks, including text-to-image generation, text-to-video generation, and image-to-video generation. The model leverages advanced deep learning architectures and training/inference techniques to enable flexible video synthesis, which could generate video content of up to 15 seconds, up to 720p resolution, and arbitrary aspect ratios. Specifically, we introduce Spatial-Temporal Diffusion Transformer (STDiT), an efficient diffusion framework for videos that decouples spatial and temporal attention. We also introduce a highly compressive 3D autoencoder to make representations compact and further accelerate training with an ad hoc training strategy. Through this initiative, we aim to foster innovation, creativity, and inclusivity within the community of AI content creation. By embracing the open-source principle, Open-Sora democratizes full access to all the training/inference/data preparation codes as well as model weights. All resources are publicly available at: https://github.com/hpcaitech/Open-Sora.

The pursuit of controllability as a higher standard of visual content creation has yielded remarkable progress in customizable image synthesis. However, achieving controllable video synthesis remains challenging due to the large variation of temporal dynamics and the requirement of cross-frame temporal consistency. Based on the paradigm of compositional generation, this work presents VideoComposer that allows users to flexibly compose a video with textual conditions, spatial conditions, and more importantly temporal conditions. Specifically, considering the characteristic of video data, we introduce the motion vector from compressed videos as an explicit control signal to provide guidance regarding temporal dynamics. In addition, we develop a Spatio-Temporal Condition encoder (STC-encoder) that serves as a unified interface to effectively incorporate the spatial and temporal relations of sequential inputs, with which the model could make better use of temporal conditions and hence achieve higher inter-frame consistency. Extensive experimental results suggest that VideoComposer is able to control the spatial and temporal patterns simultaneously within a synthesized video in various forms, such as text description, sketch sequence, reference video, or even simply hand-crafted motions. The code and models will be publicly available at https://videocomposer.github.io.

We introduce Lumiere – a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion – a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution – an approach that inherently makes global temporal consistency difficult to achieve. By deploying both spatial and (importantly) temporal down- and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales. We demonstrate state-of-the-art text-to-video generation results , and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation.

Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous images of a target human subject or a scene are required for training. Second, a learned model has limited generalization capability. A pose-to-human vid2vid model can only synthesize poses of the single person in the training set. It does not generalize to other humans that are not in the training set. To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time. Our model achieves this few-shot generalization capability via a novel network weight generation module utilizing an attention mechanism. We conduct extensive experimental validations with comparisons to strong baselines using several large-scale video datasets including human-dancing videos, talking-head videos, and street-scene videos. The experimental results verify the effectiveness of the proposed framework in addressing the two limitations of existing vid2vid approaches.

Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence. In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by utilizing static images as a form of crucial guidance. I2VGen-XL consists of two stages: i) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and ii) the refinement stage enhances the video's details by incorporating an additional brief text and improves the resolution to 1280$\times$720. To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos. Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data. The source code and models will be publicly available at \url{https://i2vgen-xl.github.io}.

We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to either limited views or inconsistent NVS, thereby affecting the performance of 3D object generation. In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS. We also propose improved 3D optimization techniques to use SV3D and its NVS outputs for image-to-3D generation. Extensive experimental results on multiple datasets with 2D and 3D metrics as well as user study demonstrate SV3D's state-of-the-art performance on NVS as well as 3D reconstruction compared to prior works.

Existing security and identity-based vehicular communication protocols used in Vehicular Ad-hoc Networks (VANETs) to achieve conditional privacy-preserving mostly rely on an ideal hardware device called tamper-proof device (TPD) equipped in vehicles. Achieving fast authentication during the message verification process is usually challenging in such strategies and further they suffer performance constraints from resulting overheads. To address such challenges, this paper proposes a novel Chinese remainder theorem (CRT)-based conditional privacy-preserving authentication scheme for securing vehicular authentication. The proposed protocol only requires realistic TPDs, and eliminates the need for pre-loading the master key onto the vehicle's TPDs. Chinese remainder theorem can dynamically assist the trusted authorities (TAs) whilst generating and broadcasting new group keys to the vehicles in the network. The proposed scheme solves the leakage problem during side channel attacks, and ensures higher level of security for the entire system. In addition, the proposed scheme avoids using the bilinear pairing operation and map-to-point hash operation during the authentication process, which helps achieving faster verification even under increasing number of signature. Moreover, the security analysis shows that our proposed scheme is secure under the random oracle model and the performance analysis shows that our proposed scheme is efficient in reducing computation and communication overheads.

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose ViewCrafter, a novel method for synthesizing high-fidelity novel views from single or sparse images with the prior of video diffusion model. Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames with significantly improved camera pose control accuracy. To further enlarge the generation range of novel views, we tailored a progressive view synthesis strategy to expand the point cloud and the areas covered by the novel views, which can be further integrated with a camera trajectory planning algorithm to automatically reveal and address occlusions in different scenes. With ViewCrafter, we can facilitate various applications, such as immersive experiences with real-time rendering by efficiently optimizing a 3D-GS representation using the reconstructed 3D points and the generated novel views, and scene-level text-to-3D generation for more imaginative content creation. Extensive experiments on diverse datasets demonstrate the strong generalization capability and superior performance of our method in synthesizing high-fidelity novel views. Our project webpage and code are available at https://drexubery.github.io/ViewCrafter/.

We present DreamPose, a diffusion-based method for generating animated fashion videos from still images. Given an image and a sequence of human body poses, our method synthesizes a video containing both human and fabric motion. To achieve this, we transform a pre-trained text-to-image model (Stable Diffusion [16]) into a pose-and-image guided video synthesis model, using a novel finetuning strategy, a set of architectural changes to support the added conditioning signals, and techniques to encourage temporal consistency. We fine-tune on a collection of fashion videos from the UBC Fashion dataset [50]. We evaluate our method on a variety of clothing styles and poses, and demonstrate that our method produces state-of-the-art results on fashion video animation. Video results are available on our project page: https://grail.cs.washington.edu/projects/dreampose

Image and video synthesis are closely related areas aiming at generating content from noise. While rapid progress has been demonstrated in improving image-based models to handle large resolutions, high-quality renderings, and wide variations in image content, achieving comparable video generation results remains problematic. We present a framework that leverages contemporary image generators to render high-resolution videos. We frame the video synthesis problem as discovering a trajectory in the latent space of a pre-trained and fixed image generator. Not only does such a framework render high-resolution videos, but it also is an order of magnitude more computationally efficient. We introduce a motion generator that discovers the desired trajectory, in which content and motion are disentangled. With such a representation, our framework allows for a broad range of applications, including content and motion manipulation. Furthermore, we introduce a new task, which we call cross-domain video synthesis, in which the image and motion generators are trained on disjoint datasets belonging to different domains. This allows for generating moving objects for which the desired video data is not available. Extensive experiments on various datasets demonstrate the advantages of our methods over existing video generation techniques. Code will be released at https://github.com/snap-research/MoCoGAN-HD.

AIMS In patients with atrial fibrillation (AF) and heart failure (HF), strict and regular rate control with atrioventricular junction ablation and biventricular pacemaker (Ablation + CRT) has been shown to be superior to pharmacological rate control in reducing HF hospitalizations. However, whether it also improves survival is unknown. METHODS AND RESULTS  In this international, open-label, blinded outcome trial, we randomly assigned patients with severely symptomatic permanent AF >6 months, narrow QRS (≤110 ms) and at least one HF hospitalization in the previous year to Ablation + CRT or to pharmacological rate control. We hypothesized that Ablation + CRT is superior in reducing the primary endpoint of all-cause mortality. A total of 133 patients were randomized. The mean age was 73 ± 10 years, and 62 (47%) were females. The trial was stopped for efficacy at interim analysis after a median of 29 months of follow-up per patient. The primary endpoint occurred in 7 patients (11%) in the Ablation + CRT arm and in 20 patients (29%) in the Drug arm [hazard ratio (HR) 0.26, 95% confidence interval (CI) 0.10-0.65; P = 0.004]. The estimated death rates at 2 years were 5% and 21%, respectively; at 4 years, 14% and 41%. The benefit of Ablation + CRT of all-cause mortality was similar in patients with ejection fraction (EF) ≤35% and in those with >35%. The secondary endpoint combining all-cause mortality or HF hospitalization was significantly lower in the Ablation + CRT arm [18 (29%) vs. 36 (51%); HR 0.40, 95% CI 0.22-0.73; P = 0.002]. CONCLUSIONS Ablation + CRT was superior to pharmacological therapy in reducing mortality in patients with permanent AF and narrow QRS who were hospitalized for HF, irrespective of their baseline EF. STUDY REGISTRATION ClinicalTrials.gov Identifier: NCT02137187.

The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos, a task that was challenging or impossible with prior methods. It has also led to the creation of many new applications in content creation. In this article, we provide an overview of GANs with a special focus on algorithms and applications for visual synthesis. We cover several important techniques to stabilize GAN training, which has a reputation for being notoriously difficult. We also discuss its applications to image translation, image processing, video synthesis, and neural rendering.

Diffusion Transformer has demonstrated powerful capability and scalability in generating high-quality images and videos. Further pursuing the unification of generation and editing tasks has yielded significant progress in the domain of image content creation. However, due to the intrinsic demands for consistency across both temporal and spatial dynamics, achieving a unified approach for video synthesis remains challenging. We introduce VACE, which enables users to perform Video tasks within an All-in-one framework for Creation and Editing. These tasks include reference-to-video generation, video-to-video editing, and masked video-to-video editing. Specifically, we effectively integrate the requirements of various tasks by organizing video task inputs, such as editing, reference, and masking, into a unified interface referred to as the Video Condition Unit (VCU). Furthermore, by utilizing a Context Adapter structure, we inject different task concepts into the model using formalized representations of temporal and spatial dimensions, allowing it to handle arbitrary video synthesis tasks flexibly. Extensive experiments demonstrate that the unified model of VACE achieves performance on par with task-specific models across various subtasks. Simultaneously, it enables diverse applications through versatile task combinations. Project page: https://ali-vilab.github.io/VACE-Page/.

Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning. In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a conditional GAN model with a novel multi-scale text-conditioning scheme that improves text-video associations. By combining the proposed conditioning scheme with a deep GAN architecture, TFGAN generates high quality videos from text on challenging real-world video datasets. In addition, we construct a synthetic dataset of text-conditioned moving shapes to systematically evaluate our conditioning scheme. Extensive experiments demonstrate that TFGAN significantly outperforms existing approaches, and can also generate videos of novel categories not seen during training.

Abstract It has long been recognized that sample size calculations for cluster randomized trials require consideration of the correlation between multiple observations within the same cluster. When measurements are taken at anything other than a single point in time, these correlations depend not only on the cluster but also on the time separation between measurements and additionally, on whether different participants (cross-sectional designs) or the same participants (cohort designs) are repeatedly measured. This is particularly relevant in trials with multiple periods of measurement, such as the cluster cross-over and stepped-wedge designs, but also to some degree in parallel designs. Several papers describing sample size methodology for these designs have been published, but this methodology might not be accessible to all researchers. In this article we provide a tutorial on sample size calculation for cluster randomized designs with particular emphasis on designs with multiple periods of measurement and provide a web-based tool, the Shiny CRT Calculator, to allow researchers to easily conduct these sample size calculations. We consider both cross-sectional and cohort designs and allow for a variety of assumed within-cluster correlation structures. We consider cluster heterogeneity in treatment effects (for designs where treatment is crossed with cluster), as well as individually randomized group-treatment trials with differential clustering between arms, for example designs where clustering arises from interventions being delivered in groups. The calculator will compute power or precision, as a function of cluster size or number of clusters, for a wide variety of designs and correlation structures. We illustrate the methodology and the flexibility of the Shiny CRT Calculator using a range of examples.

Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, simplifying the process of producing diverse and individual content. Current methods excel in generating short videos (up to 16s), but produce hard-cuts when naively extended to long video synthesis. To overcome these limitations, we present StreamingT2V, an autoregressive method that generates long videos of up to 2 minutes or longer with seamless transitions. The key components are: (i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the preceding chunk via an attentional mechanism, leading to consistent chunk transitions, (ii) a longterm memory block called appearance preservation module (APM), which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene, and (iii) a randomized blending approach that allows for the autoregressive application of a video enhancer on videos of indefinite length, ensuring consistency across chunks. Experiments show that StreamingT2V produces more motion, while competing methods suffer from video stagnation when applied naively in an autoregressive fashion. Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator, surpassing competitors in both consistency and motion.

Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. The generation of a single frame requires the model to process the entire sequence, including the future. We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly. To further reduce latency, we extend distribution matching distillation (DMD) to videos, distilling 50-step diffusion model into a 4-step generator. To enable stable and high-quality distillation, we introduce a student initialization scheme based on teacher’s ODE trajectories, as well as an asymmetric distillation strategy that supervises a causal student model with a bidirectional teacher. This approach effectively mitigates error accumulation in autoregressive generation, allowing long-duration video synthesis despite training on short clips. Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models. It enables fast streaming generation of high-quality videos at 9.4 FPS on a single GPU thanks to KV caching. Our approach also enables streaming video-to-video translation, image-to-video, and dynamic prompting in a zero-shot manner. We release our code and pretrained models.

The Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. However, previous methods in V2A have limited generation quality in terms of temporal synchronization and audio-visual relevance. We present Diff-Foley, a synchronized Video-to-Audio synthesis method with a latent diffusion model (LDM) that generates high-quality audio with improved synchronization and audio-visual relevance. We adopt contrastive audio-visual pretraining (CAVP) to learn more temporally and semantically aligned features, then train an LDM with CAVP-aligned visual features on spectrogram latent space. The CAVP-aligned features enable LDM to capture the subtler audio-visual correlation via a cross-attention module. We further significantly improve sample quality with `double guidance'. Diff-Foley achieves state-of-the-art V2A performance on current large scale V2A dataset. Furthermore, we demonstrate Diff-Foley practical applicability and generalization capabilities via downstream finetuning. Project Page: see https://diff-foley.github.io/

Synthesizing high-fidelity videos from real-world multi-view input is challenging due to the complexities of real-world environments and high-dynamic movements. Previous works based on neural radiance fields have demonstrated high-quality reconstructions of dynamic scenes. However, training such models on real-world scenes is time-consuming, usually taking days or weeks. In this paper, we present a novel method named MixVoxels to efficiently represent dynamic scenes, enabling fast training and rendering speed. The proposed MixVoxels represents the 4D dynamic scenes as a mixture of static and dynamic voxels and processes them with different networks. In this way, the computation of the required modalities for static voxels can be processed by a lightweight model, which essentially reduces the amount of computation as many daily dynamic scenes are dominated by static backgrounds. To distinguish the two kinds of voxels, we propose a novel variation field to estimate the temporal variance of each voxel. For the dynamic representations, we design an inner product time query method to efficiently query multiple time steps, which is essential to recover the high-dynamic movements. As a result, with 15 minutes of training for dynamic scenes with inputs of 300-frame videos, MixVoxels achieves better PSNR than previous methods. For rendering, MixVoxels can render a novel view video with 1K resolution at 37 fps. Codes and trained models are available at https://github.com/fengres/mixvoxels.

BackgroundAlthough preoperative radiotherapy (RT) is widely used as the initial treatment for locally advanced rectal cancer (RC) in the neoadjuvant setting, factors determining clinical response have not been adequately defined. Radiosensitivity has recently been shown to be greatly affected by immune function of the host.MethodsIn 48 cases of advanced RC, we retrospectively examined the density of tumor infiltrating CD4(+) and CD8(+) T cells using immunohistochemical staining of biopsy samples before CRT, and examined the correlation with tumor response.ResultsThe numbers of both CD4(+) and CD8(+) tumor-infiltrating lymphocytes (TIL) in pre-CRT biopsy samples were strongly correlated with tumor reduction ratio evaluated by barium enema. Moreover, the densities of CD4(+) and CD8(+) TIL were significantly associated with histological grade after CRT. The density of CD8(+) TIL was an independent prognostic factor for achieving complete response after CRT.ConclusionsIn RC patients, T lymphocyte-mediated immune reactions play an important role in tumor response to CRT, and the quantitative measurement of TIL in biopsy samples before CRT can be used as a predictor of the clinical effectiveness of CRT for advanced RC.

Aims Although cardiac resynchronization therapy (CRT) is effective in patients with systolic heart failure (HF) and a wide QRS interval, a substantial proportion of patients remain non-responsive. The SonR contractility sensor embedded in the right atrial lead enables individualized automatic optimization of the atrioventricular (AV) and interventricular (VV) timings. The RESPOND-CRT study investigated the safety and efficacy of the contractility sensor system in HF patients undergoing CRT. Methods and results RESPOND-CRT was a prospective, randomized, double-blinded, multicentre, non-inferiority trial. Patients were randomized (2:1, respectively) to receive weekly, automatic CRT optimization with SonR vs. an Echo-guided optimization of AV and VV timings. The primary efficacy endpoint was the rate of clinical responders (patients alive, without adjudicated HF-related events, with improvement in New York Heart Association class or quality of life), at 12 months. The study randomized 998 patients. Responder rates were 75.0% in the SonR arm and 70.4% in the Echo arm (mean difference, 4.6%; 95% CI, −1.4% to 10.6%; P < 0.001 for non-inferiority margin −10.0%) (Table 2). At an overall mean follow-up of 548 ± 190 days SonR was associated with a 35% risk reduction in HF hospitalization (hazard ratio, 0.65; 95% CI, 0.46–0.92; log-rank P = 0.01). Conclusion Automatic AV and VV optimization using the contractility sensor was safe and as effective as Echo-guided AV and VV optimization in increasing response to CRT. ClinicalTrials.gov number NCT01534234

We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame. By exploiting the simple yet effective tuning strategy with narrow bands, the proposed method realizes a feasible framework for handling video sequences on-the-fly with high training efficiency. The storage overhead induced by using explicit grid representations can be significantly reduced through the use of model difference based compression. We also introduce an efficient strategy to further accelerate model optimization for each frame. Experiments on challenging video sequences demonstrate that our approach is capable of achieving a training speed of 15 seconds per-frame with competitive rendering quality, which attains $1000 \times$ speedup over the state-of-the-art implicit methods. Code is available at https://github.com/AlgoHunt/StreamRF.

BackgroundThis study evaluates the dose distribution of reversed planned tangential beam intensity modulated radiotherapy (IMRT) compared to standard wedged tangential beam three-dimensionally planned conformal radiotherapy (3D-CRT) of the chest wall in unselected postmastectomy breast cancer patientsMethodsFor 20 unselected subsequent postmastectomy breast cancer patients tangential beam IMRT and tangential beam 3D-CRT plans were generated for the radiotherapy of the chest wall. The prescribed dose was 50 Gy in 25 fractions. Dose-volume histograms were evaluated for the PTV and organs at risk. Parameters of the dose distribution were compared using the Wilcoxon matched pairs test.ResultsTangential beam IMRT statistically significantly reduced the ipsilateral mean lung dose by an average of 21% (1129 cGy versus 1437 cGy). In all patients treated on the left side, the heart volume encompassed by the 70% isodose line (V70%; 35 Gy) was reduced by an average of 43% (5.7% versus 10.6%), and the mean heart dose by an average of 20% (704 cGy versus 877 cGy). The PTV showed a significantly better conformity index with IMRT; the homogeneity index was not significantly different.ConclusionsTangential beam IMRT significantly reduced the dose-volume of the ipsilateral lung and heart in unselected postmastectomy breast cancer patients.

Abstract Aims Our aim was to develop a machine learning (ML)-based risk stratification system to predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients undergoing cardiac resynchronization therapy (CRT). Methods and results Multiple ML models were trained on a retrospective database of 1510 patients undergoing CRT implantation to predict 1- to 5-year all-cause mortality. Thirty-three pre-implant clinical features were selected to train the models. The best performing model [SEMMELWEIS-CRT score (perSonalizEd assessMent of estiMatEd risk of mortaLity With machinE learnIng in patientS undergoing CRT implantation)], along with pre-existing scores (Seattle Heart Failure Model, VALID-CRT, EAARN, ScREEN, and CRT-score), was tested on an independent cohort of 158 patients. There were 805 (53%) deaths in the training cohort and 80 (51%) deaths in the test cohort during the 5-year follow-up period. Among the trained classifiers, random forest demonstrated the best performance. For the prediction of 1-, 2-, 3-, 4-, and 5-year mortality, the areas under the receiver operating characteristic curves of the SEMMELWEIS-CRT score were 0.768 (95% CI: 0.674–0.861; P < 0.001), 0.793 (95% CI: 0.718–0.867; P < 0.001), 0.785 (95% CI: 0.711–0.859; P < 0.001), 0.776 (95% CI: 0.703–0.849; P < 0.001), and 0.803 (95% CI: 0.733–0.872; P < 0.001), respectively. The discriminative ability of our model was superior to other evaluated scores. Conclusion The SEMMELWEIS-CRT score (available at semmelweiscrtscore.com) exhibited good discriminative capabilities for the prediction of all-cause death in CRT patients and outperformed the already existing risk scores. By capturing the non-linear association of predictors, the utilization of ML approaches may facilitate optimal candidate selection and prognostication of patients undergoing CRT implantation.

Video-to-video synthesis (vid2vid) aims for converting high-level semantic inputs to photorealistic videos. While existing vid2vid methods can achieve short-term temporal consistency, they fail to ensure the long-term one. This is because they lack knowledge of the 3D world being rendered and generate each frame only based on the past few frames. To address the limitation, we introduce a novel vid2vid framework that efficiently and effectively utilizes all past generated frames during rendering. This is achieved by condensing the 3D world rendered so far into a physically-grounded estimate of the current frame, which we call the guidance image. We further propose a novel neural network architecture to take advantage of the information stored in the guidance images. Extensive experimental results on several challenging datasets verify the effectiveness of our approach in achieving world consistency - the output video is consistent within the entire rendered 3D world. this https URL

Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalability. In this work, we build Snap Video, a video-first model that systematically addresses these challenges. To do that, we first extend the EDM framework to take into account spatially and temporally redundant pixels and naturally support video generation. Second, we show that a U-Net—a workhorse behind image generation—scales poorly when generating videos, requiring significant computational overhead. Hence, we propose a new transformer-based architecture that trains 3.31 times faster than U-Nets (and is ∼4.5 faster at inference). This allows us to efficiently train a text-to-video model with billions of parameters for the first time, reach state-of-the-art results on a number of benchmarks, and generate videos with substantially higher quality, temporal consistency, and motion complexity. The user studies showed that our model was favored by a large margin over the most recent methods.

Background: Esophageal cancer (EC) is a common cancer with high mortality because of its rapid progression and poor prognosis. Radiotherapy is one of the most effective treatments for EC. Three-dimensional conformal radiotherapy (3D-CRT) and intensity-modulated radiotherapy (IMRT) are 2 recently developed radiotherapy techniques. IMRT is believed to be more effective than 3D-CRT in target coverage, dose homogeneity, and reducing toxicity to normal organs. However, these advantages have not been demonstrated in the treatment of EC. This meta-analysis was performed to compare IMRT and 3D-CRT in the treatment of EC in terms of dose–volume histograms and outcomes including survival and toxicity. Methods: A literature search was performed in PubMed, Embase, and the Cochrane library databases from their inceptions to Dec 30, 2016. Two authors independently assessed the included studies and extracted data. The average percent irradiated volumes of adjacent noncancerous organs were calculated and compared between IMRT and 3D-CRT. The odds ratio of overall survival (OS), and radiation pneumonitis and radiation esophagitis was also evaluated. Results: Totally 7 studies were included. Of them, 5 studies (80 patients) were included in the dosimetric comparison, 3 studies (871 patients) were included in the OS analysis, and 2 studies (205 patients) were included in the irradiation toxicity analysis. For lung in patients receiving doses ≥20 Gy and heart in patients receiving dose = 50 Gy, the average irradiated volumes of IMRT were less than those from 3D-CRT. IMRT resulted in a higher OS than 3D-CRT. However, no significant difference was observed in the incidence of radiation pneumonitis and radiation esophagitis between 2 radiotherapy techniques. Conclusion: Our data suggest that IMRT-delivered high radiation dose produces significantly less average percent volumes of irradiated lung and heart than 3D-CRT. IMRT is superior to 3D-CRT in the OS of EC while shows no benefit on radiation toxicity.

We present an approach to predict future video frames given a sequence of continuous video frames in the past. Instead of synthesizing images directly, our approach is designed to understand the complex scene dynamics by decoupling the background scene and moving objects. The appearance of the scene components in the future is predicted by non-rigid deformation of the background and affine transformation of moving objects. The anticipated appearances are combined to create a reasonable video in the future. With this procedure, our method exhibits much less tearing or distortion artifact compared to other approaches. Experimental results on the Cityscapes and KITTI datasets show that our model outperforms the state-of-the-art in terms of visual quality and accuracy.

This study proposes the developing and implementing of the high-definition level triple streaming hybrid security camera that can output three types of video-signals, which are high-definition serial digital interface, extended serial digital interface, and analog signals. In this study, to develop the high-definition level triple streaming hybrid security camera the hardware and firmware software was designed and implemented. The hardware was implemented using image sensors MN34229PL from Panasonic, main processors EN778/EN331 from Eyenix, Auto Iris functions KA909A from Fairchild, and Zoom/Focus controls A3901SEJTR-T from Microsystems Inc. In addition, to evaluate the performance test of the developed camera in this study, we requested for the performance evaluation at the broadcasting and communication convergence testing by the department of Telecommunication Technology Association, Korea. The results of the performance tests indicated that the performances of the product developed in this study were found to be excellent than the other commercial products with regard to the signal-to-noise ratio, the minimum intensity of illumination, and power consumption. The product designed in this study is expected to be widely utilized in the high-resolution security camera market.

Recent advancements in diffusion models have greatly improved the quality and diversity of synthesized content. To harness the expressive power of diffusion models, researchers have explored various controllable mechanisms that allow users to intuitively guide the content synthesis process. Although the latest efforts have primarily focused on video synthesis, there has been a lack of effective methods for controlling and describing desired content and motion. In response to this gap, we introduce MCDiff, a conditional diffusion model that generates a video from a starting image frame and a set of strokes, which allow users to specify the intended content and dynamics for synthesis. To tackle the ambiguity of sparse motion inputs and achieve better synthesis quality, MCDiff first utilizes a flow completion model to predict the dense video motion based on the semantic understanding of the video frame and the sparse motion control. Then, the diffusion model synthesizes high-quality future frames to form the output video. We qualitatively and quantitatively show that MCDiff achieves the state-the-of-art visual quality in stroke-guided controllable video synthesis. Additional experiments on MPII Human Pose further exhibit the capability of our model on diverse content and motion synthesis.

This presentation introduces a self-supervised learning approach to the synthesis of new video clips from old ones, with several new key elements for improved spatial resolution and realism: It conditions the synthesis process on contextual information for temporal continuity and ancillary information for fine control. The prediction model is doubly autoregressive, in the latent space of an autoencoder for forecasting, and in image space for updating contextual information, which is also used to enforce spatio-temporal consistency through a learnable optical flow module. Adversarial training of the autoencoder in the appearance and temporal domains is used to further improve the realism of its output. A quantizer inserted between the encoder and the transformer in charge of forecasting future frames in latent space (and its inverse inserted between the transformer and the decoder) adds even more flexibility by affording simple mechanisms for handling multimodal ancillary information for controlling the synthesis process (eg, a few sample frames, an audio track, a trajectory in image space) and taking into account the intrinsically uncertain nature of the future by allowing multiple predictions. Experiments with an implementation of the proposed approach give very good qualitative and quantitative results on multiple tasks and standard benchmarks.

Telemonitoring (TM) is a safe and efficient monitoring system for internal cardioverter defibrillator device (ICD) recipients. TM has been used to track info on the clinical status of heart failure patients treated by ICD and/or cardiac resynchronisation therapy defibrillator (CRT‐D). The aim of this study was to investigate the impact of TM on clinical outcomes in a population of CRT‐D patients with heart failure.

Generating rich and controllable motion is a pivotal challenge in video synthesis. We propose Boximator, a new approach for fine-grained motion control. Boximator introduces two constraint types: hard box and soft box. Users select objects in the conditional frame using hard boxes and then use either type of boxes to roughly or rigorously define the object's position, shape, or motion path in future frames. Boximator functions as a plug-in for existing video diffusion models. Its training process preserves the base model's knowledge by freezing the original weights and training only the control module. To address training challenges, we introduce a novel self-tracking technique that greatly simplifies the learning of box-object correlations. Empirically, Boximator achieves state-of-the-art video quality (FVD) scores, improving on two base models, and further enhanced after incorporating box constraints. Its robust motion controllability is validated by drastic increases in the bounding box alignment metric. Human evaluation also shows that users favor Boximator generation results over the base model.

We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework (MMAudio). In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples. Additionally, we improve audio-visual synchrony with a conditional synchronization module that aligns video conditions with audio latents at the frame level. Trained with a flow matching objective, MMAudio achieves new video-to-audio state-of-the-art among public models in terms of audio quality, semantic alignment, and audio-visual synchronization, while having a low inference time (1.23s to generate an 8s clip) and just 157M parameters. MMAudio also achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance. Code, models, and demo are available at: hkchengrex.github.io/MMAudio.

Background. While neoadjuvant concurrent chemoradiotherapy has improved outcomes for esophageal cancer patients, surgical complication rates remain high. The most frequent perioperative complications after trimodality therapy were cardiopulmonary in nature. The radiation modality utilized can be a strong mitigating factor of perioperative complications given the location of the esophagus and its proximity to the heart and lungs. The purpose of this study is to make a dosimetric comparison of Intensity-Modulated Radiation Therapy (IMRT), proton and 3D conformal radiotherapy (3D-CRT) with regard to reducing perioperative cardiopulmonary complications in esophageal cancer patients. Materials. Ten patients with esophageal cancer treated between 2010 and 2013 were evaluated in this study. All patients were simulated with contrast-enhanced CT imaging. Separate treatment plans using proton radiotherapy, IMRT, and 3D-CRT modalities were created for each patient. Dose-volume histograms were calculated and analyzed to compare plans between the three modalities. The organs at risk (OAR) being evaluated in this study are the heart, lungs, and spinal cord. To determine statistical significance, ANOVA and two-tailed paired t-tests were performed for all data parameters. Results. The proton plans showed decreased dose to various volumes of the heart and lungs in comparison to both the IMRT and 3D-CRT plans. There was no difference between the IMRT and 3D-CRT plans in dose delivered to the lung or heart. This finding was seen consistently across the parameters analyzed in this study. Conclusions. In patients receiving radiation therapy for esophageal cancer, proton plans are technically feasible while achieving adequate coverage with lower doses delivered to the lungs and cardiac structures. This may result in decreased cardiopulmonary toxicity and less morbidity to esophageal cancer patients.

We present a self-supervised approach with pose perceptual loss for automatic dance video generation. Our method can produce a realistic dance video that conforms to the beats and rhymes of given music. To achieve this, we firstly generate a human skeleton sequence from music and then apply the learned pose-to-appearance mapping to generate the final video. In the stage of generating skeleton sequences, we utilize two discriminators to capture different aspects of the sequence and propose a novel pose perceptual loss to produce natural dances. Besides, we also provide a new cross-modal evaluation metric to evaluate the dance quality, which is able to estimate the similarity between two modalities (music and dance). Finally, our experimental qualitative and quantitative results demonstrate that our dance video synthesis approach produces realistic and diverse results. Our source code and data are available at https://github.com/xrenaa/Music-Dance-Video-Synthesis.

Diffusion models have transformed the image-to-image (I2I) synthesis and are now permeating into videos. How-ever, the advancement of video-to-video (V2V) synthesis has been hampered by the challenge of maintaining temporal consistency across video frames. This paper proposes a consistent V2V synthesis framework by jointly leveraging spatial conditions and temporal optical flow clues within the source video. Contrary to prior methods that strictly adhere to optical flow, our approach harnesses its benefits while handling the imperfection in flow estimation. We encode the optical flow via warping from the first frame and serve it as a supplementary reference in the diffusion model. This enables our model for video synthesis by editing the first frame with any prevalent I2I models and then propagating edits to successive frames. Our V2V model, Flow Vid, demon-strates remarkable properties: (1) Flexibility: Flow Vid works seamlessly with existing I2I models, facilitating various modifications, including stylization, object swaps, and local edits. (2) Efficiency: Generation of a 4-second video with 30 FPS and 512×512 resolution takes only 1.5 minutes, which is 3.1×, 7.2×, and 10.5× faster than CoDeF, Rerender, and TokenFlow, respectively. (3) High-quality: In user studies, our FlowVid is preferred 45.7% of the time, outperforming CoDeF (3.5%), Rerender (10.2%), and TokenFlow (40.4%).

Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. This naturally suggests a bijective mapping between the video domain and the static content as well as residual information. In contrast to common stochastic image-to-video synthesis, such a model does not merely generate arbitrary videos progressing the initial image. Given this image, it rather provides a one-to-one mapping between the residual vectors and the video with stochastic outcomes when sampling. The approach is naturally implemented using a conditional invertible neural network (cINN) that can explain videos by independently modelling static and other video characteristics, thus laying the basis for controlled video synthesis. Experiments on diverse video datasets demonstrate the effectiveness of our approach in terms of both the quality and diversity of the synthesized results. Our project page is available at https://bit.ly/3dg90fV.

Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and ren-dering high-quality, spatially consistent new views. While recent methods for view synthesis based on diffusion have shown great progress, achieving consistency among various view estimates and at the same time abiding by the desired camera pose remains a critical problem yet to be solved. In this work, we demonstrate a strikingly simple method, where we utilize a pre-trained video diffusion model to solve this problem. Our key idea is that synthesizing a novel view could be reformulated as synthesizing a video of a cam-era going around the object of interest-a scanning video-which then allows us to leverage the powerful priors that a video diffusion model would have learned. Thus, to perform novel-view synthesis, we create a smooth camera trajectory to the target view that we wish to render, and denoise using both a view-conditioned diffusion model and a video diffusion model. By doing so, we obtain a highly consistent novel view synthesis, outperforming the state of the art.

We prove that the Tutte embeddings (a.k.a. harmonic/embeddings) of certain random planar maps converge to $\gamma$-Liouville quantum gravity ($\gamma$-LQG). Specifically, we treat mated-CRT maps, which are discretized matings of correlated continuum random trees, and $\gamma$ ranges from $0$ to $2$ as one varies the correlation parameter. We also show that the associated space-filling path on the embedded map converges to space-filling SLE$_{\kappa}$ for $\kappa =16/\gamma^2$ (in the annealed sense) and that simple random walk on the embedded map converges to Brownian motion (in the quenched sense). Our arguments also yield analogous statements for the Smith (square tiling) embedding of the mated-CRT map. This work constitutes the first proof that a discrete conformal embedding of a random planar map converges to LQG. Many more such statements have been conjectured. Since the mated-CRT map can be viewed as a coarse-grained approximation to other random planar maps (the UIPT, tree-weighted maps, bipolar-oriented maps, etc.), our results indicate a potential approach for proving that embeddings of these maps converge to LQG as well. To prove the main result, we establish several (independently interesting) theorems about LQG surfaces decorated by space-filling SLE. There is a natural way to use the SLE curve to divide the plane into `cells' corresponding to vertices of the mated-CRT map. We study the law of the shape of the origin-containing cell, in particular proving moments for the ratio of its squared diameter to its area. We also give bounds on the degree of the origin-containing cell and establish a form of ergodicity for the entire configuration. Ultimately, we use these properties to show (using a general theorem proved in a separate paper) that random walk on these cells converges to a time change of Brownian motion, which in turn leads to the Tutte embedding result.

Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for flexible controls, pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize realistic character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip into three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of the synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results show that the proposed method outperforms prior works by a large margin in character animation synthesis and is effective in providing a high degree of controllability (i.e., arbitrary characters, novel 3D motions, interactive scenes), thus enabling brandnew editing tasks (e.g., video character replacement).

To date, there are no data from randomized controlled studies on the benefit of cardiac resynchronization therapy (CRT) when implanted as an upgrade in patients with a previous device as compared to de novo CRT. In the CRT Survey II we compared the baseline data of patients upgraded to CRT (CRT‐P/CRT‐D) from a previous pacemaker (PM) or implantable cardioverter‐defibrillator (ICD) to de novo CRT implantation.

We adapt the CRT approach for computing Hilbert class polynomials to handle a wide range of class invariants. For suitable discriminants D, this improves its performance by a large constant factor, more than 200 in the most favourable circumstances. This has enabled record-breaking constructions of elliptic curves via the CM method, including examples with |D| > 1015.

How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident variations caused by the stochastic nature of our world. These outcomes are governed by the characteristic kinematics of objects that dictate their overall motion caused by a local interaction. Conversely, the movement of an object provides crucial information about its underlying distinctive kinematics and the interdependencies between its parts. This two-way relation motivates learning a bijective mapping between object kinematics and plausible future image sequences. Therefore, we propose iPOKE – invertible Prediction of Object Kinematics – that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis. In contrast to previous works, we do not generate arbitrary realistic videos, but provide efficient control of movements, while still capturing the stochastic nature of our environment and the diversity of plausible outcomes it entails. Moreover, our approach can transfer kinematics onto novel object instances and is not confined to particular object classes. Our project page is available at https://bit.ly/3dJN4Lf.

Neoadjuvant CRT may lead to significant tumor regression in patients with rectal cancer. Different CRT regimens with consolidation chemotherapy may lead to increased rates of complete tumor regression. The purpose of this study was to understand tumor metabolic activity following two different neoadjuvant CRT regimens using sequential PET/CT imaging in two different intervals following RT. Patients with cT2-4 N0-2 M0 rectal cancer treated by standard CRT (54Gy and 2 cycles of 5FU-based chemotherapy) or extended CRT (54Gy and 6 cycles of 5FU-based chemotherapy) underwent sequential PET/CT imaging at baseline, 6 weeks and 12 weeks from radiation completion. 99 patients undergoing standard CRT were compared to 12 patients undergoing CRT with consolidation chemotherapy. Patients treated with consolidation CRT had increased rates of complete clinical or pathological response (66 % vs. 23 %; p < 0.001). SUVmax variation between baseline and 6 weeks (88 % vs. 63 %; p < 0.001) and between baseline and 12 weeks (90 % vs. 57 %; p < 0.001) were significantly more pronounced among patients undergoing extended CRT with consolidation chemotherapy. An increase in SUVmax between 6 and 12 weeks was observed in 51 % of patients undergoing standard and 18 % of patients undergoing consolidation CRT (p = 0.04). Most of the reduction in tumor metabolism after neoadjuvant CRT occurs within the first 6 weeks from RT completion. In patients undergoing CRT with consolidation chemotherapy, tumors are less likely to regain metabolic activity between 6 and 12 weeks. Therefore, assessment of tumor response may be safely postponed to 12 weeks in patients undergoing extended CRT with consolidation chemotherapy. NCT00254683

Videos of actions are complex spatio-temporal signals, containing rich compositional structures. Current generative models are limited in their ability to generate examples of object configurations outside the range they were trained on. Towards this end, we introduce a generative model (AG2Vid) based on Action Graphs, a natural and convenient structure that represents the dynamics of actions between objects over time. Our AG2Vid model disentangles appearance and position features, allowing for more accurate generation. AG2Vid is evaluated on the CATER and Something-Something datasets and outperforms other baselines. Finally, we show how Action Graphs can be used for generating novel compositions of unseen actions.

Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an image to generate a specific motion trajectory desired by the user since there is no means to provide motion information. Conversely, language information can describe the desired motion, while not precisely defining the content of the video. This work presents a multimodal video generation framework that benefits from text and images provided jointly or separately. We leverage the recent progress in quantized representations for videos and apply a bidirectional transformer with multiple modalities as inputs to predict a discrete video representation. To improve video quality and consistency, we propose a new video token trained with self-learning and an improved mask-prediction algorithm for sampling video tokens. We introduce text augmentation to improve the robustness of the textual representation and diversity of generated videos. Our framework can incorporate various visual modalities, such as segmentation masks, drawings, and partially occluded images. It can generate much longer sequences than the one used for training. In addition, our model can extract visual information as suggested by the text prompt, e.g., “an object in image one is moving northeast”, and generate corresponding videos. We run evaluations on three public datasets and a newly collected dataset labeled with facial attributes, achieving state-of-the-art generation results on all four11Code: https://github.com/snap-research/MMVID and Webpage..

What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks. Project page is available at https://bit.ly/3cxfA2L.

Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world capturing. Despite advancements in generative AI for video creation, achieving precise control over motion for interactive video asset generation remains challenging. To this end, we propose Image Conductor, a method for precise control of camera transitions and object movements to generate video assets from a single image. An well-cultivated training strategy is proposed to separate distinct camera and object motion by camera LoRA weights and object LoRA weights. To further address cinematographic variations from ill-posed trajectories, we introduce a camera-free guidance technique during inference, enhancing object movements while eliminating camera transitions. Additionally, we develop a trajectory-oriented video motion data curation pipeline for training. Quantitative and qualitative experiments demonstrate our method's precision and fine-grained control in generating motion-controllable videos from images, advancing the practical application of interactive video synthesis. Project webpage available at https://liyaowei-stu.github.io/project/ImageConductor/

Spatio-temporal coherency is a major challenge in synthesizing high quality videos, particularly in synthesizing human videos that contain rich global and local deformations. To resolve this challenge, previous approaches have resorted to different features in the generation process aimed at representing appearance and motion. However, in the absence of strict mechanisms to guarantee such disentanglement, a separation of motion from appearance has remained challenging, resulting in spatial distortions and temporal jittering that break the spatio-temporal coherency. Motivated by this, we here propose LEO, a novel framework for human video synthesis, placing emphasis on spatio-temporal coherency. Our key idea is to represent motion as a sequence of flow maps in the generation process, which inherently isolate motion from appearance. We implement this idea via a flow-based image animator and a Latent Motion Diffusion Model (LMDM). The former bridges a space of motion codes with the space of flow maps, and synthesizes video frames in a warp-and-inpaint manner. LMDM learns to capture motion prior in the training data by synthesizing sequences of motion codes. Extensive quantitative and qualitative analysis suggests that LEO significantly improves coherent synthesis of human videos over previous methods on the datasets TaichiHD, FaceForensics and CelebV-HQ. In addition, the effective disentanglement of appearance and motion in LEO allows for two additional tasks, namely infinite-length human video synthesis, as well as content-preserving video editing. Project page: https://wyhsirius.github.io/LEO-project/.

One of the salient hurdles for wide adoption of machine learning (ML) has been efficient and high-performance edge compute. ML developers use very large, expensive, and power-intensive systems to create new models, but the final implementation needs to be deployed in a cost-effective, small, and low-power solution. This has commonly led to situations where the models that can be deployed in an edge environment are significantly smaller than state-of-the-art models leading the benchmark scoreboards [1]. A flash-based Analog Matrix Processor (AMP) is well positioned to close this development/deployment gap by providing edge inference compute that is 10-to-100× more efficient than traditional digital inference. It accomplishes this by leveraging denser circuitry via smaller memory elements (multi-level flash instead of several SRAM cells) and denser communication (256+ level signaling instead of binary signaling). Deep Neural Networks (DNNs) are an ideal application for the AMP since they can use analog-aware training to overcome the inherent non-idealities present in analog signal chains. DNNs use large pattern matchers which naturally suppress noise and map well to the AMP, which prefers to amortize the fixed costs inside the analog system over larger calculations.

Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However, their adherence to physical commonsense across real-world actions remains unclear (e.g., playing tennis, backflip). Existing benchmarks suffer from limitations such as limited size, lack of human evaluation, sim-to-real gaps, and absence of fine-grained physical rule analysis. To address this, we introduce VideoPhy-2, an action-centric dataset for evaluating physical commonsense in generated videos. We curate 200 diverse actions and detailed prompts for video synthesis from modern generative models. We perform human evaluation that assesses semantic adherence, physical commonsense, and grounding of physical rules in the generated videos. Our findings reveal major shortcomings, with even the best model achieving only 22% joint performance (i.e., high semantic and physical commonsense adherence) on the hard subset of VideoPhy-2. We find that the models particularly struggle with conservation laws like mass and momentum. Finally, we also train VideoPhy-AutoEval, an automatic evaluator for fast, reliable assessment on our dataset. Overall, VideoPhy-2 serves as a rigorous benchmark, exposing critical gaps in video generative models and guiding future research in physically-grounded video generation. The data and code is available at https://videophy2.github.io/.

Video-to-speech synthesis (also known as lip-to-speech) refers to the translation of silent lip movements into the corresponding audio. This task has received an increasing amount of attention due to its self-supervised nature (i.e., can be trained without manual labelling) combined with the ever-growing collection of audio-visual data available online. Despite these strong motivations, contemporary video-to-speech works focus mainly on small- to medium-sized corpora with substantial constraints in both vocabulary and setting. In this work, we introduce a scalable video-to-speech framework consisting of two components: a video-to-spectrogram predictor and a pre-trained neural vocoder, which converts the mel-frequency spectrograms into waveform audio. We achieve state-of-the art results for GRID and considerably outperform previous approaches on LRW. More importantly, by focusing on spectrogram prediction using a simple feedforward model, we can efficiently and effectively scale our method to very large and unconstrained datasets: To the best of our knowledge, we are the first to show intelligible results on the challenging LRS3 dataset.

We demonstrate the ability to receive live color analog television and video game signals with the use of the Rydberg atom receiver. The typical signal expected for traditional 480i National Television Standards Committee format video signals requires a bandwidth of over 3 MHz. We determine the beam sizes, powers, and detection method required for the Rydberg atoms to receive this type of signal. The beam size affects the average time the atoms remain in the interaction volume, which is inversely proportional to the bandwidth of the receiver. We find that small beam diameters (less than 100  μm) lead to much faster responses and allow for color reception. We demonstrate the effect of the beam size on bandwidth by receiving a live 480i video stream with the Rydberg atom receiver. The best video reception was achieved with a beam width of [Formula: see text]m full-width at half-max.

Video sentiment analysis can effectively establish the relationship between the emotion state and the multimodal information, while still suffer from intensive computation and low efficiency, due to the von Neumann computing architecture. Here, we present a brain-inspired hierarchical interactive in-memory computing (IMC) system, which can efficiently solve ‘von Neumann bottleneck’, enabling cross-modal interactions and semantic gap elimination. First, a 1T1M synapse array is fabricated using cost-effective, highly stable, flexible, and eco-friendly carbon materials, offering efficient analog multiply-accumulate operations. To illustrate the complexity of the proposed brain-inspired hierarchical interactive IMC system, three modules are proposed: 1) unimodal extraction module, 2) hierarchical interactive module, 3) output module. Furthermore, the proposed system is validated by applying it to video sentiment analysis. The experimental results demonstrate that the proposed system outperforms the existing state-of-the-art methods with high computational efficiency and good robustness. This work opens up a new way to achieve the deep integration of nanomaterials, deep learning, and modern electronics into IMC.

With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs about 1 min of the training data, which is significantly less than audio-driven approaches; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing and training time from several days for audio-based methods to 4 hours, which is 10 times faster. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.

Protocol sequences are used for channel access in the collision channel without feedback. Each user accesses the channel according to a deterministic zero-one pattern, called the protocol sequence. In order to minimize fluctuation of throughput due to delay offsets, we want to construct protocol sequences whose pairwise Hamming cross-correlation is as close to a constant as possible. In this paper, we present a construction of protocol sequences which is based on the bijective mapping between one-dimensional (1-D) sequence and two-dimensional (2-D) arrays by the Chinese Remainder Theorem (CRT). In the application to the collision channel without feedback, a worst-case lower bound on system throughput is derived.

In this article, we describe a methodology that aims at either breaking or proving the security of CRT-RSA implementations against fault injection attacks. In the specific case-study of the BellCoRe attack, our work bridges a gap between formal proofs and implementation-level attacks. We apply our results to three implementations of CRT-RSA, namely the unprotected one, that of Shamir, and that of Aumüller et al. Our findings are that many attacks are possible on both the unprotected and the Shamir implementations, while the implementation of Aumüller et al.  is resistant to all single-fault attacks. It is also resistant to double-fault attacks if we consider the less powerful threat model of its authors.

Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by providing input data that is as rich as the output data, e.g., image sequence plus conditioning in, video out. However, clinical documentation is usually scarce and only single images are reported and stored, thus retrospective patient-specific analysis or the generation of rich training data becomes impossible with current approaches. In this paper, we extend elucidated diffusion models for video modelling to generate plausible video sequences from single images and arbitrary conditioning with clinical parameters. We explore this idea within the context of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We use the publicly available EchoNet-Dynamic dataset for all our experiments. Our image to sequence approach achieves an $R^2$ score of 93%, which is 38 points higher than recently proposed sequence to sequence generation methods. Code and models will be available at: https://github.com/HReynaud/EchoDiffusion.

In this paper, we introduce Fairy, a minimalist yet ro-bust adaptation of image-editing diffusion models, enhancing them for video editing applications. Our approach centers on the concept of anchor-based cross-frame attention, a mechanism that implicitly propagates diffusion features across frames, ensuring superior temporal coherence and high-fidelity synthesis. Fairy not only addresses limitations of previous models on memory and processing speed, but also improves temporal consistency through a unique data augmentation strategy. This strategy renders the model equivariant to affine transformations in both source and target images. Remarkably efficient, Fairy generates 120- frame 512×384 videos (4-second duration at 30 FPS) in just 14 seconds, outpacing prior works by at least 44×. A comprehensive user study, involving 1000 generated samples, confirms that our approach delivers superior quality, decisively outperforming established methods.

Learning based 6D object pose estimation methods rely on computing large intermediate pose representations and/or iteratively refining an initial estimation with a slow render-compare pipeline. This paper introduces a novel method we call Cascaded Pose Refinement Transformers, or CRT-6D. We replace the commonly used dense intermediate representation with a sparse set of features sampled from the feature pyramid we call OSKFs(Object Surface Keypoint Features) where each element corresponds to an object keypoint. We employ lightweight deformable transformers and chain them together to iteratively refine proposed poses over the sampled OSKFs. We achieve inference runtimes 2× faster than the closest real-time state of the art methods while supporting up to 21 objects on a single model. We demonstrate the effectiveness of CRT-6D by performing extensive experiments on the LM-O and YCBV datasets. Compared to real-time methods, we achieve state of the art on LM-O and YCB-V, falling slightly behind methods with inference runtimes one order of magnitude higher. The source code is available at: https://github.com/PedroCastro/CRT-6D

The increasing deployment of machine learning at the core and at the edge for applications such as video and image recognition has resulted in a number of special purpose accelerators in this domain. However, these accelerators do not have full end-to-end software stacks for application development, resulting in hard-to-develop, proprietary, and suboptimal application programming and executables. In this paper, we describe software stack for a memristor-based hybrid (analog-digital)accelerator. The software stack consists of an ONNX converter, an application optimizer, a compiler, a driver, and emulators. The ONNX converter helps leveraging interoperable neural network models developed on frameworks that support ONNX, such as CNTK, Caffe2, Tensorflow, etc. The application optimization layer adapts these interoperable models to the underlying hardware. The compiler generates executable ISA code that the underlying accelerator can run. Finally, the emulator enables software execution without actual hardware which enables hardware design space exploration and testing. By building a software stack, we have made hybrid memristor-based ML accelerators more accessible to software developers, enabled a generation of better-performing executables, and created an environment that can be leveraged by a multitude of existing neural network models developed using other frameworks to target these accelerators.

This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data. Recent advancements in neural scene representation have achieved notable success in rendering high-quality autonomous driving scenes, but the performance significantly degrades as the viewpoint deviates from the training trajectory. To mitigate this problem, we introduce StreetCrafter, a novel controllable video diffusion model that utilizes LiDAR point cloud renderings as pixel-level conditions, which fully exploits the generative prior for novel view synthesis, while preserving precise camera control. Moreover, the utilization of pixel-level LiDAR conditions allows us to make accurate pixel-level edits to target scenes. In addition, the generative prior of StreetCrafter can be effectively incorporated into dynamic scene representations to achieve real-time rendering. Experiments on Waymo Open Dataset and PandaSet demonstrate that our model enables flexible control over viewpoint changes, enlarging the view synthesis regions for satisfying rendering, which outperforms existing methods. The code is available at https://zju3dv.github.io/streetcrafter.

Cross-view video synthesis task seeks to generate video sequences of one view from another dramatically different view. In this paper, we investigate the exocentric (third-person) view to egocentric (first-person) view video generation task. This is challenging because egocentric view sometimes is remarkably different from the exocentric view. Thus, transforming the appearances across the two different views is a non-trivial task. Particularly, we propose a novel Bi-directional Spatial Temporal Attention Fusion Generative Adversarial Network (STA-GAN) to learn both spatial and temporal information to generate egocentric video sequences from the exocentric view. The proposed STA-GAN consists of three parts: temporal branch, spatial branch, and attention fusion. First, the temporal and spatial branches generate a sequence of fake frames and their corresponding features. The fake frames are generated in both downstream and upstream directions for both temporal and spatial branches. Next, the generated four different fake frames and their corresponding features (spatial and temporal branches in two directions) are fed into a novel multi-generation attention fusion module to produce the final video sequence. Meanwhile, we also propose a novel temporal and spatial dual-discriminator for more robust network optimization. Extensive experiments on the Side2Ego and Top2Ego datasets show that the proposed STA-GAN significantly outperforms the existing methods.

This editorial refers to ‘The effect of QRS duration on cardiac resynchronization therapy in patients with a narrow QRS complex: a subgroup analysis of the EchoCRT Trial’, by J. Steffel et al ., on page doi:10.1093/eurheartj/ehv242. Guidelines give a strong recommendation for cardiac resynchronization therapy (CRT) for selected patients with heart failure.1,2 These recommendations are relatively complex and could be improved and simplified in the light of new data. In this issue of the journal, the EchoCRT investigators show, in a prospective randomized controlled trial, that patients with a QRS duration or <130 ms may be harmed by CRT even if they have evidence of left ventricular dyssynchrony on echocardiography and that this applies similarly to those with a QRS duration of <120 ms and those with QRS 120–130 ms.3 If we knew exactly how CRT worked, then making recommendations on how and in whom it should be used would be easy. However, the precise mechanism(s) by which CRT reduces morbidity and mortality are incompletely understood, which makes targeting the right patients for intervention difficult.4,5 Like many other treatments for heart failure, a single mode of action for CRT seems unlikely and therefore it would be surprising if any single measurement predicted benefit accurately. However, QRS duration certainly helps where many other measures have failed. The mechanisms underlying the effect of CRT probably vary from one patient to the next and, over time, even within an individual patient, because the cardiovascular system is dynamic by design. Potential mechanisms of effect include not only correction of inter- and intraventricular mechanical dyssynchrony, atrioventricular dyssynchrony, and mitral regurgitation, but also long-term effects …

Among many alternatives to replace sand in cement-based materials, cathode-ray tube (CRT) glass emerges as a suitable replacement for many reasons. This paper provides a state-of-the-art review on the use of cathode-ray tube (CRT) glass waste in cement-based concrete and mortar in accordance with PRISMA guidelines. The new aspects of the research are the literature coverage up to 2021 which would make it distinct from other articles. This review would act as a catalyst to use CRT glass waste in concrete mixtures. A total of 61 papers from literature were analyzed with emphasis on the fresh, mechanical, and durability performance of cement-based materials containing CRT glass waste as fine aggregates. The analysis revealed that the majority of the studies agreed that replacing sand with CRT glass waste increased the consistency where the low permeability of the CRT glass caused this effect. Strength of cement-based materials, on the other hand, decreased due to the weaker bond between the cement paste and the aggregates. The low water absorption of the CRT glass defined its effect on the durability properties of cement-based materials, such as drying shrinkage and water absorption capacity, leading to an improved performance. In addition, CRT glass waste activated the alkali-silica reaction in cement-based materials causing undesirable expansion. Additionally, several investigations proposed solutions to mitigate the lead leaching associated with the lead content found in the CRT glass. In general, it was assessed that CRT glass waste could be a valid component in the production of sustainable cement-based materials, especially for radiation shielding applications. The recommendations for future research are also suggested.

Time-lapse videos usually contain visually appealing content but are often difficult and costly to create. In this paper, we present an end-to-end solution to synthesize a time-lapse video from a single outdoor image using deep neural networks. Our key idea is to train a conditional generative adversarial network based on existing datasets of time-lapse videos and image sequences. We propose a multi-frame joint conditional generation framework to effectively learn the correlation between the illumination change of an outdoor scene and the time of the day. We further present a multi-domain training scheme for robust training of our generative models from two datasets with different distributions and missing timestamp labels. Compared to alternative time-lapse video synthesis algorithms, our method uses the timestamp as the control variable and does not require a reference video to guide the synthesis of the final output. We conduct ablation studies to validate our algorithm and compare with state-of-the-art techniques both qualitatively and quantitatively.

Recent research has demonstrated impressive results in video-to-speech synthesis which involves reconstructing speech solely from visual input. However, previous works have struggled to accurately synthesize speech due to a lack of sufficient guidance for the model to infer the correct content with the appropriate sound. To resolve the issue, they have adopted an extra speaker embedding as a speaking style guidance from a reference auditory information. Nevertheless, it is not always possible to obtain the audio information from the corresponding video input, especially during the inference time. In this paper, we present a novel vision-guided speaker embedding extractor using a self-supervised pretrained model and prompt tuning technique. In doing so, the rich speaker embedding information can be produced solely from input visual information, and the extra audio information is not necessary during the inference time. Using the extracted vision-guided speaker embedding representations, we further develop a diffusion-based video-to-speech synthesis model, so called DiffV2S, conditioned on those speaker embeddings and the visual representation extracted from the input video. The proposed DiffV2S not only maintains phoneme details contained in the input video frames, but also creates a highly intelligible mel-spectrogram in which the speaker identities of the multiple speakers are all preserved. Our experimental results show that DiffV2S achieves the state-of-the-art performance compared to the previous video-to-speech synthesis technique.

Smoke detection represents a critical task for avoiding large scale fire disaster in industrial environment and cities. Including intelligent video-based techniques in existing camera infrastructure enables faster response time if compared to traditional analog smoke detectors. In this work presents a hybrid approach to assess the rapid and precise identification of smoke in a video sequence. The algorithm combines a traditional feature detector based on Kalman filtering and motion detection, and a lightweight shallow convolutional neural network. This technique allows the automatic selection of specific regions of interest within the image by the generation of bounding boxes for gray colored moving objects. In the final step the convolutional neural network verifies the actual presence of smoke in the proposed regions of interest. The algorithm provides also an alarm generator that can trigger an alarm signal if the smoke is persistent in a time window of 3 s. The proposed technique has been compared to the state of the art methods available in literature by using several videos of public and non-public dataset showing an improvement in the metrics. Finally, we developed a portable solution for embedded systems and evaluated its performance for the Raspberry Pi 3 and the Nvidia Jetson Nano.

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs’ video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6 dB PSNR gains in terms of GUs’ minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.

A mated-CRT map is a random planar map obtained as a discretized mating of correlated continuum random trees. Mated-CRT maps provide a coarse-grained approximation of many other natural random planar map models (e.g., uniform triangulations and spanning tree-weighted maps), and are closely related to $\gamma$-Liouville quantum gravity (LQG) for $\gamma \in (0,2)$ if we take the correlation to be $-\cos(\pi\gamma^2/4)$. We prove estimates for the Dirichlet energy and the modulus of continuity of a large class of discrete harmonic functions on mated-CRT maps, which provide a general toolbox for the study of the quantitative properties random walk and discrete conformal embeddings for these maps. For example, our results give an independent proof that the simple random walk on the mated-CRT map is recurrent, and a polynomial upper bound for the maximum length of the edges of the mated-CRT map under a version of the Tutte embedding. Our results are also used in other work by the first two authors which shows that for a class of random planar maps --- including mated-CRT maps and the UIPT --- the spectral dimension is two (i.e., the return probability of the simple random walk to its starting point after $n$ steps is $n^{-1+o_n(1)}$) and the typical exit time of the walk from a graph-distance ball is bounded below by the volume of the ball, up to a polylogarithmic factor.

Sound design involves creatively selecting, recording, and editing sound effects for various media like cinema, video games, and virtual/augmented reality. One of the most time-consuming steps when designing sound is synchronizing audio with video. In some cases, environmental recordings from video shoots are available, which can aid in the process. However, in video games and animations, no reference audio exists, requiring manual annotation of event timings from the video. We propose a system to extract repetitive actions onsets from a video, which are then used - in conjunction with audio or textual embeddings - to condition a diffusion model trained to generate a new synchronized sound effects audio track. In this way, we leave complete creative control to the sound designer while removing the burden of synchronization with video. Furthermore, editing the onset track or changing the conditioning embedding requires much less effort than editing the audio track itself, simplifying the sonification process. We provide sound examples, source code, and pretrained models to faciliate reproducibility 1.

Over the past year, sweeping local and state-wide policies framed as bans against “CRT” are being propagated to restrict how race and racism can be taught in K-12 schools across the nation. As a result, schools are increasingly becoming a place where teachers face interpersonal and professional risk for teaching about US racial realities, including threats to their professional licenses for engaging historical or current day topics of race, inequity and injustice. In this article, we first draw on CRT to analyze how CRT-bans leverage white defensiveness and white comfort to restrict instruction and discourse about systemic rac-ism, thereby upholding it. Second, we describe a mixed methods research study with 117 teachers across the US that provides an initial look at how teachers are being harmed by these bans. The data suggests that CRT-bans are negatively impacting the racial climate of schools and contributing to the systematic pushout of teachers, particularly those committed to equity and inclusion. In addition to capturing teachers’ experiences about the bans, we specifically examine the pressure teachers are experiencing and its exacerbation of an already national problem, teacher attrition. We end the article with evidence-based recommendations on ways schools might mitigate the harm of CRT-bans on teachers.

Abstract Background: Nasopharyngeal carcinoma (NPC) is a prevalent malignancy in Asia particularly southern China. Comparisons of outcomes of conformal radiotherapy (CRT) and intensity-modulated radiotherapy (IMRT) have been still debated. This meta-analysis was carried out to compare oncologic outcomes of CRT and IMRT in the treatment of NPC. Methods: A literature search was performed through PubMed, Embase, and the Cochrane library databases from their inceptions to November 10, 2018. Two authors assessed the included studies and extracted data independently. Relative studies that compared oncologic outcomes between CRT and IMRT for NPC were included. Results: A total of 13 eligible studies were included, which contained 1 RCT, 1 prospective study, and 11 retrospective studies. Our meta-analysis showed that IMRT has increased overall survival (OR = 0.51, 95% CI = 0.41–0.65, P < .00001), locoregional control rate (OR = 0.59, 95% CI = 0.52–0.67, P < .00001), disease-free survival (OR = 0.77, 95% CI = 0.65–0.91, P = .002), and metastasis-free survival (OR = 0.71, 95% CI = 0.54–0.94, P = .01) in comparison with CRT. Conclusion: The results of this meta-analysis indicate IMRT should be a better option for the treatment of NPC because patients who underwent IMRT may benefit from increased overall survival, locoregional control rate, disease-free survival, and metastasis-free survival compared with CRT.

Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized techniques, which only run a deep net forward pass on a test scene. In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video. To answer whether generalized dynamic novel view synthesis from monocular videos is possible today, we establish an analysis framework based on existing techniques and work toward the generalized approach. We find a pseudo-generalized process without scene-specific appearance optimization is possible, but geometrically and temporally consistent depth estimates are needed. Despite no scene-specific appearance optimization, the pseudo-generalized approach improves upon some scene-specific methods.

The intuitive nature of drag-based interaction has led to its growing adoption for controlling object trajectories in image-to-video synthesis. Still, existing methods that perform dragging in the 2D space usually face ambiguity when handling out-of-plane movements. In this work, we augment the interaction with a new dimension, i.e., the depth dimension, such that users are allowed to assign a relative depth for each point on the trajectory. That way, our new interaction paradigm not only inherits the convenience from 2D dragging, but facilitates trajectory control in the 3D space, broadening the scope of creativity. We propose a pioneering method for 3D trajectory control in image-to-video synthesis by abstracting object masks into a few cluster points. These points, accompanied by the depth information and the instance information, are finally fed into a video diffusion model as the control signal. Extensive experiments validate the effectiveness of our approach, dubbed LeviTor, in precisely manipulating the object movements when producing photo-realistic videos from static images. Our code is available at: https://github.com/ant-research/LeviTor.

High dynamic range (HDR) image synthesis from multiple low dynamic range exposures continues to be actively researched. The extension to HDR video synthesis is a topic of significant current interest due to potential cost benefits. For HDR video, a stiff practical challenge presents itself in the form of accurate correspondence estimation of objects between video frames. In particular, loss of data resulting from poor exposures and varying intensity makes conventional optical flow methods highly inaccurate. We avoid exact correspondence estimation by proposing a statistical approach via maximum a posterior estimation, and under appropriate statistical assumptions and choice of priors and models, we reduce it to an optimization problem of solving for the foreground and background of the target frame. We obtain the background through rank minimization and estimate the foreground via a novel multiscale adaptive kernel regression technique, which implicitly captures local structure and temporal motion by solving an unconstrained optimization problem. Extensive experimental results on both real and synthetic data sets demonstrate that our algorithm is more capable of delivering high-quality HDR videos than current state-of-the-art methods, under both subjective and objective assessments. Furthermore, a thorough complexity analysis reveals that our algorithm achieves better complexity-performance tradeoff than conventional methods.

We present a novel method for synthesizing both temporally and geometrically consistent street-view panoramic video from a single satellite image and camera trajectory. Existing cross-view synthesis approaches focus on images, while video synthesis in such a case has not yet received enough attention. For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view. As for synthesis in the 3D space, we implement a cascaded network architecture with two hourglass modules to generate point-wise coarse and fine features from semantics and per-class latent vectors, followed by projection to frames and an up-sampling module to obtain the final realistic video. By leveraging computed correspondences, the produced street-view video frames adhere to the 3D geometric scene structure and maintain temporal consistency. Qualitative and quantitative experiments demonstrate superior results compared to other state-of-the-art synthesis approaches that either lack temporal consistency or realistic appearance. To the best of our knowledge, our work is the first one to synthesize cross-view images to videos..

The current work presents the first experimental demonstration of real-time Ethernet (ETH) trial services and 4K-Ultra High Definition (UHD) video application transmission over a 2λ Wavelength Division Multiplexing (WDM) analog Fiber-Wireless (FiWi) mmWave X-haul network, supporting dynamic flexible capacity allocation through an integrated silicon photonic Si3N4 Reconfigurable Optical Add Drop Multiplexer (ROADM) and 60 GHz wireless transmission across a 7 m V-band link distance, while controlled by a Software Defined Network (SDN) controller based on Open Daylight. Analog X-haul transport of the radio signals is based on an Intermediate Frequency over Fiber (IFoF) scheme centered at 1.5 GHz using an Orthogonal Frequency Division Multiplexing (OFDM) radio-waveform with a bandwidth of 204 MHz, generated by a Field Programmable Gated Array (FPGA)-based RF System-on-Chip (SoC) processor that converts on-the-fly the real-time ETH downlink traffic to analog radio and vice-versa for the uplink. Dynamic allocation of the X-haul traffic capacity is handled through the use of the 802.1Q Virtual Local Area Network (VLAN) tag-mechanism, which controls the forwarding operation to the proper DAC and InP EML for optical modulation using Intensity Modulation/Direct Detection (IM/DD) schemes, while the wavelength routing operation is handled by the low-loss four-port Si3N4 ROADM featuring only 2.5 dB fiber-to-fiber losses based on a cascaded MZI-interleaver design on the TriPleX platform, routing the real-time traffic to a second mmWave antenna site. Detailed measurements and traffic statistics indicate end-to-end latency of less than 260 μs and a packet loss less than 0.0054% across a dynamic range of at least 6.5 dB in the optical domain, while the high user bandwidth and signal quality are validated by an uninterrupted 4K-UHD video transmission across the FiWi X-haul mmWave transport network. The current work aims to shape a complete technology roadmap for Point-to-Multipoint FiWi transport network architectures with high spectral efficiency X-haul transport for dense areas and 5G/6G hotspots of future mmWave Centralized Radio Access Networks (C-RANs).

Background/Aim: Neutrophil-to-lymphocyte ratio (NLR) is an indicator of systemic inflammation and could be a predictive factor in malignant tumors. The aim of this study was to investigate the impact of NLR in patients with lower rectal cancer who received preoperative chemo-radiotherapy (CRT). Patients and Methods: Forty-eight patients with lower rectal cancer who underwent preoperative CRT and curative resection were enrolled. Blood samples were obtained before and after CRT. The relationship of NLR with clinical outcome was investigated. Results: Post-CRT NLR was higher compared to pre-CRT NLR. The patients with higher post-CRT NLR tended to have worse pathological response to CRT compared to those with low post-CRT NLR. The patients with high post-CRT NLR showed poorer 5-year overall survival and 3-year disease free survival while there was no correlation according to pre-CRT NLR. The univariate analysis showed that post-CRT stage and post-CRT NLR were associated with a poorer 5-year overall survival. Conclusion: NLR after preoperative CRT could be a potential prognostic indicator for patients with lower rectal cancer.

Diffusion models have made tremendous progress in text-driven image and video generation. Now text-to-image foundation models are widely applied to various down-stream image synthesis tasks, such as controllable image generation and image editing, while downstream video synthesis tasks are less explored for several reasons. First, it requires huge memory and computation overhead to train a video generation foundation model. Even with video foundation models, additional costly training is still required for downstream video synthesis tasks. Second, although some works extend image diffusion models into videos in a training-free manner, temporal consistency cannot be well preserved. Finally, these adaption methods are specifically designed for one task and fail to generalize to different tasks. To mitigate these issues, we propose a training-free general-purpose video synthesis framework, coined as BIVDiff, via bridging specific image diffusion models and general text-to-video foundation diffusion models. Specifically, we first use a specific image diffusion model (e.g., ControlNet and Instruct Pix2Pix) for frame-wise video generation, then perform Mixed Inversion on the generated video, and finally input the inverted latents into the video diffusion models (e.g., VidRD and ZeroScope) for temporal smoothing. This decoupled framework enables flexible image model selection for different purposes with strong task generalization and high efficiency. To validate the effectiveness and general use of BIVDiff, we perform a wide range of video synthesis tasks, including controllable video generation, video editing, video inpainting, and outpainting.

Recent advances in diffusion models have showcased promising results in the text-to-video (T2V) synthesis task. However, as these T2V models solely employ text as the guidance, they tend to struggle in modeling detailed temporal dynamics. In this paper, we introduce a novel T2V framework that additionally employ audio signals to control the temporal dynamics, empowering an off-the-shelf T2I diffusion to generate audio-aligned videos. We propose audio-based regional editing and signal smoothing to strike a good balance between the two contradicting desiderata of video synthesis, i.e., temporal flexibility and coherence. We empirically demonstrate the effectiveness of our method through experiments, and further present practical applications for contents creation.

We present FloVD, a novel video diffusion model for camera-controllable video generation. FloVD leverages optical flow to represent the motions of the camera and moving objects. This approach offers two key benefits. Since optical flow can be directly estimated from videos, our approach allows for the use of arbitrary training videos without groundtruth camera parameters. Moreover, as background optical flow encodes 3D correlation across different viewpoints, our method enables detailed camera control by leveraging the background motion. To synthesize natural object motion while supporting detailed camera control, our framework adopts a two-stage video synthesis pipeline consisting of optical flow generation and flow-conditioned video synthesis. Extensive experiments demonstrate the superiority of our method over previous approaches in terms of accurate camera control and natural object motion synthesis.

Video-to-Video synthesis (Vid2Vid) has achieved remarkable results in generating a photo-realistic video from a sequence of semantic maps. However, this pipeline suffers from high computational cost and long inference latency, which largely depends on two essential factors: 1) network architecture parameters, 2) sequential data stream. Recently, the parameters of image-based generative models have been significantly compressed via more efficient network architectures. Nevertheless, existing methods mainly focus on slimming network architectures and ignore the size of the sequential data stream. Moreover, due to the lack of temporal coherence, image-based compression is not sufficient for the compression of the video task. In this paper, we present a spatial-temporal compression framework, \textbf{Fast-Vid2Vid}, which focuses on data aspects of generative models. It makes the first attempt at time dimension to reduce computational resources and accelerate inference. Specifically, we compress the input data stream spatially and reduce the temporal redundancy. After the proposed spatial-temporal knowledge distillation, our model can synthesize key-frames using the low-resolution data stream. Finally, Fast-Vid2Vid interpolates intermediate frames by motion compensation with slight latency. On standard benchmarks, Fast-Vid2Vid achieves around real-time performance as 20 FPS and saves around 8x computational cost on a single V100 GPU.

Video frame synthesis is an important task in computer vision and has drawn great interests in wide applications. However, existing neural network methods do not explicitly impose tensor low-rankness of videos to capture the spatiotemporal correlations in a high-dimensional space, while existing iterative algorithms require hand-crafted parameters and take relatively long running time. In this paper, we propose a novel multi-phase deep neural network Transform-Based Tensor-Net that exploits the low-rank structure of video data in a learned transform domain, which unfolds an Iterative Shrinkage-Thresholding Algorithm (ISTA) for tensor signal recovery. Our design is based on two observations: (i) both linear and nonlinear transforms can be implemented by a neural network layer, and (ii) the soft-thresholding operator corresponds to an activation function. Further, such an unfolding design is able to achieve nearly real-time at the cost of training time and enjoys an interpretable nature as a byproduct. Experimental results on the KTH and UCF-101 datasets show that compared with the state-of-the-art methods, i.e., DVF and Super SloMo, the proposed scheme improves Peak Signal-to-Noise Ratio (PSNR) of video interpolation and prediction by 4.13 dB and 4.26 dB, respectively.

Text-to-video (T2V) generation is a rapidly growing research area that aims to translate the scenes, objects, and actions within complex video text into a sequence of coherent visual frames. We present FlowZero, a novel framework that combines Large Language Models (LLMs) with image diffusion models to generate temporally-coherent videos. FlowZero uses LLMs to understand complex spatio-temporal dynamics from text, where LLMs can generate a comprehensive dynamic scene syntax (DSS) containing scene descriptions, object layouts, and background motion patterns. These elements in DSS are then used to guide the image diffusion model for video generation with smooth object motions and frame-to-frame coherence. Moreover, FlowZero incorporates an iterative self-refinement process, enhancing the alignment between the spatio-temporal layouts and the textual prompts for the videos. To enhance global coherence, we propose enriching the initial noise of each frame with motion dynamics to control the background movement and camera motion adaptively. By using spatio-temporal syntaxes to guide the diffusion process, FlowZero achieves improvement in zero-shot video synthesis, generating coherent videos with vivid motion.

Most of the existing works in video synthesis focus on generating videos using adversarial learning. Despite their success, these methods often require input reference frame or fail to generate diverse videos from the given data distribution, with little to no uniformity in the quality of videos that can be generated. Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames. To this end, we develop a novel approach that jointly optimizes the input latent space, the weights of a recurrent neural network and a generator through non-adversarial learning. Optimizing for the input latent space along with the network weights allows us to generate videos in a controlled environment, i.e., we can faithfully generate all videos the model has seen during the learning process as well as new unseen videos. Extensive experiments on three challenging and diverse datasets well demonstrate that our proposed approach generates superior quality videos compared to the existing state-of-the-art methods.

We present a learning-based approach with pose perceptual loss for automatic music video generation. Our method can produce a realistic dance video that conforms to the beats and rhymes of almost any given music. To achieve this, we firstly generate a human skeleton sequence from music and then apply the learned pose-to-appearance mapping to generate the final video. In the stage of generating skeleton sequences, we utilize two discriminators to capture different aspects of the sequence and propose a novel pose perceptual loss to produce natural dances. Besides, we also provide a new cross-modal evaluation to evaluate the dance quality, which is able to estimate the similarity between two modalities of music and dance. Finally, a user study is conducted to demonstrate that dance video synthesized by the presented approach produces surprisingly realistic results. The results are shown in the supplementary video at this https URL

We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs variational autoencoders to extract image features, which are then concatenated with video latents along the sequence dimension. It relies exclusively on inherent 3D self-attention mechanisms to incorporate them, eliminating the need for additional parameters or modules. A novel cross-video pairing strategy and a multi-stage training regimen are introduced to balance identity consistency and facial editability while enhancing video naturalness. Extensive experiments demonstrate Concat-ID's superiority over existing methods in both single and multi-identity generation, as well as its seamless scalability to multi-subject scenarios, including virtual try-on and background-controllable generation. Concat-ID establishes a new benchmark for identity-preserving video synthesis, providing a versatile and scalable solution for a wide range of applications.

Recently, pseudo-analog transmission based on SoftCast has been proposed to improve the received quality of video/image by eliminating the cliff effect in traditional digital transmission. In this paper, we propose a Compressed Pseudo-analog Transmission System (ComPaTS) for remote sensing images over bandwidth-constrained wireless channels. This novel scheme is developed based on the observation that the inherent dropping strategy in pseudo-analog transmission is impractical for remote sensing images in which the transmission bandwidth is generally insufficient. In ComPaTS, to guarantee the content diversity gain under pseudo-analog transmission, block-based Compressive Sensing (CS) is applied to the wavelet domain of each remote sensing image, where the sampling ratio is proportional to the importance of different blocks. The main work of ComPaTS is to leverage the sampling ratio in block-based CS and the resource allocation in pseudo-analog transmission in order to minimize system distortion. Two components of system distortion, i.e., source distortion and channel distortion are analyzed respectively. To characterize the coupling relationship between these two different types of distortion, a joint bandwidth-power distortion optimization problem is formulated. Furthermore, we also propose a two-stage allocation algorithm to solve the problem efficiently. The simulation results demonstrate that the proposed ComPaTS scheme significantly outperforms reference schemes in terms of peak signal-to-noise ratio under different bandwidth-constrained scenarios.

Mental fatigue can impair physical, technical, and tactical performance in sports. Since most previous research used general cognitive tasks to elicit mental fatigue, the aim of this study was to investigate whether a more sport-specific task could induce the effects of mental fatigue and impair the subsequent physical and technical performance in a soccer small-sided game. Ten soccer players performed two small-sided games on two different days in a crossover design. Before each small-sided game, they performed a video-based tactical task (30 min) and a control task (documentary watching, 30 min) in a randomized and counterbalanced order. Mental effort was measured through a visual analog scale after the tactical and control tasks. Subjective ratings of perceived exertion were assessed through the RPE questionnaire after the end of the SSG. Physical performance was assessed during the SSG through GPS technology. Results showed no differences (p > 0.05) in physical performance between the two conditions. None of the technical variables were negatively affected by the video-based tactical condition, with the number of total passes (p = 0.003; ES = 0.72 medium) and successful passes (p = 0.003; ES = 0.82 large) results even improved by the video-based tactical task. The mental effort required by the video-tactical task was significantly higher than the control task (p = 0.002; ES = 2.09 huge). However, overall RPE did not differ between conditions. The video-based tactical task did not elicit mental fatigue and did not impair subsequent physical and technical performance. The higher ecological validity of the task and the higher motivation of the participants might have contributed to the results.

Hybrid digital–analog (HDA) transmission is beco-ming an attractive solution for mobile video delivery because it not only has graceful degradation with channel variations but also yields high power efficiency. However, the heavy bandwidth demand of analog transmission is still an unsolved problem, limiting the received video quality when the bandwidth is not sufficient. To address this problem, we propose adopting Shannon–Kotel’nikov (SK) mapping for HDA video transmission and design an HDA scheme called SK-Cast. SK-Cast consists of a digital and an analog branch. In the digital branch, SK-Cast compresses the video sequence using an high efficiency video coding digital encoder to produce a base layer. The base layer is transmitted through digital methods with strong protection. The residual signals are then decorrelated using three-dimensional discrete cosine transform transform. The SK mapping is exploited to transmit these coefficients, as they can achieve efficient bandwidth compression. We address the resource allocation problems in SK-Cast, including the allocation between digital and analog branches and the allocation among analog symbols. The simulation results show that the SK-Cast outperforms the state-of-the-art HDA systems, including WSVC and SharpCast, and a digital scalable video coding system.

This paper presents a generative adversarial learning-based human upper body video synthesis approach to generate an upper body video of target person that is consistent with the body motion, face expression, and pose of the person in source video. We use upper body keypoints, facial action units and poses as intermediate representations between source video and target video. Instead of directly transferring the source video to the target video, we firstly map the source person's facial action units and poses into the target person's facial landmarks, then combine the normalized upper body keypoints and generated facial landmarks with spatio-temporal smoothing to generate the corresponding target video's image. Experimental results demonstrated the effectiveness of our method.

Recently, pseudo analog transmission has gained increasing attentions due to its ability to alleviate the cliff effect in video multicast scenarios. The existing pseudo analog systems are optimized under the minimum mean squared error criterion. However, their power allocation strategies do not take the perceptual video quality into consideration. In this article, we propose a human-perception-based pseudo analog video transmission system named ROIC-Cast, which aims to intelligently enhance the transmission quality of the region-of-interest (ROI) parts. Firstly, the classic deep learning based saliency detection algorithm is adopted to decompose the continuous video sequences into ROI and non-ROI blocks. Secondly, an effective compression method is used to reduce the data amount of side information generated by the ROI extraction module. Then, the power allocation scheme is formulated as a convex problem, and the optimal transmission power for both ROI and non-ROI blocks is derived in a closed form. Finally, the simulations are conducted to validate the proposed system by comparing with a few of existing systems, e.g., KMV-Cast, SoftCast, and DAC-RAN. The proposed ROIC-Cast can achieve over 4.1 dB peak signal-to-noise ratio gains of ROI compared with other systems, given the channel signal-to-noise ratio as −5 dB, 0 dB, 5 dB, and 10 dB, respectively. This significant performance improvement is due to the automatic ROI extraction, high-efficiency data compression as well as adaptive power allocation.

Ultrasound video classification enables automated diagnosis and has emerged as an important research area. However, publicly available ultrasound video datasets remain scarce, hindering progress in developing effective video classification models. We propose addressing this shortage by synthesizing plausible ultrasound videos from readily available, abundant ultrasound images. To this end, we introduce a latent dynamic diffusion model (LDDM) to efficiently translate static images to dynamic sequences with realistic video characteristics. We demonstrate strong quantitative results and visually appealing synthesized videos on the BUSV benchmark. Notably, training video classification models on combinations of real and LDDM-synthesized videos substantially improves performance over using real data alone, indicating our method successfully emulates dynamics critical for discrimination. Our image-to-video approach provides an effective data augmentation solution to advance ultrasound video analysis. Code is available at https://github.com/MedAITech/U_I2V.

In this paper, we report on a parallel free-viewpoint video synthesis algorithm that can efficiently reconstruct a high-quality 3D scene representation of sports scenes. The proposed method focuses on a scene that is captured by multiple synchronized cameras featuring wide-baselines. The following strategies are introduced to accelerate the production of a free-viewpoint video taking the improvement of visual quality into account: (1) a sparse point cloud is reconstructed using a volumetric visual hull approach, and an exact 3D ROI is found for each object using an efficient connected components labeling algorithm. Next, the reconstruction of a dense point cloud is accelerated by implementing visual hull only in the ROIs; (2) an accurate polyhedral surface mesh is built by estimating the exact intersections between grid cells and the visual hull; (3) the appearance of the reconstructed presentation is reproduced in a view-dependent manner that respectively renders the non-occluded and occluded region with the nearest camera and its neighboring cameras. The production for volleyball and judo sequences demonstrates the effectiveness of our method in terms of both execution time and visual quality.

This paper proposes a novel hardware architecture for real-time face detection, which is efficient and suitable for embedded systems. The proposed architecture is based on AdaBoost learning algorithm with Haar-like features and it aims to apply face detection to a low-cost FPGA that can be applied to a legacy analog video camera as a target platform. We propose an efficient method to calculate the integral image using the cumulative line sum. We also suggest an alternative method to avoid division, which requires many operations to calculate the standard deviation. A detailed structure of system elements for image scale, integral image generator, and pipelined classifier that purposed to optimize the efficiency between the processing speed and the hardware resources is presented. The performance of the proposed architecture is described in comparison with the detection results of OpenCV using the same input images. For verification of the actual face detection on analog cameras, we designed an emulation platform using a low-cost Spartan-3 FPGA and then experimented the proposed architecture. The experimental results show that the processing time for face detection on analog video camera is 42 frames per second, which is about 3 times faster than previous works for low-cost face detection.

Spiking Neural Networks (SNNs) can be configured to produce almost-equivalent accurate Analog Neural Networks (ANNs) by various ANN-SNN conversion methods. Most of these methods are applied to classification and object detection networks tested on frame-based datasets. In this work, we demonstrate a converted SNN for image segmentation and applied to a natural video dataset. Instead of resetting the network state with each input frame, we capitalize on the temporal redundancy between adjacent frames in a natural scene, and propose an interval reset method where the network state is reset after a fixed number of frames. We studied the trade-off between accuracy and latency with the number of interval reset frames. We also applied layer-specific normalization and early stopping to speed up network convergence and to reduce the latency. Our results show that the SNN achieved a 35.7x increase in convergence speed with only 1.5% accuracy drop using an interval reset of 20 frames.

Multiview video can provide more immersive perception than traditional single 2-D video. It enables both interactive free navigation applications as well as high-end autostereoscopic displays on which multiple users can perceive genuine 3-D content without glasses. The multiview format also comprises much more visual information than classical 2-D or stereo 3-D content, which makes it possible to perform various interesting editing operations both on pixel-level and object-level. This survey provides a comprehensive review of existing multiview video synthesis and editing algorithms and applications. For each topic, the related technologies in classical 2-D image and video processing are reviewed. We then continue to the discussion of recent advanced techniques for multiview video virtual view synthesis and various interactive editing applications. Due to the ongoing progress on multiview video synthesis and editing, we can foresee more and more immersive 3-D video applications will appear in the future.

In recent years, diffusion models have emerged as the most powerful approach in image synthesis. However, applying these models directly to video synthesis presents challenges, as it often leads to noticeable flickering contents. Although recently proposed zero-shot methods can alleviate flicker to some extent, we still struggle to generate coherent videos. In this paper, we propose DiffSynth, a novel approach that aims to convert image synthesis pipelines to video synthesis pipelines. DiffSynth consists of two key components: a latent in-iteration deflickering framework and a video deflickering algorithm. The latent in-iteration deflickering framework applies video deflickering to the latent space of diffusion models, effectively preventing flicker accumulation in intermediate steps. Additionally, we propose a video deflickering algorithm, named patch blending algorithm, that remaps objects in different frames and blends them together to enhance video consistency. One of the notable advantages of DiffSynth is its general applicability to various video synthesis tasks, including text-guided video stylization, fashion video synthesis, image-guided video stylization, video restoring, and 3D rendering. In the task of text-guided video stylization, we make it possible to synthesize high-quality videos without cherry-picking. The experimental results demonstrate the effectiveness of DiffSynth. All videos can be viewed on our project page. Source codes will also be released.

Hybrid digital–analog (HDA) video transmission schemes have shown advantages in avoiding the cliff effect. However, most current HDA schemes assume perfect transmission of the digital signal, which is hardly the case in practice. In this paper, we propose an adaptive recursive distortion estimate for the HDA system (ARDE-HDA), which recursively estimates the decoder-side distortion from the encoder and adaptively allocates the transmission power between the digital and analog signals in HDA. First, we derive the closed-form expression of a recursive distortion estimation (RDE) method, which does not require the digital part of the HDA output to be decoded perfectly, and both transmission error and superposition process of digital and analog parts are taken into consideration. Then, based on the deduced RDE model, an adaptive power allocation is proposed for the digital and analog parts to minimize the decoder-side distortion. Finally, simulation results are presented, which show the accuracy of the proposed RDE model and the ARDE-HDA method. Our method can achieve an average of 4.10 dB gain over existing HDA methods and 15.14-dB gain over the Softcast method in terms of the peak signal-to-noise ratio.

Distribution Matching Distillation (DMD) is a promising score distillation technique that compresses pre-trained teacher diffusion models into efficient one-step or multi-step student generators. Nevertheless, its reliance on the reverse Kullback-Leibler (KL) divergence minimization potentially induces mode collapse (or mode-seeking) in certain applications. To circumvent this inherent drawback, we propose Adversarial Distribution Matching (ADM), a novel framework that leverages diffusion-based discriminators to align the latent predictions between real and fake score estimators for score distillation in an adversarial manner. In the context of extremely challenging one-step distillation, we further improve the pre-trained generator by adversarial distillation with hybrid discriminators in both latent and pixel spaces. Different from the mean squared error used in DMD2 pre-training, our method incorporates the distributional loss on ODE pairs collected from the teacher model, and thus providing a better initialization for score distillation fine-tuning in the next stage. By combining the adversarial distillation pre-training with ADM fine-tuning into a unified pipeline termed DMDX, our proposed method achieves superior one-step performance on SDXL compared to DMD2 while consuming less GPU time. Additional experiments that apply multi-step ADM distillation on SD3-Medium, SD3.5-Large, and CogVideoX set a new benchmark towards efficient image and video synthesis.

We tackle the problem of synthesizing a video of multiple moving people as seen from a novel view, given only an input video and depth information or human poses of the novel view as prior. This problem requires a model that learns to transform input features into target features while maintaining temporal consistency. To this end, we learn an invariant feature from the input video that is shared across all viewpoints of the same scene and a view-dependent feature obtained using the target priors. The proposed approach, View-LSTM, is a recurrent neural network structure that accounts for the temporal consistency and target feature approximation constraints. We validate View-LSTM by designing an end-to-end generator for novel-view video synthesis. Experiments on a large multi-view action recognition dataset validate the proposed model.

In this paper, we propose a new method to automatically generate a video sequence from a single image and a user provided motion stroke. Generating a video sequence based on a single input image has many applications in visual content creation, but it is tedious and time-consuming to produce even for experienced artists. Automatic methods have been proposed to address this issue, but most existing video prediction approaches require multiple input frames. In addition, generated sequences have limited variety since the output is mostly determined by the input frames, without allowing the user to provide additional constraints on the result. In our technique, users can control the generated animation using a sketch stroke on a single input image. We train our system such that the trajectory of the animated object follows the stroke, which makes it both more flexible and more controllable. From a single image, users can generate a variety of video sequences corresponding to different sketch inputs. Our method is the first system that, given a single frame and a motion stroke, can generate animations by recurrently generating videos frame by frame. An important benefit of the recurrent nature of our architecture is that it facilitates the synthesis of an arbitrary number of generated frames. Our architecture uses an autoencoder and a generative adversarial network (GAN) to generate sharp texture images, and we use another GAN to guarantee that transitions between frames are realistic and smooth. We demonstrate the effectiveness of our approach on the MNIST, KTH, and Human 3.6M datasets.

Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time applicability. To address these challenges, we propose a simple yet effective tuning-free framework for character video synthesis, named MovieCharacter, designed to streamline the synthesis process while ensuring high-quality outcomes. Our framework decomposes the synthesis task into distinct, manageable modules: character segmentation and tracking, video object removal, character motion imitation, and video composition. This modular design not only facilitates flexible customization but also ensures that each component operates collaboratively to effectively meet user needs. By leveraging existing open-source models and integrating well-established techniques, MovieCharacter achieves impressive synthesis results without necessitating substantial resources or proprietary datasets. Experimental results demonstrate that our framework enhances the efficiency, accessibility, and adaptability of character video synthesis, paving the way for broader creative and interactive applications.

Wireless virtual reality (VR) applications that provide users extraordinary viewing experience are now drawing great attentions. Transmitting VR video via wireless channel to users’ head-mounted display devices efficiently with low latency is very important for many emerging VR applications. In this paper, we propose a pseudo-analog transmission framework named OmniCast, which provides graceful quality degradation and competitive performance for unpredictably varying wireless channels while featuring low latency, low complexity, and low energy cost. In particular, we analyze the influence of projection between the spherical representation and the 2-D plane representation, and derive a power optimization scheme to minimize the distortion on the sphere. In addition, we develop an approach to measure the efficiency of decorrelation transform in the spherical domain. Based on that, an appropriate transform option can be determined. Experimental results show that the proposed framework improves the transmission efficiency of omnidirectional videos, while achieving elegant quality degradation for channel fluctuation in a wide channel SNR range.

We investigate how to enhance the physical fidelity of video generation models by leveraging synthetic videos derived from computer graphics pipelines. These rendered videos respect real-world physics, such as maintaining 3D consistency, and serve as a valuable resource that can potentially improve video generation models. To harness this potential, we propose a solution that curates and integrates synthetic data while introducing a method to transfer its physical realism to the model, significantly reducing unwanted artifacts. Through experiments on three representative tasks emphasizing physical consistency, we demonstrate its efficacy in enhancing physical fidelity. While our model still lacks a deep understanding of physics, our work offers one of the first empirical demonstrations that synthetic video enhances physical fidelity in video synthesis. Website: https://kevinz8866.github.io/simulation/

We propose a hybrid digital-analog framework for wireless video transmission, which benefits from both the high distortion-power performance of digital systems and the graceful performance degradation of analog systems. The proposed framework models video frames as a parallel Gaussian source, which is separated into digital and analog parts through scalar quantization. It features entropy coding and channel coding in digital transmission and power scaling in analog transmission. The key challenge in this framework is how to allocate the constrained power and bandwidth resources between and among digital and analog components to achieve minimal distortion at the receiver. Given the worst-case channel signal-to-noise ratio, we are able to derive a closed-form expression of the overall distortion. However, minimizing it is a mixed-integer non-linear programming problem, which is generally non-deterministic polynomial-time hard. By making reasonable and justified simplifications, we approach the optimal solution through a practical scheme. Evaluations show that the proposed scheme outperforms the state-of-the-art analog scheme SoftCast by a large margin. The gain in received video peak signal-to-noise ratio is up to 5.0 dB for various types of videos.

Stereo video synthesis from monocular input is challenging in spatial computing and virtual reality due to the lack of high-quality stereo video pairs for training and the difficulty of maintaining spatio-temporal consistency between frames. Existing methods primarily address these issues by directly applying novel view synthesis (NVS) techniques to video, while facing limitations such as the inability to effectively represent dynamic scenes and the requirement for extensive training data. In this paper, we introduce a novel self-supervised stereo video synthesis paradigm via a video diffusion model, termed SpatialDreamer, which meets the challenges head-on. Firstly, to address the stereo video data insufficiency, we propose a Depth based Video Generation module DVG, which employs a forward-backward rendering mechanism to generate paired videos with geometric and temporal priors. Leveraging data generated by DVG, we propose RefinerNet along with a self-supervised synthetic framework designed to facilitate efficient and dedicated training. More importantly, we devise a consistency control module, which consists of a metric of stereo deviation strength and a Temporal Interaction Learning module TIL for geometric and temporal consistency ensurance respectively. We evaluated the proposed method against various benchmark methods, with the results showcasing its superior performance. Our project website is at: https://spatialdreamer.github.io.

The pseudo-analog wireless video transmission technology can improve the effectiveness, reliability, and robustness of the conventional digital system in video broadcast scenarios. Although some prototypes of IEEE 802.11 series have been developed for researchers to do simulations and experiments, they are usually expensive and provide very limited access to the physical layer. More importantly, these prototypes cannot be used to verify the correctness of the new proposed pseudo-analog wireless video transmission algorithms directly due to limited modulation modes they can support. In this paper, we present a novel design of software radio platform (SDR)-based pseudo-analog wireless video transceiver which is completely transparent and allows users to learn all the implementation details. Firstly, we prove that the analog method can also achieve the optimal performance as the digital method from the perspective of the rate-distortion theory. Then, we describe the two hardware implementation difficulties existed in the designing process including the data format modification and the non-linear distortion. Next, we introduce the implementation details of the designed transceiver. Finally, we analyze the performance of the designed transceiver. Specifically, the results show that the designed system can work effectively in both simulations and experiments.

Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. At the same time, scaling these models to enable longer, more dynamic video synthesis often remains computationally intractable. To address this challenge, we introduce the concept of Generative Temporal Nursing (GTN), where we aim to alter the generative process on the fly during inference to improve control over the temporal dynamics and enable generation of longer videos. We propose a method for GTN, dubbed VSTAR, which consists of two key ingredients: 1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis based on the original single prompt leveraging LLMs, which gives accurate textual guidance to different visual states of longer videos, and 2) Temporal Attention Regularization (TAR) - a regularization technique to refine the temporal attention units of the pre-trained T2V diffusion models, which enables control over the video dynamics. We experimentally showcase the superiority of the proposed approach in generating longer, visually appealing videos over existing open-sourced T2V models. We additionally analyze the temporal attention maps realized with and without VSTAR, demonstrating the importance of applying our method to mitigate neglect of the desired visual change over time.

Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant achievements in various image and video generation tasks, including the domain of medical imaging. However, generating echocardiography videos based on semantic anatomical information remains an unexplored area of research. This is mostly due to the constraints imposed by the currently available datasets, which lack sufficient scale and comprehensive frame-wise annotations for every cardiac cycle. This paper aims to tackle the aforementioned challenges by expanding upon existing video diffusion models for the purpose of cardiac video synthesis. More specifically, our focus lies in generating video using semantic maps of the initial frame during the cardiac cycle, commonly referred to as end diastole. To further improve the synthesis process, we integrate spatial adaptive normalization into multiscale feature maps. This enables the inclusion of semantic guidance during synthesis, resulting in enhanced realism and coherence of the resultant video sequences. Experiments are conducted on the CAMUS dataset, which is a highly used dataset in the field of echocardiography. Our model exhibits better performance compared to the standard diffusion technique in terms of multiple metrics, including FID, FVD, and SSIM.

As a natural extension of the image synthesis task, video synthesis has attracted a lot of interest recently. Many image synthesis works utilize class labels or text as guidance. However, neither labels nor text can provide explicit temporal guidance, such as when an action starts or ends. To overcome this limitation, we introduce semantic video scene graphs as input for video synthesis, as they represent the spatial and temporal relationships between objects in the scene. Since video scene graphs are usually temporally discrete annotations, we propose a video scene graph (VSG) encoder that not only encodes the existing video scene graphs but also predicts the graph representations for unlabeled frames. The VSG encoder is pre-trained with different contrastive multi-modal losses. A semantic scene graph-to-video synthesis framework (SSGVS), based on the pre-trained VSG encoder, VQ-VAE, and auto-regressive Transformer, is proposed to synthesize a video given an initial scene image and a non-fixed number of semantic scene graphs. We evaluate SSGVS and other state-of-the-art video synthesis models on the Action Genome dataset and demonstrate the positive significance of video scene graphs in video synthesis. The source code is available at https://github.com/yrcong/SSGVS.

Abstract:Technical labor is still typically made invisible in the functioning of academic libraries and other information institutions even as they begin to disseminate technical and craft knowledge through makerspaces and other sites of library innovation. This paper seeks to recover one type of technical labor, digitization, as information work that embodies mental and manual activities and is both materially and intellectually productive. This paper draws on findings from an empirical study conducted by the author from 2015–2017 that used qualitative-interpretive methods to study the discursive and material practices of professional media preservationists as they worked to digitize analog video recordings in small-scale, high-quality ("artisanal") digitization projects. One key finding of this research is that in order to produce "legitimate" digital copies within their institutional contexts, media preservationists must coordinate their physical and mental activities to develop understanding of the invisible electrical signals that carry the encoded video information, blending objective and subjective modalities of knowledge. These findings have implications for understanding how the invisible labor of digitization has significant mental as well as manual dimensions, contributing to ongoing debates in information studies and the digital humanities on the relationship between "doing" and "signifying" in terms of knowledge work.

Demand for 2K video synthesis is rising with increasing consumer expectations for ultra-clear visuals. While diffusion transformers (DiTs) have demonstrated remarkable capabilities in high-quality video generation, scaling them to 2K resolution remains computationally prohibitive due to quadratic growth in memory and processing costs. In this work, we propose Turbo2K, an efficient and practical framework for generating detail-rich 2K videos while significantly improving training and inference efficiency. First, Turbo2K operates in a highly compressed latent space, reducing computational complexity and memory footprint, making high-resolution video synthesis feasible. However, the high compression ratio of the VAE and limited model size impose constraints on generative quality. To mitigate this, we introduce a knowledge distillation strategy that enables a smaller student model to inherit the generative capacity of a larger, more powerful teacher model. Our analysis reveals that, despite differences in latent spaces and architectures, DiTs exhibit structural similarities in their internal representations, facilitating effective knowledge transfer. Second, we design a hierarchical two-stage synthesis framework that first generates multi-level feature at lower resolutions before guiding high-resolution video generation. This approach ensures structural coherence and fine-grained detail refinement while eliminating redundant encoding-decoding overhead, further enhancing computational efficiency.Turbo2K achieves state-of-the-art efficiency, generating 5-second, 24fps, 2K videos with significantly reduced computational cost. Compared to existing methods, Turbo2K is up to 20$\times$ faster for inference, making high-resolution video generation more scalable and practical for real-world applications.

Existing programmable simulators enable researchers to customize different driving scenarios to conduct in-lab automotive driver simulations. However, software-based simulators for cognitive research generate and maintain their scenes with the support of 3D engines, which may affect users' experiences to a certain degree since they are not sufficiently realistic. Now, a critical issue is the question of how to build scenes into real-world ones. In this paper, we introduce the first step in utilizing video-to-video synthesis, which is a deep learning approach, in OpenDS framework, which is an open-source driving simulator software, to present simulated scenes as realistically as possible. Off-line evaluations demonstrated promising results from our study, and our future work will focus on how to merge them appropriately to build a close-to-reality, real-time driving simulator.

Although substantial progress has been made in audiodriven talking video synthesis, there still remain two major difficulties: existing works 1) need a long sequence of training dataset (>1h) to synthesize co-speech gestures, which causes a significant limitation on their applicability; 2) usually fail to generate long sequences, or can only generate long sequences without enough diversity. To solve these challenges, we propose a Disentangled Recurrent Representation Learning framework to synthesize long diversified gesture sequences with a short training video of around 2 minutes. In our framework, we first make a disentangled latent space assumption to encourage unpaired audio and pose combinations, which results in diverse "one-to-many" mappings in pose generation. Next, we apply a recurrent inference module to feed back the last generation as initial guidance to the next phase, enhancing the long-term video generation of full continuity and diversity. Comprehensive experimental results verify that our model can generate realistic synchronized full-body talking videos with training data efficiency.

Surgical data science (SDS) is a field that analyzes patient data before, during, and after surgery to improve surgical outcomes and skills. However, surgical data is scarce, heterogeneous, and complex, which limits the applicability of existing machine learning methods. In this work, we introduce the novel task of future video generation in laparoscopic surgery. This task can augment and enrich the existing surgical data and enable various applications, such as simulation, analysis, and robot-aided surgery. Ultimately, it involves not only understanding the current state of the operation but also accurately predicting the dynamic and often unpredictable nature of surgical procedures. Our proposed method, VISAGE (VIdeo Synthesis using Action Graphs for Surgery), leverages the power of action scene graphs to capture the sequential nature of laparoscopic procedures and utilizes diffusion models to synthesize temporally coherent video sequences. VISAGE predicts the future frames given only a single initial frame, and the action graph triplets. By incorporating domain-specific knowledge through the action graph, VISAGE ensures the generated videos adhere to the expected visual and motion patterns observed in real laparoscopic procedures. The results of our experiments demonstrate high-fidelity video generation for laparoscopy procedures, which enables various applications in SDS.

Diffusion-based generative models have exhibited considerable success in conditional video synthesis and editing. Nevertheless, prevailing video diffusion models primarily rely on conditioning with specific input modalities, predominantly text, restricting their adaptability to alternative modalities without necessitating retraining of modality-specific components. In this work, we present EnergyViD, a universal spatio-temporal Energy-guided Video Diffusion model designed for zero-shot video synthesis and editing across diverse conditions. Specifically, we leverage off-the-shelf pre-trained networks to construct generic energy functions, guiding the generation process under specific conditions without the need for retraining. To precisely capture temporal dynamics related to motion conditions (e.g., pose sequences), we introduce a novel kernel Maximum Mean Discrepancy (MMD)-based energy function, which minimizes the global distribution discrepancy between the conditioning input and the generated video. Our extensive qualitative and quantitative experiments demonstrate that our algorithm consistently produces high-quality results across a wide range of motion and non-motion conditions, including text, face ID, style, poses, depths, sketches, canny edges, and segmentation maps, in the context of zero-shot video synthesis and editing. We will release source code upon acceptance of the paper.

Generative neural network models have been used to create impressive synthetic images. However, artificial video synthesis is still hard, even for these models. The best videos that generative models can currently create are a few seconds long, distorted, and low resolution. We propose and implement a model to synthesize videos at 1024 × 1024 × 32 resolution that include human facial expressions by using static images generated from a Generative Adversarial Network trained on human facial images. To the best of our knowledge, this is the first work that generates realistic videos that are larger than 256 × 256 resolution from single starting images. Our model improves video synthesis in both quantitative and qualitative ways as compared to two state-of-the-art models: TGAN and MocoGAN. In a quantitative comparison, we achieve a best Average Content Distance (ACD) score of 0.167, as compared to 0.305 and 0.201 for TGAN and MocoGAN, respectively

We propose a novel high dynamic range (HDR) video synthesis algorithm from alternatively exposed low dynamic range (LDR) videos. We first develop a superpixel-based motion estimation technique using an illumination-invariant feature descriptor. Then, we merge the warped frames to obtain the HDR frame. Experimental results demonstrate that the proposed algorithm provides high-quality HDR videos compared to state-of-the-art algorithms.

Bullet-time videos have been widely used in movies, TV advertisements, and computer games, and can produce an immersive and smooth orbital free-viewpoint of frozen action. However, existing bullet-time video synthesis methods remain challenging in practical applications, especially in complex situations with poor camera calibration and a variety of camera array structures. This paper proposes a novel bullet-time video synthesis method based on a virtual dynamic target axis. We adopt an image similarity transformation strategy to eliminate image distortion in the bullet-time video. We use a high-order polynomial curve fitting strategy to reserve more bullet-time video frame content. The proposed dynamic target axis strategy can support various camera array structures, including camera arrays with and without a common field of view. In addition, this strategy can also tolerate poor camera calibration situations with unevenly distributed reprojection errors to some extent and synthesize smooth bullet-time videos without high-precision camera calibration. Qualitative and quantitative experiments in real environments and on simulation platforms demonstrate the high performance of our bullet-time video synthesis method. Compared with the state-of-the-art methods, the proposed method shows superiority.

Understanding semantic intricacies and high-level concepts is essential in image sketch generation, and this challenge becomes even more formidable when applied to the domain of videos. To address this, we propose a novel optimization-based framework for sketching videos represented by the frame-wise B\'ezier curve. In detail, we first propose a cross-frame stroke initialization approach to warm up the location and the width of each curve. Then, we optimize the locations of these curves by utilizing a semantic loss based on CLIP features and a newly designed consistency loss using the self-decomposed 2D atlas network. Built upon these design elements, the resulting sketch video showcases impressive visual abstraction and temporal coherence. Furthermore, by transforming a video into SVG lines through the sketching process, our method unlocks applications in sketch-based video editing and video doodling, enabled through video composition, as exemplified in the teaser.

Video synthesis and prediction are essential tasks in computer vision, finding diverse applications in areas such as video editing, virtual reality, and autonomous driving. This paper presents a comparative analysis of three state-of-the-art video synthesis models: Vid2Vid, Fast Vid2Vid, and Dynamic Multi-scale Voxel Flow Network (DMVFN). Vid2Vid and Fast Vid2Vid are generative models that specialize in synthesizing realistic videos based on semantic segmentation maps. In contrast, DMVFN addresses video synthesis using a voxel-based representation and adopts a multi-scale strategy, generating voxel grids at different resolutions to capture both global and local motion information. In result analysis of video synthesis models, Vid2Vid generator and discriminator generate visually captivating videos with accurate object shapes and colors, with accuracy 84.76% and 85.25%, respectively. Whereas, Fast Vid2Vid has achieved a remarkable speed while maintaining a satisfactory quality level with generator and discriminator accuracy of 88.82% and 87.68% together. Finally, DMVFN excels at capturing complex motion patterns and preserving temporal coherence, so it has achieved 86.64% generator and 86.96% discriminator accuracy, although it may have limitations in generating subtle details. Overall, this comparative analysis provides valuable insight into the strengths and limitations of each model, assisting researchers and practitioners in selecting appropriate video synthesis techniques for specific applications.

The paper proposes a method to generate expressive talking head videos artificially in any context by synthesizing and syncing speech and video, with the only provided inputs of a facial image and an audio. The method allows the audio either to be provided directly in a pre-recorded format, or to be synthesized using a Text-to-Speech architecture from the textual input words provided. In contrast to previous methods, emphasis has been on generating more expressive, lively and appealing talking head videos through enhanced facial expressions, head dynamics and naturally enhanced audio. The proposed method works by training Long Short Term Memory (LSTM) and Multi-Layer Perceptron (MLP) networks to disentangle content and speaker identity from the input audio for generating facial landmarks dependent on these representations, in contrast to directly mapping audio with the pixels. The disentangled audio content enhances the lip movements and facial regions motions, whereas the speaker information enhances expressions and head dynamics. The method also proposes the prediction of facial landmarks, allowing enhanced, faster and more appropriate head dynamics. The paper also presents extensive qualitative and quantitative evaluation of the proposed methodology, and demonstrations of some generated talking head videos in comparison to previous methods.

In this paper we present a system to restore analog videos of historical archives. These videos often contain severe visual degradation due to the deterioration of their tape supports that require costly and slow manual interventions to recover the original content. The proposed system uses a multi-frame approach and is able to deal also with severe tape mistracking, which results in completely scrambled frames. Tests on real-world videos from a major historical video archive show the effectiveness of our demo system.

Understanding semantic intricacies and high‐level concepts is essential in image sketch generation, and this challenge becomes even more formidable when applied to the domain of videos. To address this, we propose a novel optimization‐based framework for sketching videos represented by the frame‐wise Bézier Curves. In detail, we first propose a cross‐frame stroke initialization approach to warm up the location and the width of each curve. Then, we optimize the locations of these curves by utilizing a semantic loss based on CLIP features and a newly designed consistency loss using the self‐decomposed 2D atlas network. Built upon these design elements, the resulting sketch video showcases notable visual abstraction and temporal coherence. Furthermore, by transforming a video into vector lines through the sketching process, our method unlocks applications in sketch‐based video editing and video doodling, enabled through video composition.

Edge-AI offloading is a promising solution for real-time object detection with resource-constrained edge devices. However, current edge architecture, which uses conventional digital video compression and modulation/coding, cannot provide stable object detection due to the scarcity of wireless resources and the explosive increase of edge devices. We employ pseudo-analog video transmission for Edge-AI offloading systems to cope with the issue. Although pseudo-analog video transmission cannot provide clear images, it has a substantial advantage for Edge-AI offloading systems, i.e., it can transmit higher-resolution video with limited bandwidth in lower Signal-to-Noise Ratio (SNR) links and do not need to control resolution according to wireless channel quality. This paper explores the impact of pseudo-analog video transmission on object detection accuracy.

BackgroundThe Framework Method is becoming an increasingly popular approach to the management and analysis of qualitative data in health research. However, there is confusion about its potential application and limitations.DiscussionThe article discusses when it is appropriate to adopt the Framework Method and explains the procedure for using it in multi-disciplinary health research teams, or those that involve clinicians, patients and lay people. The stages of the method are illustrated using examples from a published study.SummaryUsed effectively, with the leadership of an experienced qualitative researcher, the Framework Method is a systematic and flexible approach to analysing qualitative data and is appropriate for use in research teams even where not all members have previous experience of conducting qualitative research.

Cronbach’s alpha is a statistic commonly quoted by authors to demonstrate that tests and scales that have been constructed or adopted for research projects are fit for purpose. Cronbach’s alpha is regularly adopted in studies in science education: it was referred to in 69 different papers published in 4 leading science education journals in a single year (2015)—usually as a measure of reliability. This article explores how this statistic is used in reporting science education research and what it represents. Authors often cite alpha values with little commentary to explain why they feel this statistic is relevant and seldom interpret the result for readers beyond citing an arbitrary threshold for an acceptable value. Those authors who do offer readers qualitative descriptors interpreting alpha values adopt a diverse and seemingly arbitrary terminology. More seriously, illustrative examples from the science education literature demonstrate that alpha may be acceptable even when there are recognised problems with the scales concerned. Alpha is also sometimes inappropriately used to claim an instrument is unidimensional. It is argued that a high value of alpha offers limited evidence of the reliability of a research instrument, and that indeed a very high value may actually be undesirable when developing a test of scientific knowledge or understanding. Guidance is offered to authors reporting, and readers evaluating, studies that present Cronbach’s alpha statistic as evidence of instrument quality.

An unresolved issue in the field of implementation research is how to conceptualize and evaluate successful implementation. This paper advances the concept of “implementation outcomes” distinct from service system and clinical treatment outcomes. This paper proposes a heuristic, working “taxonomy” of eight conceptually distinct implementation outcomes—acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability—along with their nominal definitions. We propose a two-pronged agenda for research on implementation outcomes. Conceptualizing and measuring implementation outcomes will advance understanding of implementation processes, enhance efficiency in implementation research, and pave the way for studies of the comparative effectiveness of implementation strategies.

Over the past few years, there has been a steadily increasing interest on the part of economists in happiness research. We argue that reported subjective well-being is a satisfactory empirical approximation to individual utility and that happiness research is able to contribute important insights for economics. We report how the economic variables income, unemployment and inflation affect happiness as well as how institutional factors, in particular the type of democracy and the extent of government decentralization, systematically influence how satisfied individuals are with their life. We discuss some of the consequences for economic policy and for economic theory.

This textbook aims to provide a balanced introduction to research methods for today’s undergraduate business students. It does this by synthesising rigorous coverage of methodologies with an accessible ‘real-world’ approach. The text follows course learning objectives for undergraduates in business and provides examples drawn from the full range of business subjects from marketing and strategy to human resource management. The text has unique features: for example, it introduces the four frameworks approach to the research project. The four frameworks approach provides beginner researchers, as well as more advanced researchers, with a simple model that will guide them in the development of their research projects. It facilitates researchers in the task of developing properly focused, fully integrated research projects. The textbook is very sympathetic to the challenges facing a student engaging with the subject for the first time and provides an integrated and balanced approach to quantitative and qualitative research. The writing is simple and direct and the examples and case studies presented were selected particularly with an undergraduate readership in mind. In summary, the text provides a unique and simple yet comprehensive introduction to research methods for business students. Business Research Methods is a valuable resource for all undergraduate business students, particularly for secondand third-year students on business research methods courses. It provides an excellent introduction to the work of undertaking research in an academic environment. This text is essential reading for all business students required to undertake research projects. Postgraduate students, and indeed students from other nonbusiness disciplines, will also find the text very helpful. URI: http://hdl.handle.net/2086/16559 ISBN: 9781473760356 Peer Reviewed: Yes Date: 2019 Creative Commons Licence: N/A Show attachments and full item record This item appears in the following Collection(s) School of Applied Social Sciences [1729] © 2011 De Montfort University Connect with DORA: Business research methods

Using mitotic cultures synchronised by a feed-starve protocol or by elutriation, we have shown that the yeast DNA polymerase I gene is periodically expressed with its transcript increasing at least 100-fold in late Gl with a peak around the Gl/S phase boundary. This is precisely the same interval of the cell cycle in which three other yeast DNA synthesis genes, CDC8, CDC9 and CDC21, have been found to be periodically expressed (White et al 1987. Expl. Cell. Res., in press). The polymerase I transscript is also regulated in meiosis, showing an overall fluctuation in level of some 20-fold, with a peak at about mid-S phase. In addition, following irradiation with 50J/m2 ultraviolet light, there was a 20-fold increase in the transcript, starting after 30 minutes and reaching a peak two hours later. These results indicate that DNA polymerase I is subject to a complex control and imply that it has a role in both DNA synthesis and DNA repair. INTRODUCTION Many of the processes of DNA metabolism are discontinuous in nature, for instance DNA synthesis occupies only a small proportion of either the mitotic cell cycle or meiosis, and DNA repair is only required occasionally. Therefore the enzymes involved in these processes could be subject to some form of regulation which allows their synthesis only when the gene product is required. In certain cases this regulation could be highly complex since a number of enzymes function in several different aspects of DNA metabolism. A prominent example is DNA ligase which acts in DNA synthesis to join Okazaki fragments and also in repair and recombination to complete the final sealing step in nicked duplex DNA. We have studied the regulation of the DNA ligase gene, CDC9, in yeast and found that in both meiosis and the mitotic cell cycle there is a brief but marked increase in CDC9 transcript levels just prior to DNA synthesis (1,2), suggesting that transcription of this gene is confined to a short interval immediately before S phase. Similarly, there is an increase in levels of both DNA ligase message and © I R L Press Limited, Oxford, England. 5017 Nucleic Acids Research enzyme after exposure of yeast cells to ultraviolet (UV) light, y-irradiation or methyl methane sulphonate (3). Another enzyme involved in many aspects of DNA metabolism is DNA polymerase. In yeast there are two species of nuclear DNA polymerase and although their respective roles are not clearly established, their biochemical properties suggest that DNA polymerase I is responsible for DNA replication(4,5,6). There have, in fact, been two previous attempts to study expression of yeast DNA polymerase, each involving the mitotic cell cycle alone (7,8). Synchronised cells were used in these experiments and total DNA polymerase activity was assayed throughout the cell cycle. No attempt was made to differentiate between species of DNA polymerase and, in summary, the results suggest that overall polymerase activity fluctuates with a peak occurring at approximately early S phase. Possibly a more direct means of examining gene expression is to follow fluctuations in level of the transcript by hybridisation with a DNA probe. This is particularly useful when several species of an enzyme exist in a cell since hybridisation is highly specific for a particular transcript. The yeast DNA polymerase I gene has recently been cloned (9,10) thus providing a specific DNA probe for monitoring its transcript. In the experiments described here, we have exploited this to examine regulation of the polymerase I gene not only in the mitotic cell cycle but also in meiosis and after UV-irradiation. MATERIALS AND METHODS Strains and Media The genotypes of the Saccharomyces cerevisiae strains were as follows; MC16 MATa leu2-3 his4-712 ade2-1 lys2-1 SUF2, NCYC239 and SK-1 are prototrophic diploids, while details of the cdc4-3 diploid MH14 have been given previously (11). Construction of the SK-1 related a/a and a/a diploids has also been described previously (1). YPD (1% Difco yeast extract, 2% Bacto peptone and 2% glucose) or YPA (1% potassium acetate in place of the 2% glucose) were used as rich media and Difco Yeast Nitrogen Base (0.67%; 0.5% glucose) as minimal medium with the appropriate nutritional supplements. Synchronisation procedures Mitotic cultures of NCYC239 were synchronised by the feed-starve protocol of Williamson and Scopes (12)and the use of the Beckman JElOX Elutriator Rotor to synchronise cells of budding yeast has been described previously (2).

These are questions not easily answered by the quantitative research designs used commonly within the medical profession. They are however the type of questions best answered by qualitative research methods instead. An area of research which has really gained momentum in recent years, qualitative research is often regarded in some sense as competing with the more established (at least in medical circles) quantitative research. This is unfortunate, since the two approaches should be seen as complementary, providing different perspectives and answering different specific questions within any one broad area.

Yin begins the fourth edition of his 6 chapter book by explaining that case study research is a “linear, but iterative process.” This statement is supported by a visual which is displayed on the first page of each chapter. Each chapter contains one step in the linear process of case design (planning, designing, preparing, collecting, analyzing, and sharing) as well as it highlights how each step requires the researcher to review and re-examine former decisions. As Yin points out, each chapter can stand alone, yet it is linked to the other stages of the research process.

BackgroundThe syntheses of multiple qualitative studies can pull together data across different contexts, generate new theoretical or conceptual models, identify research gaps, and provide evidence for the development, implementation and evaluation of health interventions. This study aims to develop a framework for reporting the synthesis of qualitative health research.MethodsWe conducted a comprehensive search for guidance and reviews relevant to the synthesis of qualitative research, methodology papers, and published syntheses of qualitative health research in MEDLINE, Embase, CINAHL and relevant organisational websites to May 2011. Initial items were generated inductively from guides to synthesizing qualitative health research. The preliminary checklist was piloted against forty published syntheses of qualitative research, purposively selected to capture a range of year of publication, methods and methodologies, and health topics. We removed items that were duplicated, impractical to assess, and rephrased items for clarity.ResultsThe Enhancing transparency in reporting the synthesis of qualitative research (ENTREQ) statement consists of 21 items grouped into five main domains: introduction, methods and methodology, literature search and selection, appraisal, and synthesis of findings.ConclusionsThe ENTREQ statement can help researchers to report the stages most commonly associated with the synthesis of qualitative health research: searching and selecting qualitative research, quality appraisal, and methods for synthesising qualitative findings. The synthesis of qualitative research is an expanding and evolving methodological area and we would value feedback from all stakeholders for the continued development and extension of the ENTREQ statement.

Background Purposive sampling has a long developmental history and there are as many views that it is simple and straightforward as there are about its complexity. The reason for purposive sampling is the better matching of the sample to the aims and objectives of the research, thus improving the rigour of the study and trustworthiness of the data and results. Four aspects to this concept have previously been described: credibility, transferability, dependability and confirmability. Aims The aim of this paper is to outline the nature and intent of purposive sampling, presenting three different case studies as examples of its application in different contexts. Results Presenting individual case studies has highlighted how purposive sampling can be integrated into varying contexts dependent on study design. The sampling strategies clearly situate each study in terms of trustworthiness for data collection and analysis. The selected approach to purposive sampling used in each case aligns to the research methodology, aims and objectives, thus addressing each of the aspects of rigour. Conclusions Making explicit the approach used for participant sampling provides improved methodological rigour as judged by the four aspects of trustworthiness. The cases presented provide a guide for novice researchers of how rigour may be addressed in qualitative research.

BackgroundChoosing a suitable sample size in qualitative research is an area of conceptual debate and practical uncertainty. That sample size principles, guidelines and tools have been developed to enable researchers to set, and justify the acceptability of, their sample size is an indication that the issue constitutes an important marker of the quality of qualitative research. Nevertheless, research shows that sample size sufficiency reporting is often poor, if not absent, across a range of disciplinary fields.MethodsA systematic analysis of single-interview-per-participant designs within three health-related journals from the disciplines of psychology, sociology and medicine, over a 15-year period, was conducted to examine whether and how sample sizes were justified and how sample size was characterised and discussed by authors. Data pertinent to sample size were extracted and analysed using qualitative and quantitative analytic techniques.ResultsOur findings demonstrate that provision of sample size justifications in qualitative health research is limited; is not contingent on the number of interviews; and relates to the journal of publication. Defence of sample size was most frequently supported across all three journals with reference to the principle of saturation and to pragmatic considerations. Qualitative sample sizes were predominantly – and often without justification – characterised as insufficient (i.e., ‘small’) and discussed in the context of study limitations. Sample size insufficiency was seen to threaten the validity and generalizability of studies’ results, with the latter being frequently conceived in nomothetic terms.ConclusionsWe recommend, firstly, that qualitative health researchers be more transparent about evaluations of their sample size sufficiency, situating these within broader and more encompassing assessments of data adequacy. Secondly, we invite researchers critically to consider how saturation parameters found in prior methodological studies and sample size community norms might best inform, and apply to, their own project and encourage that data adequacy is best appraised with reference to features that are intrinsic to the study at hand. Finally, those reviewing papers have a vital role in supporting and encouraging transparent study-specific reporting.

A qualitative description design is particularly relevant where information is required directly from those experiencing the phenomenon under investigation and where time and resources are limited. Nurses and midwives often have clinical questions suitable to a qualitative approach but little time to develop an exhaustive comprehension of qualitative methodological approaches. Qualitative description research is sometimes considered a less sophisticated approach for epistemological reasons. Another challenge when considering qualitative description design is differentiating qualitative description from other qualitative approaches. This article provides a systematic and robust journey through the philosophical, ontological, and epistemological perspectives, which evidences the purpose of qualitative description research. Methods and rigor issues underpinning qualitative description research are also appraised to provide the researcher with a systematic approach to conduct research utilizing this approach. The key attributes and value of qualitative description research in the health care professions will be highlighted with the aim of extending its usage.

BackgroundThere is widespread interest in measuring healthcare provider attitudes about issues relevant to patient safety (often called safety climate or safety culture). Here we report the psychometric properties, establish benchmarking data, and discuss emerging areas of research with the University of Texas Safety Attitudes Questionnaire.MethodsSix cross-sectional surveys of health care providers (n = 10,843) in 203 clinical areas (including critical care units, operating rooms, inpatient settings, and ambulatory clinics) in three countries (USA, UK, New Zealand). Multilevel factor analyses yielded results at the clinical area level and the respondent nested within clinical area level. We report scale reliability, floor/ceiling effects, item factor loadings, inter-factor correlations, and percentage of respondents who agree with each item and scale.ResultsA six factor model of provider attitudes fit to the data at both the clinical area and respondent nested within clinical area levels. The factors were: Teamwork Climate, Safety Climate, Perceptions of Management, Job Satisfaction, Working Conditions, and Stress Recognition. Scale reliability was 0.9. Provider attitudes varied greatly both within and among organizations. Results are presented to allow benchmarking among organizations and emerging research is discussed.ConclusionThe Safety Attitudes Questionnaire demonstrated good psychometric properties. Healthcare organizations can use the survey to measure caregiver attitudes about six patient safety-related domains, to compare themselves with other organizations, to prompt interventions to improve safety attitudes and to measure the effectiveness of these interventions.

Sex and gender differences are often overlooked in research design, study implementation and scientific reporting, as well as in general science communication. This oversight limits the generalizability of research findings and their applicability to clinical practice, in particular for women but also for men. This article describes the rationale for an international set of guidelines to encourage a more systematic approach to the reporting of sex and gender in research across disciplines. A panel of 13 experts representing nine countries developed the guidelines through a series of teleconferences, conference presentations and a 2-day workshop. An internet survey of 716 journal editors, scientists and other members of the international publishing community was conducted as well as a literature search on sex and gender policies in scientific publishing. The Sex and Gender Equity in Research (SAGER) guidelines are a comprehensive procedure for reporting of sex and gender information in study design, data analyses, results and interpretation of findings. The SAGER guidelines are designed primarily to guide authors in preparing their manuscripts, but they are also useful for editors, as gatekeepers of science, to integrate assessment of sex and gender into all manuscripts as an integral part of the editorial process.

This essay reviews progress in empirical economics since Leamer’s (1983) critique. Leamer highlighted the benefits of sensitivity analysis, a procedure in which researchers show how their results change with changes in specification or functional form. Sensitivity analysis has had a salutary but not a revolutionary effect on econometric practice. As we see it, the credibility revolution in empirical work can be traced to the rise of a design-based approach that emphasizes the identification of causal effects. Design-based studies typically feature either real or natural experiments and are distinguished by their prima facie credibility and by the attention investigators devote to making the case for a causal interpretation of the findings their designs generate. Design-based studies are most often found in the microeconomic fields of Development, Education, Environment, Labor, Health, and Public Finance, but are still rare in Industrial Organization and Macroeconomics. We explain why IO and Macro would do well to embrace a design-based approach. Finally, we respond to the charge that the design-based revolution has overreached.

Thematic analysis is widely used in qualitative psychology research, and in this article, we present a particular style of thematic analysis known as Template Analysis. We outline the technique and consider its epistemological position, then describe three case studies of research projects which employed Template Analysis to illustrate the diverse ways it can be used. Our first case study illustrates how the technique was employed in data analysis undertaken by a team of researchers in a large-scale qualitative research project. Our second example demonstrates how a qualitative study that set out to build on mainstream theory made use of the a priori themes (themes determined in advance of coding) permitted in Template Analysis. Our final case study shows how Template Analysis can be used from an interpretative phenomenological stance. We highlight the distinctive features of this style of thematic analysis, discuss the kind of research where it may be particularly appropriate, and consider possible limitations of the technique. We conclude that Template Analysis is a flexible form of thematic analysis with real utility in qualitative psychology research.

Breast cancer is a complex and heterogeneous disease. Gene expression profiling has contributed significantly to our understanding of this heterogeneity at a molecular level, refining taxonomy based on simple measures such as histological type, tumour grade, lymph node status and the presence of predictive markers like oestrogen receptor and human epidermal growth factor receptor 2 (HER2) to a more sophisticated classification comprising luminal A, luminal B, basal-like, HER2-positive and normal subgroups. In the laboratory, breast cancer is often modelled using established cell lines. In the present review we discuss some of the issues surrounding the use of breast cancer cell lines as experimental models, in light of these revised clinical classifications, and put forward suggestions for improving their use in translational breast cancer research.

Background Qualitative descriptive designs are common in nursing and healthcare research due to their inherent simplicity, flexibility and utility in diverse healthcare contexts. However, the application of descriptive research is sometimes critiqued in terms of scientific rigor. Inconsistency in decision making within the research process coupled with a lack of transparency has created issues of credibility for this type of approach. It can be difficult to clearly differentiate what constitutes a descriptive research design from the range of other methodologies at the disposal of qualitative researchers. Aims This paper provides an overview of qualitative descriptive research, orientates to the underlying philosophical perspectives and key characteristics that define this approach and identifies the implications for healthcare practice and policy. Methods and results Using real-world examples from healthcare research, the paper provides insight to the practical application of descriptive research at all stages of the design process and identifies the critical elements that should be explicit when applying this approach. Conclusions By adding to the existing knowledge base, this paper enhances the information available to researchers who wish to use the qualitative descriptive approach, influencing the standard of how this approach is employed in healthcare research.

ABSTRACT Fully qualitative surveys, which prioritise qualitative research values, and harness the rich potential of qualitative data, have much to offer qualitative researchers, especially given online delivery options. Yet the method remains underutilised, and there is little in the way of methodological discussion of qualitative surveys. Underutilisation and limited methodological discussion perhaps reflect the dominance of interviewing in qualitative research, and (misplaced) assumptions about qualitative survey data lacking depth. By discussing our experiences of developing online surveys as a tool for qualitative research, we seek to challenge preconceptions about qualitative surveys, and to demonstrate that qualitative surveys are an exciting, flexible method with numerous applications, and advantages for researchers and participants alike. We offer an overview and practical design information, illustrated with examples from some of our studies.

BackgroundA compelling ethical rationale supports patient engagement in healthcare research. It is also assumed that patient engagement will lead to research findings that are more pertinent to patients’ concerns and dilemmas. However; it is unclear how to best conduct this process. In this systematic review we aimed to answer 4 key questions: what are the best ways to identify patient representatives? How to engage them in designing and conducting research? What are the observed benefits of patient engagement? What are the harms and barriers of patient engagement?MethodsWe searched MEDLINE, EMBASE, PsycInfo, Cochrane, EBSCO, CINAHL, SCOPUS, Web of Science, Business Search Premier, Academic Search Premier and Google Scholar. Included studies were published in English, of any size or design that described engaging patients or their surrogates in research design. We conducted an environmental scan of the grey literature and consulted with experts and patients. Data were analyzed using a non-quantitative, meta-narrative approach.ResultsWe included 142 studies that described a spectrum of engagement. In general, engagement was feasible in most settings and most commonly done in the beginning of research (agenda setting and protocol development) and less commonly during the execution and translation of research. We found no comparative analytic studies to recommend a particular method. Patient engagement increased study enrollment rates and aided researchers in securing funding, designing study protocols and choosing relevant outcomes. The most commonly cited challenges were related to logistics (extra time and funding needed for engagement) and to an overarching worry of a tokenistic engagement.ConclusionsPatient engagement in healthcare research is likely feasible in many settings. However, this engagement comes at a cost and can become tokenistic. Research dedicated to identifying the best methods to achieve engagement is lacking and clearly needed.

BackgroundThis study aims to review the literature regarding the barriers to sampling, recruitment, participation, and retention of members of socioeconomically disadvantaged groups in health research and strategies for increasing the amount of health research conducted with socially disadvantaged groups.MethodsA systematic review with narrative synthesis was conducted. Searches of electronic databases Medline, PsychInfo, EMBASE, Social Science Index via Web of Knowledge and CINHAL were conducted for English language articles published up to May 2013. Qualitative and quantitative studies as well as literature reviews were included. Articles were included if they reported attempts to increase disadvantaged group participation in research, or the barriers to research with disadvantaged groups. Groups of interest were those described as socially, culturally or financially disadvantaged compared to the majority of society. Eligible articles were categorised according to five phases of research: 1) sampling, 2) recruitment and gaining consent, 3) data collection and measurement, 4) intervention delivery and uptake, and 5) retention and attrition.ResultsIn total, 116 papers from 115 studies met inclusion criteria and 31 previous literature reviews were included. A comprehensive summation of the major barriers to working with various disadvantaged groups is provided, along with proposed strategies for addressing each of the identified types of barriers. Most studies of strategies to address the barriers were of a descriptive nature and only nine studies reported the results of randomised trials.ConclusionsTo tackle the challenges of research with socially disadvantaged groups, and increase their representation in health and medical research, researchers and research institutions need to acknowledge extended timeframes, plan for higher resourcing costs and operate via community partnerships.

We examine key aspects of data quality for online behavioral research between selected platforms (Amazon Mechanical Turk, CloudResearch, and Prolific) and panels (Qualtrics and Dynata). To identify the key aspects of data quality, we first engaged with the behavioral research community to discover which aspects are most critical to researchers and found that these include attention, comprehension, honesty, and reliability. We then explored differences in these data quality aspects in two studies (N ~ 4000), with or without data quality filters (approval ratings). We found considerable differences between the sites, especially in comprehension, attention, and dishonesty. In Study 1 (without filters), we found that only Prolific provided high data quality on all measures. In Study 2 (with filters), we found high data quality among CloudResearch and Prolific. MTurk showed alarmingly low data quality even with data quality filters. We also found that while reputation (approval rating) did not predict data quality, frequency and purpose of usage did, especially on MTurk: the lowest data quality came from MTurk participants who report using the site as their main source of income but spend few hours on it per week. We provide a framework for future investigation into the ever-changing nature of data quality in online research, and how the evolving set of platforms and panels performs on these key aspects.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

Many qualitative research studies acknowledge the possibility of social desirability bias (a tendency to present reality to align with what is perceived to be socially acceptable) as a limitation that creates complexities in interpreting findings. Drawing on experiences conducting interviews and focus groups in rural Ethiopia, this article provides an empirical account of how one research team developed and employed strategies to detect and limit social desirability bias. Data collectors identified common cues for social desirability tendencies, relating to the nature of the responses given and word choice patterns. Strategies to avoid or limit bias included techniques for introducing the study, establishing rapport, and asking questions. Pre-fieldwork training with data collectors, regular debriefing sessions, and research team meetings provided opportunities to discuss social desirability tendencies and refine approaches to account for them throughout the research. Although social desirability bias in qualitative research may be intractable, it can be minimized.

This text also provides balanced coverage of qualitative and quantitative approaches by integrating a variety of examples from recent and classic sociological research. The text challenges students to debate and discuss the strengths and weaknesses of both approaches. An important goal of this text is to introduce students to the core principles of social research in a way that is straightforward and engaging.

Background Billions of dollars are lost annually in health research that fails to create meaningful benefits for patients. Engaging in research co-design – the meaningful involvement of end-users in research – may help address this research waste. This rapid overview of reviews addressed three related questions, namely (1) what approaches to research co-design exist in health settings? (2) What activities do these research co-design approaches involve? (3) What do we know about the effectiveness of existing research co-design approaches? The review focused on the study planning phase of research, defined as the point up to which the research question and study design are finalised. Methods Reviews of research co-design were systematically identified using a rapid overview of reviews approach (PROSPERO: CRD42019123034). The search strategy encompassed three academic databases, three grey literature databases, and a hand-search of the journal Research Involvement and Engagement . Two reviewers independently conducted the screening and data extraction and resolved disagreements through discussion. Disputes were resolved through discussion with a senior author (PB). One reviewer performed quality assessment. The results were narratively synthesised. Results A total of 26 records (reporting on 23 reviews) met the inclusion criteria. Reviews varied widely in their application of ‘research co-design’ and their application contexts, scope and theoretical foci. The research co-design approaches identified involved interactions with end-users outside of study planning, such as recruitment and dissemination. Activities involved in research co-design included focus groups, interviews and surveys. The effectiveness of research co-design has rarely been evaluated empirically or experimentally; however, qualitative exploration has described the positive and negative outcomes associated with co-design. The research provided many recommendations for conducting research co-design, including training participating end-users in research skills, having regular communication between researchers and end-users, setting clear end-user expectations, and assigning set roles to all parties involved in co-design. Conclusions Research co-design appears to be widely used but seldom described or evaluated in detail. Though it has rarely been tested empirically or experimentally, existing research suggests that it can benefit researchers, practitioners, research processes and research outcomes. Realising the potential of research co-design may require the development of clearer and more consistent terminology, better reporting of the activities involved and better evaluation.

Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem‐relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image‐like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation‐based analysis, inversion of network‐extracted features, reduced‐order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem‐specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

Saturation is commonly used to determine sample sizes in qualitative research, yet there is little guidance on what influences saturation. We aimed to assess saturation and identify parameters to estimate sample sizes for focus group studies in advance of data collection. We used two approaches to assess saturation in data from 10 focus group discussions. Four focus groups were sufficient to identify a range of new issues (code saturation), but more groups were needed to fully understand these issues (meaning saturation). Group stratification influenced meaning saturation, whereby one focus group per stratum was needed to identify issues; two groups per stratum provided a more comprehensive understanding of issues, but more groups per stratum provided little additional benefit. We identify six parameters influencing saturation in focus group data: study purpose, type of codes, group stratification, number of groups per stratum, and type and degree of saturation.

Purpose The purpose of this paper is to explain the rationale for choosing the qualitative approach to research human resources practices, namely, recruitment and selection, training and development, performance management, rewards management, employee communication and participation, diversity management and work and life balance using deductive and inductive approaches to analyse data. The paper adopts an emic perspective that favours the study of transfer of human resource management practices from the point of view of employees and host country managers in subsidiaries of western multinational enterprises in Ghana. Design/methodology/approach Despite the numerous examples of qualitative methods of data generation, little is known particularly to the novice researcher about how to analyse qualitative data. This paper develops a model to explain in a systematic manner how to methodically analyse qualitative data using both deductive and inductive approaches. Findings The deductive and inductive approaches provide a comprehensive approach in analysing qualitative data. The process involves immersing oneself in the data reading and digesting in order to make sense of the whole set of data and to understand what is going on. Originality/value This paper fills a serious gap in qualitative data analysis which is deemed complex and challenging with limited attention in the methodological literature particularly in a developing country context, Ghana.

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

To update evidence‐based recommendations for the treatment of patients with ankylosing spondylitis (AS) and nonradiographic axial spondyloarthritis (SpA).

Four methods for reviewing a body of research literature – narrative review, descriptive review, vote-counting, and meta-analysis – are compared. Meta-analysis as a formalized, systematic review method is discussed in detail in terms of its history, current status, advantages, common analytic methods, and recent developments. Meta-analysis is found to be underutilized in IS. Suggestions on encouraging the use of meta-analysis in IS research and procedures recommended for meta-analysis are also provided.

PurposeQuality of life (QOL) is an important concept in the field of health and medicine. QOL is a complex concept that is interpreted and defined differently within and between disciplines, including the fields of health and medicine. The aims of this study were to systematically review the literature on QOL in medicine and health research and to describe the country of origin, target groups, instruments, design, and conceptual issues.MethodsA systematic review was conducted to identify research studies on QOL and health-related quality of life (HRQOL). The databases Scopus, which includes Embase and MEDLINE, CINAHL, and PsycINFO were searched for articles published during one random week in November 2016. The ten predefined criteria of Gill and Feinstein were used to evaluate the conceptual and methodological rigor.ResultsQOL research is international and involves a variety of target groups, research designs, and QOL measures. According to the criteria of Gill and Feinstein, the results show that only 13% provided a definition of QOL, 6% distinguished QOL from HRQOL. The most frequently fulfilled criteria were: (i) stating the domains of QOL to be measured; (ii) giving a reason for choosing the instruments used; and (iii) aggregating the results from multiple items.ConclusionQOL is an important endpoint in medical and health research, and QOL research involves a variety of patient groups and different research designs. Based on the current evaluation of the methodological and conceptual clarity of QOL research, we conclude that the majority QOL studies in health and medicine have conceptual and methodological challenges.

BackgroundNavigating the world of qualitative thematic analysis can be challenging. This is compounded by the fact that detailed descriptions of methods are often omitted from qualitative discussions. While qualitative research methodologies are now mature, there often remains a lack of fine detail in their description both at submitted peer reviewed article level and in textbooks. As one of research’s aims is to determine the relationship between knowledge and practice through the demonstration of rigour, more detailed descriptions of methods could prove useful. Rigour in quantitative research is often determined through detailed explanation allowing replication, but the ability to replicate is often not considered appropriate in qualitative research. However, a well described qualitative methodology could demonstrate and ensure the same effect.MethodsThis article details the codebook development which contributed to thematic analysis of qualitative data. This analysis formed part of a mixed methods multiphase design research project, with both qualitative and quantitative inquiry and involving the convergence of data and analyses. This design consisted of three distinct phases: quantitative, qualitative and implementation phases.Results and conclusionsThis article is aimed at researchers and doctoral students new to thematic analysis by describing a framework to assist their processes. The detailed description of the methods used supports attempts to utilise the thematic analysis process and to determine rigour to support the establishment of credibility. This process will assist practitioners to be confident that the knowledge and claims contained within research are transferable to their practice. The approach described within this article builds on, and enhances, current accepted models.

Objectives Wearable devices are currently at the heart of just about every discussion related to the Internet of Things. The requirement for self-health monitoring and preventive medicine is increasing due to the projected dramatic increase in the number of elderly people until 2020. Developed technologies are truly able to reduce the overall costs for prevention and monitoring. This is possible by constantly monitoring health indicators in various areas, and in particular, wearable devices are considered to carry this task out. These wearable devices and mobile apps now have been integrated with telemedicine and telehealth efficiently, to structure the medical Internet of Things. This paper reviews wearable health care devices both in scientific papers and commercial efforts. Methods MIoT is demonstrated through a defined architecture design, including hardware and software dealing with wearable devices, sensors, smart phones, medical application, and medical station analyzers for further diagnosis and data storage. Results Wearables, with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the environment, vital signs, and fitness. Conclusions Wearable devices are now used for a wide range of healthcare observation. One of the most important elements essential in data collection is the sensor. During recent years with improvement in semiconductor technology, sensors have made investigation of a full range of parameters closer to realization.

Participatory research (PR) encompasses research designs, methods, and frameworks that use systematic inquiry in direct collaboration with those affected by an issue being studied for the purpose of action or change. PR engages those who are not necessarily trained in research but belong to or represent the interests of the people who are the focus of the research. Researchers utilizing a PR approach often choose research methods and tools that can be conducted in a participatory, democratic manner that values genuine and meaningful participation in the research process. This article serves as an introduction to participatory research methods, including an overview of participatory research, terminology across disciplines, elements that make a research method participatory, and a model detailing the choice points that require decisions about which tools and methods will produce the desired level of participation at each stage of the research process. Intentional choices of participatory research methods, tools, and processes can help researchers to more meaningfully engage stakeholders and communities in research, which in turn has the potential to create relevant, meaningful research findings translated to action.

BackgroundWhile the patient and public involvement (PPI) evidence base has expanded over the past decade, the quality of reporting within papers is often inconsistent, limiting our understanding of how it works, in what context, for whom, and why.ObjectiveTo develop international consensus on the key items to report to enhance the quality, transparency, and consistency of the PPI evidence base. To collaboratively involve patients as research partners at all stages in the development of GRIPP2.MethodsThe EQUATOR method for developing reporting guidelines was used. The original GRIPP (Guidance for Reporting Involvement of Patients and the Public) checklist was revised, based on updated systematic review evidence. A three round Delphi survey was used to develop consensus on items to be included in the guideline. A subsequent face-to-face meeting produced agreement on items not reaching consensus during the Delphi process.ResultsOne hundred forty-three participants agreed to participate in round one, with an 86% (123/143) response for round two and a 78% (112/143) response for round three. The Delphi survey identified the need for long form (LF) and short form (SF) versions. GRIPP2-LF includes 34 items on aims, definitions, concepts and theory, methods, stages and nature of involvement, context, capture or measurement of impact, outcomes, economic assessment, and reflections and is suitable for studies where the main focus is PPI. GRIPP2-SF includes five items on aims, methods, results, outcomes, and critical perspective and is suitable for studies where PPI is a secondary focus.ConclusionsGRIPP2-LF and GRIPP2-SF represent the first international evidence based, consensus informed guidance for reporting patient and public involvement in research. Both versions of GRIPP2 aim to improve the quality, transparency, and consistency of the international PPI evidence base, to ensure PPI practice is based on the best evidence. In order to encourage its wide dissemination this article is freely accessible on The BMJ and Research Involvement and Engagement journal websites.

BackgroundThe main objective of this methodological manuscript was to illustrate the role of using qualitative research in emergency settings. We outline rigorous criteria applied to a qualitative study assessing perceptions and experiences of staff working in Australian emergency departments.MethodsWe used an integrated mixed-methodology framework to identify different perspectives and experiences of emergency department staff during the implementation of a time target government policy. The qualitative study comprised interviews from 119 participants across 16 hospitals. The interviews were conducted in 2015–2016 and the data were managed using NVivo version 11. We conducted the analysis in three stages, namely: conceptual framework, comparison and contrast and hypothesis development. We concluded with the implementation of the four-dimension criteria (credibility, dependability, confirmability and transferability) to assess the robustness of the study,ResultsWe adapted four-dimension criteria to assess the rigour of a large-scale qualitative research in the emergency department context. The criteria comprised strategies such as building the research team; preparing data collection guidelines; defining and obtaining adequate participation; reaching data saturation and ensuring high levels of consistency and inter-coder agreement.ConclusionBased on the findings, the proposed framework satisfied the four-dimension criteria and generated potential qualitative research applications to emergency medicine research. We have added a methodological contribution to the ongoing debate about rigour in qualitative research which we hope will guide future studies in this topic in emergency care research. It also provided recommendations for conducting future mixed-methods studies. Future papers on this series will use the results from qualitative data and the empirical findings from longitudinal data linkage to further identify factors associated with ED performance; they will be reported separately.

Inductive/deductive hybrid thematic analysis offers significant opportunities for researchers, but its application within integrative mixed methods research has yet to be fully explored. Firstly, this article contributes by demonstrating the compatibility of inductive/deductive hybrid thematic analysis with quantitative work in a mixed methods approach to research. Secondly, the article then innovates by highlighting the value of this approach within a critical realist meta-theoretical perspective. Here, the critical realist concepts of abduction and retroduction are crucial, both in terms of facilitating the convergence of methods and in the generation of new theory. This article will be of relevance to researchers interested in integrating inductive/deductive hybrid thematic analysis with quantitative methods within a coherent and enabling philosophical paradigm.

BackgroundCoproduction, a collaborative model of research that includes stakeholders in the research process, has been widely advocated as a means of facilitating research use and impact. We summarise the arguments in favour of coproduction, the different approaches to establishing coproductive work and their costs, and offer some advice as to when and how to consider coproduction.DebateDespite the multiplicity of reasons and incentives to coproduce, there is little consensus about what coproduction is, why we do it, what effects we are trying to achieve, or the best coproduction techniques to achieve policy, practice or population health change. Furthermore, coproduction is not free risk or cost. Tensions can arise throughout coproduced research processes between the different interests involved. We identify five types of costs associated with coproduced research affecting the research itself, the research process, professional risks for researchers and stakeholders, personal risks for researchers and stakeholders, and risks to the wider cause of scholarship. Yet, these costs are rarely referred to in the literature, which generally calls for greater inclusion of stakeholders in research processes, focusing exclusively on potential positives. There are few tools to help researchers avoid or alleviate risks to themselves and their stakeholders.ConclusionsFirst, we recommend identifying specific motivations for coproduction and clarifying exactly which outcomes are required for whom for any particular piece of research. Second, we suggest selecting strategies specifically designed to enable these outcomes to be achieved, and properly evaluated. Finally, in the absence of strong evidence about the impact and process of coproduction, we advise a cautious approach to coproduction. This would involve conscious and reflective research practice, evaluation of how coproduced research practices change outcomes, and exploration of the costs and benefits of coproduction. We propose some preliminary advice to help decide when coproduction is likely to be more or less useful.

Amazon Mechanical Turk (MTurk) is widely used by behavioral scientists to recruit research participants. MTurk offers advantages over traditional student subject pools, but it also has important limitations. In particular, the MTurk population is small and potentially overused, and some groups of interest to behavioral scientists are underrepresented and difficult to recruit. Here we examined whether online research panels can avoid these limitations. Specifically, we compared sample composition, data quality (measured by effect sizes, internal reliability, and attention checks), and the non-naivete of participants recruited from MTurk and Prime Panels—an aggregate of online research panels. Prime Panels participants were more diverse in age, family composition, religiosity, education, and political attitudes. Prime Panels participants also reported less exposure to classic protocols and produced larger effect sizes, but only after screening out several participants who failed a screening task. We conclude that online research panels offer a unique opportunity for research, yet one with some important trade-offs.

We show, based on ratings by finance journal reviewers of generated output, that the recently released AI chatbot ChatGPT can significantly assist with finance research. In principle, these results should be generalisable across research domains. There are clear advantages for idea generation and data identification. The technology, however, is weaker on literature synthesis and developing appropriate testing frameworks. Importantly, we further demonstrate that the extent of private data and researcher domain expertise input, are key factors in determining the quality of output. We conclude by considering the implications, particularly the ethical implications, which arise from this new technology.

At the turn of the new millennium, in an article published in Language Teaching Research in 2000, Dörnyei and Kormos proposed that ‘active learner engagement is a key concern’ for all instructed language learning. Since then, language engagement research has increased exponentially. In this article, we present a systematic review of 20 years of language engagement research. To ensure robust coverage, we searched 21 major journals on second language acquisition (SLA) and applied linguistics and identified 112 reports satisfying our inclusion criteria. The results of our analysis of these reports highlighted the adoption of heterogeneous methods and conceptual frameworks in the language engagement literature, as well as indicating a need to refine the definitions and operationalizations of engagement in both quantitative and qualitative research. Based on these findings, we attempted to clarify some lingering ambiguity around fundamental definitions, and to more clearly delineate the scope and target of language engagement research. We also discuss future avenues to further advance understanding of the nature, mechanisms, and outcomes resulting from engagement in language learning.

The goal of this review was to systematize empirical research that was published in peer-reviewed English-language journals between 1995 and 2015 on the prevalence, predictors, and implications of adolescents’ use of pornography. This research showed that adolescents use pornography, but prevalence rates varied greatly. Adolescents who used pornography more frequently were male, at a more advanced pubertal stage, sensation seekers, and had weak or troubled family relations. Pornography use was associated with more permissive sexual attitudes and tended to be linked with stronger gender-stereotypical sexual beliefs. It also seemed to be related to the occurrence of sexual intercourse, greater experience with casual sex behavior, and more sexual aggression, both in terms of perpetration and victimization. The findings of this review need to be seen against the background of various methodological and theoretical shortcomings, as well as several biases in the literature, which currently precludes internally valid causal conclusions about effects of pornography on adolescents.

Systematic review techniques are about to become the “new normal” in reviews of management research. However, there is not yet much advice on how to organize the sample selection process as part of such reviews. This article addresses this void and analyzes this vital part of systematic reviews in more detail. In particular, it offers a critical review of systematic literature reviews published in the Academy of Management Annals and the International Journal of Management Reviews between 2004 and 2018. Based on this methodological literature review, the article presents issues to consider in the most critical choices during the sample selection process. Furthermore, this review identifies several descriptive features such as the mean number of research items included in systematic reviews, the mean number of databases used, and the mean coverage period of such reviews. These numbers may be used as benchmark figures in future reviews.

Osteoarthritis (OA) is a degenerative joint disorder commonly encountered in clinical practice, and is the leading cause of disability in elderly people. Due to the poor self-healing capacity of articular cartilage and lack of specific diagnostic biomarkers, OA is a challenging disease with limited treatment options. Traditional pharmacologic therapies such as acetaminophen, non-steroidal anti-inflammatory drugs, and opioids are effective in relieving pain but are incapable of reversing cartilage damage and are frequently associated with adverse events. Current research focuses on the development of new OA drugs (such as sprifermin/recombinant human fibroblast growth factor-18, tanezumab/monoclonal antibody against β-nerve growth factor), which aims for more effectiveness and less incidence of adverse effects than the traditional ones. Furthermore, regenerative therapies (such as autologous chondrocyte implantation (ACI), new generation of matrix-induced ACI, cell-free scaffolds, induced pluripotent stem cells (iPS cells or iPSCs), and endogenous cell homing) are also emerging as promising alternatives as they have potential to enhance cartilage repair, and ultimately restore healthy tissue. However, despite currently available therapies and research advances, there remain unmet medical needs in the treatment of OA. This review highlights current research progress on pharmacologic and regenerative therapies for OA including key advances and potential limitations.

BackgroundEffective community-partnered and patient-centered outcomes research needs to address community priorities. However, optimal sampling methods to engage stakeholders from hard-to-reach, vulnerable communities to generate research priorities have not been identified.MethodsIn two similar rural, largely Hispanic communities, a community advisory board guided recruitment of stakeholders affected by chronic pain using a different method in each community: 1) snowball sampling, a chain- referral method or 2) purposive sampling to recruit diverse stakeholders. In both communities, three groups of stakeholders attended a series of three facilitated meetings to orient, brainstorm, and prioritize ideas (9 meetings/community). Using mixed methods analysis, we compared stakeholder recruitment and retention as well as priorities from both communities’ stakeholders on mean ratings of their ideas based on importance and feasibility for implementation in their community.ResultsOf 65 eligible stakeholders in one community recruited by snowball sampling, 55 (85 %) consented, 52 (95 %) attended the first meeting, and 36 (65 %) attended all 3 meetings. In the second community, the purposive sampling method was supplemented by convenience sampling to increase recruitment. Of 69 stakeholders recruited by this combined strategy, 62 (90 %) consented, 36 (58 %) attended the first meeting, and 26 (42 %) attended all 3 meetings. Snowball sampling recruited more Hispanics and disabled persons (all P < 0.05). Despite differing recruitment strategies, stakeholders from the two communities identified largely similar ideas for research, focusing on non-pharmacologic interventions for management of chronic pain. Ratings on importance and feasibility for community implementation differed only on the importance of massage services (P = 0.045) which was higher for the purposive/convenience sampling group and for city improvements/transportation services (P = 0.004) which was higher for the snowball sampling group.ConclusionsIn each of the two similar hard-to-reach communities, a community advisory board partnered with researchers to implement a different sampling method to recruit stakeholders. The snowball sampling method achieved greater participation with more Hispanics but also more individuals with disabilities than a purposive-convenience sampling method. However, priorities for research on chronic pain from both stakeholder groups were similar. Although utilizing a snowball sampling method appears to be superior, further research is needed on implementation costs and resources.

This chapter explores the processes of reviewing literature as a research method. The logic of the family of research approaches called systematic review is analysed and the variation in techniques used in the different approaches explored using examples from existing reviews. The key distinctions between aggregative and configurative approaches are illustrated and the chapter signposts further reading on key issues in the systematic review process.

Qualitative researchers are under increasing time demands to rapidly collect, analyze, and disseminate the results of their findings. Adaptations to qualitative methods may be required to enable the use of timely and relevant qualitative data across multiple disciplinary settings. The aim of this review is to briefly explore the ways in which data collection and analysis methods have been adapted in qualitative research to deal with short study timeframes. We carried out a two-phased systematic review of the literature and determined there were six primary reasons why rapid techniques were used: (a) reduce time, (b) reduce cost, (c) increase the amount of collected data, (d) improve efficiency, (e) improve accuracy, and (f) obtain a closer approximation to the narrated realities of research participants. In addition, we analyzed the characteristics of the articles, how traditional methods were adapted and evaluated, the benefits and limitations of using rapid techniques, and future recommendations.

Honey is one of the most appreciated and valued natural products introduced to humankind since ancient times. Honey is used not only as a nutritional product but also in health described in traditional medicine and as an alternative treatment for clinical conditions ranging from wound healing to cancer treatment. The aim of this review is to emphasize the ability of honey and its multitude in medicinal aspects. Traditionally, honey is used in the treatment of eye diseases, bronchial asthma, throat infections, tuberculosis, thirst, hiccups, fatigue, dizziness, hepatitis, constipation, worm infestation, piles, eczema, healing of ulcers, and wounds and used as a nutritious supplement. The ingredients of honey have been reported to exert antioxidant, antimicrobial, anti-inflammatory, antiproliferative, anticancer, and antimetastatic effects. Many evidences suggest the use of honey in the control and treatment of wounds, diabetes mellitus, cancer, asthma, and also cardiovascular, neurological, and gastrointestinal diseases. Honey has a potential therapeutic role in the treatment of disease by phytochemical, anti-inflammatory, antimicrobial, and antioxidant properties. Flavonoids and polyphenols, which act as antioxidants, are two main bioactive molecules present in honey. According to modern scientific literature, honey may be useful and has protective effects for the treatment of various disease conditions such as diabetes mellitus, respiratory, gastrointestinal, cardiovascular, and nervous systems, even it is useful in cancer treatment because many types of antioxidant are present in honey. In conclusion, honey could be considered as a natural therapeutic agent for various medicinal purposes. Sufficient evidence exists recommending the use of honey in the management of disease conditions. Based on these facts, the use of honey in clinical wards is highly recommended.

Quantitative and qualitative are, importantly, words to describe the kind of data gleaned from an experiment and not the phenomena themselves The kind of data we extract from an experiment depends on the experiment design and the parameters we as researchers set before beginning. Thus, external phenomena of the world are interpreted through a chosen experimental framework – whether this is quantitative or qualitative depends on the research question.

This systematic review used “science mapping” as a means of understanding the evolution of research in educational administration (EA). The review sought to document the size, growth trajectory, and geographic distribution of EA research, identify high impact scholars and documents, and illuminate the “intellectual structure” of the field. Although science mapping has been applied widely in science, medicine, and social sciences, it is still new in the field of education. The authors identified 22,361 peer-reviewed articles published in 22 Scopus-indexed EA journals between 1960 and 2018. The authors used VOSviewer, Excel, and Tableau software to analyze the data set. The review found that the EA knowledge base has grown dramatically since 1960 with an accelerating rate growth and increasing gender and geographic diversity during the past two decades. Using co-citation analysis, the review identified canonical documents, defined as highly influential documents whose impact has been sustained for a period of several decades. The review also identified four key Schools of Thought that have emerged over time focusing on Leadership for Learning, Leadership and Cultural Change, School Effectiveness and School Improvement, and Leading Teachers. More broadly, our findings highlighted a paradigm shift from “school administration” to “school leadership” over the course of the six decades. Another significant finding identified “leadership for student learning and development” as the “cognitive anchor” of the intellectual structure of the EA knowledge base. The authors conclude that science mapping offers a new and useful means of unpacking the historical development of fields of study.

BackgroundResearch reporting guidelines are increasingly commonplace and shown to improve the quality of published health research and health outcomes. Despite severe health inequities among Indigenous Peoples and the potential for research to address the causes, there is an extended legacy of health research exploiting Indigenous Peoples. This paper describes the development of the CONSolIDated critERtia for strengthening the reporting of health research involving Indigenous Peoples (CONSIDER) statement.MethodsA collaborative prioritization process was conducted based on national and international statements and guidelines about Indigenous health research from the following nations (Peoples): Australia (Aboriginal and Torres Strait Islanders), Canada (First Nations Peoples, Métis), Hawaii (Native Hawaiian), New Zealand (Māori), Taiwan (Taiwan Indigenous Tribes), United States of America (First Nations Peoples) and Northern Scandinavian countries (Sami). A review of seven research guidelines was completed, and meta-synthesis was used to construct a reporting guideline checklist for transparent and comprehensive reporting of research involving Indigenous Peoples.ResultsA list of 88 possible checklist items was generated, reconciled, and categorized. Eight research domains and 17 criteria for the reporting of research involving Indigenous Peoples were identified. The research reporting domains were: (i) governance; (ii) relationships; (iii) prioritization; (iv) methodologies; (v) participation; (vi) capacity; (vii) analysis and findings; and (viii) dissemination.ConclusionsThe CONSIDER statement is a collaborative synthesis and prioritization of national and international research statements and guidelines. The CONSIDER statement provides a checklist for the reporting of health research involving Indigenous peoples to strengthen research praxis and advance Indigenous health outcomes.

BackgroundOver the last 10 years, patient engagement in health research has emerged as the next evolution in healthcare research. However, limited evidence about the clear role and scope of patient engagement in health research and a lack of evidence about its impact have influenced the uptake, implementation and ongoing evolution of patient engagement. The present study aims to conduct a scoping review to identify methods for and outcomes of patient engagement in health research.MethodsAn adaptation of the scoping review methodology originally described by Arksey and O’Malley and updated by Levac, Colquhoun and O’Brien was applied. Sources from a formal database search and relevant documents from a grey literature search were compiled into data extraction tables. Articles were synthesised into key themes according to the (1) methods and (2) outcomes of patient engagement in health research.ResultsThe total yield for the scoping review was 55 records from across Canada, the United Kingdom and the United States. While evidence about the methods used to engage patients in health research is increasing, stronger evidence of specific patient and healthcare system outcomes is required. This necessitates further mobilisation of research that explores outcomes and that validates specific tools to evaluate engagement. Additionally, theoretical frameworks that can better inform and sustain patient engagement across the lifecycle of health research are lacking.ConclusionFurther increasing the volume and reach of evidence about patient engagement in health research will support the paradigmatic shift needed to normalise the patient’s role in research beyond ‘subject’ or ‘participant’, so as to ultimately improve patient health outcomes and better address healthcare reform in Canada.

Social scientists have a robust history of contributing to better understandings of and responses to disease outbreaks. The implementation of qualitative research in the context of infectious epidemics, however, continues to lag behind in the delivery, credibility, and timeliness of findings when compared with other research designs. The purpose of this article is to reflect on our experience of carrying out three research studies (a rapid appraisal, a qualitative study based on interviews, and a mixed-methods survey) aimed at exploring health care delivery in the context of COVID-19. We highlight the importance of qualitative data to inform evidence-based public health responses and provide a way forward to global research teams who wish to implement similar rapid qualitative studies. We reflect on the challenges of setting up research teams, obtaining ethical approval, collecting and analyzing data in real-time and sharing actionable findings.

Purpose The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field. Design/methodology/approach Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions. Findings The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research. Originality/value Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.

ABSTRACT The practice of assigning authorship for a scientific publication tends to raise two normative questions: 1) “who should be credited as an author?”; 2) “who should not be credited as an author but should still be acknowledged?”. With the publication of the revised version of The European Code of Conduct for Research Integrity (ECCRI), standard answers to these questions have been called into question. This article examines the ways in which the ECCRI approaches these two questions and compares these approaches to standard definitions of “authorship” and “acknowledgment” in guidelines issued by the International Committee of Medical Journal Editors (ICMJE) and the World Association of Medical Editors (WAME). In light of two scenarios and the problems posed by these kinds of “real-world” examples, we recommend specific revisions to the content of the ECCRI in order not only to provide a more detailed account of the tasks deserving of acknowledgment, but to improve the Code’s current definition of authorship.

Generalizability in qualitative research has been a controversial topic given that interpretivist scholars have resisted the dominant role and mandate of the positivist tradition within social sciences. Aiming to find universal laws, the positivist paradigm has made generalizability a crucial criterion for evaluating the rigor of quantitative research. This positivist echo has led generalizability to acquire a quantitative meaning, inappropriate for describing qualitative studies. The purpose of qualitative research has, thus, been directed toward providing in-depth explanations and meanings rather than generalizing findings. Through a critical review of empirical and theoretical studies, this commentary seeks to show that in qualitative domains, generalizability is possible provided that, first, generalizability is the main objective of the study; second, due precautions concerning the philosophy and terminology selected are taken. Hence, this commentary contributes to the literature on qualitative research by making suggestions for more consistent and unanimous procedures to adopt in qualitative inquiries.

Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition. However, research on end-to-end audiovisual models is very limited. In this work, we present an end-to-end audiovisual model based on residual networks and Bidirectional Gated Recurrent Units (BGRUs). To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the image pixels and audio waveforms and performs within-context word recognition on a large publicly available dataset (LRW). The model consists of two streams, one for each modality, which extract features directly from mouth regions and raw waveforms. The temporal dynamics in each stream/modality are modeled by a 2-layer BGRU and the fusion of multiple streams/modalities takes place via another 2-layer BGRU. A slight improvement in the classification rate over an end-to-end audio-only and MFCC-based model is reported in clean audio conditions and low levels of noise. In presence of high levels of noise, the end-to-end audiovisual model significantly outperforms both audio-only models.

As the movement toward evidence-based health policy continues to emphasize the importance of including patient and public perspectives, syntheses of qualitative health research are becoming more common. In response to the focus on independent assessments of rigor in these knowledge products, over 100 appraisal tools for assessing the quality of qualitative research have been developed. The variety of appraisal tools exhibit diverse methods and purposes, reflecting the lack of consensus as to what constitutes appropriate quality criteria for qualitative research. It is a daunting task for those without deep familiarity of the field to choose the best appraisal tool for their purpose. This article provides a description of the structure, content, and objectives of existing appraisal tools for those wanting to evaluate primary qualitative research for a qualitative evidence synthesis. We then discuss common features of appraisal tools and examine their implications for evidence synthesis.

ABSTRACT Background: Data-based decision-making in education often focuses on the use of summative assessment data in order to bring about improvements in student achievement. However, many other sources of evidence are available across a wide range of indicators. There is potential for school leaders, teachers and students to use these diverse sources more fully to support their work on a range of school improvement goals. Purpose and sources of evidence: To explore data-based decision-making for school improvement, this theoretical paper discusses recent research and literature from different areas of data use in education. These areas include the use of formative assessment data, educational research study findings and ‘big data’. In particular, the discussion focuses on how school leaders and teachers can use different sources of data to improve the quality of education. Main argument: Based on the literature reviewed, an iterative model of data use for school improvement is described, consisting of defining goals for data use, collecting different types of data or evidence (e.g. formal data, informal data, research evidence and ‘big data’), sense-making, taking improvement actions and evaluation. Drawing on the literature, research insights are discussed for each of these components, as well as identification of the research gaps that still exist. It is noted that the process of data use does not happen in isolation: data use is influenced by system, organisation and team/individual level factors. Conclusions: When it comes to using data to improve the quality of teaching and learning, it is evident that some of the most important enablers and barriers include data literacy and leadership. However, what is less well understood is how we can promote the enablers and remove the barriers to unlock, more fully, the potential of data use. Only then can data use lead to sustainable school improvement.

For millennia Indigenous communities worldwide have maintained diverse knowledge systems informed through careful observation of dynamics of environmental changes. Although Indigenous communities and their knowledge systems are recognized as critical resources for understanding and adapting to climate change, no comprehensive, evidence-based analysis has been conducted into how environmental studies engage Indigenous communities. Here we provide the first global systematic review of levels of Indigenous community participation and decision-making in all stages of the research process (initiation, design, implementation, analysis, dissemination) in climate field studies that access Indigenous knowledge. We develop indicators for assessing responsible community engagement in research practice and identify patterns in levels of Indigenous community engagement. We find that the vast majority of climate studies (87%) practice an extractive model in which outside researchers use Indigenous knowledge systems with minimal participation or decision-making authority from communities who hold them. Few studies report on outputs that directly serve Indigenous communities, ethical guidelines for research practice, or providing Indigenous community access to findings. Further, studies initiated with (in mutual agreement between outside researchers and Indigenous communities) and by Indigenous community members report significantly more indicators for responsible community engagement when accessing Indigenous knowledges than studies initiated by outside researchers alone. This global assessment provides an evidence base to inform our understanding of broader social impacts related to research design and concludes with a series of guiding questions and methods to support responsible research practice with Indigenous and local communities.

We present Audiovisual SlowFast Networks, an architecture for integrated audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are deeply integrated with a Faster Audio pathway to model vision and sound in a unified representation. We fuse audio and visual features at multiple layers, enabling audio to contribute to the formation of hierarchical audiovisual concepts. To overcome training difficulties that arise from different learning dynamics for audio and visual modalities, we introduce DropPathway, which randomly drops the Audio pathway during training as an effective regularization technique. Inspired by prior studies in neuroscience, we perform hierarchical audiovisual synchronization to learn joint audiovisual features. We report state-of-the-art results on six video action classification and detection datasets, perform detailed ablation studies, and show the generalization of AVSlowFast to learn self-supervised audiovisual features. Code will be made available at: this https URL.

ABSTRACT The threat to reproducibility and awareness of current rates of research misbehavior sparked initiatives to better academic science. One initiative is preregistration of quantitative research. We investigate whether the preregistration format could also be used to boost the credibility of qualitative research. A crucial distinction underlying preregistration is that between prediction and postdiction. In qualitative research, data are used to decide which way interpretation should move forward, using data to generate hypotheses and new research questions. Qualitative research is thus a real-life example of postdiction research. Some may object to the idea of preregistering qualitative studies because qualitative research generally does not test hypotheses, and because qualitative research design is typically flexible and subjective. We rebut these objections, arguing that making hypotheses explicit is just one feature of preregistration, that flexibility can be tracked using preregistration, and that preregistration would provide a check on subjectivity. We then contextualize preregistrations alongside another initiative to enhance credibility in qualitative research: the confirmability audit. Besides, preregistering qualitative studies is practically useful to combating dissemination bias and could incentivize qualitative researchers to report constantly on their study's development. We conclude with suggested modifications to the Open Science Framework preregistration form to tailor it for qualitative studies.

The use of the term immersion to describe a multitude of varying experiences in the absence of a definitional consensus has obfuscated and diluted the term. The non-exhaustive literature review presented in this paper indicates that immersion is a psychological concept as opposed to being a property of the system or technology that facilitates an experience. An adaptable definition of immersion is synthesized based on the findings from the literature review: a state of deep mental involvement in which the individual may experience disassociation from the awareness of the physical world due to a shift in their attentional state. This definition is used to contrast and differentiate interchangeably used terms such as presence from immersion and outline the implications for conducting immersion research on audiovisual experiences. A new methodology for quantifying immersion is proposed and avenues for future work are briefly discussed.

Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a two-stage learning framework to perform self-supervised class-aware sounding object localization. First, we propose to learn robust object representations by aggregating the candidate sound localization results in the single source scenes. Then, class-aware object localization maps are generated in the cocktail-party scenarios by referring the pre-learned object knowledge, and the sounding objects are accordingly selected by matching audio and visual object category distributions, where the audiovisual consistency is viewed as the self-supervised signal. Experimental results in both realistic and synthesized cocktail-party videos demonstrate that our model is superior in filtering out silent objects and pointing out the location of sounding objects of different classes. Code is available at this https URL.

Abstrak: Metode pembelajaran berbasis audiovisual merupakan suatu metode pembelajaran yang menggabungkan media audio dan visual atau bisa disebut dengan media pandang-dengar. Media audiovisual ini sangat tepat digunakan ditingkat sekolah dasar untuk meningkatkan motivasi belajar siswa sejak dini. Didalam penelitian ini penulis memiliki beberapa aspek pokok bahasan yang dituju, yaitu (1) mengawasi bagaimana proses persiapan dalam menerapkan pembelajaran berbasis audiovisual, (2) berpengaruh terhadap motivasi belajar siswa, (3) melihat keefektifan penggunaan metode berbasis audiovisual dalam pembelajaran berani. Jenis penelitian yang digunakan dalam penelitian ini adalah jenis pendekatan Studi Kepustakaan (kajian pustaka). Jenis metode penelitian kepustakaan yang digunakan untuk membahas penggunaan media audiovisual yang untuk meningkatkan motivasi belajar siswa ditingkat sekolah dasar. Untuk mengumpulkan data penelitian, penulis menggunakan teknik dokumentasi, yaitu memeriksa data dengan berupa dokumen, sumber data yang sifatnya benda mati, bisa berupa rekaman, surat-surat, aset, foto dsb.   Kata Kunci Media Audiovisual, Efektivitas, Motivasi Belajar, dan Pembelajaran Daring

People express emotions through different modalities. Utilization of both verbal and nonverbal communication channels allows to create a system in which the emotional state is expressed more clearly and therefore easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human–machine interaction. This article presents analysis of audiovisual information to recognize human emotions. A cross-corpus evaluation is done using three different databases as the training set (SAVEE, eNTERFACE’05 and RML) and AFEW (database simulating real-world conditions) as a testing set. Emotional speech is represented by commonly known audio and spectral features as well as MFCC coefficients. The SVM algorithm has been used for classification. In case of facial expression, faces in key frames are found using Viola–Jones face recognition algorithm and facial image emotion classification done by CNN (AlexNet). Multimodal emotion recognition is based on decision-level fusion. The performance of emotion recognition algorithm is compared with the validation of human decision makers.

Abstract We introduce a new resource: the SAYCam corpus. Infants aged 6–32 months wore a head-mounted camera for approximately 2 hr per week, over the course of approximately two-and-a-half years. The result is a large, naturalistic, longitudinal dataset of infant- and child-perspective videos. Over 200,000 words of naturalistic speech have already been transcribed. Similarly, the dataset is searchable using a number of criteria (e.g., age of participant, location, setting, objects present). The resulting dataset will be of broad use to psychologists, linguists, and computer scientists.

Technological advancements and ease of Internet accessibility have made using Internet-based audiovisual software a viable option for researchers conducting focus groups. Online platforms overcome any geographical limitations placed on sampling by the location of potential participants and so enhance opportunities for real-time discussions and data collection in groups that otherwise might not be feasible. Although researchers have been adopting Internet-based options for some time, empirical evaluations and published examples of focus groups conducted using audiovisual technology are sparse. It therefore cannot yet be established whether conducting focus groups in this way can truly mirror face-to-face discussions in achieving the authentic interaction to generate data. We use our experiences to add to the developing body of literature by analyzing our critical reflections on how procedural aspects had the potential to influence the data we collected using audiovisual technology to conduct synchronous focus groups. As part of a mixed methods study, we chose to conduct focus groups in this way to access geographically dispersed populations and to enhance sample variation. We conducted eight online focus groups using audiovisual technology with both academic researchers and health-care practitioners across the four regions of the United Kingdom. A reflexive journal was completed throughout the planning, conduct and analysis of the focus groups. Content analysis of journal entries was carried out to identify procedural factors that had the potential to affect the data collected during this study. Five themes were identified (Stability of group numbers, Technology, Environment, Evaluation, and Recruitment), incorporating several categories of issues for consideration. Combined with the reflections of the researcher and published experiences of others, suggested actions to minimize any potential impacts of issues which could affect interactions are presented to assist others who are contemplating this method of data collection.

The audiovisual translation (AVT) sector has undergone rapid changes in recent years. It would be uncontroversial to state that the various stakeholders: academics; freelancers; technology providers, and language service providers (LSP) are likely to hold diverse and interesting views about what the future holds and how they might be called upon to adapt to recent and future changes. We have conducted qualitative research with representatives of these stakeholders in an attempt to ascertain their concerns and also their predictions for the future. Our motivation was to discover where stakeholders see the sector in the next 10 years. The research was conducted in 2016 and 2018 during the Languages and Media conference on a sample of 160 experts from various sector stakeholder groups. The findings show a broad range of issues that can be summarised into three main themes: the status of the language service provider; the need for standards and metrics; and the importance of training.   Lay summary Audiovisual translation, or media translation such as subtitling and dubbing, has changed a great deal in recent years. Professionals involved in the creation of translations for television and film, which includes the ever-more popular platforms such as Netflix, are likely to have differing views on what the future holds for their industry, especially given the rising volume of translations made by machine translation systems which are then edited by human translators. We have conducted research among professionals involved in the audiovisual translation production process at a conference that takes place in Berlin every two years:  Languages and the Media. This was an ideal place for such work since it attracts subtitlers, translators for dubbing, people who work in TV content translation, and trainers of media translators.  We were hoping to discover the views of a wide range of people about what might happen in the short to medium term in the industry. The research was conducted in 2016 and 2018 among 160 professionals, such as subtitlers and employees of streaming platforms. The findings reveal some issues connected to translating for the media and point to the need for measuring translation quality and investing more resources into media translation training.

Feminist theorization and activism are the precursors to gender studies. This chapter on gender and audiovisual translation (AVT) therefore begins with an overview of those feminist ideas that entered translation studies from the 1980s onward

We introduce STAViS, a spatio-temporal audiovisual saliency network that combines spatio-temporal visual and auditory information in order to efficiently address the problem of saliency estimation in videos. Our approach employs a single network that combines visual saliency and auditory features and learns to appropriately localize sound sources and to fuse the two saliencies in order to obtain a final saliency map. The network has been designed, trained end-to-end, and evaluated on six different databases that contain audiovisual eye-tracking data of a large variety of videos. We compare our method against 8 different state-of-the-art visual saliency models. Evaluation results across databases indicate that our STAViS model outperforms our visual only variant as well as the other state-of-the-art models in the majority of cases. Also, the consistently good performance it achieves for all databases indicates that it is appropriate for estimating saliency "in-the-wild". The code is available at https://github.com/atsiami/STAViS.

An automatic speech recognition (ASR) system is a key component in current speech-based systems. However, the surrounding acoustic noise can severely degrade the performance of an ASR system. An appealing solution to address this problem is to augment conventional audio-based ASR systems with visual features describing lip activity. This paper proposes a novel end-to-end, multitask learning (MTL), audiovisual ASR (AV-ASR) system. A key novelty of the approach is the use of MTL, where the primary task is AV-ASR, and the secondary task is audiovisual voice activity detection (AV-VAD). We obtain a robust and accurate audiovisual system that generalizes across conditions. By detecting segments with speech activity, the AV-ASR performance improves as its connectionist temporal classification (CTC) loss function can leverage from the AV-VAD alignment information. Furthermore, the end-to-end system learns from the raw audiovisual inputs a discriminative high-level representation for both speech tasks, providing the flexibility to mine information directly from the data. The proposed architecture considers the temporal dynamics within and across modalities, providing an appealing and practical fusion scheme. We evaluate the proposed approach on a large audiovisual corpus (over 60 hours), which contains different channel and environmental conditions, comparing the results with competitive single task learning (STL) and MTL baselines. Although our main goal is to improve the performance of our ASR task, the experimental results show that the proposed approach can achieve the best performance across all conditions for both speech tasks. In addition to state-of-the-art performance in AV-ASR, the proposed solution can also provide valuable information about speech activity, solving two of the most important tasks in speech-based applications.

Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify predictive uncertainty. Our contribution in this work is to propose an uncertainty aware multimodal Bayesian fusion framework for activity recognition. We demonstrate a novel approach that combines deterministic and variational layers to scale Bayesian DNNs to deeper architectures. Our experiments using in- and out-of-distribution samples selected from a subset of Moments-in-Time (MiT) dataset show a more reliable confidence measure as compared to the non-Bayesian baseline and the Monte Carlo dropout (MC dropout) approximate Bayesian inference. We also demonstrate the uncertainty estimates obtained from the proposed framework can identify out-of-distribution data on the UCF101 and MiT datasets. In the multimodal setting, the proposed framework improved precision-recall AUC by 10.2% on the subset of MiT dataset as compared to non-Bayesian baseline.

Deep learning using audiovisual data from chimpanzee percussive behaviors enables action recognition in the wild.

Audiovisual speech integration combines information from auditory speech (talker’s voice) and visual speech (talker’s mouth movements) to improve perceptual accuracy. However, if the auditory and visual speech emanate from different talkers, integration decreases accuracy. Therefore, a key step in audiovisual speech perception is deciding whether auditory and visual speech have the same source, a process known as causal inference. A well-known illusion, the McGurk Effect, consists of incongruent audiovisual syllables, such as auditory “ba” + visual “ga” (AbaVga), that are integrated to produce a fused percept (“da”). This illusion raises two fundamental questions: first, given the incongruence between the auditory and visual syllables in the McGurk stimulus, why are they integrated; and second, why does the McGurk effect not occur for other, very similar syllables (e.g., AgaVba). We describe a simplified model of causal inference in multisensory speech perception (CIMS) that predicts the perception of arbitrary combinations of auditory and visual speech. We applied this model to behavioral data collected from 60 subjects perceiving both McGurk and non-McGurk incongruent speech stimuli. The CIMS model successfully predicted both the audiovisual integration observed for McGurk stimuli and the lack of integration observed for non-McGurk stimuli. An identical model without causal inference failed to accurately predict perception for either form of incongruent speech. The CIMS model uses causal inference to provide a computational framework for studying how the brain performs one of its most important tasks, integrating auditory and visual speech cues to allow us to communicate with others.

ABSTRACT Objective: produce and validate an educational technology in the form of video for people and families who experience colostomy and cancer. Method: educational technology development study conducted in two phases: production and validation of audiovisual technology, which includes the stages of construction and validation of the script, video production and audio-visual validation. Twelve people participated in the validation, being seven expert judges and two families (five people). The appearance and content were validated by Content Validity Index greater than or equal to 0.70% (IVC ≥0.7) and interview. Results: the script content has been validated by the expert judges, with a Global Content Validity Index equal to 0.99 and by the target audience with a Global Content Validity Index of 1. The validation of audio-visual content obtained a Global Content Validity Index equal to 0.99. Conclusion: the validated audiovisual educational technology represents a resource enhancement for the educational practices of nursing in the care of people with stoma and their families.

The imposing reality of audiovisual translation has long challenged the concept of 'equivalence' and has put this notion against the ropes almost from the very first moments of its creation. Furthermore, the advent of new tendencies in audiovisual consumption, accompanied by a variety of localization practices, challenges the traditional concept of 'translation' as we know it. This article reviews which characteristics of audiovisual translation have called into question the very essence of translation, as it has been traditionally understood. After an explanation of new practices mainly created by the new audiences or prosumers, a list of concepts and terms that try to give account to this new reality is discussed, among them localisation, transadaptation, adaptation, transcreation and transmedia narratives, as well as remakes and format licensing. Conclusions call for a new concept of equivalence that also embraces new types of relations between original and target texts, such as iconic and narrative equivalence.

The purpose of this article is to provide information and data that will contribute to the enhancement of teaching methodologies for online learning and teaching at all educational levels and disciplines (including adult education). More specifically, it attempts to shed light on media trends and prospects as educational activities and techniques, as well as on the utmost importance of the use of television content as audiovisual educational content. This venture focuses on the cases of Cyprus and Greece following literature materials and reviews, research results, and findings of previous numerous studies and research papers from and through the Internet that were considered as background. The aforementioned were applied in a pilot case study with adult educators as adult learners (18 years and older), providing literature data and historical elements as a source of further study. The findings from the pilot case study revealed that the television content can also shape (adult) learners’ perceptions on how they understand and learn in an online environment in regard to the generational cohort they belong. Furthermore, the results disclosed that an online educational process utilizing audiovisual media technologies and audiovisual content (audiovisual media communications) may support technology-enhanced learning through non-verbal communication in the new streamlined digital era in which we live. An important conclusion of this article is that the (inter)national genealogical characteristics and habits, the inherent and special characteristics, and the socio-cultural identity of learners, as well as the various (inter)national social-phenomena (e.g., media socio-phenomenon, Internet phenomenon, revival phenomenon, etc.) of the past and present, should always be taken into account by education administrators and educators, in order to maintain a quality and sustainable future education.

By employing generative deep learning techniques, Deepfakes are created with the intent to create mistrust in society, manipulate public opinion and political decisions, and for other malicious purposes such as blackmail, scamming, and even cyberstalking. As realistic deepfake may involve manipulation of either audio or video or both, thus it is important to explore the possibility of detecting deepfakes through the inadequacy of generative algorithms to synchronize audio and visual modalities. Prevailing performant methods, either detect audio or video cues for deepfakes detection while few ensemble the results after predictions on both modalities without inspecting relationship between audio and video cues. Deepfake detection using joint audiovisual representation learning is not explored much. Therefore, this paper proposes a unified multimodal framework, Multimodaltrace, which extracts learned channels from audio and visual modalities, mixes them independently in IntrAmodality Mixer Layer (IAML), processes them jointly in IntErModality Mixer Layers (IEML) from where it is fed to multilabel classification head. Empirical results show the effectiveness of the proposed framework giving state-of-the-art accuracy of 92.9% on the FakeAVCeleb dataset. The cross-dataset evaluation of the proposed framework on World Leaders and Presidential Deepfake Detection Datasets gives an accuracy of 83.61% and 70% respectively. The study also provides insights into how the model focuses on different parts of audio and visual features through integrated gradient analysis.

The world of audiovisual online markets is rapidly changing. Not long ago, it was dominated by linear television, transmitted terrestrially, through cable networks or via satellite. Recently, streaming services such as Netflix, YouTube, Amazon Prime and others have emerged as new suppliers of audiovisual content. In this quickly changing industry, competition interrelations between such different formats such as traditional TV, videos on YouTube, and streaming via Netflix are subject to controversy. In particular, doubt is cast on services such as YouTube exerting competitive pressure on services such as Netflix and traditional TV. Based upon a survey with 2920 participants, we provide an empirical analysis of consumption behavior of audiovisual contents. Using descriptive and analytical statistics, including multiple equation models, we show that there are specific areas within audiovisual content markets, where YouTube exerts considerable competitive pressure on both Netflix and classic TV, for instance, through prime time video entertainment. However, our analysis yields differentiated results as we also identify areas, where competition intensity between different service types appear to be low, for instance, through daytime and regarding the intention to shorten waiting time.

Abstract Behaviorally, it is well established that human observers integrate signals near-optimally weighted in proportion to their reliabilities as predicted by maximum likelihood estimation. Yet, despite abundant behavioral evidence, it is unclear how the human brain accomplishes this feat. In a spatial ventriloquist paradigm, participants were presented with auditory, visual, and audiovisual signals and reported the location of the auditory or the visual signal. Combining psychophysics, multivariate functional MRI (fMRI) decoding, and models of maximum likelihood estimation (MLE), we characterized the computational operations underlying audiovisual integration at distinct cortical levels. We estimated observers’ behavioral weights by fitting psychometric functions to participants’ localization responses. Likewise, we estimated the neural weights by fitting neurometric functions to spatial locations decoded from regional fMRI activation patterns. Our results demonstrate that low-level auditory and visual areas encode predominantly the spatial location of the signal component of a region’s preferred auditory (or visual) modality. By contrast, intraparietal sulcus forms spatial representations by integrating auditory and visual signals weighted by their reliabilities. Critically, the neural and behavioral weights and the variance of the spatial representations depended not only on the sensory reliabilities as predicted by the MLE model but also on participants’ modality-specific attention and report (i.e., visual vs. auditory). These results suggest that audiovisual integration is not exclusively determined by bottom-up sensory reliabilities. Instead, modality-specific attention and report can flexibly modulate how intraparietal sulcus integrates sensory signals into spatial representations to guide behavioral responses (e.g., localization and orienting).

Digital technology has played a crucial role not only in the process of production and distribution of audiovisual content, but also in the process of localization and consumption of audiovisual products. This has had an obvious impact on audiovisual translation as a field, both in professional circles as well as in scholarly research. To date, this field of research has grown exponentially, parallel to the production, consumption, interaction with and general interest in audiovisual products. At the same time, translation studies have been growing as a discipline and, consequently, has been influencing audiovisual translation (AVT) research. This article presents a due brief overview of the four turns audiovisual translation has taken to date (the descriptive, the cultural, the sociological and the cognitive turns), and identifies some of the major steps already undertaken, as well as possible research avenues that such turns are currently opening.

Can we leverage the audiovisual information already present in video to improve self-supervised representation learning? To answer this question, we study various pre-training architectures and objectives within the masked autoencoding framework, motivated by the success of similar methods in natural language and image understanding. We show that we can achieve significant improvements on audiovisual downstream classification tasks, surpassing the state-of-the-art on VGGSound and AudioSet. Furthermore, we can leverage our audiovisual pretraining scheme for multiple unimodal downstream tasks using a single audiovisual pretrained model. We additionally demonstrate the transferability of our representations, achieving state-of-the-art audiovisual results on Epic Kitchens without pre-training specifically for this dataset.

Audio-based automatic speech recognition (A-ASR) systems are affected by noisy conditions in real-world applications. Adding visual cues to the ASR system is an appealing alternative to improve the robustness of the system, replicating the audiovisual perception process used during human interactions. A common problem observed when using audiovisual automatic speech recognition (AV-ASR) is the drop in performance when speech is clean. In this case, visual features may not provide complementary information, introducing variability that negatively affects the performance of the system. The experimental evaluation in this study clearly demonstrates this problem when we train an audiovisual state-of-the-art hybrid system with a deep neural network (DNN) and hidden Markov models (HMMs). This study proposes a framework that addresses this problem, improving, or at least, maintaining the performance when visual features are used. The proposed approach is a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual features, keeping only useful information. The framework is implemented with a subset of the audiovisual CRSS-4ENGLISH-14 corpus which consists of 61 h of speech from 105 subjects simultaneously collected with multiple cameras and microphones. The proposed framework is compared with conventional HMMs with observation models implemented with either a Gaussian mixture model or DNNs. We also compare the system with a multi-stream HMM system. The experimental evaluation indicates that the proposed framework outperforms alternative methods under all configurations, showing the robustness of the gating-based framework for AV-ASR.

Audiovisual integration occurs frequently and has been shown to exhibit age-related differences via behavior experiments or time-frequency analyses. In the present study, we examined whether functional connectivity influences audiovisual integration during normal aging. Visual, auditory, and audiovisual stimuli were randomly presented peripherally; during this time, participants were asked to respond immediately to the target stimulus. Electroencephalography recordings captured visual, auditory, and audiovisual processing in 12 old (60–78 years) and 12 young (22–28 years) male adults. For non-target stimuli, we focused on alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz) bands. We applied the Phase Lag Index to study the dynamics of functional connectivity. Then, the network topology parameters, which included the clustering coefficient, path length, small-worldness global efficiency, local efficiency and degree, were calculated for each condition. For the target stimulus, a race model was used to analyze the response time. Then, a Pearson correlation was used to test the relationship between each network topology parameters and response time. The results showed that old adults activated stronger connections during audiovisual processing in the beta band. The relationship between network topology parameters and the performance of audiovisual integration was detected only in old adults. Thus, we concluded that old adults who have a higher load during audiovisual integration need more cognitive resources. Furthermore, increased beta band functional connectivity influences the performance of audiovisual integration during normal aging.

Menurut observasi penulis dilapangan sewaktu guru mengajar di dalam kelas ada beberapa siswa yang mendapat kesukaran dalam memahami mata pelajaran matematika. Dimana siswa akhirnya menjadi jenuh, menganggap matematika pelajaran yang susah, sehingga siswa menilai bahwa matematika merupakan pelajaran yang membosankan. Untuk mengurangi hal tersebut dibutuhkan media pembelajaran terbaru dan menarik untuk merangsang daya kreatifitas para siswa, diantaranya menggunakan media animasi audiovisual  Powtoon. Metode yang digunakan adalah  RnD (Pengembangan). Hasil diperoleh bahwa media audiovisual PowToon memenuhi nilai kelayakan yang digunakan sebagai media pembelajaran. Dari hasil validasi materi menunjukkan kategori valid dengan memperoleh nilai 3,53. Untuk aspek media memperoleh nilai 3,28 dengan kategori valid, untuk aspek tampilan program memperoleh  nilai 3,5 dengan kategori valid, dan untuk aspek kualitas teknis dan keefektifan program memperoleh nilai 3,5 dengan kategori valid.

Asynchronous arrival of multisensory information at the periphery is a ubiquitous property of signals in the natural environment due to differences in the propagation time of light and sound. Rapid adaptation to these asynchronies is crucial for the appropriate integration of these multisensory signals, which in turn is a fundamental neurobiological process in creating a coherent perceptual representation of our dynamic world. Indeed, multisensory temporal recalibration has been shown to occur at the single trial level, yet the mechanistic basis of this rapid adaptation is unknown. Here, we investigated the neural basis of rapid recalibration to audiovisual temporal asynchrony in human participants using a combination of psychophysics and electroencephalography (EEG). Consistent with previous reports, participant’s perception of audiovisual temporal synchrony on a given trial (t) was influenced by the temporal structure of stimuli on the previous trial (t−1). When examined physiologically, event related potentials (ERPs) were found to be modulated by the temporal structure of the previous trial, manifesting as late differences (>125 ms post second-stimulus onset) in central and parietal positivity on trials with large stimulus onset asynchronies (SOAs). These findings indicate that single trial adaptation to audiovisual temporal asynchrony is reflected in modulations of late evoked components that have previously been linked to stimulus evaluation and decision-making.

People can seamlessly integrate a vast array of information from what we see and hear in the noisy and uncertain world. However, the neural underpinnings of audiovisual integration continue to be the topic of debate. Using strict inclusion criteria, we performed activation likelihood estimation meta-analysis on 121 neuroimaging experiments with a total of 2,092 participants. We found that audiovisual integration is linked with the coexistence of multiple integration sites including early cortical, subcortical, and higher association areas. Although activity was consistently found within the superior temporal cortex, different portions of this cortical region were identified depending on the analytical contrast used, complexity of the stimuli, and modality within which attention was directed. The context-dependent neural activity related to audiovisual integration suggests a flexible rather than fixed neural pathway for audiovisual integration. Together, our findings highlight a flexible multiple pathways model for audiovisual integration, with superior temporal cortex as the central node in these neural assemblies.

Human speech can be comprehended using only auditory information from the talker's voice. However, comprehension is improved if the talker's face is visible, especially if the auditory information is degraded as occurs in noisy environments or with hearing loss. We explored the neural substrates of audiovisual speech perception using electrocorticography, direct recording of neural activity using electrodes implanted on the cortical surface. We observed a double dissociation in the responses to audiovisual speech with clear and noisy auditory component within the superior temporal gyrus (STG), a region long known to be important for speech perception. Anterior STG showed greater neural activity to audiovisual speech with clear auditory component, whereas posterior STG showed similar or greater neural activity to audiovisual speech in which the speech was replaced with speech-like noise. A distinct border between the two response patterns was observed, demarcated by a landmark corresponding to the posterior margin of Heschl's gyrus. To further investigate the computational roles of both regions, we considered Bayesian models of multisensory integration, which predict that combining the independent sources of information available from different modalities should reduce variability in the neural responses. We tested this prediction by measuring the variability of the neural responses to single audiovisual words. Posterior STG showed smaller variability than anterior STG during presentation of audiovisual speech with noisy auditory component. Taken together, these results suggest that posterior STG but not anterior STG is important for multisensory integration of noisy auditory and visual speech.

Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achieve class-aware sounding objects localization without category annotations, i.e., localizing the sounding object and recognizing its category. To address this problem, we propose a two-stage step-by-step learning framework to localize and recognize sounding objects in complex audiovisual scenarios using only the correspondence between audio and vision. First, we propose to determine the sounding area via coarse-grained audiovisual correspondence in the single source cases. Then visual features in the sounding area are leveraged as candidate object representations to establish a category-representation object dictionary for expressive visual character extraction. We generate class-aware object localization maps in cocktail-party scenarios and use audiovisual correspondence to suppress silent areas by referring to this dictionary. Finally, we employ category-level audiovisual consistency as the supervision to achieve fine-grained audio and sounding object distribution alignment. Experiments on both realistic and synthesized videos show that our model is superior in localizing and recognizing objects as well as filtering out silent ones. We also transfer the learned audiovisual network into the unsupervised object detection task, obtaining reasonable performance.

Pembelajaran yang efektif dapat ditunjang dengan hubungan komunikasi antara pendidik dan peserta didik serta lingkungan belajar yang baik. Berdasarkan hasil pengamatan disekolah sebagian besar prestasi belajar siswa masih rendah maka perlu diterapkan suatu inovasi baru untuk mengoptimalkan proses dan hasil belajar. Tujuan penelitian ini adalah untuk mengetahui efektivitas penggunaan pendekatan kontekstual berbantuan media audiovisual dalam meningkatkan motivasi dan prestasi belajar siswa pada pembelajaran IPS Terpadu. Desain penelitian ini menggunakan metode Expermental quasy design menggunakan Pretest-Posttest Control Group Design. Hasil penelitian menunjukan prestasi belajar pada kelas eksperimen lebih tinggi yaitu dengan rata-rata 82 dibandingkan kelas kontrol dengan rata-rata 65 artinya penggunaan pendekatan kontekstual berbantuan media audiovisual lebih efektif dibandingkan metode konvensional dalam meningkatkan prestasi belajar peserta didik pada pembelajaran IPS Terpadu. Peningkatan motivasi belajar siswa yang signifikan antara sebelum dan sesudah pembelajaran dengan menggunakan pembelajaran kontekstual berbantuan media audiovisual.

In recent years, immersion has become a frequently emphasized factor in the geovisualization research agenda. A principal reason for this growing interest is the assumption that immersive virtual environments (IVE) facilitate the formation of spatial presence, generally understood as the sense of “being there”. In a virtually mediated environment, the feeling of being there is of particular concern for cartographic ambitions, in terms of generating insights through geospatial representation. Current literature indicates that immersive VR systems stimulate the experience of spatial presence; however, this assumption is mainly based upon user studies in the visual communication channel. Moreover, research on IVE for geovisualization matters has to date been focused on visual-graphical rather than on auditive or even multisensory representations in virtual space. In this context, the present paper aims to evaluate the potential of audiovisual cartography with immersive virtual environments. Following a brief discussion of basic concepts, such as immersion, spatial presence and embodiment, we will integrate these aspects into a geovisualization immersion pipeline (GIP), as a framework with which to systematically link the technical and cognitive aspects of IVE. In the subsequent sections, we will examine this framework in the audio channel by analyzing how sound is implemented and perceived in GeoIVE. As we shall see, the positive effect of a combined audio-visual vs. exclusively visual presentation is supported by a series of user studies of sound effects, making audiovisual cartography with IVE a rich and worthwhile field of research.ZusammenfassungVirtuelle Realität (VR) und Immersion haben sich in den vergangenen Jahren auf der Forschungsagenda der Kartographie fest etabliert. Ein Hauptgrund dieses wachsenden Interesses ist die Annahme, dass immersive virtuelle Umgebungen räumliches Präsenzerleben induzieren, so dass sich Rezipient/inn/en in einer medialen Umgebung anwesend fühlen und Handlungsmöglichkeiten innerhalb derselben wahrnehmen. Für das grundsätzliche Ziel der Kartographie, raumbezogenes Wissen durch Repräsentationen zu vermitteln, ist das Phänomen räumlichen Präsenzerlebens von besonderem Interesse. Rezente Studien weisen zwar darauf hin, dass immersive VR-Systeme ein hohes Präsenzerleben erzeugen können, stützen sich dabei jedoch in erster Linie auf Untersuchungen des visuellen Kommunikationskanals. Ebenso hat sich die kartographische Erforschung immersiver Umgebungen bislang vor allem visuellen bzw. graphischen, weniger aber auditiven und multisensorischen Repräsentationsformen gewidmet. Vor diesem Hintergrund versucht der vorliegende Text, das Potenzial einer audiovisuellen Kartographie in immersiven virtuellen Umgebungen zu bewerten. Der einleitenden Diskussion zentraler Termini (z.B. Immersion, räumliches Präsenzerleben, Embodiment) folgt eine Verknüpfung dieser Schlüsselbegriffe innerhalb einer kartographischen Immersionspipeline – eine Modellvorstellung, die technische und kognitive Aspekte immersiver Umgebungen systematisch verbindet. Ausgehend von dieser Modellverstellung wird in den nachfolgenden Abschnitten untersucht, wie akustische Information in kartographischen VR-Systemem wahrgenommen und implementiert werden kann. Anhand einer Reihe empirischer Studien wird gezeigt, dass audiovisuelle Darstellungen verglichen mit ausschließlich visuellen Repräsentationen das räumliche Präsenzerleben verstärken können und somit audiovisuelle Kartographie in immersiven virtuellen Umgebungen zu einem reichen und lohnenden Gebiet zukünftiger Forschung machen.

Students have different levels of material understanding, including in understandingmonotonous Islamic Education materials using lecture methods. Sometimes students find it difficult to capture what teachers describe, especially in complex materials such as fiqh and long materials such asislamic culturalhistory. Thepurposeofthisresearch is to conducta learning innovationto address students' problems related to material understanding. The innovation that will be discussed in this article is the development of audiovisual media for Islamic Religious Education (PAI). The research method useslibrary research collectedfrom books and journals. With the development of audiovisual media innovation, it is hoped that students can better understand islamic education materials and provide a new atmosphere in learning. As a result of this study with technological developments, accessing audivisual media in learning is easier. The most popular site that provides audiovisual media access is Youtobe. The role of educators in the playback of learning videos as mentors. So that with this medium can shape the effectiveness of student learning.

We assessed how synchronous speech listening and lip reading affects speech recognition in acoustic noise. In simple audiovisual perceptual tasks, inverse effectiveness is often observed, which holds that the weaker the unimodal stimuli, or the poorer their signal-to-noise ratio, the stronger the audiovisual benefit. So far, however, inverse effectiveness has not been demonstrated for complex audiovisual speech stimuli. Here we assess whether this multisensory integration effect can also be observed for the recognizability of spoken words. To that end, we presented audiovisual sentences to 18 native-Dutch normal-hearing participants, who had to identify the spoken words from a finite list. Speech-recognition performance was determined for auditory-only, visual-only (lipreading) and auditory-visual conditions. To modulate acoustic task difficulty, we systematically varied the auditory signal-to-noise ratio. In line with a commonly-observed multisensory enhancement on speech recognition, audiovisual words were more easily recognized than auditory-only words (recognition thresholds of −15 dB and −12 dB, respectively). We here show that the difficulty of recognizing a particular word, either acoustically or visually, determines the occurrence of inverse effectiveness in audiovisual word integration. Thus, words that are better heard or recognized through lipreading, benefit less from bimodal presentation. Audiovisual performance at the lowest acoustic signal-to-noise ratios (45%) fell below the visual recognition rates (60%), reflecting an actual deterioration of lipreading in the presence of excessive acoustic noise. This suggests that the brain may adopt a strategy in which attention has to be divided between listening and lip reading.

Emotion recognition using audiovisual features is a challenging task for human-machine interaction systems. Under ideal conditions (perfect illumination, clean speech signals, and non-occluded visual data) many systems are able to achieve reliable results. However, few studies have considered developing multimodal systems and training strategies to build systems that can perform well under non ideal conditions. Audiovisual models still face challenging problems such as misalignment of modalities, lack of temporal modeling, and missing features due to noise or occlusions. In this article, we implement a model that combines auxiliary networks, a transformer architecture, and an optimized training mechanism to achieve a robust system for audiovisual emotion recognition that addresses, in a principled way, these challenges. Our evaluation analyzes how well this model performs in ideal conditions and when modalities are missing. We contrast this method with other multimodal fusion methods for emotion recognition. Our experimental results based on two audiovisual databases demonstrate that the proposed framework achieves: 1) improvements in emotion recognition accuracy, 2) better alignment and fusion of audiovisual features at the model level, 3) awareness of temporal information, and 4) robustness to non-ideal scenarios.

It has been established that there is an interaction between audition and vision in the appraisal of our living environment, and that this appraisal is influenced by personal factors. Here, we test the hypothesis that audiovisual aptitude influences appraisal of our sonic and visual environment. To measure audiovisual aptitude, an auditory deviant detection experiment was conducted in an ecologically valid and complex context. This experiment allows us to distinguish between accurate and less accurate listeners. Additionally, it allows to distinguish between participants that are easily visually distracted and those who are not. To do so, two previously conducted laboratory experiments were re-analyzed. The first experiment focuses on self-reported noise annoyance in a living room context, whereas the second experiment focuses on the perceived pleasantness of using outdoor public spaces. In the first experiment, the influence of visibility of vegetation on self-reported noise annoyance was modified by audiovisual aptitude. In the second one, it was found that the overall appraisal of walking across a bridge is influenced by audiovisual aptitude, in particular when a visually intrusive noise barrier is used to reduce highway traffic noise levels. We conclude that audiovisual aptitude may affect the appraisal of the living environment.

Several end-to-end deep learning approaches have been recently presented which simultaneously extract visual features from the input images and perform visual speech classification. However, research on jointly extracting audio and visual features and performing classification is very limited. In this work, we present an end-to-end audiovisual model based on Bidirectional Long Short-Term Memory (BLSTM) networks. To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the pixels and spectrograms and perform classification of speech and nonlinguistic vocalisations. The model consists of multiple identical streams, one for each modality, which extract features directly from mouth regions and spectrograms. The temporal dynamics in each stream/modality are modeled by a BLSTM and the fusion of multiple streams/modalities takes place via another BLSTM. An absolute improvement of 1.9% in the mean F1 of 4 nonlingusitic vocalisations over audio-only classification is reported on the AVIC database. At the same time, the proposed end-to-end audiovisual fusion system improves the state-of-the-art performance on the AVIC database leading to a 9.7% absolute increase in the mean F1 measure. We also perform audiovisual speech recognition experiments on the OuluVS2 database using different views of the mouth, frontal to profile. The proposed audiovisual system significantly outperforms the audio-only model for all views when the acoustic noise is high.

In multisensory integration, processing in one sensory modality is enhanced by complementary information from other modalities. Intersensory timing is crucial in this process because only inputs reaching the brain within a restricted temporal window are perceptually bound. Previous research in the audiovisual field has investigated various features of the temporal binding window, revealing asymmetries in its size and plasticity depending on the leading input: auditory–visual (AV) or visual–auditory (VA). Here, we tested whether separate neuronal mechanisms underlie this AV–VA dichotomy in humans. We recorded high-density EEG while participants performed an audiovisual simultaneity judgment task including various AV–VA asynchronies and unisensory control conditions (visual-only, auditory-only) and tested whether AV and VA processing generate different patterns of brain activity. After isolating the multisensory components of AV–VA event-related potentials (ERPs) from the sum of their unisensory constituents, we ran a time-resolved topographical representational similarity analysis (tRSA) comparing the AV and VA ERP maps. Spatial cross-correlation matrices were built from real data to index the similarity between the AV and VA maps at each time point (500 ms window after stimulus) and then correlated with two alternative similarity model matrices: AVmaps = VAmaps versus AVmaps ≠ VAmaps. The tRSA results favored the AVmaps ≠ VAmaps model across all time points, suggesting that audiovisual temporal binding (indexed by synchrony perception) engages different neural pathways depending on the leading sense. The existence of such dual route supports recent theoretical accounts proposing that multiple binding mechanisms are implemented in the brain to accommodate different information parsing strategies in auditory and visual sensory systems. SIGNIFICANCE STATEMENT Intersensory timing is a crucial aspect of multisensory integration, determining whether and how inputs in one modality enhance stimulus processing in another modality. Our research demonstrates that evaluating synchrony of auditory-leading (AV) versus visual-leading (VA) audiovisual stimulus pairs is characterized by two distinct patterns of brain activity. This suggests that audiovisual integration is not a unitary process and that different binding mechanisms are recruited in the brain based on the leading sense. These mechanisms may be relevant for supporting different classes of multisensory operations, for example, auditory enhancement of visual attention (AV) and visual enhancement of auditory speech (VA).

Although it is clear that sighted listeners use both auditory and visual cues during speech perception, the manner in which multisensory information is combined is a matter of debate. One approach to measuring multisensory integration is to use variants of the McGurk illusion, in which discrepant auditory and visual cues produce auditory percepts that differ from those based on unimodal input. Not all listeners show the same degree of susceptibility to the McGurk illusion, and these individual differences are frequently used as a measure of audiovisual integration ability. However, despite their popularity, we join the voices of others in the field to argue that McGurk tasks are ill-suited for studying real-life multisensory speech perception: McGurk stimuli are often based on isolated syllables (which are rare in conversations) and necessarily rely on audiovisual incongruence that does not occur naturally. Furthermore, recent data show that susceptibility to McGurk tasks does not correlate with performance during natural audiovisual speech perception. Although the McGurk effect is a fascinating illusion, truly understanding the combined use of auditory and visual information during speech perception requires tasks that more closely resemble everyday communication: namely, words, sentences, and narratives with congruent auditory and visual speech cues.

[T]echnology is no longer something that stands over and against human beings; it structures human life and social relations from within. Technical forms of organization, technical networks and equipment, the entire material aspect of technology, have penetrated into the depths of who we are, into our bodies and our communicative relations, in the process bringing about deep changes to the way our societies cohere.

ABSTRACT A firm step in order to impose a quota for the catalogues of video-on-demand service providers has been given by the EU through the amendment of the Audiovisual Media Services Directive in 2018. This paper aims to analyse the potential implications of this change, in relation to the way the promotion of European works has been implemented so far, since some comments can be made in light of this revision. It will be argued that more interesting than the obligation to comply with a quota, defined by the geographical origin of audiovisual works, is the idea of making VOD service providers part of every national audiovisual production and financing system via direct or indirect financial contributions. It is concluded that indirect obligations could offer a wider room for manoeuvre to better help focus public policy decisions.

Latar Belakang: Stunting merupakan kondisi gagal tumbuh untuk mencapai pertumbuhan normal yang diakibatkan oleh status gizi kurang dalam periode waktu lama. Pencegahan serta penanganan stunting dapat dilakukan dengan meningkatkan pengetahuan ibu yang memiliki anak stunting agar tidak berlanjut pada anak selanjutnya. Pemberian edukasi pada ibu dapat dilakukan menggunakan metode brainstorming dan audiovisual. Tujuan: Mengetahui pengaruh pemberian edukasi dengan metode brainstorming dan audiovisual terhadap pengetahuan ibu tentang stunting di Desa Gununglurah, Cilongok, Kabupaten Banyumas. Metode: Desain penelitian adalah quasy experimental with time series design. Subjek penelitian terdiri dari 34 ibu yang memiliki balita stunting. Data pengetahuan ibu mengenai stunting dikumpulkan menggunakan kuesioner pretest dan posttest. Pemberian edukasi gizi mengenai stunting dilakukan dengan metode brainstorming (curah pendapat) menggunakan alat bantu leaflet. Pemberian edukasi gizi mengenai stunting dengan metode audiovisual dilakukan menggunakan film ilustrasi. Hasil: Rerata skor pengetahuan ibu pada saat pretest adalah 6,44±1,65 sedangkan skor pada saat posttest naik menjadi 7,38±1,76. Analisis statistik menggunakan uji beda Wilcoxon menunjukkan adanya perbedaan signifikan antara pengetahuan ibu mengenai stunting pada waktu sebelum dan sesudah dilakukan intervensi (p=0,009). Kesimpulan: Terdapat perbedaan pengetahuan ibu yang signifikan mengenai stunting pada waktu sebelum dan sesudah pemberian edukasi gizi dengan metode brainstorming dan audiovisual.

Audio and vision are two main modalities in video data. Multimodal learning, especially for audiovisual learning, has drawn considerable attention recently, which can boost the performance of various computer vision tasks. However, in video summarization, most existing approaches just exploit the visual information while neglecting the audio information. In this brief, we argue that the audio modality can assist vision modality to better understand the video content and structure and further benefit the summarization process. Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task and develop an audiovisual recurrent network (AVRN) to achieve this. Specifically, the proposed AVRN can be separated into three parts: 1) the two-stream long-short term memory (LSTM) is used to encode the audio and visual feature sequentially by capturing their temporal dependency; 2) the audiovisual fusion LSTM is used to fuse the two modalities by exploring the latent consistency between them; and 3) the self-attention video encoder is adopted to capture the global dependency in the video. Finally, the fused audiovisual information and the integrated temporal and global dependencies are jointly used to predict the video summary. Practically, the experimental results on the two benchmarks, i.e., SumMe and TVsum, have demonstrated the effectiveness of each part and the superiority of AVRN compared with those approaches just exploiting visual information for video summarization.

Abstract Background: Health promotion is part of GPs' commitments. Some waiting rooms have therefore been implemented with audiovisual aids (posters, pamphlets or screens) for health promotion purposes. Few studies have assessed the effect of audiovisual aids in primary care. Objectives: To identify, describe and appraise studies that have investigated the effects of audiovisual aids on health promotion in primary healthcare waiting rooms. To determine which factors influence this impact through literature review. Methods: Systematic review. Two independent researchers using predefined keywords searched databases. Additional publications were extracted from the reference lists of the selected articles. The selection of the articles was performed on the title and abstract, followed by complete reading and assessment. Bias and level of evidence were analysed. Results: A total of 909 articles were collected. Most of them were not in primary care settings. Fourteen peer-reviewed articles fully meeting inclusion criteria were included and analysed. Good quality studies were scarce. Eight of these articles using videos or slideshows on TV screens or tablets indicated effects: three of them were significant on patient knowledge with acceptable evidence and three on health behaviour on surrogate endpoints. Audiovisual aids seem to be used or noticed by patients and can induce conversations with physicians. The relevant factors that might influence these effects (duration of exposure, conception quality, theme, target population and time spent in the waiting room) are insufficiently investigated. Conclusion: Audiovisual aids broadcasting messages using screens (TVs, computers, tablets, and smartphones with Bluetooth® pairing) probably enhance patients’ knowledge. A change in health behaviour remains controversial.

Intracranial human recordings are a valuable and rare resource of information about the brain. Making such data publicly available not only helps tackle reproducibility issues in science, it helps make more use of these valuable data. This is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset has several unique features. First, it includes a large amount of intracranial electroencephalography (iEEG) data (51 participants, age range of 5–55 years, who all performed the same task). Second, it includes functional magnetic resonance imaging (fMRI) recordings (30 participants, age range of 7–47) during the same task. Eighteen participants performed both iEEG and fMRI versions of the task, non-simultaneously. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This dataset can be used to study neural mechanisms of multimodal perception and language comprehension, and similarity of neural signals across brain recording modalities. Measurement(s) brain activity measurement Technology Type(s) Intracranial EEG • functional magnetic resonance imaging Factor Type(s) Short audiovisual film stimulus Sample Characteristic - Organism Homo sapiens Measurement(s) brain activity measurement Technology Type(s) Intracranial EEG • functional magnetic resonance imaging Factor Type(s) Short audiovisual film stimulus Sample Characteristic - Organism Homo sapiens

Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data. In this paper, we present a flexible audiovisual model that introduces a soft-clustering module as the audio and visual content detector, and regards the pervasive property of audiovisual concurrency as the latent supervision for inferring the correlation among detected contents. To ease the difficulty of audiovisual learning, we propose a novel curriculum learning strategy that trains the model from simple to complex scene. We show that such ordered learning procedure rewards the model the merits of easy training and fast convergence. Meanwhile, our audiovisual model can also provide effective unimodal representation and cross-modal alignment performance. We further deploy the well-trained model into practical audiovisual sound localization and separation task. We show that our localization model significantly outperforms existing methods, based on which we show comparable performance in sound separation without referring external visual supervision. Our video demo can be found at this https URL.

Game atmosphere and game audio are critical factors linked to the commercial success of video games. However, game atmosphere has been neither operationalized nor clearly defined in games user research literature, making it difficult to study. We define game atmosphere as the emerging subjective experience of a player caused by the strong audiovisual thematic cohesion (i.e., the harmonic fit of sounds and graphics to a shared theme) of video game elements. We studied players' experience of thematic cohesion in two between-subjects, independent-measures experiments (N=109) across four conditions differing in their level of audiovisual thematic fit. Participants' experiences were assessed with physiological and psychometric measurements to understand the effect of game atmosphere on player experience. Results indicate that a lack of thematic fit between audio and visuals lowers the degree of perceived atmosphere, but that while audiovisual thematic dissonance may lead to higher-intensity negative-valence facial events, it does not impact self-reported player experience or immersion.

Creative dubbing and subtitling are presented in this article as didactic resources to improve integrated foreign language (FL) skills (specifically oral and written production). The increasing attention that the application of audiovisual translation (AVT) to FL learning has been receiving in the last two decades calls for further exploration into the potential benefits of modern AVT modes, such as funsubbing and fundubbing, understood as the creative translation of an audiovisual text, be it into subtitles or through the production of a new audio track. After considering the educational power of creative AVT and providing the corresponding theoretical justification, the article will present a methodological proposal on how to use these AVT modes in online environments. Finally, preliminary data derived from a short-term trial will be analysed and discussed.

Audiovisual integration significantly changes over the lifespan, but age-related functional connectivity in audiovisual temporal asynchrony integration tasks remains underexplored. In the present study, electroencephalograms (EEGs) of 27 young adults (22–25 years) and 25 old adults (61–76 years) were recorded during an audiovisual temporal asynchrony integration task with seven conditions [auditory (A), visual (V), AV, A50V, A100V, V50A and V100A]. We calculated the phase lag index (PLI)-weighted connectivity networks modulated by the audiovisual tasks and found that the PLI connections showed obvious dynamic changes after stimulus onset. In the theta (4–7 Hz) and alpha (8–13 Hz) bands, the AV and V50A conditions induced stronger functional connections and higher global and local efficiencies, reflecting a stronger audiovisual integration effect, which was attributed to the auditory information arriving at the primary auditory cortex earlier than the visual information reaching the primary visual cortex. Importantly, the functional connectivity and network efficiencies of old adults revealed higher global and local efficiencies and higher degree in both the theta and alpha bands. These larger network efficiencies indicated that old adults might experience more difficulties in attention and cognitive control during the audiovisual integration task with temporal asynchrony than young adults. There were significant associations between network efficiencies and peak time of integration only in young adults. We propose that an audiovisual task with multiple conditions might arouse the appropriate attention in young adults but would lead to a ceiling effect in old adults. Our findings provide new insights into the network topography of old adults during audiovisual integration and highlight higher functional connectivity and network efficiencies due to greater cognitive demand.

Abstract Infants as young as 2 months can integrate audio and visual aspects of speech articulation. A shift of attention from the eyes towards the mouth of talking faces occurs around 6 months of age in monolingual infants. However, it is unknown whether this pattern of attention during audiovisual speech processing is influenced by speech and language experience in infancy. The present study investigated this question by analysing audiovisual speech processing in three groups of 4‐ to 8‐month‐old infants who differed in their language experience: monolinguals, unimodal bilinguals (infants exposed to two or more spoken languages) and bimodal bilinguals (hearing infants with Deaf mothers). Eye‐tracking was used to study patterns of face scanning while infants were viewing faces articulating syllables with congruent, incongruent and silent auditory tracks. Monolinguals and unimodal bilinguals increased their attention to the mouth of talking faces between 4 and 8 months, while bimodal bilinguals did not show any age difference in their scanning patterns. Moreover, older (6.6 to 8 months), but not younger, monolinguals (4 to 6.5 months) showed increased visual attention to the mouth of faces articulating audiovisually incongruent rather than congruent faces, indicating surprise or novelty. In contrast, no audiovisual congruency effect was found in unimodal or bimodal bilinguals. Results suggest that speech and language experience influences audiovisual integration in infancy. Specifically, reduced or more variable experience of audiovisual speech from the primary caregiver may lead to less sensitivity to the integration of audio and visual cues of speech articulation.

Audiovisual stimulation, such as auditory stimulation, light stimulation, and audiovisual combined stimulation, as a non‐invasive stimulation, which can induce gamma oscillation, has received increased attention in recent years, and it has been preliminarily applied in the clinical rehabilitation of brain dysfunctions, such as cognitive, language, motor, mood, and sleep dysfunctions. However, the exact mechanism underlying the therapeutic effect of 40‐Hz audiovisual stimulation remains unclear; the clinical applications of 40‐Hz audiovisual stimulation in brain dysfunctions rehabilitation still need further research.

The spiking neural networks (SNNs), as brain-inspired neural networks, have received noteworthy attention due to their advantages of low power consumption, high parallelism, and high fault tolerance. While SNNs have shown promising results in uni-modal data tasks, their deployment in multimodal audiovisual classification remains limited, and the effectiveness of capturing correlations between visual and audio modalities in SNNs needs improvement. To address these challenges, we propose a novel model called spiking multimodel transformer (SMMT) that combines SNNs and Transformers for multimodal audiovisual classification. The SMMT model integrates uni-modal subnetworks for visual and auditory modalities with a novel spiking cross-attention module for fusion, enhancing the correlation between visual and audio modalities. This approach leads to competitive accuracy in multimodal classification tasks with low energy consumption, making it an effective and energy-efficient solution. Extensive experiments on a public event-based data set (N-TIDIGIT&MNIST-DVS) and two self-made audiovisual data sets of real-world objects (CIFAR10-AV and UrbanSound8K-AV) demonstrate the effectiveness and energy efficiency of the proposed SMMT model in multimodal audiovisual classification tasks. Our constructed multimodal audiovisual data sets can be accessed at https://github.com/Guo-Lingyue/SMMT.

Visual information can improve the performance of automatic speech recognition (ASR), especially in the presence of background noise or different speech modes. A key problem is how to fuse the acoustic and visual features leveraging their complementary information and overcoming the alignment differences between modalities. Current audiovisual ASR (AV-ASR) systems rely on linear interpolation or extrapolation as a pre-processing technique to align audio and visual features, assuming that the feature sequences are aligned frame-by-frame. These pre-processing methods oversimplify the phase difference between lip motion and speech, lacking flexibility and impairing the performance of the system. This paper addresses the fusion of audiovisual features with an alignment neural network (AliNN), relying on recurrent neural network (RNN) with attention model. The proposed front-end model can automatically learn the alignment from the data. The resulting aligned features are concatenated and fed to conventional back-end ASR systems. The proposed front-end system is evaluated with matched and mismatch channel conditions, under clean and noisy recordings. The results show that our proposed approach can relatively outperform the baseline by 24.9% with Gaussian mixture model with hidden Markov model (GMM-HMM) back-end and 2.4% with deep neural network with hidden Markov model (DNN-HMM) back-end.

Nowadays, audiovisual media technologies and audiovisual content (audiovisual media communications) play an important role in our physical/psychological health, education, and lifelong learning, causing the redefinition of the teaching methodology. As presented in the literature, the use of audiovisual media communications presuppose a new way of approaching effective teaching, which requires the educators on all educational levels and disciplines to display with competence many advanced skills and abilities. The aim of this research is to provide data that will contribute to the effective teaching utilizing audiovisual media communications in adult education. This research is a secondary research from two researches, which are qualitative and based on a quantitative method of analyzing. The primary data were collected through experiment method from adults (18 years and older), in Cyprus and Greece. The results confirm the current debate of using audiovisual media technologies within the educational process in technology-enhanced learning in education, both from the literature, and from the findings and results of various researches. This research is part of a larger, ongoing research that explores the multidisciplinary field that incorporates media, audiovisual content, and education (MACE), information and communications technologies (ICTs) in adult education (in Greece and Cyprus).

Recent studies suggest that sub-clinical levels of autistic symptoms may be related to reduced processing of artificial audiovisual stimuli. It is unclear whether these findings extent to more natural stimuli such as audiovisual speech. The current study examined the relationship between autistic traits measured by the Autism spectrum Quotient and audiovisual speech processing in a large non-clinical population using a battery of experimental tasks assessing audiovisual perceptual binding, visual enhancement of speech embedded in noise and audiovisual temporal processing. Several associations were found between autistic traits and audiovisual speech processing. Increased autistic-like imagination was related to reduced perceptual binding measured by the McGurk illusion. Increased overall autistic symptomatology was associated with reduced visual enhancement of speech intelligibility in noise. Participants reporting increased levels of rigid and restricted behaviour were more likely to bind audiovisual speech stimuli over longer temporal intervals, while an increased tendency to focus on local aspects of sensory inputs was related to a more narrow temporal binding window. These findings demonstrate that increased levels of autistic traits may be related to alterations in audiovisual speech processing, and are consistent with the notion of a spectrum of autistic traits that extends to the general population.

OBJECTIVE The purpose of this study was to evaluate the understandability and actionability of audiovisual educational materials on diabetes in Korea using the Patient Education Materials Evaluation Tool (PEMAT), as well as determine the usefulness of these materials. METHODS A total of 85 audiovisual materials were collected from Korean websites of territory general hospitals, national health institutions, research associations, and major search engines relating to diabetes that were created between 2006 and 2015. Of these, 34 materials that met the inclusion and exclusion criteria were analyzed. Five trained researchers evaluated the materials independently. RESULTS More than half of the materials (58.8%) had been created by nongovernment organizations. Slightly more than half (n = 19) of the audiovisual materials were streaming-style animation. The average PEMAT score (58.5%) for these materials was moderate. Compared to "understandability" ratings (49.5%), "actionability" ratings were low (31.4%); indeed, fourteen materials had an actionability of 0%. The average usefulness score of the materials was 4.3 points out of a possible 7. There were few suitable audiovisual materials for patient education on diabetes. CONCLUSIONS These findings will be useful for developing new audiovisual educational materials for diabetes patients with high understandability, actionability, and usefulness.

ABSTRACT Background Children with high level of preoperative anxiety during their visit to dental office are more likely to develop maladaptive behavior postoperatively. First dental experience is always critical in molding child's attitude toward dentistry. Various behavior management methods are being employed during dental treatment to complete anticipated treatment in children. Aim The aim of the study was to evaluate the effectiveness of cognitive behavioral play therapy and audiovisual distraction for management of preoperative anxiety in children. Materials and methods A total of 45 children of age 6–10 years with moderate-to-severe anxiety were allocated into three groups: group I—cognitive behavioral play therapy (CBT), group II—audiovisual (AV) distraction, and group III—tell-show-do technique (control group). Children in the CBT group were allowed to play with building blocks, asked to draw a picture and then showed a modeling video of co-operative child undergoing dental treatment. Children in group II were subjected to passive distraction with audiovisual aids, whereas group III (control) children were managed with the conventional TSD technique. Baseline and postintervention objective and subjective anxiety scores were measured with a pulse oximeter and facial image scale (FIS), respectively. Results A statistically significant reduction in the subjective and objective anxiety scores is observed in all the three groups (p = 0.001) in both intragroup and intergroup comparisons. On intergroup comparison, the reduction in subjective and objective anxiety scores was higher in CBT (p = 0.0) than in AV distraction and TSD groups (p = 0.05). Conclusion Active distraction with cognitive behavioral play therapy is found to be more effective in reducing the preoperative anxiety in children compared to audiovisual distraction and tell-show-do technique. Clinical significance Identification and management of preoperative anxiety in children is most critical for successful dental treatment. Active distraction is an effective psychological approach for behavior management in anxious children. How to cite this article Rajeswari SR, Chandrasekhar R, Vinay C, et al. Effectiveness of Cognitive Behavioral Play Therapy and Audiovisual Distraction for Management of Preoperative Anxiety in Children. Int J Clin Pediatr Dent 2019;12(5):419–422.

The natural environments in which infants and children learn speech and language are noisy and multimodal. Adults rely on the multimodal nature of speech to compensate for noisy environments during speech communication. Multiple mechanisms underlie mature audiovisual benefit to speech perception, including reduced uncertainty as to when auditory speech will occur, use of correlations between the amplitude envelope of auditory and visual signals in fluent speech, and use of visual phonetic knowledge for lexical access. This paper reviews evidence regarding infants’ and children’s use of temporal and phonetic mechanisms in audiovisual speech perception benefit. The ability to use temporal cues for audiovisual speech perception benefit emerges in infancy. Although infants are sensitive to the correspondence between auditory and visual phonetic cues, the ability to use this correspondence for audiovisual benefit may not emerge until age four. A more cohesive account of the development of audiovisual speech perception may follow from a more thorough understanding of the development of sensitivity to and use of various temporal and phonetic cues.

Audio-video group emotion recognition is a challenging task since it is difficult to gather a broad range of potential information to obtain meaningful emotional representations. Humans can easily understand emotions because they can associate implicit contextual knowledge (contained in our memory) when processing explicit information they can see and hear directly. This paper proposes an end-to-end architecture called implicit knowledge injectable cross attention audiovisual deep neural network (K-injection audiovisual network) that imitates this intuition. The K-injection audiovisual network is used to train an audiovisual model that can not only obtain audiovisual representations of group emotions through an explicit feature-based cross attention audiovisual subnetwork (audiovisual subnetwork), but is also able to absorb implicit knowledge of emotions through two implicit knowledge-based injection subnetworks (K-injection subnetwork). In addition, it is trained with explicit features and implicit knowledge but can easily make inferences using only explicit features. We define the region of interest (ROI) visual features and Melspectrogram audio features as explicit features, which obviously are present in the raw audio-video data. On the other hand, we define the linguistic and acoustic emotional representations that do not exist in the audio-video data as implicit knowledge. The implicit knowledge distilled by adapting video situation descriptions and basic acoustic features (MFCCs, pitch and energy) to linguistic and acoustic K-injection subnetworks is defined as linguistic and acoustic knowledge, respectively. When compared to the baseline accuracy for the testing set of 47.88%, the average of the audiovisual models trained with the (linguistic, acoustic and linguistic-acoustic) K-injection subnetworks achieved an overall accuracy of 66.40%.

The fields of neural prosthetic technologies and Brain-Computer Interfaces (BCIs) have witnessed in the past 15 years an unprecedented development, bringing together theories and methods from different scientific fields, digital media, and the arts. More in particular, artists have been amongst the pioneers of the design of relevant applications since their emergence in the 1960s, pushing the boundaries of applications in real-life contexts. With the new research, advancements, and since 2007, the new low-cost commercial-grade wireless devices, there is a new increasing number of computer games, interactive installations, and performances that involve the use of these interfaces, combining scientific, and creative methodologies. The vast majority of these works use the brain-activity of a single participant. However, earlier, as well as recent examples, involve the simultaneous interaction of more than one participants or performers with the use of Electroencephalography (EEG)-based multi-brain BCIs. In this frame, we discuss and evaluate “Enheduanna—A Manifesto of Falling,” a live brain-computer cinema performance that enables for the first time the simultaneous real-time multi-brain interaction of more than two participants, including a performer and members of the audience, using a passive EEG-based BCI system in the context of a mixed-media performance. The performance was realised as a neuroscientific study conducted in a real-life setting. The raw EEG data of seven participants, one performer and two different members of the audience for each performance, were simultaneously recorded during three live events. The results reveal that the majority of the participants were able to successfully identify whether their brain-activity was interacting with the live video projections or not. A correlation has been found between their answers to the questionnaires, the elements of the performance that they identified as most special, and the audience's indicators of attention and emotional engagement. Also, the results obtained from the performer's data analysis are consistent with the recall of working memory representations and the increase of cognitive load. Thus, these results prove the efficiency of the interaction design, as well as the importance of the directing strategy, dramaturgy and narrative structure on the audience's perception, cognitive state, and engagement.

The purpose of this study was to determine the effect of audiovisual media application of patient identification implementation on patient knowledge at the University of North Sumatra Hospital in 2019. The study used a quantitative approach, with quasi-experimental methods, one group pre-test-post-test design. The results showed that before counseling, 40 patients included in the category of sufficient knowledge, and 80 people in the category of insufficient knowledge. After counseling using audiovisual media, data obtained that, as many as 12 patients included in the category of good knowledge, 68 people in the category of sufficient knowledge, and 40 people in the category of lack of knowledge. These results indicate that there was an increase in the number of patients in the knowledge category. Conclusion, there is an influence of counseling given by the use of audiovisual media to increase the number of patients in the knowledge category.   Keywords: Patient Identification, Audiovisual Media, Counseling

Abstract :Among preschoolers,  illness is  a scary thing. Children have limitations in coping mechanisms to overcome the crisis. Audiovisual intervention in watching cartoons is a distraction technique to reduce anxiety in children. The purpose of the study was to analyze the effect of audiovisual cartoon movie on anxiety levels during injection procedures in preschool children. The design of this study was using Pre-experimental pretest and posttest one group design. The research collecting the data using purposive sampling technique on 28 respondents. Independent variable audiovisual watching cartoons, while dependent variable is anxiety level. The instrument used is SOP and HAR-S anxiety scale. Statistical tests using the Paired Sample T-Test, with significance p <0.05. From the results of the statistical analysis, the sig value was obtained (p = 0.001, t=11.71) which means an audiovisual intervention by on watching  cartoons movie was effective to reduce  anxiety levels during the injection procedure in preschool children. We suggest that audiovisual intervention by watching cartoons movie  can be applied as one of the nursing interventions to reduce anxiety during injection procedures in preschool children.   Keywords: Anxiety, Audiovisual, Injection Procedure, Preschool Children

Audiovisual speech recognition is a favorable solution to multimodality human–computer interaction. For a long time, it has been very difficult to develop machines capable of generating or understanding even fragments of natural languages; the fused sight, smelling, touching, and so on provide machines with possible mediums to perceive and understand. This article presents a detailed review of recent advances in audiovisual speech recognition area. After explicitly representing audiovisual speech recognition development phase divided by timeline, we focus on typical audiovisual speech database descriptions in terms of single view and multi-view, since the public databases for general purpose should be the first concern for audiovisual speech recognition tasks. For the following challenges that are inseparably related to the feature extraction and dynamic audiovisual fusion, the principal usefulness of deep learning-based tools, such as deep fully convolutional neural network, bidirectional long short-term memory network, 3D convolutional neural network, and so on, lies in the fact that they are relatively simple solutions of such problems. As the principle analyses and comparisons related to computational load, accuracy, and applicability of well-developed audiovisual speech recognition frameworks have been conducted, we further illuminate our insights into the future audiovisual speech recognition architecture design. We argue that end-to-end audiovisual speech recognition model and deep learning-based feature extractors will guide multimodality human–computer interaction directly to a solution.

This study aims to determine the effectiveness of audiovisual education methods on self management in hypertensive patients. This study used a quasi-experimental design with a non-equivalent control group pre and post test design, with a total sample of 38 respondents consisting of an intervention group and a control group. Obtained a significant difference in the average self-management of the intervention group after the audiovisual education method was performed with (p = 0,000). There was no relationship between age (p = 0.71), sex (p = 0.955) and self-management in hypertensive patients. While self management will increase after the audiovisual education method is carried out and controlled by knowledge (p = 0.005). The conclusion of this study is the audiovisual education method can improve self management in hypertensive patients.  Keywords: Audiovisual Education, Hypertension, Self Management

The purpose of this article is twofold: to consider whether and how the European Union has a strong influence in global audiovisual politics and economy and to analyse whether the United States keeps playing a key role in global audiovisual flows or its domination is challenged by the European Union or other countries. These two purposes are analysed in the light of the rise of global online platforms, seen as the new dominant development in the audiovisual industry. In order to understand the European Union role and influence, the article highlights three interlinked factors: European/internal audiovisual policies, market profile and foreign audiovisual diplomacy. In the end, the article examines the competition that European Union faces in the global film economy, focusing notably on the US/Hollywood, China, India and global online platforms.

Recent research has suggested that dynamic emotion recognition involves strong audiovisual association; that is, facial or vocal information alone automatically induces perceptual processes in the other modality. We hypothesized that different emotions may differ in the automaticity of audiovisual association, resulting in differential audiovisual information processing. Participants judged the emotion of a talking-head video under audiovisual, video-only (with no sound), and audio-only (with a static neutral face) conditions. Among the six basic emotions, disgust had the largest audiovisual advantage over the unimodal conditions in recognition accuracy. In addition, in the recognition of all the emotions except for disgust, participants' eye-movement patterns did not change significantly across the three conditions, suggesting mandatory audiovisual information processing. In contrast, in disgust recognition, participants' eye movements in the audiovisual condition were less eyes-focused than the video-only condition and more eyes-focused than the audio-only condition, suggesting that audio information in the audiovisual condition interfered with eye-movement planning for important features (eyes) for disgust. In addition, those whose eye-movement pattern was affected less by concurrent disgusted voice information benefited more in recognition accuracy. Disgust recognition is learned later in life and thus may involve a reduced amount of audiovisual associative learning. Consequently, audiovisual association in disgust recognition is less automatic and demands more attentional resources than other emotions. Thus, audiovisual information processing in emotion recognition depends on the automaticity of audiovisual association of the emotion resulting from associative learning. This finding has important implications for real-life emotion recognition and multimodal learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The purpose of this paper is to document the impact of movies/cinema on Indian culture and to investigate the ongoing cultural transformations that have happened over a point of time in India owing to the contributions made by Indian cinema. Moreover, an attempt is made to understand how Indian movies have initiated various changes with regards to thought process on one hand and lifestyle on the other. There has been changes with regards to the institutions of marriage and instances of live in relationships have come of age and there has been a constant and continuous shift from joint families to nuclear families. Elaborating on the changes in the cultural practices; preferences have changed with regards to food habits, clothing, choice of career options and belief system. The distinctive contribution of the present paper is to offer an understanding of the impact of movies on Indian culture especially on the youth extracting information from the structured and in-depth interviews. This paper also tries to document the behavioral changes in the youth as a behavior patterns.

Abstract: Beginning with the 2006-07 season, the New York Metropolitan Opera began broadcasting live performances into select movie theatres around the world. This article explores the phenomenon using an approach known as medium theory. It draws from the work of three analysts in that tradition who have focused on the performing and recording arts: Edmund Carpenter, John Ellis, and James Monaco. The author coins the term “digital broadcast cinema” (DBC) to refer to the virtual experience of seeing a live opera on a big screen in high definition. The Met’s project is assessed with respect to the conventions that govern theatre, broadcast television, and cinema, and with reference to how it both enhances and compromises the traditional concert-going experience. Resume : Lors de la saison 2006-2007, le Metropolitan Opera de New York a commence a diffuser des representations en direct dans certaines salles de cinema autour du monde. Cet article explore le phenomene au moyen d’une approche communement appelee la theorie des medias. Celle-ci se fonde sur l’oeuvre de trois analystes specialises dans les arts du spectacle et de l’enregistrement : Edmund Carpenter, John Ellis, et James Monaco. L’auteur formule l’expression « cinema numerique radiodiffusee » pour decrire l’experience virtuelle de voir un opera presente au grand ecran en direct et en haute definition. L’auteur evalue ce projet du Met par rapport aux conventions propres au theâtre, a la television grand public et au cinema et aux manieres dont le projet parvient a ameliorer et a empirer l’experience traditionnelle d’assister a un concert.

This paper accompanies four films which use CCTV to create Live Cinema performances which challenge both the narrative conventions of cinema and the expectations that we have of CCTV images. The paper details the ways in which this occurs and concludes with some lessons for future CCTV film-making and performance. The films can be viewed at: http://paoleb-ccvv.blogspot.com/

The aim of this paper is to analyze the potential contribution of broadcasting live performing arts in movie theaters to the democratization of high culture. To accomplish this, we analyze the profile of attendees to these events to explore whether this new form of cultural consumption can attract individuals who do not normally attend live performances, thereby renewing high-brow audiences. Using data from the Spanish Survey of Cultural Habits and Practices in Spain (2018–2019), we find that individuals who frequently attend movies are more likely to attend live opera broadcasts in movie theaters, even those who do not consume live opera or even do not listen to opera at all. This is a positive insight suggesting that this initiative may be successful to engage new audiences to high culture.

The cultural Heritage building is a valuable trace of the past social life for a city. One peculiar typology of building is the theatre or, more recently, the cinema. Within this indoor space, the community of people actively live an emotional feeling over the time causing memories linked to not only the show itself, but also to the place. Furthermore, cinemas are often located in city centers where buildings are historical-oriented form the architectural point of view and for this reason an added-value needs to be recognized. Nowadays, the introduction of pay-TVs has dramatically compromised the cinema business and so-making the turnout of clients more limited. In addition, the recent COVID-19 pandemic situation has further and severally damaged the economy linked to cinema. In this scenario, the modernization and the improvement of old cinemas is becoming an interesting topic in the field of civil engineering. In fact, the new technologies make the experience more comfortable and enthusiastic because of the modern video and audio tools. On the other hand, the expectation of the clients is full-filled when the cinema is able to provide a large choice of views; which means arranging many projection rooms. In order to empathize this aspect, the structural strengthening for a multiplex conversion of an historical cinema is herein reported. The structure required a seismic joint (separating the masonry to the reinforced concrete structural bodies) and a deep strengthening of a very long span beam due to a relevant overloading (in seismic loads combination) caused by the new architectonical project. The numerical simulations demonstrated the validity of different bending strengthening systems (i.e. FRP-plate and Beton Plaquè) in terms of both load bearing capacity and maximum deflection. This overview is a part of a larger study in the way of a global structural interventions which will also involve the masonry members.

This article – initially published in Spanish by Toma Uno in 2012 – explores the Catalonian project Cinema en curs, an annual, recurring and now international programme of film education that takes place with students aged 10–18 in schools and colleges. First detailing both the rationale and methodology motivating the project, the article goes on to discuss core aspects of the approach of Cinema en curs: the Lumière minutes exercise and the short films which serve as the culmination of students’ work on the project. In doing so, the article also explores the value of approaches to film education that help learners develop an appreciation of their own lived experiences and the places in which they live. A new introduction from the authors places the article’s discussion of Cinema en curs in a contemporary context.

Abstract:This article develops an analysis of the relationship between paratextual framing and institutional identity in the body of productions broadcast to cinemas by RSC Live and NT Live, proposing that paratexts are an integral yet consistently overlooked mediator of meaning in Shakespearean live theater broadcasts. Since the National Theatre’s broadcast of All’s Well that End’s Well in 2009 and the Royal Shakespeare Company’s debut live theater broadcast of Richard II in 2013, Shakespearean performance has dominated the UK’s event cinema market. This article examines the interpretive significance of paratextual conventions developed by these two companies in negotiating institutional identity and shaping narratives of Shakespeare’s cultural value. It brings these marginal materials to the center of critical analysis, proposing an original critical vocabulary by which to examine the diversity of the broadcast paratexts which have developed around, and been shaped by, Shakespearean performance in the medium. A sustained analysis of the frames of interpretation offered in Shakespearean broadcast performances exposes the interpretive labor these marginal materials regularly perform, highlighting the value of analyzing Shakespearean live theater broadcasts holistically.

In this visual essay, I discuss how clothing made for an expanded cinema performance entitled Film as Fabric took on significance. The live work shows analogue film as fabric and stitching as editing to highlight historical relationships between textile practice and experimental filmmaking. The work developed through iterations from 2013 to 2017 as a part of doctorate research carried out from 2012 to 2020. My position as an experimental filmmaker with a background in embroidery grounded the study. Annabel Nicolson’s seminal live expanded cinema performance Reel Time (1973), in which she punctured 16mm film with her unthreaded sewing machine, was also a point of departure. Feminist critique from both disciplines ‐ film and textile studies ‐ supported the possibility of repressed relationships between the fields. Feminist scholars have repeatedly problematized contextualization of experimental filmmaking as narrow and misplaced (Blaetz 2007; Hatfield 2006; Reynolds 2009, 2012; Rhodes 1979). Until now, connections with textile practice in experimental filmmaking have been largely overlooked, most likely because domestic crafts have a long association with women’s work (Barber 1994), are central to deep rooted Western stereotypes of femininity, and remain denigrated art forms (Parker 2010). The development of Film as Fabric led to the creation of hybrid tools, forms and gestures, such as optical sounds and moving images made from fabric and stitch patterns. These were measured on the body, cut with dressmaking scissors, stitched, spliced into loops and projected in former cotton mills. This process allowed sounds associated with textile production and my personal textile ancestry to re-surface in new ways. The live context demands attention is given to material practices, which typically take place out of sight in the home and the film cutting room and have also recently undergone significant shifts in cultural status.

ABSTRACT Historical narratives seek to give us a shared reality, argued through recourse to evidence. Both impulses are under threat in the Age of the Anthropocene. This article introduces Live Interactive Documentary, a ‘performance dissemination’ model for history that deploys digital tools to merge cinema and lecture into a new form. It is designed to respond to Hayden White’s challenge in ‘The Burden of History’ and Bruno Latour’s call to gather as a bulwark to misinformation and the manipulative, political cooption of postmodern scepticism. Live Interactive Documentary creates a spectacle of archive and expertise, injected with post-postmodern values of polyphony and audience exchange, grounded in the local. This essay describes the form that I piloted with a team that includes historian Robert Nelson and composer, musician and media artist Brent Lee. Live Interactive Documentary is a model of ‘moving (image) history’ that seeks to cross boundaries between practice and theory, as well as history, film, multimedia performance art and participation. This hybrid cinema model draws upon theories of historiography, film and new media. It digitises earlier models of theatre and film exhibition and responds to the challenges of the Anthropocene by prioritising negotiation, complexity and gathering face-to-face in the real space of our analogue world.

I am a cinematic being of the Anthropocene. As a concerned citizen and environmental educator, I immerse myself in film. Gummo is a 1997 film by Harmony Korine that deeply resonates with me as a testament to the capacity and desire for humanity to realise the potential to rise from the epochal fall of the Anthropocene. I propose that my relationship with Gummo as arche-cinema is not just a process of watching and interpreting Korine’s cinematic world, but also (re)projecting my dreams of a new reality for the whole-Earth ecosystem onto the world-out-there. I suggest that my entanglement with Gummo exemplifies my climating and becoming-climate as film in our current human-induced climate crises, and in this way, I argue that I am learning to live-with climate change through film.

The feature film Ajari Aku Islam (Teach Me Islam) was directed by Deni Pusung in 2019 and is based on the story of Jaymes Riyanto (Setuningsih, N., 2020) who worked also as one a film producer and scriptwriter. The film was mostly shot in the city center of Medan such as at the Chinese Pecinan Area Kesawan, Masjid Agung Sumatra Utara, and the old settlement of Jalan Sei Sirah. The film shows the love relationship between Kenny Huang, who has Chinese roots, in the Confucianist religion, and Fidya Lubis, a Muslim woman from Batak ethnic group. They both come from the middle economic class and live in the city center of Medan. According to the producer Jaymes Riyanto, the film has the intention to remove the ethnic gap and show the potential co-existence of Muslims and non-Muslims in Medan (Padang Time, 2019). Using Christian Metz's semiotic analysis, the paper will show how the film due to using the stereotypes in its mise en scéne reproduces on the contrary, even more, the prejudices and solidifies the stigmatization and segregation among the two ethnicities. However, this paper also suggests potential measures to overcome the social disparity and ethnic segregation such as through the just urban transportation system, the urban green infrastructure, and the production of local cinema culture which can produce inter-ethnic and religious dialogues among ethnic groups.

O presente texto é uma reflexão das inquietações de um cineasta indígena sobre os dias atuais, o qual teve como ponto de partida a conversa entre os realizadores indígenas Graciela Guarani e Alexandre Pankararu por meio de uma Live realizada pela Fundação Cultural Badesc de Florianópolis, SC. Ao longo do diálogo vários pontos foram abordados, como propor outras linguagens e gêneros de produção, ver o cinema indígena como área de emprego, pensar sobre a distribuição e o público dessas produções. Onde independente de todos os aspectos levantados, o cinema indígena é e sempre será uma forma de resistência e enfrentamento.

Abstract Carlos Saura’s “pure musicals,” as he calls them, are highly based on the formal properties of the image and the expressive use of light in a minimalist scenic space. Although, they have not been declared screendance pieces (as per Rosenberg 2012), which conjoin rhythmic body movements with screen-based, technologically mediated methods of rendering, they are full-fledged screendance examples, being hybrid, symbiotic, and integral (Richard James Allen, 2006). This article concentrates on Saura’s musicals from 2005 onwards – Flamenco (1995), Iberia (2005), Fados (2007), Flamenco Flamenco (2010), Argentina (Zonda, folclore argentino, 2015) and Jota de Saura (2016) – particularly the immersive mediation operated through the use of live video feed as an intermedial sensorial device. Saura’s silky, glossy, and lustrous images form an optical-haptic continuum. The twofold bodies, the digital doubles and the flesh-and-bone act as inducers of crystallization in Gilles Deleuze’s perception of modern cinema (1985), inasmuch as they interact and alternate in a cinematic flux, forming a circuit. Thus, an image of a recorded stage performance enters into a relationship with cinema, a medium already endowed with reflective features, producing the crystallization of these screendance films in all their Saurian immersivenness and sensoriality.

By looking at ethnography as a multi-sited and evolving field in the tradition of George Marcus and Michael Fischer, the article returns to the cinematic concept of ‘expanded cinema’, focusing on current forms of expansion in audiovisual ethnographic representation (i.e., ‘expanded ethnography’). In doing so, it analyses the live cinematic performances of Supereverything* (The Light Surgeons, 2011–2017) and Invisible Cities (59 Productions, 2019) in terms of convergences, correspondences and intermedial staging, all of which dialectically synthesise the expanded field as it emerges from within the world system. The article deconstructs the aesthetical dialectics that produce the collective feeling of enlargement of the ethnographic field from a singular stage to a multiplicity of actors and stages (fields) via staged live interconnections made between intermedia technologies and social/bodily intersubjective relations, as they emerged via exploratory practices on and beyond the limits of the stage. By using sources deriving from cinematic theatre and philosophy, the article argues that the illusion of enlargement of the effigy of the world picture is techno/socially manufactured as part of the marketed media turn to imagination and subjectivity, with political consequences for ethnographic representation and its ‘expanded’ claim to a reality beyond the material history of the cosmopolis.

The films and live cinema of Vincent Moon are considered in this chapter as ‘psychedelic’: a form of filmmaking and film performances that can open the doors of perception to invisible realms of percepts, affects and durations that are beyond or below ordinary human perception. According to Paul Schrader, films can evoke such spiritual dimensions, in particular through what he called the transcendental style of film, and what Gilles Deleuze termed the time-image. As an audio-visual ethnographer of world religions who is distinctly influenced by shamanic and animistic traditions, Moon brings the transcendental style back to its plane of immanence. His live cinema performances have a ritualistic and ecstatic aspect that recalls the esoteric history of haunted media. Moon's enthralling film performances induce altered states of mind, tap into spiritual realities and immerse the audience in magic.

Humans and creatures live by communication. The start moment of existence is the beginning of communication. But communication has taken different forms in different eras. Communication may be visual, auditory or sensory. In the world of beings, one of these communication methods is used as needed. In the field of visual communication, one of the most important parts is communication through images. This type of communication has been formed since the early times of human existence. Its signs are also petroglyphs that have been found in caves since the past centuries. This relationship changed shape in different eras and turned into painting, photography, and then into cinema and television images.Apart from being an art, cinema is an audio-visual medium that uses a combination of other arts to communicate with the audience. Nowadays, it is used in various ways to establish a communication bridge between cinema producers and social audiences. Because with it, they can convey the content they want to their audience. In essence, cinema is a bridge between the film producer and the target audience. But the type of use of this powerful and effective tool is different. In European and American countries, which are the founders of this means of communication, they use it well. But in the countries of the third world, especially the countries of the Middle East, especially Afghanistan, they could not use this magical tool to establish communication in order to change their society. The reason is the lack of sufficient knowledge of psychology, sociology, cultural studies and cinema technology and the power of image art to optimally use this means of mass communication.

We present a filming process with synchronized cameras and lights which allows a scene to be photographed in two lighting conditions nearly simultaneously with a global shutter cinema camera. The technique enables novel applications for relighting, matting, and direct-indirect reflectance component separation for both technical and creative fields.

Este artigo propõe refletir sobre o filme “Eles Vivem” (Carpenter, 1988), do diretor John Carpenter, considerando as formulações de Freud e Lacan sobre a constituição do sujeito e o desenvolvimento lacaniano sobre o discurso do capitalista. Carpenter, em sua obra cinematográfica, nos apresenta o capitalismo sobre a ótica da classe trabalhadora que sofre com os efeitos causados pelo sistema. Busca-se, neste estudo, abordar, a partir da teoria psicanalítica em articulação com o cinema, a relação do sujeito com os objetos de consumo.

Nowadays cinema's influence on public opinion can also extend to a different realm. Cinema being the most powerful medium for both entertainment and to influencing society by its reflection. From thought-provoking fictions to eye-opening non-fiction, movies have the capability to transform public opinion and even initiate societal change. In this paper, we will try to explore the impact of cinema on social development and how the filmmakers are using it as a tool to address social issues. Cinema serves as a catalyst for development of social construct by addressing critical issues like discrimination, patriarchy, system failure in most creative way. Being the most powerful medium, it also can be explained as the reflection of the world we live in and they portray the struggles, triumphs, and challenges faced by individuals. By portraying real-life issues on screen, films develop a niche for viewers to engage with various perspectives. If anyone compares the cinema of yesteryears with today’s contemporary cinema, one would notice a drastic transformation not just in techniques but also in the type of themes directors are incorporating for the audience. Concepts are closer to social themes, depicting the real picture of the world.This work tries to deal with the fact that how commercial hindi cinema has changed over time and established into a new form altogether. A form where not just hero and heroine are fighting to win over their love but also to tell the story making audience more aware about society. This paper is deep analytical study of Rajkumar Hirani’s films to find social construct embed in cinema nowadays. The study analyses four popular films of Rajkumar Hirani: Munnabhai M.B.B.S. (2003), Lage Raho Munna Bhai (2006), 3 Idiots (2009), PK (2014). This paper delves into how Hirani’s films where he goes beyond traditional commercial elements and address social issues while engaging with contemporary audiences in meaningful ways.

While often unacknowledged, gags and gag comedy remain a vital part of Hollywood cinema. Following the runaway success of Who Framed Roger Rabbit (1988), an increase in the production of films combining live-action and animation has helped them become a central venue for gag comedy. However, while live-action/animation hybrids can be enormously successful, they are also extremely difficult and risky ventures, both in terms of their technical challenges and production costs. Given this, what is the rationale behind combining live-action and animation for comedy at all? As the author argues, both theoretical considerations of animation and hybrids as well as the discourse from creators of such films frame live-action/animation as particularly well suited for gag comedy due to its ability to balance the freedom of animation with the grounding realism of live-action. Films such as The Adventures of Rocky and Bullwinkle (2000), Looney Tunes: Back in Action (2003), and The SpongeBob Movie: Sponge Out of Water (2015) serve to highlight the interaction between cartoonish animation and realistic live-action as well as key tensions that arise in combining the two forms. Examining the opportunities and challenges provided by these combinations helps explain why hybrid films remain a common vehicle for gag comedy today and an important subset of Hollywood comedy as a whole.

The aim of the paper is to discuss Vestígios, an live cinema composition that is played from a purpose-built software. Vestígios is an audiovisual work in which images are reactive to sounds. In this piece I explore the notions of audiovision and synesthesia to integrate the audible and visible information in a live cinema work. Vestígios explores the movement between signs and noises, information and degeneration.The artistic process consists of working from collections of images and sounds that are live performed. In Vestígios, various audiovisual information was organized into inventories, which I present in the piece as ghostly spectra that reappear adrift. The piece works on the notion of technical image and spectrum to develop its poetics. The creative methodology considers and archaeological perspective in order to explore the relationship between media information, simulacrum and memory. The purpose is to create an audiovisual environment that evokes the condition of imaginary in the post-historical period, as defined by the Philosopher Vilém Flusser. In XXI Century, faster computers provide the technological conditions to carry out audiovisual events in a way widely different from works composed using tape or electronic video. Computers allow artists to organize images and sounds in very flexible and variable archives. In this context, the artist can act as a collector, working with dynamic inventories of audiovisual information. Considering this possibility, Vestígios is a work that may be recomposed each performance.

This paper discusses the concept of "live cinema" — a sub-genre of digital media art — through the ideas and perspectives of theoretical authors, from the silent film era to the digital media art era. It reflects on issues relating to narratives and interactivity, analyzing four works of live cinema: Super Everything, Massive Attack vs Adam Curtis, Moda Vestra and Safara — Lucid Dream 2020. By way of comparison, the same issues are addressed in three artistic projects from other subgenres of digital media art: Cadavre Exequis (interactive film), Stephen Kloss (audiovisual performance), Hantrakul & Kaczmarek (electronic music). The author proposes to develop a theoretical basis of reference, to compose linear narratives in live cinema performances, through untouchable interfaces and the performers' body movements. The present study is the intermediate results of a PhD thesis, with a practice-based research methodology in artistic practice.

“Cine Rabeca” is a documentary that expands onto the stage and blends live music with ethnographic archive. It is a cine-concert, where musicians Luiz Paixão and Renata Rosa are permanently on stage, dialoguing with their own filmic memories. During the performance, a new time is composed out of what is seen, heard and imagined. The passage of time is felt in the contrast between images of the rural world of Pernambuco, Brazil, in 1990-2000 and the image of the presence of the musicians on stage. In this cinematographic re-encounter with their trajectories, Paixão and Rosa improvise with their rabecas (fiddles) and recreate themselves. Through music, they circulate amongst rural and ancestral festivities and the narrative is permeated with the language of memory, which explores traces and reminiscences.Time and memory articulate themselves in a singular space that experiments with a form of collaboration between the languages of music, film, and anthropology. This paper explores the process of making this expanded documentary and how it has remained live during the last years, constantly transforming itself amongst the borders of the stage and the screen, between life and film. It shows a process of unfinishing that transcends the finalization of documentaries; of the very life that follows ethnographic recordings.

Doctoral programs in the fine arts, instead of coming up with their own ways of doing things, tend to adopt standards from the humanities, which themselves tend to adopt standards from science. Because of being preoccupied with trying to look like other disciplines, artistic thinking within artistic doctorates gets suppressed. But if we look into science directly and not through this second-hand approach, we can find aspects of scientific thinking that are closer to art than to the humanities. In this paper, I give examples of artistic thinking in the work of various scientists and mathematicians: a non-fiction book that uses fiction (Douglas Hofstadter’s book Gödel, Escher, Bach), a linguistic analysis that concludes with a story (Livia Polanyi’s book Telling the American Story), scientific lectures with unusual formal aspects (Roger Penrose’s “VJing” of multiple layers of foils through an overhead projector, David Deutsch’s Lectures on Quantum Computation), and a collective hiding behind a fictional mathematician (Nicolas Bourbaki). I also briefly introduce the problem of verbal overshadowing and the effects it may have on the creative process in art.

El live cinema es una práctica que fusiona las artes audiovisuales con las prácticas performativas. Aun estando mediada por las nuevas tecnologías digitales, produce una experiencia colectiva, un momento singular, efímero e irreproducible. Partiendo del concepto de pliegue como la forma idónea para describir esta práctica, se estudia la búsqueda estética o el motor creador que comparten artistas dentro de esta corriente, encontrándolo en las relaciones que establecen entre los flujos digitales y la realidad corpórea. El live cinema quedaría determinado, no solo como un pliegue entre el lenguaje cinematográfico y nuevas formas de creación audiovisual en la era digital, sino también como una práctica enfocada en explorar nuevas posibles formas de relacionarnos con el flujo de datos digitales.

This article discusses aspects of identity, memory and love in the film Café com Canela, directed by Glenda Nicácio and Ary Rosa (Brasil, 2017). The feature film tells the story of Margarida (Valdineia Soriano) a retired teacher who lives isolated from the world after losing her only child. One day, Violeta (Aline Brunne), knocks on her door and from that moment on, takes on the mission of returning a little contentment to the life of her former teacher, Margarida. The text analyzes the film, sound and visual narratives of care and affection and how these feelings structure the concept of Black Cinema in the Feminine. It was concluded that the stories about affection, friendship and care of and with black people allow reflections on black lives from the verbs: Believe, Live and Share. The text also highlights black ancestry and identity as affective processes, capable of constituting new images of the black Brazilian population.

El artículo explora la evolución del cine hacia el cine expandido y su impacto en las artes escénicas contemporáneas, especialmente en los live cinema show. Desde la arqueología de los medios, se analiza la interacción entre la imagen, la tecnología y la actuación en vivo en las “realizaciones escénicas”. Se destaca la relevancia de comprender esta convergencia para ampliar los límites de la narrativa visual en el contexto teatral actual.

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We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. We first propose complementary measures to quantify bias with respect to protected attributes such as gender and age. We then present algorithms for computing fairness-aware re-ranking of results. For a given search or recommendation task, our algorithms seek to achieve a desired distribution of top ranked results with respect to one or more protected attributes. We show that such a framework can be tailored to achieve fairness criteria such as equality of opportunity and demographic parity depending on the choice of the desired distribution. We evaluate the proposed algorithms via extensive simulations over different parameter choices, and study the effect of fairness-aware ranking on both bias and utility measures. We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice. Our approach resulted in tremendous improvement in the fairness metrics (nearly three fold increase in the number of search queries with representative results) without affecting the business metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users worldwide. Ours is the first large-scale deployed framework for ensuring fairness in the hiring domain, with the potential positive impact for more than 630M LinkedIn members.

The literature base regarding the development of sporting talent is extensive, and includes empirical articles, reviews, position papers, academic books, governing body documents, popular books, unpublished theses and anecdotal evidence, and contains numerous models of talent development. With such a varied body of work, the task for researchers, practitioners and policy makers of generating a clear understanding of what is known and what is thought to be true regarding the development of sporting talent is particularly challenging. Drawing on a wide array of expertise, we address this challenge by avoiding adherence to any specific model or area and by providing a reasoned review across three key overarching topics: (a) the performer; (b) the environment; and (c) practice and training. Within each topic sub-section, we review and calibrate evidence by performance level of the samples. We then conclude each sub-section with a brief summary, a rating of the quality of evidence, a recommendation for practice and suggestions for future research. These serve to highlight both our current level of understanding and our level of confidence in providing practice recommendations, but also point to a need for future studies that could offer evidence regarding the complex interactions that almost certainly exist across domains.

Abstract There is little doubt that the attraction, development, and retention of talent are nowadays one of the most critical challenges faced by companies worldwide. Despite the increasing scholarly attention during the last years many questions remain, particularly, those related to how (and why) talent management (TM) is conceived, implemented and developed within organizations, not to mention about its outcomes or effectiveness. We argue that organizational context has been underappreciated in TM research, which is an omission since context affects the occurrence, meaning and implementation of TM. Therefore, we edited a special issue which seeks to contribute to advance our knowledge of how contextual factors affect the conceptualization, implementation and effectiveness of TM. In this opening article, we offer a brief overview of how context is integrated in previous TM research. We then introduce the four articles in this special issue and their contributions which addresses gap in TM research and, finally, we offer some suggestions on how to improve contextualized TM research.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.,This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.,This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.,This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.,This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

ABSTRACT At the turn of the millennium, a review paper was published in this journal on talent identification and development in soccer (Williams & Reilly, 2000). In the current paper, we assess progress made in this field over the last twenty years relative to the areas for future research highlighted in the original review. We evaluate developments in light of the calls made by Williams and Reilly to: a) undertake more multidisciplinary rather than mono-disciplinary research; b) embrace longitudinal rather than cross-sectional research designs; c) expand the research base on female football; and, d) better identify the subjective criteria used by scouts when selecting one player over another for entry into a formalised training environment. The body of mono-disciplinary research on this topic continues to expand, and progress has been made in publishing multidisciplinary, prospective, longitudinal data sets, along with advanced statistical modelling procedures, as well as in identifying the experiential criteria used by scouts. We found some variables in these studies have predictive value from adolescence to adult performance level in soccer. We present suggestions for future research to enhance knowledge and understanding of the best practices underpinning the identification and development of future generations of professional players.

The modern-day landscape of Olympic and Professional sport is arguably more competitive than ever. One consequence of this is the increased focus on identifying and developing early athletic talent. In this paper, we highlight key challenges associated with talent (athlete) identification and development and propose possible solutions that could be considered by research and practice. The first challenge focuses on clarifying the purposes of talent identification initiatives such as defining what talent is and how its meaning might evolve over time. Challenge two centers on ways to best identify, select and develop talent, including issues with different approaches to identification, the need to understand the impact of development and the need to have appropriate resourcing in the system to support continued development of knowledge. Finally, we discuss two challenges in relation to the ‘healthiness’ of talent identification and development. The first examines whether a talent identification and development system is ‘healthy’ for athletes while the second focuses on how sport stakeholders could discourage the apparent trend toward early specialization in youth sport settings. Whilst this paper discusses the research in relation to these challenges, we propose multiple possible solutions that researchers and practitioners could consider for optimizing their approach to talent identification and development. In summary, talent is a complex and largely misunderstood phenomenon lacking robust research evidence, and given concerns that it is potentially unhealthy, talent identification and selection at younger ages is not recommended.

Purpose The transformational changes to business environments brought about by the fourth industrial revolution create a perfect storm for strategic human resource management, prompting a need to explore the implications of this context for talent management theory and practice. The paper aims to discuss these issues. Design/methodology/approach In-depth interviews were conducted with HR directors and senior leaders within engineering-led organisations to explore current challenges experienced across each stage of the talent pipeline: attraction and recruitment, training and development, career development, talent mobility and succession planning. Findings The speed of technological change brought about by Industry 4.0 had created a significant gap between current capability of employees and the rapidly evolving requirements of their roles, prompting a need to consider new and more effective approaches to talent development. Middle managers are increasingly recognised as overlooked critical talent within this context of unprecedented change, given their essential role in change management. In addition, whilst lateral hiring remains a common talent management practice, in the case of Industry 4.0 this equates to fighting a war for talent that does not exist. Practical implications This study suggests that there is a need for evolution of talent management theory and practice towards a more dynamic, systems-thinking orientation, acknowledging the interrelated nature of different talent management activities. Originality/value This paper provides an in-depth insight into the impact of the unprecedented change brought about by Industry 4.0 on contemporary talent management practice, considering how theory and practice might need to evolve to enable individuals and organisations to keep up with the rate of technological change.

Since the expansion of the universe was first established by Edwin Hubble and Georges Lemaître about a century ago, the Hubble constant H0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_0$$\end{document} which measures its rate has been of great interest to astronomers. Besides being interesting in its own right, few properties of the universe can be deduced without it. In the last decade, a significant gap has emerged between different methods of measuring it, some anchored in the nearby universe, others at cosmological distances. The SH0ES team has found H0=73.2±1.3kms-1Mpc-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_0 = 73.2 \pm 1.3 \; \;\,\hbox {kms}^{-1} \,\hbox {Mpc}^{-1}$$\end{document} locally, whereas the value found for the early universe by the Planck Collaboration is H0=67.4±0.5kms-1Mpc-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_0 = 67.4 \pm 0.5 \; \;\,\hbox {kms}^{-1} \,\hbox {Mpc}^{-1}$$\end{document} from measurements of the cosmic microwave background. Is this gap a sign that the well-established ΛCDM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varLambda} {\text{CDM}}$$\end{document} cosmological model is somehow incomplete? Or are there unknown systematics? And more practically, how should humble astronomers pick between competing claims if they need to assume a value for a certain purpose? In this article, we review results and what changes to the cosmological model could be needed to accommodate them all. For astronomers in a hurry, we provide a buyer’s guide to the results, and make recommendations.

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Researchers showed how discourses of talent management (TM) perpetuate structural barriers of exclusion and discrimination. Traditional TM discourses have to be questioned if authentic talent development (ATD) practices are to flourish. This requires a change from the focus on talent identification to ATD’s focus on talent cultivation. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Abstract HR managers have different beliefs about the nature, value, and instrumentality of talent—referred to as ‘talent philosophies’. In line with cognitive psychology, we reason that talent philosophies are similar to mental models that influence how HR managers interpret and use talent management (TM) practices within their organizations. In this article, we explore the prevalence of four different talent philosophies (exclusive/stable; exclusive/developable; inclusive/stable; inclusive/developable) in a sample of 321 HR managers. We then explore how talent philosophies relate to organizational context (i.e. size, ownership form, multinational orientation) as well as to HR managers’ perceptions of their organization’s TM practices. Cluster analysis corroborated the presence of the four talent philosophies in our dataset. All four talent philosophies were represented almost equally often in the overall dataset. Organizational size was found to be related to talent philosophies, such that HR managers who worked in smaller organizations were more likely to hold an inclusive talent philosophy. We also found support for the relationship between talent philosophies and perceptions of the exclusiveness or inclusiveness of the organization’s definition of talent, and its degree of workforce differentiation. Contrary to expectations, results did not support a link between talent philosophies and perceived talent identification criteria.

Purpose This study aims to investigate the significance of an emerging concept – green talent management (TM) and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions. Design/methodology/approach Two hundred and thirty-five structured questionnaires were administered to the academic staff in five universities located in Northern Cyprus, and the data was analyzed using partial least square structural equation modeling with the aid of WarpPLS (7.0). Findings This study provides evidences that green hard and soft TM exerts significant influence on employees’ innovative work behavior. Similarly, transformational leadership and artificial intelligence were confirmed to have a significant impact on employees’ innovative work behavior. Moreover, the study found transformational leadership and artificial intelligence to significantly moderate the relationship between green hard TM and employees’ innovative work behavior. Research limitations/implications The study provides theoretical and managerial implications of findings that will assist the leaders in higher educational institutions in harnessing the potential of green TM in driving their employees’ innovative work behavior toward the achievement of sustainable competitive advantage in the market where they operate. Originality/value The attention of researchers in the recent time has been on the way to address the challenge facing organizational leaders on how to develop and retain employee that will contribute to the sustainability of their organization toward the achievement of sustainable competitive advantage in the market they operate. Meanwhile, the studies exploring these concerns are limited. In view of this, this study investigates the significance of an emerging concept – green talent management and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.

Abstract Having established their first overseas operation, the next big challenge for many emerging economy multinational enterprises (EMNEs) is to align their HRs with their strategic ambition. Their lack of internationally experienced talent has become a major obstacle to strategy implementation: they need to fill leadership roles with international responsibility based abroad and at home, and they need to develop talent for future international leadership roles. The key challenge for catch-up strategies thus is to attract, develop, and retain talents who can lead international operations. In this paper, we develop a research agenda on strategic management and HRM in EMNEs that aims to explain the obstacles EMNEs face and facilitates the development of better talent management practice. In particular, we propose to integrate strategic management and HRM perspectives not only to advance theories but to enhance the relevance of both lines of scholarship to practice.

The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used by organizations to manage their workforce. This paper discusses the benefits and costs associated with the use of talent analytics within an organization as well as to highlight the differences between talent analytics and other sub-fields of business analytics. It will discuss a number of case studies on how talent analytics can improve organizational decision-making. From the case studies, we will identify key channels through which the adoption of talent analytics can improve the performance of the HR function and eventually of the whole organization. While discussing the opportunities that talent analytics offer organizations, this paper highlights the costs (in terms of data governance and ethics) that the widespread use of talent analytics can generate. Finally, it highlights the importance of trust in supporting the successful implementation of talent analytics projects.

The differential value created by talented employees and their contribution to organizations in the hypercompetitive and complex global economy has made talent management a strategic priority for organizations. Talent management has been advocated as an important strategy to retain talented employees, but academic studies exploring their relationship are limited. Building on the Resource-Based View (RBV) theory and Social Exchange Theory (SET), the present article studies the relationship between talent management and employee retention. In addition, a conceptual model explaining the role of talent perception congruence and organizational justice in the relationship between talent management and employee retention is developed by incorporating the Perceived Organizational Justice Theory and Congruence Theory in talent management context. This article may assist in setting the direction for future research in the area of talent management and help managers understand the significant roles of talent perception congruence and organizational justice in determining the talent management outcomes.

The widespread use of online recruitment services has led to an information explosion in the job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is the bridge for adapting the right candidates to the right positions. Existing studies on Person-Job Fit usually focus on measuring the matching degree between talent qualification and job requirements mainly based on the manual inspection of human resource experts, which could be easily misguided by the subjective, incomplete, and inefficient nature of human judgment. To that end, in this article, we propose a novel end-to-end Topic-based Ability-aware Person-Job Fit Neural Network (TAPJFNN) framework, which has a goal of reducing the dependence on manual labor and can provide better interpretability about the fitting results. The key idea is to exploit the rich information available in abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, two hierarchical topic-based ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measure the different contribution of each job experience to a specific ability requirement. In addition, we design a refinement strategy for Person-Job Fit prediction based on historical recruitment records. Furthermore, we introduce how to exploit our TAPJFNN framework for enabling two specific applications in talent recruitment: talent sourcing and job recommendation. Particularly, in the application of job recommendation, a novel training mechanism is designed for addressing the challenge of biased negative labels. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness and interpretability of the TAPJFNN and its variants compared with several baselines.

Achievement in different domains, such as academics, music, or visual arts, plays a central role in all modern societies. Different psychological models aim to describe and explain achievement and its development in different domains. However, there remains a need for a framework that guides empirical research within and across different domains. With the talent-development-in-achievement-domains (TAD) framework, we provide a general talent-development framework applicable to a wide range of achievement domains. The overarching aim of this framework is to support empirical research by focusing on measurable psychological constructs and their meaning at different levels of talent development. Furthermore, the TAD framework can be used for constructing domain-specific talent-development models. With examples for the application of the TAD framework to the domains of mathematics, music, and visual arts, the review provided supports the suitability of the TAD framework for domain-specific model construction and indicates numerous research gaps and open questions that should be addressed in future research.

Purpose The purpose of this paper is to provide a definition of talent and talent management in the luxury hotel sector with a focus on talent retention strategies. Design/methodology/approach A qualitative approach was used and 27 face-to-face semi-structured interviews were conducted with managers in luxury hotels in four countries (the USA, the UK, Australia and Greece). Findings Talent refers to those who “go above and beyond”. Talent retention strategies in luxury hotels include a friendly, family-oriented and open access culture, teamwork, compensation, succession planning and training and development. A hybrid exclusive and inclusive approach to talent management (TM) is proposed with the implicit engagement of the individuals. Practical implications Luxury hotels should choose TM practices that fit the organizational culture with a focus on retention strategies that are tailor-made to the individual or groups of individuals. Opportunities to progress, succession planning and employee participation to TM are valued in the industry. Originality/value This study provides findings from empirical comparative research conducted in four different countries, whereas most published work on TM focuses on bibliographic reviews. It provides a conceptualization of talent and TM. This study frames the nature of TM in hotels and advances the knowledge of talent retention strategies found to be effective in hospitality.

Talent management is a major global challenge confronting most organizations in the world. Due to scarcity of talent, organizations around the world are competing for the same pool of talents to acquire and retain talents in order to maintain their operations and continue to grow in terms of service and profitability. Aim: to investigate the effect of talent management on organization performance at Shebin El-Kom hospitals. Subjects and methods: A descriptive correlational research design was conducted in two selected hospitals, Menoufia University and Shebin El-Kom Teaching hospitals. Two standardized questionnaires were used, which are, Talent Management and Organization Performance questionnaires. Random sample (273) of nurses composed the study sample. Results: The present study' findings revealed that nurses in Menoufia University hospital had a higher satisfaction level than nurses in Shebin El-Kom teaching hospital regarding total score of talent attraction, retention and organization performance with highly statistical significant difference. Conclusion: There was a highly significant positive correlation between organization performance and each of the three components of talent management. Recommendations: All health care organizations should introduce talent management strategy in their strategic planning to remain competitive in today’s healthcare market.

Abstract While research shows that talent management (TM) practices are linked to individual and organisational outcomes, little is known about the mechanisms through which these processes unfold, especially in the public sector of emerging market economies. This exploratory study investigates TM in the public sector context of Bahrain addressing an important research gap. Drawing on Organisational Support Theory (OST) and strategic human resource management (SHRM) literature, we test the role of line managerial support for development (LMSD) and organisational support for development (OSD) as mechanisms through which TM practices influence individual talent performance. Results from a survey based on a talent pool of a Bahraini governmental administrative institution show that LMSD fully mediates the relationship between TM practices and each of the individual talent performance indicators, apart from task performance. We also find that the indirect relationship between TM and individual talent performance was significant at low levels of OSD. The study contributes to unlocking the TM black box in the public sector of developing countries by highlighting the key role of line managers in the implementation of TM practices revealing a substitution effect between LMSD and OSD, as interchangeable sources of developmental support that can enhance talent performance in this context.

This brief article focuses on developing talents in young people and improving the culture of the entire school by democratizing more opportunities for creative productivity for all students. The economic, cultural, and social development of all nations depends on increasing the reservoir of highly talented people.

Purpose The purpose of this paper is to determine the causal relationship between talent management dimensions such as Human Capital Index and perceived organizational support (POS) and the impact thereof on the turnover intentions of Generation-Y employees. This study tries to find out how the intensions of employees to quit relates significantly to the talent management practices in Indian hospitality organizations. Design/methodology/approach This study proposed an integrated conceptual model based on previous literature where the relationship between the relevant constructs is verified. A cross-sectional survey design is used for data collection, which is ideally suited for the descriptive and predictive functions associated with correlation research and for assessing the interrelationship among various variables in the study. Findings Pearson’s correlations showed a practically significant positive relationship between the organization’s talent management practices and POS. It is also inferred that perceived supervisory support does not mediate the relationship between talent management practices and intention to quit. The findings of this study also confirm that employees’ perception of the organization’s actions has direct effects on their perception of support from their supervisors. The study also found a practically significant negative relationship between POS and the employee’s intention to quit, where high levels of POS are associated with a decreased intention to quit the organization. Originality/value From a theoretical aspect, this study provided a quantitative method for assessing the Generation-Y employees’ perceptions in relation to other relevant constructs. Practically, this study provided a framework for the management to understand that the perceptions regarding talent practices and support influence an employee’s intent to leave an organization.

Purpose This study aims to provide a systematic literature review (SLR) of extant research on talent management (TM) in hospitality and tourism (H&T). The objectives are first, to offer an empirical mapping analysis of TM research in H&T; second, to identify and discuss key research topics; and third, to recommend avenues for future research. Design/methodology/approach The authors conducted a SLR of TM literature in the context of H&T by analysing peer-reviewed papers published between January 2000 and October 2020. Following the preferred reporting items for systematic reviews and meta-analysis process, three independent coders undertook a content analysis of 74 papers for the SLR. Findings The authors found that TM in H&T offers contextualised evidence consistent on five key research topics: TM practices; conceptualisation of talent and TM; TM antecedents and outcomes; key TM stakeholders; and talent education and graduate careers. The analysis indicated that most studies are qualitative, focussed on Europe and North America and that primarily examine hotels. The small number of TM studies in H&T along with the lack of strong theoretical groundings show that the field is at the early stages of the growing stage, yet the sharp increase demonstrates an exponential growth in interest. Practical implications This study highlights the importance of TM and indicates that businesses in the sector need to reconsider the definition of talent, their approaches to TM and the bundling of TM practices to improve their effectiveness. It also calls for partnerships among the industry’s key stakeholders and a revisiting of agendas for educators. Originality/value To the best of the authors’ knowledge, this is the first study that offers a holistic examination of research on a wide range of TM topics within various H&T sectors over the past 20 years. It clarifies the conceptualisation of talent and TM in this field and provides insights, as well as context-specific evidence on the management of H&T talent. This study is the first to argue that the research stream is moving towards a growth stage and offers recommendations on advancing the field.

In business-to-business (B2B) markets, the success of key account management (KAM) teams depends on how they are structured and how they handle customer relationships. The authors conceptualize relationships among selling team members as a within-seller (intrafirm) network and the relationships between selling team members and buyer representatives as a buyer–seller (interfirm) network. Drawing on both structural (buyer–seller density, within-seller density, and within-seller centralization) and functional (buyer–seller similar function ties and within-seller cross-functional ties) composition attributes of these networks, the authors examine how the interplay between these networks drives seller account profitability. Using data from 207 key account managers across B2B industries, the authors uncover a nuanced pattern of interplay across the two networks. Densities in the two networks are mutually substitutive, but density in the buyer–seller networks and centralization in the within-seller networks serve complementary roles. Cross-function ties in the within-seller network serve a complementary role too, but only in relation to similar function ties in the buyer–seller network. In contrast, within-seller centralization supports both network density and similar function ties in the buyer–seller network and, thus, emerges as a valuable KAM team characteristic. These findings suggest multiple ways for firms to align interfirm and intrafirm KAM networks to improve account profitability.

Despite the significant contribution of buyer-driven knowledge transfer activities (BDKTAs) to innovation and operational performance, studies that analyze social sustainability in manufacturing (suppliers) firms are still scarce. This paper examines the mediation relationship of knowledge acquisition and investment in environmental management between BDKTAs and social performance improvements (SPIs). The paper contributes to the understanding of buyer knowledge transfer activities, with a focus on the knowledge acquisition capabilities and investments in environmental management, and the effect on SPIs. The hypotheses were examined with partial least squares structural equation modeling (PLS-SEM) with data collected from 239 firms. Buyer knowledge transfer activities are likely to increase the willingness of suppliers to make specific environmental investments into operations in waste reduction procedures, the recycling of materials, and pollution prevention training of employees. We proposed that buyer knowledge transfer activities are necessary to survive and grow and thus there is a need to acquire knowledge resources to achieve organizational sustainability. Buyer knowledge transfer activities are necessary to make investment decisions in environmental management programs. Firms that focus on buyer knowledge transfer activities and internal investments into environmental management can attain sustainability objectives.

Purpose This paper aims to examine the technology usage for talent management and its effect on organizational performance. Design/methodology/approach The grounded theory approach was used for this research. Semi-structured interviews with 122 senior HR officers of national and multinational companies in India were conducted after extensive literature review. NVivo 8.0 software was used for the analysis of the interview data. Findings Technology usage for talent management contributes to talent analytics and strategic HR management (SHRM). It was found that talent analytics and SHRM lead to developing a high-performing talent pool, which in turn contributes to organizational performance. Originality/value This study used the grounded theory approach to develop the proposed conceptual model for organizational performance using talent management technology. This study delivers important insights for talent managers, HR technology marketers and developers of technology.

Coaches are faced with the difficult task of identifying and selecting athletes to their team. Despite its widespread practice in sport, there is still much to learn about improving the identification and selection process. Evidence to date suggests selection decisions (at different competitive levels) can be inaccurate, bias driven, and sometimes even illogical. These mistakes are believed to contribute to “talent wastage,” the effect of a coach’s wrongful selection and/or deselection of an athlete to/from a team. Errors of this scale can lead to negative repercussions for all stakeholders involved and therefore deserve further exploration. It is the purpose of this paper to shed light on the potential factors influencing talent wastage and to illuminate possible psychological pitfalls when making decisions under uncertainty.

Autonomy via agents using large language models (LLMs) for personalized, standardized tasks boosts human efficiency. Automating web tasks (like booking hotels within a budget) is increasingly sought after. Fulfilling practical needs, the web agent also serves as an important proof-of-concept example for various agent grounding scenarios, with its success promising advancements in many future applications. Prior research often handcrafts web agent strategies (e.g., prompting templates, multi-agent systems, search methods, etc.) and the corresponding in-context examples, which may not generalize well across all real-world scenarios. On the other hand, there has been limited study on the misalignment between a web agent's observation/action representation and the pre-training data of the LLM it's based on. This discrepancy is especially notable when LLMs are primarily trained for language completion rather than tasks involving embodied navigation actions and symbolic web elements. Our study enhances an LLM-based web agent by simply refining its observation and action space to better align with the LLM's capabilities. This approach enables our base agent to significantly outperform previous methods on a wide variety of web tasks. Specifically, on WebArena, a benchmark featuring general-purpose web interaction tasks, our agent AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively, and boosts the success rate by 26.6 points (+161%) over similar plain web agents with its observation and action space alignment. We achieve this without using in-context examples, new agent roles, online feedback or search strategies. AgentOccam's simple design highlights LLMs' impressive zero-shot performance on web tasks, and underlines the critical role of carefully tuning observation and action spaces for LLM-based agents.

Organizations thrive on attaining and sustaining competitiveness because of the turbulent, rapidly changing business environment. The research shows that it is possible to improve organizations’ competitiveness by considering the multidimensional model viewpoints, which include the strategic talent management ecosystem viewpoints and the strategic agile management ecosystem viewpoints. The multidimensional model includes the essential constructs for developing strategic talent management, i.e., acquiring talented employees, target development of talented employees, designing talent teams, and talent leadership, as well as the following essential constructs for developing strategic agile management, i.e., designing agile teams, agile leadership, and support agile management culture. The research was implemented on 532 owners/managers in organizations. Structural equation modeling was used to test the hypotheses. Results show that developing a strategic talent management ecosystem positively affects developing a strategic agile management ecosystem and leads to higher organization competitiveness. A developed multidimensional model will help owners and managers comprehensively embrace and implement agility in their organization to increase competitiveness.

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The aim of this paper is to present a systematic literature revision (SLR) that shows the relationship between the concept of employer branding (EB) and talent management (TM). Based on the EB model proposed by Backhaus and Tikoo in 2004, and the macro-contingent model for talent management introduced in 2019 by King and Vaiman, we intend both to analyse the theory correlating the EB concept and TM, and to identify TM dimensions that are reflected on the EB concept. A systematic literature revision was carried out using the Preferred Reporting Items for Systematic Reviews (PRISMA) protocol in order to identify and sum up the most relevant studies of the last 10 years concerning these topics. Findings show that scientific literature on the subject grew considerably in the last four years, reflecting the rising concern over the creation of an employer brand at the organisational level. EB explores talent attraction and retention particularly, though, unexpectedly, is also becoming a concept explored by nations to attract a qualified workforce. Analysing selected articles, we may conclude that EB is clearly considered as a tool within the largest process of talent management.

As corporate globalization is restricted through changes in macro‐environments, firms traditionally reliant on global talent pools are suffering. This is particularly true for high‐tech firms' seeking to source high‐skilled STEM (science, technology, engineering, and mathematics) talent. The aim of this study, in line with the special issue, is to explore the impact of macro‐level trends in national government policy and political climates on high‐tech firms seeking to source high‐skilled STEM talent. By applying coevolutionary reasoning, we develop propositions that link the macro environment with corporate strategizing. A multi‐respondent, qualitative research design is adopted, involving interviews and focus groups with 40 practitioners and industry experts. Through qualitative data analysis, we identify emergent adaptations to corporate talent sourcing activities for dealing with increasing STEM shortages linked to deglobalization. As the data highlight, the adaptations are a consequence of both internal strategic factors as well as external institutional forces and the interplay between the two. Implications for future research and practice are discussed.

This study aims to critically examine talent management practices and strategies from ethical and responsible management perspectives.,It achieves its aim through conceptual analysis by theorising through the lenses of talent philosophies, the organisational justice theory, the stakeholder theory and extant literature.,A responsible talent management construct and mode to guide the practice of talent management in a socially responsible way is developed. It argues that inclusivity; corporate responsibility; and equity and equal employment opportunity are the key underlying principles of a responsible talent management system. This study further argues that responsible talent management practices promote achievement of multilevel sustainable outcomes such as decent work, employee well-being and organisational well-being.,Emphasising responsible management and ethical concerns in organisational talent strategies and practices is non-negotiable, given the current level of interest in sustainable work and employment and in the quest to achieve sustainable human and organisational outcomes through management and organisational practices.,The development of a responsible talent management construct and model is original and novel and is expected to shape thinking and drive new research directions in the field of talent management. It further contributes directly to knowledge and practice by demonstrating how organisations can manage their talents in a responsible way.

PurposeThe present study aims to examine the impact of relational communication, operationalized in terms of information sharing, quality and frequency of information, on buyer's trust and relationship satisfaction in a buyer–vendor relationship. The study also tests the mediating role of trust and the moderating role of relationship commitment in relational communication and satisfaction relationship.Design/methodology/approachThe data for the study were collected through a questionnaire survey from 321 managers of the different firms who were directly or indirectly involved in making procurement or purchasing decision in the firm and were familiar with the firm's supplier relationships.FindingsRelational communication was found to be positively related to relational satisfaction and trust partially mediated this relationship. Further, relationship commitment moderated relational communication-satisfaction relationship, such that the positive affect of relational communication on relational satisfaction was accentuated when buyer experienced higher relationship commitment towards the supplier.Research limitations/implicationsUsing single source, self-reported questionnaire data and cross-sectional research design are the limitations of this study. Studies in future should consider a dyadic perspective. The study outlines the need to explore investments and strategies in enhancing relational communication in buyer–vendor relationships.Originality/valueAnchored in theoretical foundations of social exchange theory, the study integrates and tests behavioral aspects of buyer–vendor relationship. Testing an integrated model with direct and indirect effects of relational communication on relationship satisfaction in buyer–vendor is a significant contribution of the research. The study also contributes by examining relational exchanges in buyer-vendor relationships in India, an underrepresented context in buyer–supplier relationship (BSR) literature.

Abstract Our study examines the role digitalisation plays in how various stakeholder groups (HR and line managers) identify talent and whether digitalisation transforms the role of HR professionals in identifying talent. By employing a qualitative case study with multiple embedded units of analysis, we show how the same digital talent management technology produced different ways of identifying talent even within the one organisation. In one social context, the material properties of the technology dominated, while in another the existing social context and relationships prevailed over the material properties of the technology. Our findings have implications for understanding digital transformations by acknowledging what factors influence the role that digitalisation and automation have on the perceived legitimacy of HR professionals. We discuss these implications for talent management scholarship and the HR profession.

Talent Management (T.M.) constitutes a modern and emerging research area in Human Resources Management (HRM). Using a systematic literature approach, we searched in Talent Management literature in the healthcare sector context. We conclude that the number of related studies is minimal. The benefits of implementing Talent Management strategies in healthcare organizations are essential for the organization’s sustainable development and the talented staff and healthcare services patients. Our goal is to undertake a systematic literature review to identify these factors related to talent management practices suitable for healthcare organizations and professionals. We have conducted, according to PRISMA guidelines, a systematic literature review (2010–2020) in the electronic databases PubMed, CINAHL, Cochrane Database of Systematic Reviews, Health Source/Nursing Academic Edition. Search terms related to T.M. were (“Talent Management” AND “Talent Healthcare”). Strict inclusion and exclusion criteria were set for observational studies, while grey and unpublished literature, uncontrolled studies, protocols, commentaries, and conference proceedings were excluded. All included items were assessed for their quality according to set criteria. Six hundred and eighty-four studies were identified, of which 24 met the requirements. The resulting Talent Management Factors were grouped into nine categories: Programming, Attraction, Development, Preservation, Performance Assessment, Work Climate, Culture, Succession Planning, and Leadership. Based on these factors, we provide a holistic picture of the referred domain’s leading developments. The paper determines the Talent Management factors and explains what happens in practice. In this way, we contribute to building a theoretical framework for T.M. in terms of the organizational context.

Talent flow analysis is a process for analyzing and modeling the flows of employees into and out of targeted organizations, regions, or industries. A clear understanding of talent flows is critical for many applications, such as human resource planning, brain drain monitoring, and future workforce forecasting. However, existing studies on talent flow analysis are either qualitative or limited by coarse level quantitative modeling. To this end, in this paper, we provide a fine-grained data-driven approach to model the dynamics and evolving nature of talent flows by leveraging the rich information available in job transition networks. Specifically, we first investigate how to enrich the sparse talent flow data by exploiting the correlations between the stock price movement and the talent flows of public companies. Then, we formalize the talent flow modeling problem as to predict the increments of the edge weights in the dynamic job transition network. In this way, the problem is transformed into a multi-step time series forecasting problem. A deep sequence prediction model is developed based on the recurrent neural network model, which consumes multiple input sources derived from dynamic job transition networks. Finally, experimental results on real-world data show that the proposed model outperforms other benchmark models in terms of prediction accuracy. The results also indicate that the proposed model can provide reasonable performance even if the historical talent flow data are not completely available.

Abstract This paper aims to provide insight into talent management and employee recognition variables, in an effort to understand if these management practices are, in a way, implemented in the Research Institutions in Qatar. It also aims to examine the link of these two constructs to employee performance and to suggest a pathway to improve the current practices, if any, in order to enhance the work performance. The sample of the study consisted of 180 full time employees who were randomly selected and surveyed using an online personalized questionnaire. The results of the empirical research uncover that talent management and employee recognition can significantly affect the level of employee performance, as well contributing to the organizational success and positioning. This study also found that talent management and employee recognition are interrelated variables that affect employee performance. Talent Management and employee performance are seen as strategic tools to implement strategic objectives and to enhance employees and organization’s performance. The study concludes with a set of recommendations for both practitioners and researchers working in Qatar and beyond. The results of the study emphasize the importance of managing employee recognition in shaping work performance. It seems to be individuals’ need for recognition, esteem and social status continues to be a higher order need irrespective of time and context. This paper is the first paper in Qatar and the Middle East that examines the role of talent management and employee recognition in shaping employee performance in the context of research institutions.

drishtiias.com/printpdf/global-talent-competitiveness-index Why in News India has moved up eight places to the 72 position in the Global Talent Competitiveness Index (GTCI) 2020. Switzerland topped the list of 132 nations, followed by the US and Singapore . Rank Country Score 1 Switzerland 81.23 2 US 79.09 3 Singapore 78.48 4 Sweden 75.82 5 Denmark 75.18 6 Netherlands 74.99 7 Finland 74.47 8 Luxembourg 73.94 nd

We analyze the competitive effects of mergers in markets with buyer power. Using mechanism design arguments, we show that without cost synergies, mergers harm buyers, regardless of buyer power. However, buyer power mitigates the harm to a buyer from a merger of symmetric suppliers. With buyer power, a merger increases incentives for entry, increases investment incentives for rivals, and can increase investment incentives for merging parties. Because buyer power reduces the profitability of a merger, it increases the profitability of perfect collusion relative to a merger. Cost synergies can eliminate merger harm but also render otherwise profitable mergers unprofitable.

A Large Language Model (LLM) powered GUI agent is a specialized autonomous system that performs tasks on the user's behalf according to high-level instructions. It does so by perceiving and interpreting the graphical user interfaces (GUIs) of relevant apps, often visually, inferring necessary sequences of actions, and then interacting with GUIs by executing the actions such as clicking, typing, and tapping. To complete real-world tasks, such as filling forms or booking services, GUI agents often need to process and act on sensitive user data. However, this autonomy introduces new privacy and security risks. Adversaries can inject malicious content into the GUIs that alters agent behaviors or induces unintended disclosures of private information. These attacks often exploit the discrepancy between visual saliency for agents and human users, or the agent's limited ability to detect violations of contextual integrity in task automation. In this paper, we characterized six types of such attacks, and conducted an experimental study to test these attacks with six state-of-the-art GUI agents, 234 adversarial webpages, and 39 human participants. Our findings suggest that GUI agents are highly vulnerable, particularly to contextually embedded threats. Moreover, human users are also susceptible to many of these attacks, indicating that simple human oversight may not reliably prevent failures. This misalignment highlights the need for privacy-aware agent design. We propose practical defense strategies to inform the development of safer and more reliable GUI agents.

Since the explosion in popularity of ChatGPT, large language models (LLMs) have continued to impact our everyday lives. Equipped with external tools that are designed for a specific purpose (e.g., for flight booking or an alarm clock), LLM agents exercise an increasing capability to assist humans in their daily work. Although LLM agents have shown a promising blueprint as daily assistants, there is a limited understanding of how they can provide daily assistance based on planning and sequential decision making capabilities. We draw inspiration from recent work that has highlighted the value of ‘LLM-modulo’ setups in conjunction with humans-in-the-loop for planning tasks. We conducted an empirical study (N = 248) of LLM agents as daily assistants in six commonly occurring tasks with different levels of risk typically associated with them (e.g., flight ticket booking and credit card payments). To ensure user agency and control over the LLM agent, we adopted LLM agents in a plan-then-execute manner, wherein the agents conducted step-wise planning and step-by-step execution in a simulation environment. We analyzed how user involvement at each stage affects their trust and collaborative team performance. Our findings demonstrate that LLM agents can be a double-edged sword — (1) they can work well when a high-quality plan and necessary user involvement in execution are available, and (2) users can easily mistrust the LLM agents with plans that seem plausible. We synthesized key insights for using LLM agents as daily assistants to calibrate user trust and achieve better overall task outcomes. Our work has important implications for the future design of daily assistants and human-AI collaboration with LLM agents.

The concept of talent management has attracted most of the researchers’ attention in the recent years. In today’s business environment, talent management becomes one important source of competitive advantage that creates value for all companies. In the current economic downturn, there is a high demand for talent in the organizations. The aim of this study is to examine talent management process in organizations. This article is based on a review of the academic and popular talent management literatures. The paper focused by clarifying the definitions and the importance of talent management and what factors influencing the growth of talent management.

Purpose In the tourism industry, immersive technologies become increasingly vital, amplifying traveler experiences and industry growth. By studying “e-booking” applications prevalent in hotels, this study aims to analyze the impact of integrating an anthropomorphic virtual agent (AVA) on user perceptions of humanness and service usage intent. Design/methodology/approach Two experiments were conducted to examine the effects of using an AVA and explain the psychological mechanism of how AVA’s attributes increase intention to use “e-booking” application. Findings The results highlight the positive influence of AVA on the intention to use. They illustrate the psychological mechanism of how AVA’s attributes (agency and emotionality) influence perceived humanness and intention to use. More specifically, the results indicate that perceived humanness mediated the effect of an AVA on intention to use. Research limitations/implications Further research should delve into additional capabilities related to humanness. Practical implications This study provides useful insights for hotels’ managers about incorporating AVAs in digital services to enhance the perceived humanness of AVAs. The findings suggest that such efforts could yield benefits, especially when they involve conveying that AVAs possess agency and emotionality. Originality/value To the best of the author’s knowledge, this study is the first to investigate how AVA impacts hotel human–computer interaction. It examines agency and emotionality features on humanness perception and behavioral intent. It also guides successful digitalized hotel service development and design, expanding existing research on human–virtual agent digital services, which mainly focuses on superficial traits like face and gender.

Decision-making is the process of selecting alternative actions to achieve a particular goal. Increased movement of the number of passengers using air transportation mode, making the growth of ticket booking application also increase. A system judgment is required to determine which airplane ticket booking application to use. This study discusses the process of choosing an airplane ticket booking application using Analytical Hierarchy Process (AHP) method by using Systems and Software Quality Requirements and Evaluation ISO / IEC 25010:2011 criteria measurement of quality in use. The processed data is obtained from the geometric average of three respondents ie representatives from information technology experts, the public and travel agent. Analysis and data processing using expert choice tools. There are 3 applications airplane ticket booking online in this research deliberately disguised his name. This is because it is feared to have a negative effect on the existing business competition. The results obtained from this study that the application of ticket booking that have the highest value recommended for use is Application A, followed by Application B and Application C

Tool-Based Agent Systems (TBAS) allow Language Models (LMs) to use external tools for tasks beyond their standalone capabilities, such as searching websites, booking flights, or making financial transactions. However, these tools greatly increase the risks of prompt injection attacks, where malicious content hijacks the LM agent to leak confidential data or trigger harmful actions. Existing defenses (OpenAI GPTs) require user confirmation before every tool call, placing onerous burdens on users. We introduce Robust TBAS (RTBAS), which automatically detects and executes tool calls that preserve integrity and confidentiality, requiring user confirmation only when these safeguards cannot be ensured. RTBAS adapts Information Flow Control to the unique challenges presented by TBAS. We present two novel dependency screeners, using LM-as-a-judge and attention-based saliency, to overcome these challenges. Experimental results on the AgentDojo Prompt Injection benchmark show RTBAS prevents all targeted attacks with only a 2% loss of task utility when under attack, and further tests confirm its ability to obtain near-oracle performance on detecting both subtle and direct privacy leaks.

Technological developments and can changes in the mind and desires of guests in booking hotel rooms. Online travel agents are one of the most advanced products that are currently booming, and also for changes room reservation to be direct or go shows switching to using online travel agents. The purpose of this study was to know information why the guest booking room directly going to online travel agents, in order to improve e-commerce effort of services to maintain guest while still choosing Mutiara Malioboro Hotel Yogyakarta as a place to stay. Research is an online travel agent that can increase hotel occupancy rates because they are accessible, efficient and effective at competitive prices, as well as hotel costs with sales costs, fees and employee usage fees. Management support in OTA is also needed in an effort to increase the number of orders through OTA.

The flow of human capital into and out of organizations is a crucial aspect of organizational functioning, yet the bulk of the theory and research adopts a US-centric perspective. The purpose of this edited volume is for scholars embedded in contexts around the world to describe the relevance and implications (or lack thereof) of turnover theories in their particular context. We take a broad view of talent, focusing on the departure of human capital in general without necessarily restricting the analysis to those who disproportionately contribute to organizational success, and the authors focus on institutional contexts and culture because of their role in shaping employee norms and behaviors. We partnered with author teams embedded in countries and regions with a focus on capturing variance in contexts across the GLOBE clusters: Anglo (England), Confucian Asian (China; South Korea), Eastern European (Bulgaria), Germanic European (Germany), Latin American (Mexico), Latin European (Spain), Middle Eastern (Turkey), Nordic European (Denmark), Southern Asian (India), and Sub-Saharan African (South Africa). We provided each author team discretion to express their own voice, while also providing a common set of goals across chapters for consistency of contribution: a description of the institutional, legal, and cultural context as it relates to employee mobility, a review of context-specific research literature leading to a description of how the mechanisms and processes in prominent turnover theories may operate differently in a particular context, and implications for research and practice related to talent turnover and retention. Considering the contributions as a set, we identify important themes and overarching recommendations for scholars interested in studying employee retention and turnover around the globe.

The development of the internet has influenced the development of the world economy. Various buying and selling transactions that previously could only be done face-to-face, have now developed into transactions via the internet known as e-business or e-commerce. The hotel room online booking system was created to make it easier for consumers to book rooms 24 hours a day. With the availability of the online booking feature, consumers can access hotel information in detail and more transparently, besides that, consumers can also see reviews which can be used as their consideration in choosing hotels and planning holidays. Traveloka's significant development as an Indonesian online travel agent unicorn plays an important role in accelerating the growth rate of the online travel ecosystem, especially for the domestic market. There are many factors that must be examined in finding information, placing orders, and purchasing online. Therefore, this research is focused on online booking behavior. This study aims to determine the influence between variables based on Theory of Planned Behavior, which consists of attitude toward the online booking behavior, subjective norm, perceived behavioral control, online booking intention and online booking behavior at Traveloka. Data was collected from 133 respondents of domestic tourists who have made online bookings at Traveloka. Data were analyzed using Partial Least Square (PLS) statistics with the Smart PLS 3.0 M3 program to determine the complexity of the relationship between latent variables and their indicators. The results of this study indicate that attitude toward the behavior and subjective norms have a positive and significant effect on online booking intention. Meanwhile, perceived behavioral control has no significant effect on online booking intention. Another finding is that online booking intention and perceived behavioral control are known to have a positive and significant effect on online booking behavior. Traveloka management and marketers are also expected to be able to use the results of this research to evaluate and take corrective action on aspects that are deemed inadequate and manage the ease of use of the application to increase online booking intentions through the Traveloka application.

AI-controlled chatbots have been used in travel services for some time and range from simple hotel reservations to personalized travel recommendations. However, the acceptance of chatbots compared to human interlocutors has not yet been extensively studied experimentally in the tourism context. In this experimental, randomized, vignette-based, preregistered 2 (agent: AI chatbot/human counterpart) × 3 (situation: positive/neutral/negative) between-subjects design, we hypothesized that booking intention is reduced in chatbots compared to human agents and in situations where the booking can only be made under more negative than the original conditions. Additionally, we hypothesized an interaction effect between agent and situation, presuming that the decrease in booking intention in negative situations would be less strong for human agents than for chatbots. Structural equation modelling of the data indicates strong support for the Technology Acceptance Model in the booking context. As presumed, the booking intention was lower in the negative situation and borderline lower for the chatbot. The interaction effect was shown descriptively in the data. Chatbots are recognized during the booking process and less accepted to support bookings than their human counterparts. Therefore, managers should design chatbots as human-like as possible to avoid losing sales when outsourcing customer contact activities to AI technologies.

To achieve both a large number of platform users and the sustainability of service intermediation platform ecosystems, this study attempts to clarify appropriate platform fee settings and promising market types. It focuses on hotel-booking platforms and platform-based markets. We conduct agent-based simulation experiments with the conditions of (a) platform fee setting, (b) supply and demand balance, and (c) the consumer type categorized as “mandatorily” purchasing consumers (e.g., business travelers whose purchase of a hotel service is mandatory) and “optionally” purchasing consumers (who can refuse services if their requirements are not satisfied). Our simulation results reveal that when the platforms focus on mandatorily purchasing consumers, they could acquire larger platform users through fee settings, depending on the balance of supply and demand; however, they could not maintain ecosystem sustainability in any of the cases. When the platform focuses on optionally purchasing consumers, it can achieve both a large number of platform users and ecosystem sustainability with platform fee settings of consumer incentives, especially in excessive supply markets.

The world globalization is widely used, and there are several definitions that may fit this one word. However the reality remains that globalization has impacted and is impacting each individual on this planet. It is defined to be greater movement of people, goods, capital and ideas due to increased economic integration, which in turn is propelled, by increased trade and investment. It is like moving towards living in a borderless world. With the reality of globalization, the travel industry has benefited significantly. It could be said that globalization is benefiting from the flight industry. Regardless of the way one looks at it, more persons are traveling each day and are exploring several places that were distant places on a map. Equally, technology has been growing at an increasingly rapid pace and is being utilized by several persons all over the world. With the combination of globalization and the increase in technology and the frequency in travel there is a need to provide an intelligent application that is capable to meeting the needs of travelers that utilize mobile phones all over. It is a solution that fits in perfectly to a user’s busy lifestyle, offers ease of use and enough intelligence that makes a user’s experience worthwhile. Having recognized this need, the Agent based Mobile Airline Search and Booking System is been developed that is built to work on the Android to perform Airline Search and booking using Biometric. The system also possess agent learning capability to perform the search of Airlines based on some previous search pattern .The development been carried out using JADE-LEAP Agent development kit on Android.

In this paper, an agent-based model is presented to test the feasibility of different configurations of Demand Responsive Shared Transport (DRST) services in a real context. DRST services provide “just-in-time” mobility solutions by dynamically assigning a fleet of vehicles to passenger booking requests taking advantages of Information and Communication Technologies. First results show the impact of different route choice strategies on the system performance and can be useful to help the planning and designing of such services.

Purpose – The aim of this research is to analyze the relationship between satisfaction, trust, repurchase intention, and switching intention in online-booking hotel platforms. Design/methodology/approach – The research involved 600 respondents who have ever used an online-booking hotel platform in all stages of the transaction process (from the start until the end of service). DETERMINANTS OF REPURCHASE INTENTION AND SWITCHING INTENTION: ANALYSIS OF ONLINE TRAVEL AGENT, PEER-TO-PEER ACCOMMODATION, AND VIRTUAL HOTEL OPERATOR PLATFORMS ODREDNICE NAMJERE PONOVNE KUPOVINE I NAMJERE PREBACIVANJA: ANALIZA PLATFORMI ONLINE PUTNIČKIH AGENATA, SMJEŠTAJA (U VLASNIŠTVU) RAVNOPRAVNIH KORISNIKA (ENG. PEER-TO-PEER ACCOMMODATION) TE VIRTUALNIH HOTELSKIH OPERATORA

The Agent-Based Ticket Booking System was created in response to the inherent challenges and frustrations of utilizing standard ticket booking methods. Whether it was buying tickets for flights, events, or any other purpose it usually involves filling out lengthy forms, visiting real counters, or navigating challenging websites. These processes can annoy customers in addition to being time-consuming. Furthermore, the involvement of intermediary organizations may result in an increase in the cost of the tickets. Through the elimination of intermediaries and the use of AI-driven conversational interfaces, this initiative seeks to simplify, lower costs, and enhance the ticket purchase experience. The Agent-Based Ticket Booking System redefines the whole ticketing process with its user-friendly interface, offering a seamless and easy booking experience.

Automating web navigation which aims to build a web agent that follows user instructions to complete tasks like booking flights by inter-acting with websites, has received increasing attention due to its practical value. Although existing web agents are mostly equipped with visual perception, planning, and memory abilities, their reasoning process are still deviate from human cognition. In this work, we study the human thought pattern to empower agent with more human-like abilities in web navigation. To tackle this problem, we propose a novel multimodal web agent framework called WebExperT, which is designed to emulate the human planning process of "thinking fast and slow" to effectively decompose complex user instructions. Furthermore, WebExperT leverages experiential learning by reflecting from failure for continuously refining planning and decision-making outcomes. Experimental re-sults on the M IND 2W EB benchmark demonstrate the superiority of WebExperT in both supervised and unsupervised settings.

This study responds to the Technological Acceptance Model (TAM) critics regarding the model's missing self-regulatory and motivational variables. The research integrates personal innovativeness and perceived risk in the TAM model and explores the interrelationships among those constructs. A self-administered questionnaire was used to collect the data for this study, and 293 consumers of the Indonesia Online Travel Agent (OTA) industry participated. The data were analysed using Partial Least Square, which employed the inner and outer model evaluations to analyse the data. The results demonstrated that Generation Z's attitude toward using the OTA application is positively affected by its perceived usefulness and ease of use. Moreover, three variables significantly affect Generation Z's behavioural intention to use e-commerce applications. Personal innovativeness significantly affects perceived transaction risks and attitudes toward using the OTA application. These results imply that in order to enhance customers’ willingness to keep using the application, the OTA practitioners should ensure that their customers experiencing the ease-of-use dan the usefulness of the application, so that the customers will have a greater willingness to use the same application in the future.

With more accessibility to the Internet and modernization of e-payment systems, the approach to address the travel requirements has dramatically changed over the years. The service offered by the Online Travel Agents (OTAs) has a huge impact on a very competitive online marketplace. The purpose of this research is to observe, examine, and analyze key factors which a consumers’ decision to use a particular travel agent website for e-hotel booking can be predicted. The data are compiled using IBM SPSS. The key methods are applied for data analysis in addition to other statistical methods including factor analysis and multinomial logistic regression analysis. The analysis of data reveals a positive association of website quality factors, product related factors, and consumer relationship factors with consumers’ decision to book from a certain travel agent website. Viewing the big picture, consumer relationship factors are found to be more influential as compared to website quality and product related factors. Moreover, the researchers reveal product price as the most influential variable, but it indicates no statistical significance with consumers’ decision. There are many different price products which are available across different travel agent websites. The convenience of payment method is found to be a significant attribute associated with the consumers’ decision to book from a travel website. In addition, attributes of travel products variety and online reviews provided by the travel websites are observed to be statistically significant. This research indicates the trends of consumer decision-making for e-hotel booking in Indonesia.

Website quality is one of the features that organisations increasingly use to maintain a competitive advantage. In the hospitality sector in particular, the growing use of a website as a sales channel has necessitated that hotel distribution companies understand the influence of website quality on their customers’ purchase behaviour. The purpose of this study was to assess the impact of Business-to-Business (B2B) third-party hotel booking website quality features on travel agents’ satisfaction, and their subsequent purchase intention. Quantitative data was collected from South African travel agents using an online survey. Three website quality dimensions namely ease of use, information quality and visual appearance, as well as customer satisfaction and purchase intention were measured. Results indicate that there is a positive relationship between website quality and customer satisfaction, and between customer satisfaction and purchase intention. Information quality appears to have the strongest relationship with customer satisfaction. Regression analyses revealed that the relationship between website quality and purchase intention is mediated by customer satisfaction. The study makes a predominantly empirical contribution as there does not appear to be a similar study conducted within an African context. From a practical contribution perspective, the findings may assist third-party hotel distribution companies with meeting the demands of travel agents, and thereby improve their overall business performance.

The advancement of technology makes it possible for ordering flight tickets online instead of conventionally using travel agent service. Also, the increasing number of smartphone users makes the mobile application as the most often utilized medium for tickets booking. However, user loyalty is still an issue for flight booking service providers because there are so many options and users has flexibility in choosing the service provider anytime. This study aims to determine the factors that influence perceived value and satisfaction that increase user loyalty of flight ticket booking application in Indonesia based on user’s reason to make a transaction which are utilitarian or hedonic. This study used a quantitative approach by surveying 1.160 respondents who had purchased flight tickets through applications. Data were processed by Covariance Based Structural Equation Modeling (CB-SEM) using AMOS 24.0. The results show that there is a relation between perceived value, user satisfaction, and user loyalty. In addition, this study is also found that hedonic users are more prioritize experiences before, during, and after making a transaction, and utilitarian users are more prefer transaction that fast, safe, and easy.

Phone automation agents aim to autonomously perform a given natural-language user request, such as scheduling appointments or booking a hotel. While much research effort has been devoted to screen understanding and action planning, complex tasks often necessitate user interaction for successful completion. Aligning the agent with the user's expectations is crucial for building trust and enabling personalized experiences. This requires the agent to proactively engage the user when necessary, avoiding actions that violate their preferences while refraining from unnecessary questions where a default action is expected. We argue that such subtle agent-initiated interaction with the user deserves focused research attention. To promote such research, this paper introduces a task formulation for detecting the need for user interaction and generating appropriate messages. We thoroughly define the task, including aspects like interaction timing and the scope of the agent's autonomy. Using this definition, we derived annotation guidelines and created a diverse dataset for the task, leveraging an existing UI automation dataset. We tested several text-based and multimodal baseline models for the task, finding that it is very challenging for current LLMs. We suggest that our task formulation, dataset, baseline models and analysis will be valuable for future UI automation research, specifically in addressing this crucial yet often overlooked aspect of agent-initiated interaction. This work provides a needed foundation to allow personalized agents to properly engage the user when needed, within the context of phone UI automation.

In peer-to-peer (P2P) accommodation, consumers face high levels of consumption uncertainty from both P2P sites and hosts. Our study applies principal–agent theory to develop a dual processing model in examining how to support consumers’ repurchase intention by reducing consumers’ uncertainty perceptions associated with booking and staying. We selected Airbnb as our context and collected survey data from American consumers. The results show that perceived uncertainty (hosts) has a direct effect on repurchase intention, whereas perceived uncertainty (site) has an indirect effect. Further, perceived utility and trust in the last host can help reduce fears of opportunism (hosts), whereas site quality can alleviate fears of opportunism (site). Our study contributes to the literature by developing a dual-processing model of repurchase intention to clarify how consumers’ repurchase intention can be enhanced. The results can provide important guidelines regarding how to support consumers’ repurchase intention. We also provide suggestions regarding how to deal with additional uncertainties post COVID-19.

Technology information development changed the customer behaviour in desire in booking hotel room. Online Travel Agent is one of the most advanced products that is currently booming and change the room reservation pattern from direct reservation or go show to room reservation by Online Travel Agent. The purpose of this study was to find the information why the booking pattern changed from direct reservation to online travel agent in order to improve the e-commerce in maintain the guest to still choosing Mercure Nexa Bandung as their preferred hotel to stay in Bandung. The result of the study shows Online Travel Agent can increase hotel occupancy rate because they are accessible, efficient and effective at competitive prices as well the hotel costs with sales cost, fees and employee payroll expenses. Hotel’s management support in developing OTA is needed as an effort to increase number of room reservation from OTA.

We envision a multi-agent federation layer to coordinate systems composed of independent Earth observation missions. The goal of this federation is to allow clients requesting acquisitions of large areas to easily access several constellations of satellites and communication sites to compose and download their acquisitions, in a reduced time compared to conventional uncoordinated requests. We identify several scientific tracks and challenges related to agent-based approaches such as coordination, planning and learning, to implement to two key federation functions: (i) multi-mission coverage feasibility and dispatching and (ii) communication site booking.

With the rapid advancement of artificial intelligence, the proliferation of autonomous agents has introduced new challenges in interoperability, scalability, and coordination. The Internet of Agents (IoA) aims to interconnect heterogeneous agents through standardized communication protocols, enabling seamless collaboration and intelligent task execution. However, existing agent communication protocols such as MCP, A2A, and ANP remain fragmented and scenario-specific. To address this gap, we propose Agent Collaboration Protocols (ACPs), a comprehensive protocol suite for the IoA. ACPs include registration, discovery, interaction, and tooling protocols to support trustable access, capability orchestration, and workflow construction. We present the architecture, key technologies, and application workflows of ACPs, and demonstrate its effectiveness in a collaborative restaurant booking scenario. ACPs lay the foundation for building a secure, open, and scalable agent internet infrastructure.

Large Action Models (LAMs) have revolutionized intelligent automation, but their application in healthcare faces challenges due to privacy concerns, latency, and dependency on internet access. This report introduces an ondevice, multi-agent healthcare assistant that overcomes these limitations. The system utilizes smaller, task-specific agents to optimize resources, ensure scalability and high performance. Our proposed system acts as a one-stop solution for health care needs with features like appointment booking, health monitoring, medication reminders, and daily health reporting. Powered by the Qwen Code Instruct 2.5 7B model, the Planner and Caller Agents achieve an average RougeL score of 85.5 for planning and 96.5 for calling for our tasks while being lightweight for on-device deployment. This innovative approach combines the benefits of ondevice systems with multi-agent architectures, paving the way for user-centric healthcare solutions.

E-ticketing and online booking have transformed the aviation industry, hospitality, travel agencies and consumer behavior by providing new ways of communication and service support. Hence, the quality of e-ticketing and online booking websites is an important factor to drive the business. This study aims to analyze the effect of web information design, web visual design, and web brand image on purchase intentions mediated by trust. The data were obtained by conducting a survey to the community using the Online Travel Agent (OTA) website in the DKI Jakarta area. Hypothesis testing uses Structural Equation Model (SEM) analysis with SmartPLS. The results show that web information design has a direct effect on trust but has no significant effect on purchase intention either directly or mediated by the trust variable, web brand image has an effect on purchase intention mediated by trust, while web visual design has no effect on trust or purchase intention.

Aim/Purpose: This study aims to create customer segmentation who use Online Travel Agent (OTA) mobile applications in Indonesia throughout their hotel booking journey. Background: In the context of mobile hotel booking applications, research analyzing the customer experience at each customer journey stage is scarce. However, literature increasingly acknowledges the significance of this stage in comprehending customer behavior and revenue streams. Methodology: This study employs a mixed-method and exploratory approach by doing in-depth interviews with 20 participants and questionnaires from 207 participants. Interview data are analyzed using thematic analysis, while the questionnaires are analyzed using descriptive statistics. Contribution: This study enriches knowledge in understanding customer behavior that considers the usage of mobile apps as a segmentation criterion in the hotel booking journey. Findings: We developed four user personas (no sweat player, spotless seeker, social squad, and bargain hunter) that show customer segmentation based on the purpose, motivation, and actions in each journey stage (inspiration, consideration, reservation, and experience). Recommendations for Practitioners: The resulting customer segmentation enables hospitality firms to improve their current services by adapting to the needs of various segments and avoiding unanticipated customer pain points, such as incomplete information, price changes, no social proof, and limited payment options. Recommendation for Researchers: The quality and robustness of the customer segment produced in this study can be further tested based on the criteria of homogeneity, size, potential benefits, segment stability, segment accessibility, segment compatibility, and segment actionability. Impact on Society: This study has enriched the existing literature by establishing a correlation between user characteristics and how they use smartphones for tourism planning, focusing on hotel booking in mobile applications. Future Research: For future research, each customer segment’s demographic and behavioral factors can be explored further.

Purpose: Travel and leisure demand reach a considerable number through tourism development. In accommodating travel and leisure needs, the solution was found by using a third party to arrange the itinerary. This is how travel agent existence could be used as a marketing strategy. This trick also done by The Café - International Buffet Restaurant. This research was conducted to analyze the implementation of travel agent contracts in increasing the number of bookings and identify the most effective travel agent contracts in advancing the number of bookings. Research methods: Regarding finding the result, this research used the quantitative technique by applying the effectiveness formula and categorizing the result with the effectiveness level. The result is performed using tables, numbers, percentages, and charts processed by Microsoft Excel. In describing the explanatory, it uses descriptive analysis, which uses quantitative techniques to interpret the meaning of the result. The results will be solved as information on the problem formulation, conclusion, and suggestion. Results and discussion: The implementation of travel agent contract Chinese market and travel agent contracts in the non-Chinese market are the objects of this research. Implication: Based on the analysis, the travel agent contract in the non-Chinese market is more effective in increasing the number of restaurant bookings regarding its contribution exceeding the target that must be achieved.   Keywords: Effectiveness, travel agent contract, restaurant booking

Hotel reservations are currently dominated by online bookings from Online Travel Agent, therefore thisresearch was created to assist hoteliers in knowing how the role of online reviews can influence consumerpurchase intentions in making online reservations. There is no previous research that discusses this topic withthe setting in Batam, so this research was made to discuss it. This research used quantitative method and testedusing IBM SPSS, with the target population of respondents who have made hotel reservations using OTA inBatam, at the end of this study it is proven that the independent and mediating variables have a significantinfluence on the dependent variable. However, this study has limitations, which there are still invalid items andthere are still many that can be researched in more detail for further research.Keywords: Online Booking Intention, Purchase Intention, Reviewer Expertise, Online Travel Agent

Car pooling is environment friendly form of ride sharing technique which reduces bottleneck on roads and thereby also downsizing the parking challenge. However as per extant study distrust or wariness and high shuttle tariffs are major challenges for carpooling services. One way to boost trust among commuters and commuters and drivers is to bring into consideration reputation assessment mechanisms which will enhance civil assuage. This can be achieved by bringing social networks, route networks, commuter’s desires, ratings of commuters and cost optimization into account and developing a framework based on these factors. Agent mediated rapidly changing carpooling system involves booking seats to travel from a particular source to destination through software agents. One major problem in such systems is the anterior concurrence between the person who plays the role of driver and offers his vehicle for ride and the commuters. The dynamic carpooling exploit IT system to eliminate this constraint and ameliorate the nature of service. This paper assesses numerous trust and reputation systems specific to carpooling applications. These models are investigated on the underpinning of manifold idiosyncrasy like trust measurement, reputation reckoning, and threats on the system. This paper furnishes reputation reckoning framework for carpooling system that changes with the changes in environment by counting user desires, social networks and score from other commuters thereby ameliorate precursory systems by meliorate trust factor between the two involved parties. The carpooling model tacit a major trust affair because people resist sharing their vehicles or their time with strangers.

Large Language Model (LLM) agents deployed for real-world tasks face a fundamental dilemma: user requests are underspecified, yet agents must decide whether to act on incomplete information or interrupt users for clarification. Existing approaches either rely on brittle confidence thresholds that require task-specific tuning, or fail to account for the varying stakes of different decisions. We introduce a decision-theoretic framework that resolves this trade-off through the Value of Information (VoI), enabling agents to dynamically weigh the expected utility gain from asking questions against the cognitive cost imposed on users. Our inference-time method requires no hyperparameter tuning and adapts seamlessly across contexts-from casual games to medical diagnosis. Experiments across four diverse domains (20 Questions, medical diagnosis, flight booking, and e-commerce) show that VoI consistently matches or exceeds the best manually-tuned baselines, achieving up to 1.36 utility points higher in high-cost settings. This work provides a parameter-free framework for adaptive agent communication that explicitly balances task risk, query ambiguity, and user effort.

Despite advances in language and speech technologies, no open-source system enables full speech-to-speech, multi-turn dialogue with integrated tool use and agentic reasoning. We introduce AURA (Agent for Understanding, Reasoning, and Automated Tool Use), the first open-source, speech-native assistant capable of completing complex, goal-driven tasks through dynamic tool invocation and multi-turn conversation. AURA combines open-weight ASR, TTS, and LLMs in a cascaded pipeline and supports tools such as calendar booking, contact lookup, web search, and email. Its modular design allows easy integration of new tools using natural language prompts and action classes. On VoiceBench, AURA scores 92.75% on OpenBookQA-outperforming all open-weight systems and nearing GPT-4o-and 4.39 on AlpacaEval, competitive with other open-weight systems. Human evaluation shows 90% task success on complex, multi-turn speech tasks.

Online travel agencies (OTAs) rely on the global distribution system (GDS) to provide air ticket query and booking services. Air companies put their tickets onto a number of ticket counters for sale and OTAs provide value-added services to customers relying on air ticketing services provided by ticket counters. When OTAs query these services, the returned ticket information is redundant. Therefore, OTAs should choose some air ticketing services from GDS to query the latest air ticket information. At the same time, the quality (i.e., the prices of tickets provided) of air ticketing services differs because their tickets come from a relatively fixed set of air companies, who have their specific ticket pricing strategies. Since the quality of an air ticketing service is related to the ticket query it processes , to select appropriate services from all air ticketing services in terms of the content of the ticket query is a difficult task, which can not be solved by current service selection approaches. Therefore, we propose a Multi-agent-based Air ticketing Service SElection approach (MASSE). MASSE is based on a multi-agent reinforcement learning so that it can capture the dynamics of quality of air ticketing services. Extensive experiments show MASSE can reduce invalid queries to air ticketing services while retaining the quality of query results.

Tiket.com is an Online Travel Agent (OTA) that provides online hotel bookings. This study aims to analyze the effect of visual presentation and online reviews on hotel booking intention on the Tiket.com application. This research integrates visual presentation and online reviews with the Technology Acceptance Model (TAM). The variables tested consist of Visual presentation, Perceived Ease of Use, and Online review as independent variables, Perceived Usefulness as a moderating variable, and Booking Intention as the dependent variable. The type of research is quantitative with a cross-sectional design and the research subjects are users of the Tiket.com application. Data was collected by distributing questionnaires to 119 respondents. The research hypothesis was tested using the Partial Least Squares Structural Equation Model (PLS-SEM) analysis technique. Based on the results of the hypothesis, H1, H3, and H8 are not in line with previous thinking because they do not positively influence buying interest in the tiket.com application. tiket.com applications should update booking features, and more attractive colors, update the system, reduce addsens, and update a more modern look.

Due to the increasing number of automobiles on the roads worldwide, parking space problems have arisen in many areas, causing people to spend a considerable amount of time searching for a place to park. This study proposes a smart mobile parking booking system utilizing an agent-based model. The algorithm considers the driver's location, speed, desired destination, preferred parking charge, and arrival time, to determine the most suitable and available parking spot for them. The agent-oriented approach is utilized to represent the system, with each agent responsible for addressing a particular issue

: The Major problem LPG cylinder users face. They don’t know exactly the status of LPG gas completion so, it takes more delay in booking the cylinder which is uncomfortable to us. A proposes a system that will make entire LPG cylinder booking procedure automated (without human intervention). This system continuously measures the weight of the cylinder and once it reaches minimum value, it will automatically send message to the authorized LPG agent so that they can deliver the LPG cylinder in time. Along with the automated cylinder booking we also designed feature related to the safety of the user in which it continuously monitor the leakage of LPG gas and alerts the user regarding leakage to avoid major accidents. If gas is leaked, it automatically turns on the exhaust fan. Then, if room temperature is increased or decrease then monitor through temperature sensor.

The way the author walks the reader through, dividing one calendar year into four quarters, Q1, Q2, Q3, Q4, and then filling each with single drops, album releases, show dates, etc , lends itself to be an excellent, hands-on assignment for students on the mechanics of building tours Considering the number of music business books dedicated to the live and touring segment, of which there are not many, this is a welcome addition, especially because it is written from the perspective of an agent Though the touring market took a big hit with COVID-19 globally, when artists get back on the road, this will be a useful book for those who want to build their touring career Since 2005, he has produced and booked hundreds of concerts nationwide including collaborations with Lincoln Center, Central Park SummerStage in New York, and the Kennedy Center in Washington, D C He currently serves as Director of Programming at Drom, a 300 people-capacity club in downtown Manhattan and is the curator of the New York Gypsy Festival

To celebrate the 10th anniversary of the International Journal of Talent Development and Creativity, I return to the vision of the publication, by understanding what it means to develop talent and creativity in the 21st century. This paper celebrates the development of talent in the context of an arts education for neurodiverse students. Often talent is overlooked in pedagogies of neoliberalism, as the ethos for universities favour the market forces of competition and survival. In particular, post-1992 universities, face fierce competition for students due to market saturation. Students’ needs have been forgotten in unethical recruitment practices that are disguised as increased access. Unfortunately, it is to the detriment of welfarism and what bell hooks terms ‘the care of the soul’. This paper partly explores the current milieu, at university to contextualise the tenacity, resilience and vulnerability of those working and learning. Here talent and development are encouraged in young people, who have hitherto been marginalised and disadvantaged. It celebrates the successes, facilitated in part by the mediation of a small team of study skills support workers, including the author. Accordingly, a feminist approach engages auto-ethnography, and psychosocial spaces of the imaginary. Borrowing from the oeuvre of Feminism, Adult Education and Creative Possibility: Imaginative Responses, woven within the threads of this theory are small vignettes, art and poetry by one study skills support worker and the author. Speaking from the lived experience of being a child from a working-class background who is neurodiverse, I understand what it means to be marginalised in the English system of education. From the experiences of the support worker and me, this paper explores the ‘student journey’ within the context of inclusion in higher education, followed by an exploration of creative practices and reflections.

The article identifies the most important areas of development of creative marketing, which are the result of creative "imagination" of the marketer. In essence, this is the freedom to make a marketing decision based on market conditions, experience, knowledge, consumer behavior in each market segment. Thus, a great responsibility in this case lies with the marketer, who must have a non-standard thinking, the talent to creatively perceive the situation in the market and use them, as well as be able to choose a non-traditional marketing option that would immediately attract consumers, make them stay longer near this product or its advertising. This is the main difference between creative and traditional marketing - the ability to attract the attention of the buyer, to surprise him. The marketer must have a lateral thinking, which is to shift from traditional thinking, breaking the standard approaches to any particular idea, finding an original way to express it, creating something radically new. The article proposes a synthetic technology of innovation, which includes all components of the socio-economic system: society, economy and society and creates a sequence. It is proved that the use of a creative marketing approach is reflected in the product and its characteristics: novelty of the product, non-standard product, purposefulness in use, development. The article reveals the essence, advantages and disadvantages of implementing creative marketing strategies, including anticipation strategy and reactive strategy. Introduction and use of a creative approach to marketing activities of the enterprise is a key element of modern entrepreneurship, which plays a key role in achieving goals. The right combination of creative approach and pragmatic view is the key to the effective functioning of the enterprise in modern market conditions.

The present study aimed to investigate the predictive ability of the talent management practices of school principals to the affective organizational commitment of teachers in the State of Kuwait. To achieve this goal, correlation approach was used. The Talent Management Scale and the Affective Organizational Commitment Scale were applied to a random sample of 202 male and female teachers from Kuwait City schools. The results of stepwise regression analysis revealed that development and reward management practices were statistically significant predictors of affective organizational commitment predictive ability to emotional commitment, while identifying critical positions and competence training did not predict affective organizational commitment. The study recommends conducting more studies to investigate the predictive role of identifying critical positions and competence training practices in affective commitment.

This article presents a study on the development of art environments for young talented artists across 4 municipalities in the western part of Jutland, Denmark. Within this context and based on systems thinking, neosystems thinking and a holistic ecological approach to talent development, the researchers identify and describe three transcontextual art talent environments (Off-Track, Film, Show-up). Based on the neo-systemic concepts of symmathesy (Together-learning), the pivotal point in this study, was to identify organizational markers in the development of a strong talent infrastructure across the municipalities. This to enable and cultivate the artistic potential of young people. The research question of the study was: Which organizational markers cultivate the development of art talent environments in the context of four Danish municipalities in the western part of Jutland? The study is based on interviews with seven different public servants from different municipalities in the western part of Jutland and build on a prior research regarding young artists within different artistic fields and their instructors. Through a generic thematic analysis, four organizational markers were identified (organizing, strategizing, license to operate, and creative places). The paper argues that an orientation towards environments as a symmathesy and towards organizational markers can help organizations improve artistic talent environments across municipalities. Further, the role of public servants as talent stewards is discussed in the discussion.

This study aims to identify the effect of price and psychological factors on accommodation booking decisions through the Airbnb application. Data collection in this research was carried out by observation, interview, questionnaire and literature study techniques. The data analysis technique used is descriptive quantitative analysis and multiple linear regression analysis. The results of this study are expected to reveal whether tourists who book accommodation through Airbnb are merely motivated by the desire to get accommodation at a low price. Are psychological factors consisting of variables of motivation, perception, learning, and attitudes related to the desire of tourists to learn new things such as knowing the lives of local people also influence tourists in booking accommodation via Airbnb. The results of this study are independent variables consisting of price variables (X1), perception variables (X3), learning variables (X4), attitude variables (X5) significantly influence the dependent variable, namely the accommodation booking variable (Y). Whereas the motivation variable (X2) which is related to the trend indicator of the use of technology to book accommodations anywhere and anytime has no significant effect in booking accommodations through Airbnb's Online Travel Agent.

Inclusivity in Action: The Visually Impaired Service Desk Agent for Appointment Booking is designed to empower visually impaired individuals to work as customer service executives handling appointment bookings. It introduces a voice-based interaction system integrated with transcription and a virtual assistant. By leveraging Whisper for transcription, pyttsx3 for text-to-speech, and a Flask backend with a React-based frontend, the application ensures seamless support handling. The project demonstrates that with suitable AI tools and thoughtful design, accessibility barriers in corporate environments can be reduced effectively. The system facilitates end-to-end customer interaction without the need for visual interfaces, thereby placing blind users on par with sighted support agents. Call recordings are automatically transcribed, allowing the virtual assistant to respond contextually or assist the agent in navigating options. The interface is optimized for keyboard navigation and auditory feedback, ensuring full operational independence. Additionally, the solution is scalable and can be adapted to different domains like healthcare, education, and IT services. By focusing on task automation and real-time support, the application not only enhances accessibility but also improves efficiency and customer satisfaction. Through this initiative, we aim to set a precedent for inclusive design in AI-powered service desk solutions. The project underscores the importance of equal employment opportunities and the role of assistive technologies in bridging systemic gaps.

There is a lack of research into talent identification and talent development in the field of performing arts. The Voice of Holland (TVOH) has been the subject of a longitudinal empirical study as a good example of a setting for talent identification and talent development in this field. The study involved a literature review on talent approaches, talent identification, talent development, and performance. The study sought answers to the question how coaches at TVOH identify the singing talents of participants and contribute to develop these talents to a higher level of performance necessary for growth into a professional performer. Data were collected by desk research, participative observations, and qualitative interviews. The study revealed that talent identification and talent development intertwine, transformational coaching is part of performance coaching, and the preparation of talents in the field of the performing arts benefits a more integrated approach on talent development to enhance the overall performance needed to grow into a professional performer. Although the findings and conclusions are context specific for TVOH, they might be useful for teachers, coaches, and talents in the field of the performing arts and anyone else interested in the education and preparation of talents in general.

The purpose of this research is to find out the influence of online customer review on online booking intention in milenial use online travel agent B2C in Indonesia. A total of 200 respondents were using probability sampling. A questionnaire was used as a research instrument to collect data from respondents. The analisis technique used is a verification technique using of SEM. The research shows that online customer review has an influence on online booking intention.

In the coming year, it is estimated that holiday activities will continue to be part of the lifestyle of the Indonesian people. Traveloka with unicorn status is present in Indonesia as an online travel agent which in 2014 entered the hotel reservation business. Competition is very tight from competitors, Traveloka continues to strive to provide the best for its consumers. Traveloka provides services in the form of reviews, ratings and hotel facilities. The purpose of this study was to determine the effect of: consumer trust on hotel booking decisions, reviews of hotel booking decisions both directly and through trust, ratings on hotel booking decisions both directly and through trust, facilities on hotel booking decisions both directly and through trust. The increasing competition requires each retailer to strive to maintain and even win the competition between retailers by understanding consumer behavior, for online shopping consumers, as much as possible can be used as input or consideration in online shopping, and of course as a reference for future researchers. The population is an unknown number of consumers who have booked hotels on Traveloka. The sample data of 200 were obtained by purposive sampling using google form to obtain primary data. The research design used explanatory research. Data were analyzed using path analysis, where data quality tests were previously carried out. The results showed that reviews had no significant effect on trust, reviews had no significant effect on hotel booking decisions, rating had a significant effect on trustworthiness, rating had no significant effect on hotel bookings, information on facilities had a significant effect on trust, facilities information had no significant effect on hotel booking decisions. and trust has a significant effect on hotel booking decisions. Keywords: Reviews, rating, facility information, trust, hotel booking decisions. DOI: 10.7176/EJBM/12-30-04 Publication date: October 31 st 2020

: The digitalization has changed the business paradigm, including the tourism industry. Tourists are becoming more familiar with using Online Travel Agent booking and leaving the traditional booking method. This study examined the antecedents of online information adoption behavior in travel booking. Four dimensions, namely source credibility, perceived risk, argument quality, and information usefulness, were analyzed to understand the consequences of tourists’ online information adoption behavior. A total of 200 valid data from the respondents within the age range of 18-60 years old was gathered for this research. The results from SEM-AMOS statistical analysis indicated that three dimensions of e-WOM source credibility had a positive impact on consumer’s perceived risk. Moreover, the perceived risk was also proven to have an impact on the consumer’s argument quality and information usefulness. Furthermore, these two variables had a positive impact on a consumer’s online information adoption.

This study investigates the influence of reservation agents’ service quality on guests’ booking decisions at Jumeirah Bali, a five-star luxury hotel facing increasing competition from online travel agencies (OTAs). Using a quantitative descriptive approach with a causal-associative design, data were collected from 100 respondents selected through purposive sampling, and analyzed using simple linear regression supported by validity, reliability, and classical assumption tests. The results reveal that service quality significantly affects booking decisions, with a regression coefficient of 0.618 and R² of 0.485, indicating that nearly half of the variance in guest decisions is explained by service quality. Politeness, professionalism, and product knowledge were identified as the strongest dimensions, while responsiveness and attentiveness, though satisfactory, require further improvement. These findings underscore the strategic role of reservation agents as the first service encounter that shapes guest perceptions, enhances direct booking performance, and strengthens long-term loyalty in a competitive hospitality market.

City travelers rely heavily on online cab booking services for their daily transportation needs, making them an essential component of everyday commutes. Taxi businesses are seeing an increase in demand for integrated booking systems, which provide convenience for both parties. The centralized method used by many of the current online taxi booking systems to find, search for, and reserve taxis increases the danger of a single point of failure. In this paper, a mobile-driven cab-booking application named “FindUrShuttle” has been designed and deployed for real-time utilization. The developed application caters to a distributed taxi system based on intelligent agents that utilize local data. The NEXT.js, a React-based web framework, serves as the main backbone, providing the building blocks for the design and implementation of this application. Mapbox API has been incorporated into the application to find the shortest path between two points. In addition, vehicle pricing is dynamic and adjusted based on demand for vehicles. The simulation results of the developed application certainly demonstrate the effectiveness of a distributed booking system over centralized systems.

A survey of 124 executives and mid-level managers from 21 different industries discovered that 3 core issues dominate the “human side” of M&A as being most important to deal success: communication (90%), retention and “re-recruitment” of key talent (86%), and cultural comparison and integration (81%). However, the Oxford M&A Insights Project found that activities to address these aspects account for just 15%of the activities respondents feel their firms do well: communication (6%), talent management and retention (5%), and cultural analysis and integration (4%). This chapter presents proven pragmatic approaches to M&A communications, retention and “re-recruitment” of key talent, and organizational cultural comparison and integration. The tools, templates, best practices, potential pitfalls, and a case example of how to effectively address the “human side” of M&A are also addressed, along with the main participants, core activities, buyer’s and seller’s perspectives, and key cross-border considerations.

With the rapid development of information technology in hotel booking context, it is no doubt that many hotels consequently enhance the needs of integrating information technologies into their overall business operations. In this study, we developed a research model which consists of perceived value, trust toward a third party online booking site, and trust toward hotels, and tested it by using partial least square techniques. Survey data were collected from 307 individuals who have prior experiences on making a reservation using third-party online booking sites. Based upon our findings, we found that the perceived value, which was affected by both price and quality, was positively related to individuals’ intention to book. We also found that both trust toward third-party online booking sites and trust toward hotels, which was influenced by online review, have positive impacts on individuals’ intention to book. The implications of these findings for both research and practice are discussed.

ABSTRACT This study investigates the effect of hotel website quality, social presence, affective commitment, and e-trust on travelers’ online hotel booking intentions. Smart-PLS software was applied to test the hypotheses. The results of this study explain how hotel website quality, social presence, affective commitment, and e-trust have a significant relationship with online hotel booking intentions. This study identifies how hotel website quality is a critical driver in developing affective commitment, e-trust, and online hotel booking intentions. Travelers who enjoy the social experience with the hotel website platform are more motivated to book a room at that hotel. This study confirms the vital role of social presence in enhancing e-trust and online hotel booking intentions and provides evidence of the importance of the holistic model in understanding travelers’ decision-making processes, especially when booking a hotel room.

When searching and booking a hotel online, consumers are exposed to multiple cues such as customer reviews, price, and brand names. This study aims to examine the level of diagnosticity and simultaneous effects of the three important decision cues: online review, price, and brand on consumer quality evaluation and hotel booking intention. Study 1 is a randomized controlled experiment of 2 (high vs. low price) × 2 (positive vs. negative online review) × 2 (familiar vs. unknown brand). Study 2 replicates and extends study 1 by further examining the three cues’ effects on both perceived quality and booking intention, and the mediation effect of perceived quality. The results reveal three-way interaction effects of multiple cues in consumers’ decision processes and indicate that negative reviews have a dominating effect on hotel booking intention, and the level of cue diagnosticity from high to low is as follows: online review, brand familiarity, and price.

As people feel more comfortable using the Internet, online hotel bookings has become popular in recent years. Understanding the drivers of online booking intention and behavior can help hotel managers to apply corresponding strategies to increase hotel booking rates. Thus the purpose of this study is to investigate the factors influencing the use intention and behavioral intention of online hotel booking. The proposed model has assimilated factors from the extended Unified theory of Acceptance and use of Technology (UTAUT2) along with age, gender, and experience as moderators. Data were collected by conducting a field survey questionnaire completed by 488 participants. The results showed that behavioral intention is significantly and positively influenced by performance expectancy, social influence, facilitating condition, hedonic motivation, price value, and habit behavior. Use behavior is positively influenced by facilitating condition and hedonic motivation. As for moderators, gender moderates the relationships between performance expectancy, social influence, and behavioral intention. Age moderates the relationships between effort expectancy, social influence, hedonic motivation, and behavioral intention. Experience moderates the relationships between social influence, price value, and behavioral intention and between habit behavior and use behavior. Based on the results, recommendations for hotel managers are proposed. Furthermore, research limitations and future directions are discussed.

Purpose This study aims to evaluate the vitality of source, receiver and message factors on credibility assessment of online hotel reviews. Further, the current study determines the effects of perceived credibility of online reviews (PCOR) on hotel booking intentions (HBI). Design/methodology/approach The current study performs reliability analysis to determine the internal consistency of the measurement scales and to ensure the uni-dimensionality of the measurement scales the present study performs exploratory factor analysis. Further, the present study performs structural equation modeling to identify the relationships between the variables. Findings Two-sided online reviews have a more significant positive impact on PCOR as compared to the effects of receiver, review quality, review consistency and negative-sided online reviews. Originality/value This is one of the first study that analyses the impact of source, receiver and message on PCOR. Moreover, the present study offered theoretical justification behind the factors that affect the authenticity of online hotel reviews and its effects on HBI.

Background Early or timely initiation of antenatal care and regular visits based on the schedule have a tremendous effect on both maternal and fetal health. Despite the paramount benefits of early initiation of ANC within the first 12 weeks of pregnancy, women still do not have adequate and equal access to high-quality early antenatal care. Objective To determine the prevalence and factors associated with delayed first ANC booking in Ethiopia. Method A secondary data analysis was conducted using the 2016 Ethiopian demographic and health survey data. All reproductive age women who gave birth in the five years preceding the survey and who had ANC visit for their last child were included in this study. The total weighted sample size analyzed was 4,741. Due to the hierarchical nature of the EDHS data, Multi-level logistic regression model was used to identify the individual and community level factors associated with delayed first ANC booking. Result In this study, the prevalence of delayed first ANC booking was 67.31% [95% CI: 65.96% to 68.63%]. Women with secondary and higher education [Adjusted Odd Ratio (AOR)  =  0.78; 95%CI: 0.61, 0.99] and [AOR  =  0.61; 95%CI: 0.44, 0.83] respectively had lower odds of delayed first ANC booking. But woman who were multiparous and grand multiparous [AOR  =  1.21; 95%CI: 1.01, 1.45] and [AOR  =  1.50; 95%CI: 1.16, 1.93] respectively, women with the last pregnancy wanted no more [AOR  =  1.52; 95%CI: 1.10, 2.09], a woman who was living in the rural area [AOR  =  1.66; 95%CI: 1.25, 2.21], and a woman who was living in large central regions and small peripheral regions [AOR  =  2.76; 95%CI: 2.20, 3.47] and [AOR  =  2.70; 95%CI: 2.12, 3.45] respectively had higher odds of delayed first ANC booking. Conclusion Despite the documented benefits of early antenatal care initiation, late ANC booking is still predominant in Ethiopia as highlighted by this study. Maternal education, parity, wanted the last child, residence and region were significantly associated with delayed first ANC booking. Therefore, taking special attention for these high-risk groups could decrease delayed first ANC booking and this intern decreases maternal and fetal health problems by identifying and intervene early.

Purpose The purpose of this study is to investigate the effects of telepresence while using a smartphone-based virtual reality system (SBVR) to explore a hotel virtually and to determine the influence of this immersive experience on the booking intention of the potential customer. Design/methodology/approach Within the scope of this study, a conceptual research model was developed which covered utilitarian and hedonic aspects of the user experience of SBVRs and showed their relevance for the booking intention. A virtual reality application was programmed especially for the study, in which the test persons were able to virtually explore a hotel complex. A total of 569 people participated in the study. A questionnaire was used for the data collection. The structural equation modelling and hypothesis verification were carried out using the partial least squares method. Findings The immersive feeling of telepresence increases the perceived enjoyment and usefulness of the potential customer. In addition, the user's curiosity is aroused by the telepresence, which also significantly increases the perceived enjoyment as well as the perceived usefulness. The hedonic and utilitarian value of the virtual hotel experience increases the probability that the customer will book the travel accommodation. Research limitations/implications The virtual reality application developed for the study is based on static panoramic images and does not contain audio-visual elements (e.g. sound, video, animation). Audio-visual elements might increase the degree of immersion and could therefore be investigated in future research. Practical implications The results of the study show that the SBVR is a suitable marketing tool to present hotels in an informative and entertaining way, and can thereby increase sales and profits. Originality/value For the first time, this study investigates the potential of SBVRs for the virtual product presentation of hotels and provides empirical evidence that the availability of this innovative form of presentation leads to a higher booking intention.

In the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management decisions difficult and risky. In attempting to minimize losses, hotels tend to implement restrictive cancellation policies and employ overbooking tactics, which, in turn, reduce the number of bookings and reduce revenue. To tackle the uncertainty arising from booking cancellations, we combined the data from eight hotels’ property management systems with data from several sources (weather, holidays, events, social reputation, and online prices/inventory) and machine learning interpretable algorithms to develop booking cancellation prediction models for the hotels. In a real production environment, improvement of the forecast accuracy due to the use of these models could enable hoteliers to decrease the number of cancellations, thus, increasing confidence in demand-management decisions. Moreover, this work shows that improvement of the demand forecast would allow hoteliers to better understand their net demand, that is, current demand minus predicted cancellations. Simultaneously, by focusing not only on forecast accuracy but also on its explicability, this work illustrates one other advantage of the application of these types of techniques in forecasting: the interpretation of the predictions of the model. By exposing cancellation drivers, models help hoteliers to better understand booking cancellation patterns and enable the adjustment of a hotel’s cancellation policies and overbooking tactics according to the characteristics of its bookings.

Purpose The purpose of this study is to develop a framework for a freight consolidation company to adopt blockchain for the shipping community. Our research critically examine the challenges faced by a global shipping company that offer freight consolidation businesses and explore the use of Blockchain technology to enhance the competitiveness and sustainability of freight booking operations. Design/methodology/approach This paper is a case study, ECU Worldwide, with focus on transforming their operations using blockchain technology for the freight booking industry. As the case is explorative in nature, the research aim to unearth the complex blockchain adoption phenomenon in the industry as the technology is very nascent at present. The research is primarily grounded on Technology Acceptance Model (TAM) theory. Findings The research finds that blockchain technology supports solving many issues and inefficiencies of global shipping operations but there are some barriers that they need to overcome. The research provides a framework and recommendations for global company to consider when considering Blockchain technology for implementation. Our research finding shows that smart contracts can be set up at critical points along with the shipment route namely the storage, customs, carrier, transporters and consignee stage to ensure greater security and transparency. Research limitations/implications The research provides recommendations to key stakeholders involved in freight forwarding segment of logistics industry while adopting blockchain technology. Apart from the methodological limitation of the research, the research is limited to Singapore in terms of geographical coverage. Practical implications The drivers and barriers identified in the study can give practitioners insight of using blockchain for the industry. The proposed framework can assist companies in the shipping industry to prepare themselves to adopt blockchain for the community. Originality/value This case study is the first of its kind to examine the use of blockchain to explore the adoption in logistics Industry in Singapore and perhaps worldwide.

Electronic word-of-mouth on social networking sites is one of the most effective marketing resources in the lodging industry. Furthermore, Generation Y is becoming an important customer base for the luxury hotel industry and relies heavily on eWOM on social networking sties. By adopting and using four selected constructs (perceived usefulness, enjoyment, subjective norm, and perceived behavioral control) from the extended Technology Acceptance Model and the Theory of Planned Behavior, this study reveals how eWOM on social networking sites influences Generation Y’s purchase intentions when booking luxury hotels. The current study suggests that the usage of eWOM on social networking sites not only helps potential Generation Y customers select suitable luxury hotels but also allows luxury hoteliers to better cater to this growing market segment.

Sustainable urban mobility requires optimizing public transit passenger experiences. This system integrates cloud technologies and the Random Forest algorithm with bus fare ticketing systems to improve efficiency and customer pleasure. Cloud systems provide real-time data processing for price structure and route optimization in research. Cloud computing lets transportation providers quickly adjust to shifting demand, enhancing service dependability and responsiveness. The Random Forest algorithm is integrated into ticketing procedures to estimate passenger demand and optimize prices. Historical ridership data, weather, and special events are used to provide reliable fare estimates and suggestions using machine learning. This case study shows that the suggested approach improves passenger experience, waiting times, and system efficiency in a metropolitan transport network. Scalable, cloud-based infrastructure adapts to different transportation system sizes and configurations. Findings show that cloud computing and machine learning algorithms may make public transportation systems more responsive and passenger-centric. As cities seek smarter, more environmentally friendly transportation solutions, such breakthrough technology offers potential for public transit.

This study examines how mobile technology adoption influences customers' intention to book hotel rooms via smartphone. This study empirically incorporated perceived enjoyment and perceived value in the modified Technology Acceptance Model (m-TAM) and tested it as a unified model. Partial Least Square-Structural equation modelling (PLS-SEM) was applied to estimate the proposed research framework based on the survey data collected from 386 travellers who booked hotels via their smartphones. The structural model confirms perceived usefulness, perceived ease of use, perceived enjoyment, and perceived price value significantly influence consumers' behavioural intentions toward mobile hotel booking. This study confirms that TAM can be extended and employed to predict and explain the acceptance of the new technologies in service industries. This study also provides valuable theoretical contributions in developing and testing related theories and practical implications to hotel operators, online travel agencies (OTAs), and hospitality technology suppliers.

The increasing use of mobile applications by travellers and the high adaption of tourism companies into this new contact and sales platform, made it necessary to comprehensively investigate the mobile application users’ behaviours. This research combines the Stimulus-Organism-Response (S-O-R) framework and Technology Acceptance Model (TAM) to develop a theoretical background in examining travel booking behaviour of mobile application users. The conceptual model suggests that mobile application quality (MAQ) directly affects perceived ease of use (PEOU) and perceived usefulness (PU) which influence the intention to use (IU) mobile applications. Moreover, the offline brand trust (BT) has been hypothesised as a moderator between PEOU and PU's impacts on IU mobile applications. Analyses results indicated that system and service quality dimensions of MAQ significantly affect IU mobile application via PEOU and PU. Moreover, offline BT had both direct and moderator influences on the formation of IU mobile application. The study findings contributed to the theory in understanding mobile application users’ behaviours and suggested valuable managerial strategies in the m-commerce context.

Traffic violation monitoring and control is a major concern in India due to excess crowd, increasing commuters, bad traffic signal management, and rider mentality. It is obvious that physical traffic police-based monitoring alone is insufficient to monitor such large traffic volumes and simultaneously track violations. This has led to many violators going unnoticed. The violators, in turn, cause more serious mishaps on the road resulting in danger to their own life as well as to other’s life. Thus, there is a need for incorporating Artificial Intelligence (AI)-based techniques to eliminate manual intervention for the detection and catching of violators. In this paper, we propose a system to automatically detect two-wheeler violations like not wearing a helmet, usage of a phone while riding, triple riding, wheeling, and illegal parking for Indian road scenarios and eventually automating the ticketing process by capturing the violations and corresponding vehicle number in a database. We propose using a custom trained Yolo-v4 + DeepSORT for violation detection and tracking and Yolo-v4 + Tesseract for number plate detection and extraction. This implementation obtained a mean average precision (mAP) of 98.09% for violation detection and an accuracy of 99.41% for number plate detection on the test data. Further, the system detected 77 out of 93 violations with zero false positives in real-life scenarios. Thus,showing that the traffic violation system developed can be used to automate traffic violation ticketing. The developed system would be particularly useful in deriving various safety-related policies and will help to enforce strong regulation of traffic rules and build towards a smart city ecosystem via the automated AI-based traffic violation and ticketing system.

Personal assistant using a human operator need some time to process single request such as ticket booking, ordering something, and get services. One request can contain many queries for some information provided on the internet. Business performance values time efficiency so must be considered an alternative way to take request. Chatbot can give 24 hours service which can become an advantage besides using a human personal assistant. Chatbot acts like routing agent that can classify user context in conversation. Chatbot helped with natural language processing (NLP) to analyze the request and extract some keyword information. One important process in NLP is morphological analysis and part of speech (POS) tagging. POS help to parse the meaning of chat text based on a set of rules. The rule base is specific to some language and designed to capture all the keyword relies on chat text. Keyword in booking conversation term is like departure and destination city and also the date of flight. There is a variation from a user determining city and date. NLP in booking confirmation has a task to analyze various pattern describing ordering requests like city and date. Messenger bot would be an example of assistance that can help user connected to many services some like ticketing service through conversation interaction. The contribution of this research is to conduct some scenario that happening in ordering tickets. This research conduct that chatbot can help acts as customer service, based on the conducted scenario and show an F-measure score of 89.65%

BACKGROUND Road traffic injuries (RTIs) are the third leading cause of death in Saudi Arabia. Because speed is a major risk factor for severe crash-related injuries, a camera ticketing system was implemented countrywide in mid-2010 by the traffic police in an effort to improve traffic safety. There are no published studies on the effects of the system in Saudi Arabia. OBJECTIVE To examine injury severity and associated mortality at a large trauma center before and after the implementation of the ticketing system. DESIGN Retrospective, analytical. SETTING Trauma center of a tertiary care center in Riyadh. PATIENTS AND METHODS The study included all trauma registry patients seen in the emergency department for a crash-related injury (automobile occupants, pedestrians, or motorcyclists) between January 2005 and December 2014. Associations with outcome measures were assessed by univariate and multivariate methods. MAIN OUTCOME MEASURE(S) Injury severity score (ISS), Glasgow coma scale (GCS) and mortality. RESULTS The study included all trauma registry patients seen in the emergency department for a crash-related injury. All health outcomes improved in the period following implementation of the ticketing system. Following implementation, ISS scores decreased (−3.1, 95% CI −4.6, −1.6) and GCS increased (0.47, 95% CI 0.08, 0.87) after adjusting for other covariates. The odds of death were 46% lower following implementation than before implementation. When the data were log-transformed to account for skewed data distributions, the results remained statistically significant. CONCLUSIONS This study suggests positive health implications following the implementation of the camera ticketing system. Further investment in public health interventions is warranted to reduce preventable RTIs. LIMITATIONS The study findings represent a trauma center at a single hospital in Riyadh, which may not generalize to the Saudi population.

Introduction Participation in mammographic screening for breast cancer in Australia is approximately 54% among the general population, but screening among women from some culturally and linguistically diverse (CALD) backgrounds is lower. BreastScreen Victoria apply strategies to increase screening including reminder letters and phone calls; however, these are usually provided in English. Using intervention strategies generated from the Ophelia (OPtimise HEalth LIteracy and Access) community co-design process, translated mammography reminder letters and in-language phone calls were tested within two randomised control trials (RCTs). Methods and analysis Women aged 50–75 years who were due for their 2-yearly screening mammography (for RCT#1) or were under-screened, i.e. ≥27 months since last screen (for RCT#2) were randomised into intervention or control groups. RCT#1 compared sending women routine reminder letters (English only) with translated (Arabic or Italian) letters. RCT#2 compared reminder telephone calls to women in their preferred language (Arabic or Italian) to no telephone call. The primary outcome for each trial was screening booking rates within 14-days. Primary outcomes were tested using Pearson’s chi-square test. Rates within language group (incidence ratio: IR) were compared using the Cochran-Mantel-Haenszel test. Results For RCT#1 (letters) 1,032 women were randomised into the intervention arm or to usual care. Uptake of screening bookings was similar between both groups, with no differences observed by language group. For RCT#2 (phone calls), 195 women were randomised to the intervention group or to usual care. Overall, 64.2% of women in the intervention arm and 6% in the control arm booked a screening appointment within 14 days (p<0.0001). The IR (95%CI) of booking was 10.1 (3.9, 26.3) times higher among Italian women, and 11.6 (2.9, 46.5) times higher among Arabic women in the intervention compared to usual care groups. Discussion and conclusion A service improvement initiative derived from community members and breast screen providers was found to be highly effective. This evidence informed the service provider, BreastScreen Victoria, who have implemented these improvements into routine practice to improve screening among CALD groups and reduce health inequalities.

Abstract The recent trend suggests that people are more inclined to online hotel reviews to take online hotel booking decisions. The present study argues that when consumers are exposed to online hotel reviews, it draws an impression of the hotel brand in the mind of consumers, resulting in influence their hotel booking intentions. The current study determines the mediation effects of brand image between credible online hotel reviews and consumers’ hotel booking intentions. To ascertain the uni-dimensionality of the scales, the present study executes exploratory factor analysis. The ensure the internal consistency of the variable scales, the study determines Cronbach’s alpha. To measure the mediation effects, the present study performs structural equation modeling bootstrapping method. Data analysis reveals that marketers should look at hedonic brand image, which ultimately influences consumers’ hotel booking intentions. This is a unique study that ascertains the type of brand image that actually mediates between the impact of online hotel reviews and consumers’ hotel booking intentions. In addition, the paper furnishes theoretical background on how people interpret online hotel reviews and its effects.

In this paper, we examine how green communication impacts luxury hotel bookings using data from China. To determine whether green communication has an impact on guests’ decision-making processes, 384 questionnaires were distributed across four major Chinese cities, Beijing, Shanghai, Guangzhou, and Shenzhen. Among the mediators and moderators were perceived brand integrity, intrinsic green motivation, and environmental knowledge. Results indicate that the combinations of perceived brand integrity and green intrinsic motivation affect guests’ booking intentions along with environmental knowledge, according to the results of this study. Luxury hospitality managers can use this research to develop a theoretical framework for sustainable communication with green consumers.

ABSTRACT During online hotel booking, travelers are exposed to numerous hotel options, possibly making them feel overwhelmed with choices. Based on an online hotel booking scenario, two 2 × 2 factorial design experiments (Study one: 2 choice sizes × 2 amount of information; Study two: 2 choice sizes × 2 presentation of information), each with a sample of 242 participants, have been conducted to test the effect of choice size on perceived overload. The results find the choice overload effects and the associated internal mechanism, as well as the moderating effects of amount of information and presentation of information.

Purpose This paper aims to examine the influences of the number of hotel management responses and especially the textual similarity in hotel management responses to online reviews on hotel online booking. Design/methodology/approach This study used the data from 437 hotels in New York City on Expedia. The data specifically include online reviews, management responses and real-time number of online hotel bookings, which were merged to create one dataset for this study. To calculate the management response similarity, three widely recognized text mining functions of calculating textual similarity were adopted in this model. Fixed-effect panel data model was then used to examine the influence of management response to consumer online reviews on online hotel booking volume. Findings The empirical results demonstrate that the number of management responses to consumer online reviews does not significantly affect hotel booking; compared to none or only one management response, or management responses with low similarity, management responses with high similarity can significantly reduce the hotel booking on Expedia. Practical implications This study suggests that the similarity of management responses influences customers’ hotel booking, and hotel managers should avoid providing too similar management responses. Originality/value First, this study, for the first time, proposes the concept of management response similarity and its measurement methods. Second, this study takes an initial attempt to empirically test the influence of response similarity on hotel booking by using secondary data online.

PurposeThe primary purpose of this study was to examine the underlying consumer values that drive hotel booking mobile app users to engage more with the app and use the app continuously for hotel booking purposes.Design/methodology/approachBy conducting confirmatory factor analysis (CFA) and structural equation modeling (SEM) on the data collected through the Qualtrics online survey platform from 506 respondents in the United States, the proposed measurements and structural models were tested.FindingsThe findings for both Generation Xers and Millennials revealed that hedonic and social values influenced mobile app engagement, which strongly influenced mobile app loyalty. Thus, to enhance customer engagement and indirectly influence mobile app loyalty, the app value delivery should appeal to social and hedonic values. Although the utilitarian values for using apps had a potential direct impact on mobile app loyalty, the mediation analysis showed that mobile app engagement, when connecting consumption values to mobile app loyalty, served as a full mediator for Generation Xers and a partial mediator for Millennials.Practical implicationsThis study provides insights into how hotels and online travel agent (OTA) marketing managers may consider augmenting user engagement with hotel booking mobile apps. This study suggests that hoteliers and OTAs should further develop their user experience efforts to enhance the utilitarian features of their mobile app to increase revenue from repeat purchases. Additionally, this study provides implications for enhancing the hedonic and social features of hotel booking mobile apps to appeal to Millennials and Gen Xers.Originality/valueThis study developed and assessed an integrated model to investigate the relationships between consumption values, engagement and loyalty in hotel booking mobile apps. Furthermore, it examined generational cohorts' role in the relationships between these constructs.

Purpose The study aims to investigate how hotel booking attributes (i.e. perceived privacy, perceived certification and perceived assurance) engender consumers’ untrust and consequent behavioral intentions (i.e. altruistic behavior and trusting intentions). It also unveils the role of hotel attributes performance as a moderator between hotel booking attributes and consumers’ untrust. Design/methodology/approach Data were collected through an online platform by engaging 454 Chinese respondents. SPSS 25.0 and AMOS 24.0 (structural equation modeling) were used for data analysis and interpretation. Findings Results demonstrate that hotel booking attributes positively substantiate consumers’ untrust which, in turn, develops altruistic behavior and negative trusting intentions. Moreover, hotel attribute experience significantly moderates the relationships between perceived privacy, perceived certification and consumers’ untrust. Notably, hotel attribute performance insignificantly influences the association between perceived assurance and untrust. Research limitations/implications This study used the Chinese context and examined Chinese domestic travelers and the nonbranded hotel industry. Notwithstanding its limitations, the findings help hospitality and tourism firms, en bloc, to manage their review websites by explicitly disclosing policies regarding customers’ privacy and assurance, winning their trust through third-party certification and employing data scientists to develop algorithms to sieve fake information proactively. Originality/value This study develops an original conceptual framework by using the untrust model in this research. Our findings add to the research on consumer behavior, information processing, service management and trust and suggest practical implications for hospitality firms.

This research employed two experimental studies to examine the relationship of photograph content with consumers’ online travel booking intentions, the role of photograph source in this relationship, and the impact of photograph content on consumer reaction to price. Study 1’s findings suggest that perceived information value mediates the effect of photograph content on consumers’ booking intentions, with product-focused photographs perceived as being higher in information value than experience-focused photographs. Further, perceived photograph credibility was found to influence consumer reaction to photograph content, with photograph source moderating the photograph content-perceived photograph credibility relationship. Study 2’s findings suggest that photograph content moderates the price–booking intentions relationship. When the price is higher than the average in the market, consumers are likely to rely more heavily on product-focused photographs than on experience-focused photographs to inform the booking decision. Together, the findings of these studies can guide travel marketers in developing cost-effective, photograph-based online content.

Ticket fraud and ticket scalping activities often cause high costs as well as trust concerns for fans buying event tickets, especially in the secondary ticketing market. To address these issues, several publications and projects have proposed using blockchain technology to enable digital trust and ticket verifiability and thus to improve event ticketing systems. However, these approaches exhibit considerable privacy challenges and fall short concerning reliable, efficient visitor identification, which is necessary for controlling secondary market transactions. We demonstrate how a novel paradigm for end-user digital identity management, called self-sovereign identity (SSI), can be utilized to gain secondary market control. To do so, we follow a rigorous design science research approach to build and evaluate an SSI-based event ticketing framework. Our findings demonstrate that SSI-based event ticketing can enable efficient secondary market control by facilitating a practical implementation of the centralized exchange model. To generalize our results, we derive design principles for the efficient, reliable, and privacy-oriented ticket and identity verification and the use of revocation registries.

Recently, internet technology is considered to be the most used information and communication technology by organizations: it can ease the process of transactions and reinforce the relation between companies and customers. This investigation empirically examines the impact of e-ticketing technique on customer satisfaction; a convenience sample of Jordanian airline passengers that had booked flights in the last 12 months through companies offering e-ticketing services was acquired. The findings indicate that customer satisfaction with e-ticketing services was influenced by all of the independent variables measured (Data security, Customer and Technical Support, and User-Friendliness) were noted to have significant impact on customer satisfaction with e-ticketing services.

Background Antenatal care (ANC) is one of the most cost-effective and crucial components of maternal health care services. In developing countries where access to care, empowerment, and decision making power of women is low, ANC service is vital. The time at which first ANC visit was done has the utmost importance to ensure optimal health effects for both women and children. This study aimed to assess the proportion and factors associated with early antenatal care booking among pregnant women who were attending public health institutions in a remote area of Bule Hora district, Southern Ethiopia, from May to July, 2019. Methods Institutional-based cross-sectional study design was conducted at Bule Hora district public health facilities. Data were collected on systematically selected 377 pregnant women from 1st May to 30th July 2019. The sample size was determined by single population proportion formula and data were collected by using a standardized and pretested questionnaire and entered into Epidata 3.1 version, and then exported to Statistical Package for Social Science (SPSS) version 25 for analysis. The strength of association was measured by odds ratios with 95% confidence interval (CI) at a p-value of <0.05 and finally obtained results were presented by using simple frequency tables, bar graph, and texts. Results The proportion of early antenatal care booking among pregnant women attending antenatal care in the study area was 57.8%. Factors contributing to early antenatal care booking were husband’s education (Adjusted odds ratio (AOR), 2.5; 95% CI: 1.2, 4.9), knowledge on antenatal care service (AOR,1.99; 95% CI:1.2,3.3), means of approving current pregnancy (AOR,1.8; 95% CI:1.1,2.8), and being advised before starting antenatal care visit (AOR,2.1; 95% CI:1.2,3.6). Conclusion Generally, the timely initiation of ANC among pregnant mothers is not ideal. Modifiable factors like husband’s education, knowledge on antenatal care service, means of recognizing current pregnancy, and access to pre-ANC advice were found determinants for the timely initiation of ANC. Thus, it is advisable to provide proper information about antenatal care services by health care providers and enhancement of health extension program to increase community awareness before and during pregnancy at all levels of health care provision is very important.

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

Abstract Tourism is a rapidly growing industry worldwide, and internet technologies are becoming increasingly important in that industry. Online hotel booking website (OHBW) has become an important tool in the marketing of hotel services. In this study, the factors affecting the trust perception of OHBW users were investigated. Data collected from 456 consumers using OHBWs were analysed using structural equation modelling. The results showed that reputation, security, ease of use and risk significantly affected the user's perception of trust. In addition, the perception of trust has a positive effect on customer loyalty. As a result, this study aims to provide useful findings to OHBW managers and researchers who want to increase customer trust and ensure their loyalty.

Big data have contributed to deepen our understanding in regards to many human systems, particularly human mobility patterns and the structure and functioning of transportation systems. Resonating the recent call for ‘open big data,’ big data from various sources on a range of scales have become increasingly accessible to the public. However, open big data relevant to travelers within public transit tools remain scarce, hindering any further in-depth study on human mobility patterns. Here, we explore ticketing-website derived data that are publically available but have been largely neglected. We demonstrate the power, potential and limitations of this open big data, using the Chinese high-speed rail (HSR) system as an example. Using an application programming interface, we automatically collected the data on the remaining tickets (RTD) for scheduled trains at the last second before departure in order to retrieve information on unused transit capacity, occupancy rate of trains, and passenger flux at stations. We show that this information is highly useful in characterizing the spatiotemporal patterns of traveling behaviors on the Chinese HSR, such as weekend traveling behavior, imbalanced commuting behavior, and station functionality. Our work facilitates the understanding of human traveling patterns along the Chinese HSR, and the functionality of the largest HSR system in the world. We expect our work to attract attention regarding this unique open big data source for the study of analogous transportation systems.

Although virtual reality technology is increasingly being used in tourism, its potential as a shopping tool and as an avenue for marketing and selling tourism products and services has not yet been examined. Likewise, very little is known about how exploring holiday packages through virtual reality affects behavioral intention to visit tourist destinations. This study aims to compare the visit intentions evoked and the process of booking holiday travel packages between an immersive virtual reality environment (displayed through Oculus head-mounted glasses) and a traditional web-based 2D platform. A causal model is proposed and tested for both designs. Using a between-subjects experimental design with a sample of 202 individuals, the experiences of two randomly selected groups were observed as they bought holiday tour packages to Rio de Janeiro, Brazil. The first group made a simulated purchase in an immersive virtual reality environment using a head-mounted device, and the second group made the purchase on a traditional e-commerce website. The findings revealed that the scores given to sense of presence, attitude change, and perceived ease of use were greater among those who made the purchase in the more immersive virtual reality environment. However, the relationships between the variables in the causal model were stronger for the classic website than for the virtual reality setting. Attitude change positively affected intention to visit a destination more in the virtual reality environment.

The major impact of hotel industry on the environment has become a serious concern for both hoteliers and tourists, with many studies showing that tourists are having more and more expectations from hoteliers to implement environmental practices. Considering the fact that most hotel bookings are now made online through booking websites, it is important for these to show and manifest the same preoccupation with the environment, by promoting environmentally friendly initiatives. The aim of the present study is to emphasize the crossroads between sustainability and digitalization, and to increase the role of digital technologies in encouraging a sustainable development of all tourism sectors, including hospitality. The study provides information and identifies important elements to assess the environmental practices of booking websites. In order to identify the position that booking websites and platforms take towards the environmental practices, the filter section of four booking platforms is analyzed. The present study aims not only to analyze the current position of booking platforms towards the environment, but also to identify methods that improve the way they highlight their implemented environmental practices, based on tourist’ reviews. In order to identify the customers’ opinion regarding these types of practices, a total of 31,800 tourist reviews posted on Booking.com were analyzed. The results have indicated that the level of awareness related to the need of protecting the environment, in the case of both hoteliers and tourists, must increase aspects that also imply the need for booking websites to highlight and encourage environmental practices. The obtained results are useful for booking websites developers who can adapt and make the website’s interface friendlier regarding environmental practices. In addition, hotel managers and entrepreneurs can make use of these results in order to develop new types of business in the hospitality industry, and in the long run the results are useful for increasing the awareness among tourists of the need for environmental practices. Suggestions are made on how booking websites can involve themselves more in environmentally friendly initiatives and in potential future study subject discussions.

This paper introduces a novel digital ticketing platform using blockchain technology. Taking in a number of considerations by observing legacy ticketing systems and existing attempts at digital ticketing, we make use of IBM’s Hyperledger Fabric framework to design an architecture that distributes the tickets across all participating organisations. We note the potential benefits this platform has. Governing organisations maintain their right to set the rules of the platform and access the data to generate statistics. Vending organisations share access to the same underlying tickets whilst preserving competition. The platform offers passengers a variety of ways to pay for and access their tickets, using a combination of legacy and modern methods. Furthermore, we note the platform has the potential to eradicate paper ticketing and surplus voucher cards.

Information and communication technologies have empowered the consumers with ease of access to information about products and services in form of electronic word of mouth (eWOM). eWOM plays a critical role in consumer purchase decisions in the form of online reviews. The study uses the S-O-R framework to examine the impact of online reviews on online hotel booking. The data were collected using purposive sampling through an online self-administered questionnaire and analyzed using the PLS-SEM technique. The results of the study show that review valence significantly impacts review credibility whereas review length does not. Furthermore, credible reviews lead to high purchase intention. The positive impact of purchase intention on purchase in online hotel booking context is a novel finding. We suggest managers to include more positive valanced reviews to develop trust and credibility while take proper measures to reduce risk.

Recently, various applications for Vehicular Ad hoc Networks (VANETs) have been proposed and smart traffic violation ticketing is one of them. On the other hand, the new Information-Centric Networking (ICN) architectures have emerged and been investigated into VANETs, such as Vehicular Named Data Networking (VNDN). However, the existing applications in VANETs are not suitable for VNDN paradigm due to the dependency on a “named content” instead of a current “host-centric” approach. Thus, we need to design the emerging and new architectures for VNDN applications. In this paper, we propose a smart traffic violation ticketing (TVT) system for VNDN, named as SmartCop, that enables a cop vehicle (CV) to issue tickets for traffic violation(s) to the offender(s) autonomously, once they are in the transmission range of that CV. The ticket issuing delay, messaging cost, and percentage of violations detected for varying number of vehicles, violators, CVs, and vehicles speeds are estimated through simulations. In addition, we provide a road map of future research directions for enabling safe driving experience in future cars aided with VNDN technology.

Abstract To ensure the privacy of users in transport systems, researchers are working on new protocols providing the best security guarantees while respecting functional requirements of transport operators. In this paper1, we design a secure NFC m-ticketing protocol for public transport that preserves users’ anonymity and prevents transport operators from tracing their customers’ trips. To this end, we introduce a new practical set-membership proof that does not require provers nor verifiers (but in a specific scenario for verifiers) to perform pairing computations. It is therefore particularly suitable for our (ticketing) setting where provers hold SIM/UICC cards that do not support such costly computations. We also propose several optimizations of Boneh-Boyen type signature schemes, which are of independent interest, increasing their performance and efficiency during NFC transactions. Our m-ticketing protocol offers greater flexibility compared to previous solutions as it enables the post-payment and the off-line validation of m-tickets. By implementing a prototype using a standard NFC SIM card, we show that it fulfils the stringent functional requirement imposed by transport operators whilst using strong security parameters. In particular, a validation can be completed in 184.25ms when the mobile is switched on, and in 266.52ms when the mobile is switched off or its battery is flat.

ABSTRACT The Covid-19 pandemic has presented a range of economic consequences for the tourism industry. Notably, this has been striking mainly because of the rising fear toward public places and travel avoidance. Vietnam has one particular market segment – love hotels – and these hotels have not suffered as much as the overall hotel industry. Applying Structural Equation Modelling (SEM), and robustness test using Woodside's Accurate case outcome modeling (ACOM) of 2019 the study here confirms that: (i) Covid-19 knowledge increases conventional customer’s perceived risk; (ii) Perceived risk (before and after Covid-19) have a negative effect on hotel booking intention; (iii) risk perception and booking intention of customers in two hotel types differ significantly, in which there is a preference for love hotels. Despite love hotels' remarkable market value, the academic attention to this special business model, has to date, been little researched. This research contributes evidence of asymmetric perceived physical risk as well as booking intention of the two market segments namely, tourist and love hotels.

IOT based gas level detection and automatic booking notification. Now a day's LPG is a major cooking fuel as it is cost effective also. So, it is the most preferred fuel source. LPG cylinder has to be pre-booked every time. This booking process is not much efficient nowadays, because users of LPG have increasing day by day. Due to large pool of requests every day the system cannot record every request and serve. This project cares about this issue and gives additional effectiveness to it. As this project monitor's the gas level in the cylinder and if the gas level is lesser than certain level then it automatically sends a notification to both user and the gas agency using mobile network. This project uses microcontroller-based system in which a weight sensor or load sensor are used to find the gas level in the cylinder. This reading is passed to LCD module to show the gas content in the cylinder. If the gas level is less than the fixed value then GSM module sends the notification to the mobile and also informs the gas agency to record the booking on confirmation with customer. This project also ensures safety near the cylinder by detecting if any gas leakage and if any fire occurs. If any of these detected a buzzer will ring and also an exhaust fan will be turned on. The user will also be notified regarding this.

Airbnb has emerged as one of the most prominent examples of sharing economy platforms in the tourism industry. The growing popularity of Airbnb among tourists has boost revenue in the accommodation rental industry. Considering its significant impact on tourism industry growth, this paper aims to shed some light on guests’ booking intentions on Airbnb, Malaysia.,A web-based survey was designed to collect the data, which yielded 174 usable and valid responses. Structural equation modelling, partial least square technique was used to analyse the data.,The results confirmed that Airbnb’s reputation and guests’ unique experience expectation positively affect guests’ attachment to Airbnb, which, in turn, drives guest booking intention. However, price consciousness did not exert any effect on guests’ attachment to Airbnb.,The findings of this study will equip Airbnb managers, accommodation platform owners and service providers in developing appropriate marketing plans and strategies to better understand the implications of both internal and external causes that may lead to guests’ intention to book their accommodations.,This research has its merits in adding value to the tourism literature by understanding the drivers of guests’ booking intentions on Airbnb. Furthermore, the study findings contribute to the existing literature by investigating few new linkages such as the role of guests’ attachment to Airbnb as a mediator between guests’ unique experience expectation, price consciousness, reputation and guests’ booking intention.

Objective This study aimed to assess antenatal care (ANC) booking within the first trimester of pregnancy and its associated factors among urban dwellers of pregnant women in Debre Berhan town, Ethiopia. Design Cross-sectional. Setting Public and private health facilities that provide ANC services in Debre Berhan town. Outcome measure First ANC booking within the first trimester of pregnancy. Participants Urban dwellers of pregnant women in Debre Berhan town (n=384). Results A total of 387 pregnant women in Debre Berhan town were selected for this study, of which 384 responded giving a response rate of 99.2%. The proportion of pregnant women who had ANC booking within the first trimester of pregnancy was 156 (40.6%; 95% CI: 35.8% to 45.6%). In the multivariable analysis, the odds of first ANC booking within the first trimester was higher among pregnant women who had secondary school (adjusted OR (AOR): 1.84; 95% CI: 1.10 to 3.19) and more than secondary level of education (AOR: 2.26; 95% CI: 1.27 to 4.03) compared with those who had less than a secondary school level of education. Pregnant women who have any ill health with their current pregnancy (AOR: 1.99; 95% CI: 1.21 to 3.27) were more likely to start booking within the first trimester than their counterparts. The odds of ANC booking within the first trimester was threefold higher among women with knowledge of ANC (AOR: 3.05; 95% CI: 1.52 to 6.11) compared with their counterparts. Conclusion First ANC booking within the first trimester was found to be low among urban dwellers of Debre Berhan town. Secondary school and more educational level, having ill health during early pregnancy and women’s knowledge about ANC services were statistically associated with ANC booking within the first trimester of pregnancy. Therefore, improving ANC booking according to the WHO recommendation requires due attention. Further qualitative research exploring why early ANC booking remains low among urban dwellers is important to design intervention modalities.

Purpose Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia). Design/methodology/approach The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination. Findings The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia. Research limitations/implications This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration. Originality/value This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

ABSTRACT This study examines how perceived hotel website attributes influence consumers’ booking intentions. From a dynamic view, this study integrates privacy, security and consumers’ perceived protection against online purchasing risks into the empirical model obtained from existing literature. Moreover, consumers’ Internet experiences, as a dynamic learning process, are considered a moderator in the relationship between website quality and booking intentions. Empirical results support the expected hypotheses concerning the effects of website dimensions on booking intentions wherein website usability and booking intentions are the strongest. Moreover, the power law of practice exists as daily and yearly Internet experience can moderate such causal relationship. Implications for hoteliers and future researchers are also presented at the end of this article.

With the rapid progress of social economy, online tourism booking has newly become a highlight for China’s tourism trade and its development. College students, who have abundant disposable time, gradually build up as great tourist forces adept at using network resources. By both literature research and empirical research, this paper explores the factors affecting college students’ online tourism decision-making. By establishing college students’ online tourism booking behavior model based on TAM, and empirically analyzing 161 pieces of data based on SPSS22.0 and PROCESS macro plug-in, the hypotheses and modified model are verified. According to the results, perceived usefulness is still the most important factor in the college students’ online tourism booking behavior model, while perceived ease of use is another essential one. Usage attitude and usage intention are combined into a common factor—“usage attitude and intention” affecting the model; Negative Internet word-of-mouth plays a moderating role in the relationship between perceived usefulness and usage attitude and intention, and perceived usefulness and perceived ease of use together act as an intermediary in the model.

Purpose This study aims to examine the potential influence of Nonfungible Tokens (NFTs) and the Metaverse on the Halal tourism industry, with a particular emphasis on Halal brands and hotel booking platforms. This study also purpose to uncover the opportunities and challenges associated with the adoption of these technologies in the sector. Design/methodology/approach A qualitative research approach is employed, encompassing a comprehensive literature review of the halal tourism industry, NFTs and the Metaverse. Furthermore, this study utilizes case studies of brands and hotel-booking platforms that have experimented with or integrated these technologies. This study aims to provide a nuanced understanding of the implications of digital technologies in the context of the halal tourism industry. Findings The study uncovered several opportunities provided by NFTs and the Metaverse for the Halal tourism industry, such as enhanced authentication and traceability of halal products and immersive experiences tailored to Muslim travelers. However, it also identifies challenges including compliance with Islamic principles, data privacy, cybersecurity and equitable access to technology. Research limitations/implications While the study has several significant implications, it acknowledges potential limitations related to the nascent nature of NFTs and the Metaverse. Future research should delve deeper into ethical, legal and socioeconomic issues surrounding the application of these technologies in the halal tourism industry. Practical implications The findings of this study have practical implications for halal brands, hotel-booking platforms and regulators. This study provides a roadmap for harnessing the transformative power of NFTs and the Metaverse while addressing potential challenges. Social implications This research underlines the necessity for collaboration among industry stakeholders, technology providers and regulators to ensure equitable access and adherence to Islamic principles. This study preserves a more inclusive and ethically compliant use of these technologies, potentially shaping the future of the halal tourism industry. Originality/value This study focuses on the intersection of NFTs, the Metaverse and the Halal tourism industry. It provides fresh insights into the transformative potential of these technologies, aiding halal brands and hotel booking platforms in creating distinctive value propositions and experiences.

The paper investigates the factors influencing spectators’ adoption of digital (mobile) ticketing by expanding the Technology Acceptance Model (TAM; Davis, 1989) to include two variables overlooked in previous ticketing research: (1) trust of digital ticketing and (2) willingness to pay convenience fees. The study provides information on consumer behavior that may aid sports managers’ decision-making.,Through a paper-and-pencil survey of high school American football spectators (N = 523) over 12 games, the study examined perceived ease of use, perceived usefulness, the trust of digital ticketing, willingness to pay convenience fees and their effects on spectators’ intention to use digital ticketing.,Trust of digital ticketing, willingness to pay a convenience fee and the perceived ease of use were found to influence the perceived usefulness of the technology, which, in turn, had a significant effect on the intention to use. Willingness to pay fees also had a direct effect on intention.,Although every intention was made to ascertain a diverse sampling of schools (urban vs. suburban, large vs. small), it is worth noting that the sample location may be a limitation for the overall applicability.,The manuscript includes practical applications for marketers and ticket sales staff. Digital (mobile) ticketing is on the rise. As segments like high school athletics adopt the technology, spectators’ evaluation of digital ticketing must be considered, given its impact on intentions to use.,The findings support the use of willingness to pay convenience fees and trust of digital ticketing as considerations in the TAM for understanding digital ticketing.

ABSTRACT With the development of e-commerce, online booking has become an important process for tourists. Consequently, a number of online travel accommodation booking sites have emerged, allowing users to book travel-related services and leave their comments and reviews on the service. These kinds of information will influence the decision of the latecomers.Therefore, it is necessary to understand what key characteristics of online hotel reviews will affect travellers’ booking decisions, and then discuss the impact of online review system on the enterprise’s knowledge management in the hotel industry. This study first identified six factors from the literature, then collected data by using online questionnaire survey, and finally analysed the influencing factors of online booking decision by using multiple regression method. The experimental results showed that only three factors are important in the booking decision. They are quantity of review, content of review, and number of useful comments.

We develop a blockchain-based mobile ticketing system which utilizes smart contracts and multi-signature to enforce and authorize the executions of transactions. The transactions of buying and using mobile tickets are verified by the blockchain, and the immutable ledger and multi-signature on the blockchain ensures the authenticity and security of the tickets. The experimental results show that the proposed system is very efficient and requires only a nominal cost.

Photographic images help customers perceive product information more accurately and clearly. A customer’s perception of a particular product also influences their decision to purchase it. In the context of a hotel, guests evaluate digital hotel photos online during their booking decision process. While a large body of research has contributed to the understanding of how hotel online digital images shape hotel customer behaviour, little is known about the aesthetics, content, and composition of hotel images and their effects on booking decisions. In addition, previous research has routinely been criticised for having methodological limitations. These studies have routinely used surveys and experiments to explore how hotel pictures affect customer perception of the hotel and his/her booking intentions. Unlike prior studies, this research scopes a determination of the ‘selling’ properties pertinent to the hotel’s digital images placed online on the hotel-themed websites with the application of the latest technologies pursuant to visual data mining, processing and analysis. This study employed Google’s Inception v3 neural network as an AI solution for embedding and classifying hotel photo images with the further application of logistic regression and fuzzy cognitive mapping method. The results of the present study determined the hotel picture properties that may engender positive customer perception of the hotel and sequentially can precipitate hotel booking. The revealed ‘selling’ hotel image properties comprise (a) light and time of the photo shooting, (b) image colour scheme, (c) human presence, and (d) shooting angle. This study suggests a set of practical recommendations to hotel marketers to develop ‘selling’ photo images that generate hotel bookings online. The completed research is one of the first in the nascent literature stream in AI-powered computer vision solutions studies to determine the effects of photo aesthetics on online hotel bookings.

Background Antenatal care is one of the components of the maternal and child continuum of care. Timely booking ANC during pregnancy is of utmost importance to guarantee the best possible health outcomes for women and children. Inappropriate timing of the first ANC booking is associated with poor pregnancy outcomes, including perinatal death, stillbirth, and early neonatal death. According to WHO focused ANC recommendation, every mother should start booking ANC within the first 12 weeks of gestational age. However, in developing countries, including Ethiopia, many pregnant mothers were not booking ANC at the recommended time. Thus, this study aims to assess the time to first ANC booking and its determinants in Ethiopia. Methods A survival analysis was conducted to determine time to first ANC booking among 3917 weighted study subjects. The data were extracted from EDHS 2019 using STATA version 17 software. A Kaplan Meier survivor curve was computed to estimate the time of first ANC booking. A Long-rank test was used to compare the difference in survival curves. Weibull Inverse Gaussian shared frailty model was used to identify significant predictors. On multivariable analysis, variables having a p -value of ≤ 0.05 are considered statistically significant. Results The overall median survival time was four months. The significant determinant of time to first ANC booking are residence (rural [ϕ = 1.111, 95CI: 1.060, 1.164), mother educational level (primary education [ϕ = 0.945, 95CI: 0.915, 0.977], secondary and above educational [ϕ = 0.857, 95CI: 0.819, 0.896]) and wealth index (middle [0.948 (ϕ = 0.948, 95CI: 0.911, 0.986) and rich [ϕ = 0.904, 95CI: 0.865, 0.945]) Conclusion The median time for first ANC booking is 4 month, which is higher than the WHO recommended time. The timing of the first ANC booking in Ethiopia was mainly influenced by the residence of women, mother educational level, and wealth index. It is strongly recommended to expose mothers to educational materials and other awareness-creation campaigns, as well as to support disadvantaged women, such as the uneducated, poor, and those living in rural or remote areas.

Malaria is a major public health concern among pregnant women in sub‐Saharan Africa. Within the region, Nigeria has the highest malaria cases. This study aimed to determine the prevalence and factors associated with malaria parasitaemia among pregnant women at a booking clinic in Ibadan, Nigeria.

The development of the internet has influenced the development of the world economy. Various buying and selling transactions that previously could only be done face-to-face, have now developed into transactions via the internet known as e-business or e-commerce. The hotel room online booking system was created to make it easier for consumers to book rooms 24 hours a day. With the availability of the online booking feature, consumers can access hotel information in detail and more transparently, besides that, consumers can also see reviews which can be used as their consideration in choosing hotels and planning holidays. Traveloka's significant development as an Indonesian online travel agent unicorn plays an important role in accelerating the growth rate of the online travel ecosystem, especially for the domestic market. There are many factors that must be examined in finding information, placing orders, and purchasing online. Therefore, this research is focused on online booking behavior. This study aims to determine the influence between variables based on Theory of Planned Behavior, which consists of attitude toward the online booking behavior, subjective norm, perceived behavioral control, online booking intention and online booking behavior at Traveloka. Data was collected from 133 respondents of domestic tourists who have made online bookings at Traveloka. Data were analyzed using Partial Least Square (PLS) statistics with the Smart PLS 3.0 M3 program to determine the complexity of the relationship between latent variables and their indicators. The results of this study indicate that attitude toward the behavior and subjective norms have a positive and significant effect on online booking intention. Meanwhile, perceived behavioral control has no significant effect on online booking intention. Another finding is that online booking intention and perceived behavioral control are known to have a positive and significant effect on online booking behavior. Traveloka management and marketers are also expected to be able to use the results of this research to evaluate and take corrective action on aspects that are deemed inadequate and manage the ease of use of the application to increase online booking intentions through the Traveloka application.

Background Online appointment booking is a commonly used tool in several industries. There is limited evidence about the benefits and challenges of using online appointment booking in health care settings. Potential benefits include convenience and the ability to track appointments, although some groups of patients may find it harder to engage with online appointment booking. We sought to understand how patients in England used and experienced online appointment booking. Objective This study aims to describe and compare the characteristics of patients in relation to their use of online appointment booking in general practice and investigate patients’ views regarding online appointment booking arrangements. Methods This was a mixed methods study set in English general practice comprising a retrospective analysis of the General Practice Patient Survey (GPPS) and semistructured interviews with patients. Data used in the retrospective analysis comprised responses to the 2018 and 2019 GPPS analyzed using mixed-effects logistic regression. Semistructured interviews with purposively sampled patients from 11 general practices in England explored experiences of and views on online appointment booking. Framework analysis was used to allow for comparison with the findings of the retrospective analysis. Results The retrospective analysis included 1,327,693 GPPS responders (2018-2019 combined). We conducted 43 interviews with patients with a variety of experiences and awareness of online appointment booking; of these 43 patients, 6 (14%) were from ethnic minority groups. In the retrospective analysis, more patients were aware that online appointment booking was available (581,224/1,288,341, 45.11%) than had experience using it (203,184/1,301,694, 15.61%). There were deprivation gradients for awareness and use and a substantial decline in both awareness and use in patients aged >75 years. For interview participants, age and life stage were factors influencing experiences and perceptions, working patients valued convenience, and older patients preferred to use the telephone. Patients with long-term conditions were more aware of (odds ratio [OR] 1.43, 95% CI 1.41-1.44) and more likely to use (OR 1.65, 95% CI 1.63-1.67) online appointment booking. Interview participants with long-term conditions described online appointment booking as useful for routine nonurgent appointments. Patients in deprived areas were clustered in practices with low awareness and use of online appointment booking among GPPS respondents (OR for use 0.65, 95% CI 0.64-0.67). Other key findings included the influence of the availability of appointments online and differences in the registration process for accessing online booking. Conclusions Whether and how patients engage with online appointment booking is influenced by the practice with which they are registered, whether they live with long-term conditions, and their deprivation status. These factors should be considered in designing and implementing online appointment booking and have implications for patient engagement with the wider range of online services offered in general practice.

ABSTRACT This study investigates the influence of live streaming characteristics and motivational factors on consumer trust, engagement, and impulsive booking behaviors within the hospitality industry in Saudi Arabia. Leveraging the country’s rapidly expanding digital economy and high internet penetration, the research focuses on how key live streaming elements – professionalism, informativeness, entertainment, and interactivity – shape consumer trust, which in turn affects engagement and impulsive booking intentions. The study employs a quantitative research methodology, using an online survey administered to 383 participants who actively engage with live streaming platforms for hotel bookings. Structural Equation Modeling (SEM) was used to analyze the data, revealing that live streaming characteristics significantly influence trust, which subsequently drives customer engagement and impulsive bookings. However, time pressure was found to moderate these relationships, weakening the impact of engagement on impulsive booking under high-pressure conditions. The findings underscore the critical role of trust as a mediator and highlight the importance of balancing engagement strategies with appropriate levels of time pressure to optimize consumer outcomes. This study offers practical implications for hospitality businesses aiming to enhance their digital marketing strategies and provides theoretical contributions to the Stimulus-Organism-Response (S-O-R) Model, Trust construct, Impulse Buying Model, and Self-Determination Theory (SDT).

Ability to easily search and then compare items is an advantage that online platforms have over bricks and mortar formats. In such an intensely competitive airline market, this paper investigates if this exploration experience satisfies the traveler during air ticket booking, by applying the stimulus organism response model. An empirical study is used to test the proposed model by applying the structural equation modeling and interaction effects. The model describes the extent by which traveler’s repurchase intention is influenced by four important factors – search and compare assortment satisfaction, social influence and overall satisfaction. The paper extends the satisfaction to online repatronage intention and customer loyalty. This empirical study also establishes that the relationships are moderated due to civil status and ticketing number of platforms used by the customers.

This paper offers insights into experiential values and online accommodation booking in the digital transformation era of the tourism sector in relation to the Greek Generation Z cohort. More specifically, the role of experiential values, attitudes, and involvement as well as their impact on patronage intentions of the Generation Z cohort are investigated. Quantitative research conducted with an online questionnaire yielded a sample of 970 Generation Zers’ responses. Using the Structural Equation Model via the AMOS statistical package, six hypotheses were tested. Results have revealed that the construct of experiential values positively impacts directly on patronage intentions, attitudes towards the e-booking platform, and customer involvement. Additionally, Generation Z members’ attitudes towards the e-booking platform and involvement with the platform impacts patronage intentions. Furthermore, involvement with the e-booking platform has a negative effect on consumers’ attitudes towards the platform. Detailed discussion of results and further implications is also provided.

Abstract The aim of this study is to examine the effect of positive electronic word of mouth (eWOM) on green hotel booking intention using a comprehensive moderated-mediation framework. A total of 223 responses were collected through online survey. The results indicated that argument quality, source credibility, valence and consumer rating play a crucial part in consumers’ green hotel booking intention. The study contributes the eWOM, green marketing, and hospitality literature. The findings of this study will help the green hotel managers to know about the dynamic role that eWOM triggers, trust and consumers environmental concern play in green hotel booking intention.

Unit Pelaksana Tugas Information Technology (UPT IT) merupakan pusat penyedia informasi Akademik yang ada pada kampus Universitas Islam Syekh-Yusuf. ketika terjadi keluhan tentang gangguan Sistem Informasi Akademik (SINA) seperti nilai tidak sesuai, masalah registrasi atau masalah keuangan, user masih harus datang langsung ke kantor teknisi IT untuk melaporkan keluhannya , user juga bingung harus melapor kesiapa ketika teknisi IT tidak berada ditempat laporanlaporan yang masuk tidak terdokumentasi dengan baik. Untuk mengatasi masalah diatas dibutuhkan suatu aplikasi helpdesk ticketing untuk menjadi wadah user melaporkan masalah. Pendekatan pemgemabangan sistem ini menggunakan metode prototype, aplikasi ini dirancang memanfaatkan web application sebagai platformnya dengan MYSQL sebagai databasenya. Hasil dari penelitian ini adalah terancangnya aplikasi helpdesk ticketing untuk mempermudah user dalam melakukan pelaporan masalah, dan memudahkan Teknisi IT dalam mengelola  laporan.

Public transport plays an important role in the daily lives of hundreds of millions of people. The limited national and local finances starve the public transport networks of funding, although greater usage would imply important environmental and financial benefits. The recent advances in mobile technologies and services could be harnessed to increase the attractiveness and efficiency of various public transport systems. Increasingly flexible electronic ticketing, smart cards and real-time information feeds via smartphones are making transportation networks faster, cheaper and more efficient for both the passengers and operators. In this paper, we present our approach in designing and implementing an M-ticketing system for urban transport based on NFC technology. We make initial design decisions based on a study of two similar systems Oyster and U-Go. We make use of Microsoft technologies for the prototype implementation and we propose a test suite to validate the system. We conclude on presenting the experience gained from this project and propose a set of further developments in order for the prototype to become more realistic.

ABSTRACT Standing out in the tourists’ hotel booking process is important for hotels in today’s competitive environment. To effectively promote hotels, it is necessary to have a deeper grasp of the factors affecting tourists’ hotel choices. To this end, this research examines the effects of psychological distance (temporal and spatial) and social influence on tourists’ hotel booking preferences based on the Construal-Level Theory (CLT). Social influence is identified as a moderator between psychological distance and tourists’ booking preferences; whole perceived message quality is identified as a mediator. The paper concludes with theoretical implications, practical implications, and ideas for future research.

Since there is a lot of ambiguity while booking a room at the last moment due to which we usually don’t end up getting one. To avoid the occurrence of such a situation “Book The Room” app comes to your rescue. Through this app, [1] we can book the conference room well in advance based on the availability of the date and time that we have opted for. If the time limit exceeds a time span of 2 hours, then a request will be sent to the admin for necessary approval. Further to the approval, the conference room can be used. The queued requests are served on a first come first serve basis by the admin.

This paper exhibits a computerized transport ticketing for the Public Transport System utilizing RFID (Radio Frequency Identification Device) labels. Sooner or later, transport framework with cutting edge advancements like RFID, GSM and GPS will pick up spotlight because of their favorable position of higher comfort and more noteworthy life guidelines when contrasted with the customary transport frameworks. In the proposed framework, the passenger is identified automatically and the fare based on the distance travelled is deducted automatically, the use of RFID tags along with GPS is used to make the identification of passenger and the fare more accurate. The traditional paper-based tickets can be replaced by the RFID system as they are reusable and provide higher accuracy and hence they are much better. This replaces the existing paper-based bus ticketing system and also prevents corruption/unaccounted money. RFID tags are being used as tickets which are reusable and deducted the fare based on the distance travelled using GPS system by the user. This system minimizes human errors and effort.

Online ordering is the latest breakthrough in the hospitality industry, but when it comes to booking cancellations, it has a negative impact on it. To reduce and anticipate an increase in the number of booking cancellations, we developed a booking cancellations prediction model using machine learning interpretable algorithms for hotels. Both models used Random Forest and the Extra Tree Classifier share the highest precision ratios, Random Forest on the other hand has the highest recall ratio, this model predicted 79% of actual positive observations. These results prove that it is possible to predict booking cancellations with high accuracy. These results can also help hotel owners or hotel managers to predict better predictions, improve cancellation regulations, and create new tactics in business.

Online travel sales continue to increase every year. Recorded in 2019, digital transactions related to online travel reached USD 755.4 billion. One of the supports of the travel business is the tourism and hospitality industry. The online reservation system is one of the most attractive solutions in the hospitality industry. Cancellation of hotel bookings or reservations through the online system is currently one of the problems in the hotel management system. When the reservation has been canceled, the hotel will be harmed. Therefore, predicting whether a booking will be canceled or not using the help of data science is needed so that the hotel can minimize lost profits. Therefore, by using datasets related to hotel booking requests, a predictive analysis using the CRISP-DM framework is conducted. By first performing some data preparation processes, this study uses a tree-based algorithm to perform the prediction. The experiment produced that Random Forest model has the best value with an accuracy value of 0.8725 and it is found that the time difference between booking is made and arrival time is the most influential feature in predicting the level of cancellation.

This study aims to examine the factors affecting the behavioural intentions toward online hotel booking. The study integrates visual presentations and online reviews with the technology acceptance model (TAM). Partial least squares structural equation modelling (PLS-SEM) was used to test the proposed hypotheses in this research. The results report that booking intentions are mainly determined by visual presentations and perceived usefulness. The statistical analysis supports the notion that visual presentations and online reviews have a positive impact on perceived usefulness and perceived ease of use. However, online reviews do not have any significant influence on booking intention directly. The study concludes that online consumers are more likely to book a hotel online if visual presentations and online reviews appear to be useful and easy to use. The findings contribute several implications for researchers and practitioners in the hospitality field.

This study explored behavioral models based on low-cost carrier customers’ use of a ticketing app. Technology readiness, service convenience, and corporate credibility were evaluated in the model. A total of 815 valid responses were collected from customers of Tigerair, Taiwan, who had flown from one of the two major international airports in Taiwan. With technology readiness regarded as the grouping variable, the results indicated that corporate credibility significantly affected customers’ intentions to use the app, whereas the effects of service convenience on the technology exploration, technology contradiction, and the technology insecurity groups varied. Finally, this paper presents a discussion of management implications and suggestions for future studies.

Highway and bridge construction projects are subject to cost and schedule overruns, as well as workforce shortages in remote locations, both of which can result in disputes over the quality of the end product. E-ticketing technology can improve the quality, however, while decreasing cost overruns and schedule delays. Despite the benefits of E-ticketing, many state departments and agencies are unwilling to transition into this technology. This study aims to identify the potential barriers to implementing E-ticketing, determine the adoption rate of state agencies/departments, and evaluate the benefits of employing an E-ticketing platform. The changes in technological trends in highway construction that are related to material tracking, inspection, and digitization are qualitatively analyzed, using meta-synthesis and interpretative analytical techniques. Key technologies that have the potential to be integrated into the E-ticketing platform to mitigate the limitations faced at the time of implementation are also discussed. The study’s findings suggest that the implementation process and regulations of an E-ticketing platform vary drastically from state to state, and a common set of guidelines is essential for obtaining long term success. The study advocates four key recommendations for widespread implementation of the E-ticketing platform and suggests directions for further research. The results of this study will assist DOT’s decision-makers and engineers in developing a common E-ticketing platform, adopting policies and guidelines, decreasing the costs of their projects, providing the initial investment, running pilot tests, enhancing the safety of their inspectors, and completing their projects in a timely and efficient manner.

In light of travelers’ growing preference for sustainable hotel accommodation, this study investigated the relative importance of green certification labels/badges in online hotel selection/booking choice. A conjoint analysis was performed on seven online hotel attributes (including green certification labels/badges) in two specific scenarios (imagining they were booking in a pre- and post-COVID-19 setting) using 270 U.S. subjects surveyed in April 2020. The results revealed that green certification labels/badges do impact travelers’ online hotel booking choice, but not as much as cancellation policies, hotel rating, price, and location. Price in particular exerted a greater influence on travelers’ booking preferences in the post-COVID-19 scenario. Yet, a significant percentage of travelers (around 40% in both scenarios) were willing to pay more for a hotel with green certification. These results contribute to our theoretical and practical understanding of the factors that influence online hotel booking, as well as the power of green certification labels/badges in driving online hotel bookings in the pre-and post-COVID-19 contexts.

From the point of view of the national economy, the development of a transport sector is a key process which relies on many other industries. Therefore, it is inevitable to coordinate related activities and to develop the transport sector effectively, and significant synergies in relation to other sectors could be generated. This article deals with the issue of public passenger transport and its greater attractiveness through the account-based ticketing (ABT) technology application in the process of passenger handling. The article contains a review of the current literature on the subject, using both completed and partially completed studies. It presents the case of study of account-based ticketing in the Slovak Republic. ABT technology has already been introduced in many countries, for example, in the Netherlands in 2019, and in Singapore.

PurposeThe pace of booking is a critical element in the accuracy of revenue management (RM) systems. Anecdotal evidence suggests that booking windows exhibit persistent shifts due to a variety of macro and micro factors. The article outlines several causes and tests the impact of the shifts on forecasting accuracy.Design/methodology/approachA novel methodological approach is utilized to empirically shift hotel reservation windows into smaller increments. Forecasts are then estimated and tested on the incremental shifts with popular RM techniques characteristic of advance booking data. A random effects model assesses the impact of the shifts on forecast accuracy.FindingsThe results show that shifts in booking behavior can cause the accuracy of forecasting models to deteriorate. The findings stress the importance of considering these shifts in model estimation and evaluation.Practical implicationsThe results demonstrate that changes in booking behavior can be detrimental to the accuracy of RM forecasting algorithms. It is recommended that revenue managers monitor booking window shifts when forecasting with advanced booking data.Originality/valueThis study is the first to systematically assess the impact of booking window shifts on forecasting accuracy. The demonstrated approach can be implemented in future research to assess model accuracy as booking behavior changes.

AI-controlled chatbots have been used in travel services for some time and range from simple hotel reservations to personalized travel recommendations. However, the acceptance of chatbots compared to human interlocutors has not yet been extensively studied experimentally in the tourism context. In this experimental, randomized, vignette-based, preregistered 2 (agent: AI chatbot/human counterpart) × 3 (situation: positive/neutral/negative) between-subjects design, we hypothesized that booking intention is reduced in chatbots compared to human agents and in situations where the booking can only be made under more negative than the original conditions. Additionally, we hypothesized an interaction effect between agent and situation, presuming that the decrease in booking intention in negative situations would be less strong for human agents than for chatbots. Structural equation modelling of the data indicates strong support for the Technology Acceptance Model in the booking context. As presumed, the booking intention was lower in the negative situation and borderline lower for the chatbot. The interaction effect was shown descriptively in the data. Chatbots are recognized during the booking process and less accepted to support bookings than their human counterparts. Therefore, managers should design chatbots as human-like as possible to avoid losing sales when outsourcing customer contact activities to AI technologies.

E-construction is becoming increasingly attractive in the highway construction industry as the industry faces reductions in staffing and resources. Specifically, the addition of e-construction methods can offer a boost in efficiency and safety in resurfacing projects. Electronic ticketing (e-ticketing), paver-mounted thermal profiling, and intelligent compaction were recently incorporated into a study coordinated by the Kentucky Transportation Cabinet, United States, to assess these e-construction technologies on two resurfacing projects. This study aimed to investigate the accuracy of the data collected and the time savings that come with the incorporation of technology in construction. This manuscript focuses explicitly on the performance of e-ticketing, since it showed the most promising results during the study. An analysis of the data shows that e-ticketing offers the same data typically collected from traditional tickets. E-ticketing reduced the amount of time needed to retrieve material tickets and facilitated comparisons of theoretical tonnages to actual tonnages, in the same manner as traditional approaches. Inspectors also reduced their exposure to hazardous jobsite conditions through the use of e-ticketing, while contractors strengthened their operational efficiency. It is crucial that all stakeholders involved in e-ticketing projects practice partnering and collaboration to take advantage of the benefits of this emerging technology. This paper’s primary contribution is to document lessons learned and to evaluate the performance of e-ticketing technology in relation to safety, time savings, remote inspections, and accuracy compared with traditional practices on an asphalt paving project.

Recent advancements in various technologies have made remarkable developments in various fields for public welfare and public transport is one such area. In the near future public bus transport system with advanced technologies like Radio Frequency Identification Device (RFID), GSM, GPS, ZigBee and RF modules will gain spotlight due to their advantage of higher convenience and greater life standards as compared to the conventional bus systems. In this paper, a comprehensive review of all several proposed bus ticketing and bus information methods has been presented in detail. The study brings out improved solution in terms of cost, convenience, user satisfaction and future implementation. The choice of working modules and their efficient performance has been discussed along with the highlighted importance of the need of technology for welfare of common public and visually impaired.

ABSTRACT This study aims to test the role of price consciousness, reputation, trust, and perceived value on tourists’ booking intentions on Airbnb. Additionally, this study investigates the mediating role of perceived value and consumer trust in Airbnb on the link between attributed causes and guest booking intention. A cross-sectional survey was designed to examine the theoretical framework, which yielded 311 responses. The data were analyzed using structural equation modeling. The study found that reputation and price consciousness influence tourists’ cognitive evaluation of Airbnb service and affect their intention to book accommodation on the Airbnb platform. The findings of this study may serve as the basis for designing effective marketing tools to understand better causes that may drive guests’ booking intentions.

Electronic ticketing (E-ticketing) is the process of digital ticket delivery, and it can be combined with different technologies to produce a wide variety of benefits in the construction transportation industry. Many transportation departments and agencies are not willing to adopt this technology due to some limitations imposed by technology or organization policies. Therefore, the aim of this paper is to perform analysis of E-Ticketing adoption and its changes during the Covid-19 pandemic, evaluate the benefits and limitations of the technology, and determine the technology integration with other available automation platforms in construction industry. The outcome of study will help in validating that the primary uses of E-ticketing are not safety and documentation;it is also a tool for rendering automated inspections and reducing the cost and increasing the overall quality of transportation construction projects during and after the pandemic. This study incorporated an extensive literature review on policies, memos, guidelines, news articles, and research publications pertaining to the limitations, benefits, and key technologies that can be integrated into E-ticketing platforms. A qualitative analysis of recent technological trends indicated that intelligent compaction, drone surveying, infrared sensors, advanced imaging, and thermal profilers can be integrated into an E-ticketing platform to extract better results that are paramount to a project's success. Four findings from this study will assist project stakeholders in developing guidelines and policies, reducing costs, and increasing the overall safety of the project. The main contribution of the paper is a framework for the platform that combines E-ticketing and other emerging technologies to facilitate efficient automated inspections, billing, and telematics in the construction transportation industry.

Mobile ticketing services allow urban transport passengers to travel in a convenient and easy way, enhancing their travelling experience. In recent years several mobile ticketing services have started to be developed and launched, but there is still a lot to be done in terms of its effectiveness, efficiency and innovation. This paper presents a micro-location mobile ticketing solution based on Near Field Communication (NFC) and Bluetooth Low Energy (BLE) technologies, called Anda. This solution is based on a check-in/be-out scheme and requires the minimum intervention from the passenger. It is really innovative in the urban transport field, as it takes advantage of BLE technology not usually used for this purpose, it is based on a concept of post-billing with a fare optimization algorithm associated and it allows the micro-location of passengers throughout their journeys. This paper details the architecture of the solution and its mode of operation. It also presents the evaluation methodology that was followed during the pilot trial that took place in the Metropolitan Area of Porto (AMP), Portugal, during one year with 140 real passengers. A set of design lessons were identified as a result of the field tests and materialized in five mobile ticketing design dimensions, constituting important contributions to the design of future mobile ticketing services. Anda was commercially deployed in the AMP in 2018 and is used by thousands of passengers every day.

The American criminal legal system is an important site of political socialization: scholars have shown that criminal legal contact reduces turnout and that criminalization pushes people away from public institutions more broadly. Despite this burgeoning literature, few analyses directly investigate the causal effect of lower-level police contact on voter turnout. To do so, we leverage individual-level administrative ticketing data from Hillsborough County, Florida. We show that traffic stops materially decrease participation for Black and non-Black residents alike, and we also find temporal variation in the effect for Black voters. Although stops reduce turnout more for Black voters in the short term, they are less demobilizing over a longer time horizon. Although even low-level contacts with the police can reduce political participation across the board, our results point to a unique process of political socialization vis-à-vis the carceral state for Black Americans.

IT companies are popular in the present time as technology arises in the entire world. Incidents and Requests for either clients or company internal are hard to manually track as it is a day-to-day transaction. To keep all the logs, IT Companies are using a ticketing system to track all the records they are gathering. One of the most popular ticketing systems is the JIRA. Either the support team or company internal is benefitting from the use of JIRA as it easily documents all the different transactions and keeps track of different incidents and requests. One of the cons of JIRA is its limited features. The researchers conducted research where it aims to determine the effectiveness of JIRA as a ticketing system despite its limited features. The researchers used a descriptive method to analyze the pros and cons of this ticketing system. It is concluded that this JIRA Ticketing system is helpful for both support groups and internal members of the company as they track all the incidents and request tickets for their clients despite the limited features of JIRA. The researchers strongly recommend to venture out and try other analytic models and start with the ones mentioned in this study for a good foundation and idea. This is can provide the research a new set of results that can be useful to both established and startup companies.

This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor, which leverages the latest Transformers models and machine learning techniques quickly assign issues within an organization, like customer support, help desk and alike issue ticketing systems. The project provides functionality to 1) assign an issue to the correct group, 2) assign an issue to the best resolver, and 3) provide the most relevant previously solved tickets to resolvers. We leverage one ticketing system sample dataset, with over 3k+ groups and over 10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a 79.0% top 5 accuracy on resolver suggestions. We hope this research will greatly improve average issue resolution time on customer support, help desk, and issue ticketing systems.

This study aims to determine the impact of the eticketing application system on bus transportation for the peoples of Bandung. To support the research, the method used was qualitative. The results show that there are some positive impact on the implementation of e-ticketing system, one of which is to increase public interest in public transport in order to reduce traffic jam that often occurs during this time. This is because the implementation of e-ticketing systems on public transportation provides convenience and comfort in ticket booking services, both for the peoples and for the providers of transportation services. Of course the convenience and comfort can encourage peoples to use public transportation in carrying out their daily activities. Keywords— E-Ticketing, Application, System

The electronic ticketing system (e-ticket) is different from the conventional ones which propose offline-based activities. The e-ticket system uses cards to provide convenience while minimizing the incoming queue to the entrance area. However, people have difficulties using such new system and bringing eticket system not optimally working due to the people prefer paying with cash. Therefore, there is a need to measure the extent to which the e-ticket system appropriately received by visitors. The Unified Theory of Acceptance and Use of Technology (UTAUT) is used and consisting of needed hypotheses and variables: performance expectancy, effort expectancy, social influence, facilitating conditions, price value, behavioral intention, and use behavior. This study uses a quantitative approach method using SmartPLS. The results have shown what factors have significant effects on the acceptance of the use of the e-ticket system. Based on the findings, it is found that effort expectancy becomes the most significant factor toward behavioral intention. Further investigation is needed for a better determination.

ABSTRACT Guests heavily rely on photos to evaluate accommodation properties, due to hospitality’s intangible and experiential nature. However, little is known for how to effectively present photos for increasing a guest’s booking intention. Drawing from cue consistency theory, this study examines the effects of photo presentation (i.e., the number of photos, organization of photos, and congruency of the first photo with a headline) on booking intention. The findings indicate that a higher number of photos, organized photos, and congruent first photo with a headline enhance customers’ booking intention. The interaction effects suggest that the organized photos foster booking intention at a lower number of photos with an incongruent first photo with a headline. However, if the first photo is congruent with a headline, customers have no different booking intention between the higher and lower number of photos, regardless of the organization. Implications and limitations are discussed.

This paper empirically analyzes the effect of the environment on hotel customer satisfaction in Southeast Asian countries, as reflected in reviews on online booking sites. The logistic regression method is applied to extract the estimations. The empirical outcomes reveal that the environment can significantly influence customer satisfaction, which means tourists are likely to maximize their satisfaction by choosing destinations that provide a good environment. In addition, room price, hotel location, and service quality can also increase customer satisfaction. Web-based customer reviews potentially affect the booking decisions of future tourists, who carefully evaluate reviewers’ comments when making decisions about accommodation. Hotel authorities in Southeast Asian countries can improve hotel service by adopting renewable energy resources, which may in turn increase the booking interest. Furthermore, customer feedback is an essential factor, and hotel authorities can improve hotel services by considering reviewers’ comments.

This study reveals the key facets of switching costs and their antecedents within the context of online hotel booking in China using mixed methods. Survey results based on 368 valid responses indicate significant antecedents stemming from the characteristics of the online booking market and the individual behavioural and experiential characteristics of customers. However, relational switching costs show a significant positive effect on customers’ intentions to continue with their current online booking service provider, while other switching costs do not. Qualitative research further explores the multi-dimensional nature of customer-perceived switching costs and shows that consumers’ habitual purchases with an online hotel booking service provider form trust, increasing the cost of brand relationship losses and preventing customers from switching. When the self-construal of consumers is different, the homogeneity of providers will have both a positive and negative impact on switching intentions. There are also some interesting findings regarding learning costs in the online hotel booking field. This study extends knowledge about customer retention in the competitive Chinese online booking market by incorporating different types of antecedents and testing their influences on different facets of switching costs as well as the consequences.

This study develops a model for selecting between third-party booking systems from a consumer perspective based on previous information/e-commerce systems success models and a hospitality website quality evaluation model. Factors influencing consumers’ selection of third-party booking systems are identified and the relative importance of each is revealed through the analytic hierarchy process technique. Data is collected from two waves of online surveys and analyzed with the Expert Choice software package. The results reveal the following priorities of factors: security oriented, information quality, service quality, system quality, and marketing oriented. Similarities and differences between these findings and those of past studies are discussed. Based on the analytic hierarchy process results, a practitioner guide on how to design a successful third-party booking system is presented. The developed model is different from hotel selection models and displays more theoretical completeness.

E-Ticketing adalah suatu cara untuk mendokumentasikan proses penjualan online yang hasil keluarannya berupa tiket yang dapat  memudahkan orang untuk membeli tiket untuk berbagai acara semua konser dari satu situs web. Tiket dapat dibeli online melalui pembayaran dengan metode transfer ataupun kartu kredit. Customer tidak perlu lagi menghabiskan waktu untuk datang ke lokasi penjualan tiket, mereka dapat langsung memesan tiket konser yang dijual dari mana saja dan kapan saja dengan kemudahan berbagai sarana pembayaran, serta tidak perlu khawatir akan hilangnya tiket, dicuri, tertinggal, atau bahkan tercebur air, bahkan E-ticketing memungkinkan anda, membelikan tiket untuk kerabat dan orang lain. Permasalahannya adalah customer masih perlu menukarkan tiket fisik dan memilih kursi yang akan mereka tempati pada saat menonton konser, sehingga mereka harus hadir lebih awal jika ingin mendapatkan kursi yang sesuai dengan keinginannya, karena jika hadir belakangan akan menyebabkan kursi yang akan dipilih sudah ditempati oleh orang lain. Dalam hal ini penulis akan mencoba mengembangkan sistem informasi penjualan tiket konser dengan metode seating number. Hasil penelitian ini diharapkan mampu mengatasi persoalan tersebut diatas, sehingga customer bisa langsung memilih kursi saat memesan tiket konser di website, sehingga tidak merepotkan customer pada saat penukaran tiket fisik pada hari-H. Dalam pengembangan sistem ini penulis menggunakan metode prototype yang didalamnya mencakup dari pengembangan sistem pada penelitian sebelumnya yang ditulis oleh Wahyudi, M.Kom. kemudian dikembangkan fitur seating number.

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/ by/4.0) ABSTRACT Ticketing system is an IT service management platform. The ticketing tool provides solutions to all clients who are connected with one or more organizations remotely/within the infrastructure. The Ticketing System is also used as a help desk software that can help customer to get their service as efficiently as possible while maintaining their service requests. The platform of ticketing system is deployed in virtual machine which thus can be accessed by anyone within the organization.

PurposeThe purpose of the study was (1) to examine the motivational composites determining consumers' continued intention to use digital ticketing via self-service technology (SST) by integrating service satisfaction and (2) to ascertain the differences between consumers according to their psychological discomfort toward technology.Design/methodology/approachData were collected using a structured questionnaire, and a total of 323 were included in the analysis. Partial least squares structural equation modeling (PLS-SEM) was utilized to test motivational determinants of digital ticketing. In addition, permutation-based multi-group analysis was performed to investigate the differences between consumers with high and low technology discomfort.FindingsBoth intrinsic and extrinsic motivation are positive predictors of continued intention to use digital ticketing, but only intrinsic motivation has a positive impact on continued intention through service satisfaction. Moreover, the relationship between intrinsic motivation, service satisfaction and continued intention was stronger for consumers with high psychological discomfort than those with low psychological discomfort.Originality/valueGiven the conspicuous characteristics of the digital ticketing process for sporting events, the insights gained from the study may be of assistance to researchers and practitioners in understanding sport consumers' ticket consumption behavior and the determinants of SST usage.

PurposeThis study aims to investigate the effect of deepfake video advertisements on hotel booking intention by applying the media richness theory (MRT) and information manipulation theory (IMT).Design/methodology/approachA quantitative survey was conducted using a structured questionnaire to understand the effect of deepfake hotel video advertisements on booking intention. A large cross-section of 1,240 tourists was surveyed and data were analyzed with partial least squares structural equation modeling (PLS-SEM).FindingsThe outcome of this research provides the factors affecting the booking intention due to deepfake hotel video advertisements. These factors are media richness (MR), information manipulation (IM) tactics, perceived value (PV) and perceived trust (PT). Cognitive load and perceived deception (DC) negatively influence the hotel booking intention.Practical implicationsThe distinctive model that emerged is insightful for senior executives and managers in the hospitality sector to understand the influence of deepfake video advertisements. This research provides the factors of hotel booking intention due to deepfake video advertisements, which are helpful for designers, developers, marketing managers and other stakeholders in the hotel industry.Originality/valueMR and IMT are integrated with variables such as PT and PV to explore the tourists' hotel booking intention after watching deepfake video advertisements. It is the first step toward deepfake video advertisements and hotel booking intentions for tourists. It provides an empirically tested and validated robust theoretical model to understand the effect of deepfake video advertisements on hotel booking intention.

Nowadays, ticketing system plays an important role in people's daily life, especially in concerts, movies, theaters and other activities. However, the traditional ticketing system lacks the supervision of the secondary market and ticket scalpers. It leads to the phenomenon of fake tickets and highpriced tickets, which damages the interests of consumers. At the same time, paper tickets are not environmentally friendly and easily damaged. The ticket with QR code is relatively simple and has no collection value. In order to solve the above problems, in this paper, we propose ticketing system based on Non-Fungible Token (NFT). The link between the ticket and the purchaser is established through blockchain technology. Through the uniqueness of NFT, the activity tickets have more collection value. It describes the schema definition and generation of NFT tickets, secondary markets are introduced and regulated through smart contracts. Through the collection feature of NFT itself, ticket is not only used as a proof of admission, but also improves the experience of fans. Compared with the existing system, the system innovatively introduces NFT, while introducing a secondary market in the transaction and monitoring, making it authentic, supervised, transparent, and available.

Complex transport networks and lack of seamless options are barriers to the use of public transport services. Modern mobile ticketing services allow to travel in a convenient and smooth way, enhancing the customer experience. The successful design of mobile ticketing solutions requires action from complex ecosystem of organizations. This paper presents the codesign of a mobile ticketing service solution based on Bluetooth Low Energy (BLE) technology, resulting from close collaboration between different stakeholders of the ecosystem. The solution is based in a check-in/be-out scheme, requiring an intentional user action when entering the vehicle, being the alight station automatically detected by the system. The mobile phone interact with BLE beacons installed in metro and train stations and inside buses, allowing to locate the customer along the transport network. Different contributions and perspectives of each actor to the design of each activity of the customer experience as well as the final decisions that have been taken are also presented.

The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22% and 7% gain (Accuracy@10) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.

We propose TickEth, a system that aims at mitigating some of the problems encountered by the ticketing industry. The name TickEth is built by combining the words ticket, its application domain, and Ethereum, the underlying platform we adopted for the development of the prototype. Nowadays, ticketing ecosystem is wide and fragmented, and faces several problems. TickEth exploits Ethereum smart contracts to tackle the inability of checking the authenticity of tickets sold online (that can be fake or duplicates of a real ticket), the wild price range of resold tickets in the secondary market, and the unwieldy refund procedures. TickEth is an open specification, where different third-party clients can freely interact, and can be instantiated by different vendors as they like. We implemented a proof-of-concept prototype, available at: https://github.com/H221/TickEth

Status Assessment of E-Ticketing Sutainability for Trans Semarang Bus to Support Smart City using Multidimensional Scaling Approaches: One of the public service facilities using IT in the transportation sector is the eticketing of the Trans Semarang Bus. E-ticketing is a cashless bus ticket payment with the aim to facilitate the service process. The implementation of E- ticketing Trans Semarang Bus engine technology sometimes experiences the obstacles due to crowded service conditions, non-conducive network and signals, so the e-ticketing machine is error and unable to quickly detect data balance. The service clerk has not been able to be technically competent to deal with problems that occur suddenly when the e-ticketing machine is in trouble. The aim of conducting research is to assess the sustainability status of e-ticketing Trans Semarang Bus, which is viewed from the dimensions of input, process, and output, in attempt to determine the factors/attributes that influence the sustainability of the e-ticketing Trans Semarang Bus. The research employed descriptive qualitative-quantitative method with Multidimensional Scaling analysis. This research is expected to be an input in the formulation of the smart city development strategy of Semarang City Government, especially in the transportation sector. The results of the research show the sustainability status of e-ticketing Trans Semarang Bus on the good conditions of input dimension because it is supported by an integrated service, device and technology-oriented roadmap; integrated IT system framework in terms of hardware, software and networks; and IT network infrastructure. The sustainability status of e-ticketing Trans Semarang Bus is in good condition in the dimensions of the process which is supported by collaboration and cooperation among stakeholders; operational financing includes IT professionals, operations, maintenance. Meanwhile, the output dimension has a fairly sustainable status. This might be caused by other factors that need to be comprehensively evaluated.Keywords : Sustainability, Bus Trans Semarang, smart city, multidimensional scaling. Salah satu fasilitas layanan publik yang menggunakan IT di sektor transportasi adalah e-ticketing Bus Trans Semarang. Eticketing merupakan pembayaran tiket bus cashless yang bertujuan untuk mempermudah proses pelayanan. Penerapan teknologi mesin e-ticketing Bus Trans Semarang terkadang mengalami kendala akibat kondisi pelayanan yang ramai serta jaringan dan sinyal yang tidak kondusif, sehingga mesin e-ticketing menjadi error sehingga tidak mampu mendeteksi data saldo dengan cepat. Secara kompetensi teknis, petugas pelayanan belum mampu mengatasi permasalahan yang terjadi secara tiba-tiba ketika mesin e-ticketing mengalami gangguan (trouble). Tujuan penelitian mengenai penilaian status keberlanjutan e-ticketing Bus Trans Semarang yang ditinjau dari dimensi input, proses, dan output adalah untuk mengetahui faktor/atribut yang berpengaruh terhadap keberlanjutan e-ticketing Bus Trans Semarang. Penelitian ini menggunakan metode deskriptif kualitatif-kuantitatif dengan analisis Multidimensional Scaling. Penelitian ini diharapkan dapat menjadi masukan dalam perumusan strategi pengembangan kota pintar Pemerintah Kota Semarang khususnya sektor transportasi. Hasil penelitian menunjukkan bahwa status keberlanjutan e-ticketing Bus Trans Semarang pada dimensi input dalam kondisi baik karena didukung dengan roadmap terintegrasi yang berorientasi pada service, device, dan teknologi; framework sistem IT yang terintegrasi dari sisi hardware, software, dan jaringan; dan infrastruktur jaringan IT. Status keberlanjutan e-ticketing Bus Trans Semarang pada dimensi proses dalam kondisi baik karena didukung kolaborasi dan kerjasama antar stakeholders; pembiayaan operasional yang mencakup profesional IT, operasi, pemeliharaan, pelatihan, dan konsultan; dan interoperabilitas platform IT pada sisi aplikasi dan service. Sedangkan dimensi output memiliki status cukup berkelanjutan. Hal ini kemungkinan dapat disebabkan oleh faktor lain yang perlu untuk dievaluasi secara komprehensif.Kata kunci :Keberlanjutan, Bus Trans Semarang, Kota Pintar, Multidimensional Scaling.

Event tickets being sold in their electronic instances are subject to counterfeiting, profiteering, and black markets. Therefore, suitable service management mechanisms are required to overcome such deficits. This work designs, develops, and evaluates the approach of a Decentralized Ticketing platform—called DeTi—for managing the distribution of electronic event tickets and “regulating” the aftermarket. DeTi offers a dedicated service management functionality by operating through Smart Contracts of Ethereum, such that users can verify tickets’ validity for a given event. Especially, a new mechanism for users to detect fraudulent events is introduced, too. The evaluation performed indicates that DeTi invalidates or validates tickets efficiently via its decentralized and BC-based service management approach. By securing technically a set of underlying processes, DeTi obviates forging, replication, and scalping of tickets, allowing for a well-managed resale ecosystem of tickets based on and limited to the organizers’ initial pricing.

In electronic ticketing system, the malicious behavior of scalpers damages the customer’s interest and disturbs the normal order of market. In order to solve the problem of scalpers, we took two steps. Firstly, we established the electronic ticketing system based on the consortium blockchain (CB-ETS). By establishing CB-ETS, we can make the ticketing market develop better in a controlled environment and be managed by the members in the consortium blockchain. Secondly, we put forward a kind of taxation mechanism for suppressing scalpers based on CB-ETS. Together with the regulatory mechanism, our scheme can effectively reduce the scalpers’ profits and further inhibit scalpers. Through the above two steps, the scheme can effectively resist the malicious behavior of scalpers. Among them, in the process of transferring tickets, we optimized the transfer mechanism to achieve a win-win situation. Finally, we analyzed the security and efficiency of our scheme. Our scheme realizes the anonymity through the mixed currency protocol based on ring signature and guarantees the unforgeability of tickets by multi-signature in the process of modifying the invalidity of tickets. It also could resist to Dos attacks and Double-Spending attacks. The efficiency analysis shows that our scheme is significantly superior to relevant works.

This chapter presents an in-depth study of the current situation of mobile ticketing services in the context of urban passenger transport, and points out future trends and directions that will define forthcoming versions of mobile ticketing services. It defines mobile ticketing services and presents the technologies most used to deliver these solutions. This is complemented by a survey of research studies and experiences of deployments in a real environment. The mobile ticketing ecosystem is then deeply explored, where key players are identified as well as their key drivers and concerns regarding mobile ticketing services. Finally, future trends and research opportunities regarding mobile ticketing solutions are presented.

This study proposes a privacy preserving Blockchain-Based Ticketing Service (BB Tickets), which stores information about events and related tickets in the blockchain network. As the blockchain technologies can ensure information integrity, ticket buyers can use the data stored in the blockchain network to ensure the authenticity of the purchased tickets and to resolve related disputes. Furthermore, the Non-Interactive Zero-Knowledge (NIZK) scheme is utilized in the proposed system to protect user privacy. Therefore, this study contributes to providing a privacy-preserving means for users to enjoy the reliable ticketing service provided by the blockchain technologies.

PurposeWith the yearly increase of mileage and passenger volume in China's high-speed railway, the problems of traditional paper railway tickets have become increasingly prominent, including complexity of business handling process, low efficiency of ticket inspection and high cost of usage and management. This paper aims to make extensive references to successful experiences of electronic ticket applications both domestically and internationally. The research on key technologies and system implementation of railway electronic ticket with Chinese characteristics has been carried out.Design/methodology/approachResearch in key technologies is conducted including synchronization technique in distributed heterogeneous database system, the grid-oriented passenger service record (PSR) data storage model, efficient access to massive PSR data under high concurrency condition, the linkage between face recognition service platforms and various terminals in large scenarios, and two-factor authentication of the e-ticket identification code based on the key and the user identity information. Focusing on the key technologies and architecture the of existing ticketing system, multiple service resources are expanded and developed such as electronic ticket clusters, PSR clusters, face recognition clusters and electronic ticket identification code clusters.FindingsThe proportion of paper ticket printed has dropped to 20%, saving more than 2 billion tickets annually since the launch of the application of E-ticketing nationwide. The average time for passengers to pass through the automatic ticket gates has decreased from 3 seconds to 1.3 seconds, significantly improving the efficiency of passenger transport organization. Meanwhile, problems of paper ticket counterfeiting, reselling and loss have been generally eliminated.Originality/valueE-ticketing has laid a technical foundation for the further development of railway passenger transport services in the direction of digitalization and intelligence.

The adoption of mobile applications on smartphones and tablet computers has become increasingly common among businesses and service providers. It has been proven to be rapid and successful in engaging the attention of consumers, communicating new technologies, and developing processes such as ticketing systems for transport companies. This research employed agile methodology in developing an Android-based mobile ticketing system that involves assessing current processes, suggestions for improving and optimizing processes, designing software, creating, implementing, and system evaluation. The ISO 9126 Software Quality Characteristics were used as a survey questionnaire to determine the user's acceptance level on the quality of the develop system in terms of functionality, reliability, usability, efficiency, maintainability and portability where participant ratings were scaled, mean scores were measured and interpreted qualitatively using a 5-point Likert scale. Results indicate that mobile bus ticketing system (MBTS) implementation and adoption is generally acceptable using the ISO 9126 characteristics as viewed by system users and were participants in this research work. Therefore, for the adoption by the bus operator, MBTS is highly recommended for successful and more efficient transactional processes, knowledge generated is readily accessible and useful for taking decision. In the future, further development of this research can be considered and work on the incorporation of other emerging technologies. Widespread use of mobile apps allows the production of more sophisticated mobile applications and more potential to be explored. Researchers working on similar study can find operators of large bus companies operating in the northern part of the Philippines.

Electronic ticketing (E-ticketing), an innovative mode of issuing and delivering tickets, is becoming prevalent and is employed by many transport companies in Europe, America, Asia, and some parts of Africa to reduce the cost that goes with printing tickets. This study investigated E-ticketing adoption in the bus transport sector in the Kumasi Metropolis of Ghana. The study applied the Technology Acceptance Model (TAM), and the Structural Equation Modelling technique was used to analyse the factors affecting the intention to use the E-ticketing system. In that regard, the study was based on perceived usefulness, perceived ease of use, subjective norms, and demographic variables. Quantitative research approaches were used by distributing questionnaires to 392 passengers, employing the cluster and random sampling techniques. Findings from the study showed that perceived usefulness and subjective norm were the significant factors affecting passengers’ intention to adopt the E-ticketing system. Besides, perceived ease of use and subjective norms were significant in predicting passengers’ perceived usefulness of adopting E-ticketing system. The findings of this research will provide the government, industrial players, and the entire public a better understanding of the perceived factors that could enhance or serve as a barrier to passengers’ adoption of the E-ticketing system. This can also lead to the development of effective E-ticketing marketing strategies.

Public transport sustainability is becoming a major driver for public transport development. Public transport ridership represents one of the key performance indicators of sustainability in the sense of balancing the economic, social and environmental aspects of public transport. There are various methods for improving the attractiveness of public transport for passengers by reducing resistances, which discourage potential and existing passengers to use public transport. Transport ticketing is one of the methods. This article presents a methodological framework for evaluating transport ticketing technologies with the use of a transport ticketing convenience model developed by the authors as well as some survey results through the application of the developed framework on traditional smart ticketing and contactless payment card ticketing technologies. First, a methodological framework for modelling ticketing convenience based on end-to-end passenger experience is presented. Second, a ticketing convenience model for barrier-free and double-sided validation baseline ticketing systems is developed. Third, the ticketing system based on contactless bank payment cards is compared with traditional smart ticketing systems in terms of convenience. It is shown that a contactless payment cards ticketing system has greater convenience or a better, more seamless travel experience than traditional smart systems. Finally, some research perspectives on enhancing the ticketing convenience model by using mobile smartphones with NFC, BLE and GPS technology, as well as the inclusion of technologies related to ticketing such as passenger information systems into the model, are contemplated.

Buses represent the main mode for intercity passenger transportation in China. In recent years, a multichannel ticketing strategy has been widely employed in the bus passenger transportation industry. However, the mechanisms and key drivers of the channels through which bus passengers purchase tickets are underexplored. Thus, the aim of this study is to empirically apply an integrated choice and latent variable (ICLV) approach to analyze ticketing channel choice behavior and the heterogeneous preferences of bus passengers. The variables incorporated in the model include the socioeconomic characteristics of passengers, trip attributes, and latent attitudes with 12 ordinal indicators. Based on the data of 1800 participants collected from the city of Beijing, China, this study develops a ticketing channel choice ICLV model merging a discrete choice model with a structural equation model. The key factors that affect the channel preference are further discussed through a comparison with a conventional multinomial logit (MNL) model. The results reveal that the three attitudinal variables have a significant influence on ticketing channel choice. Furthermore, this study indicates that perceptual differences exist due to various socioeconomic and trip characteristics. Personal privacy is a major obstacle that prevents passengers from choosing online channels, especially for older passengers and those with lower education.

Wijaya Karya (Persero) Tbk. Yaitu perusahaan yang bergerak di bidang infrastruktur. Sebagai perusahaan yang besar WIKA memiliki aplikasi untuk membantu proses bisnisnya. Aplikasi-aplikasi tersebut sebagian besar dikelola oleh departemen Sistem Informasi. Dari kekurangan aplikasi tersebut, tentunya akan ada keluhan-keluhan pengguna aplikasi yang telah dibuat. Proses pekerjaan yang dilakukan saat ini masih belum optimal karena belum adanya report ticket yang memudahkan Agen dan Manajer untuk melihat seluruh data tiket dan tampilan dari web helpdesk ticketing system yang kurang menarik sehingga penulis ingin mengubah tampilannya. Berdasarkan masalah di atas, maka diperlukan perancangan sistem pada aplikasi Helpdesk ticketing system. Perancangan ini menggunakan metode design thinking, yang terdiri dari tahapan empathize, define, ide, prototipe dan tes. Sehingga hasil dari perancangan ini memberikan rekomendasi berupa model UI/UX pada aplikasi Helpdesk ticketing system.

Purpose – Ticketing system is common in Technical Support in Information Technology Industry. At present time, even management is using it. It serves as a way to connect the company and the client, end to end. Support Industry used this also to monitor and log all solutions and processes that come up to resolve the ticket. Client and the resources that will troubleshoot this, use the ticket to have an idea for what is the problem, when the client encountered it, and even the business impact. The researchers conducted research entitled “Impact of Critical and Auto Ticket’s: Analysis for Management and Workers Productivity in using Ticketing System" where it aims to come up with a solution on how we are going to prevent, troubleshoot, and give insight for possible business impact to those everyday issues. This will help the management’s business process to have an analysis to prevent critical issues that will affect the live production as well as give time to their resource to focus on their tasks. Method – Researchers used data collection to gather data from management, support workers, and Service Now’s open-source ticketing system to visualize the ticketing system application. Also, a purposive sampling type of survey has been used to highly represent the data. Results – Critical ticket gives a lot of pressure to the resources as they needed to resolve the incident in accordance with the service level agreement. It affects their productivity to maximize their time to resolve other escalated incidents as they need to focus with critical ones. Instead of maximizing time management and prioritizing employee’s welfare like work and life balance, auto ticket’s affects it because of continuous re-occurring and sometimes needed to be escalated as it will affect the live production. Having knowledge management helps resource to find references on how to deal with the incident. It helps them to execute workaround quickly and think of a way on how to resolve it permanently. Conclusion – It is concluded that critical and auto ticket’s affects the everyday productivity of the worker especially teaching new employees despite ongoing critical incidents. Researchers provided solutions such as knowledge Management and Dashboard to document all the solutions encountered and monitor the SLA’s and incoming tickets. Recommendation – It is recommended to have further research on how critical and auto ticket affects the mental health of resources and its direct impact to businesses. It is also recommended to have a study on how knowledge management work and help resources to identify correct workaround despite of having a lot of troubleshooting guides. Practical Implication – This research benefits data warehousing companies that can be used as a third-party application to connect support team, management, and clients. Management can create a dashboard and identify how their support team provides good quality of service with their response and resolution time. Support team can have a reference to quickly fix escalated incidents and provide ticket number to the client for easily follow up an update.

PurposeThis techno-centric and too much busy day-to-day living style of citizens pressurizes the implementation of E-ticketing service to adapt with change. Thus, this study aims to examine the factors influencing railway passengers’ E-ticketing service acceptance and usage intention in Bangladesh and to extend the widely used Technology Acceptance Model through inserting two new constructs.Design/methodology/approachThis paper employs structural equation modeling to test model’s paths developed through theoretical research framework. Moreover, a structured questionnaire was administered at different railway stations in northern and western parts of Bangladesh to collect data. Total of 302 responses were considered for statistical analysis to test hypotheses after considering anomalies and outliers in raw data.FindingsThe study results show that technology trust (TT) has the strongest impact on passengers’ E-ticketing usage intention rather than perceived ease of use and perceived usefulness (PU). Meanwhile, the easiness of using technology to reserve tickets does matter to female passengers rather than male passengers wherein PU and TT do not do that.Originality/valueThe findings of this study might be helpful for the railway authorities to improve the ticket reservation service quality online by developing the advanced booking application and minimizing the pressure on other transportation. Therefore, this empirical study will contribute to this domain for further study that ensures full satisfaction of passengers and uplift the railway passengers’ usage intention for E-ticketing which then helps the government to implement the digitization slogan with efficiency and effectiveness.

Global Forest Resource Assessment has stated that nearly 80,000 to 160,000 trees are chopped down globally daily for paper production which leads to deforestation which in turn contributes to climate change. Furthermore in any Public Transport System, once the passenger reaches the destination, the ticket is no longer useful and is eventually thrown away. To overcome these issues, a smart ticketing system to support the concept of digitalization by introducing a paperless system for Public Transport System(PTS) is presented. This Smart Ticketing System is implemented by interfacing Radio Frequency Identification(RFID) with widely used microcontrollers- Arduino Uno and Raspberry Pi. This reader system along with the GPS module is attached at both ends of the bus. Passengers carrying the RFID tags are expected to tap the card against the reader while entering and exit. GPS module is attached to get the exact coordinates of the source and destination and the fare is calculated based on the distance travelled. This fare is deducted from the RFID cards which are rechargeable.

Both in the US and abroad, various transit agencies have started to implement mobile ticketing applications, which save commuters time during ticket purchase and allows them to avoid surcharges typically incurred when purchasing tickets on-board. Mobile ticketing applications also have a high potential to help transit agencies reduce costs, give them a better understanding of commuter behaviour, and allow them to make their services more efficient. However, it is not a straightforward task to predict the customers’ reaction and gain their trust and to increase the adoption rate of this relatively new technology. A comprehensive evaluation plan is required to detect and fix critical usability problems during both the application development and system wide implementation phases. To address this need, a four-stage evaluation framework is proposed in this study for mobile ticketing technologies in public transit to improve their usability and enhance their adoptability by the potential users. The proposed evaluation framework was employed when New Jersey transit's mobile ticketing application, MyTix, introduced in 2013, was being developed.

Within the field of mobile payments, the market segment for mobile ticketing services is expected to grow, driven by the public transport sector. Although a large number of studies have explored the value of mobile payments to customers (ie service end users), there is a lack of research exploring the value of mobile payment and mobile ticketing for business customers (ie transport companies and retailers). This paper aims to address this gap and to explore the kinds of value that mobile ticketing services create for public transport companies. The research considers mobile ticketing services in six Northern European countries. The research findings suggest that within the business-to-business context, mobile ticketing services create multiple dimensions of value, namely enhanced technical functionality; financial value; better working environment; operational value; and reputational and customer relationship value. For passengers, meanwhile, value takes the form of convenience and service enhancement. These benefits are factors driving adoption of mobile ticketing and mobile payment services by public transport companies.

Currently, the sphere of sports is continuously expanding in Indonesia. E-ticketing has made it possible for fans to quickly and easily purchase tickets to soccer matches thanks to advances in technology and information. Some studies have found that the ability to distribute tickets rapidly and efficiently is one of the e-greatest ticketing benefits. The goal of this study is to evaluate how soccer fans' perceptions of the ease and advantages of ticket purchasing influence their willingness to make those purchases. Subjects in this study are the 120 football fans who regularly attend PSIS Semarang games in International Stadium Jatidiri Semarang. This study also has a quantitative component. The analysis that is used is a multivariate regression. This study shows that a minimal number of soccer fans still prefer to purchase tickets online. In addition, the study found that (1) ease of usei has a positive impact on perceived usefulness of e-ticketing, (2) ease of use has no effect on the intention to use e-ticketing, and (3) perceived usefulness of e-ticketing have a positive impact on the intention to use e-ticketing.

Due to the rapid shift of companies towards superb customer experience and satisfaction, ticketing systems have come into a prominence and represent a strategic element in business competitiveness. Different software companies have developed very effective software tools for issue tracking, nevertheless, some sub-processes and tasks within the ticketing systems are still performed manually. These manually performed tasks represent bottlenecks, especially at large organizations they result in declined productivity and increased response time. Advancements in machine learning can be used in a novel way in which they are combined with the traditional issue tracking and ticketing systems on the market, to enable optimal operational efficiency in the Customer Service and Support(CSS) Department of large-scale businesses that deal with customer reports. This paper proposes an integrated approach to customer support by treating three seemingly different bottlenecks in the ticketing system: spam detection, ticket assignment and sentiment analysis. We use primary data to implement and apply the proposed machine learning approach. The evaluation shows promising results in terms of accuracy and efficiency of our approach.

Since 2012, many major commuter railroads have deployed mobile ticketing applications (or “apps”) that allow passengers to pay fares directly using their smartphones. In light of this rapid technological change, this research aims to provide a synthesis of the current state of mobile ticketing in the United States. The 14 largest commuter railroads that have launched mobile ticketing apps are compared in four different areas: (1) the ticket validation process; (2) ticket types offered in the mobile app; (3) additional features in the app; and (4) the process for transferring to other modes using the app. The results reveal that all mobile ticketing applications considered in this analysis utilize visual inspection for validation, and that most of them also use quick response (QR) barcodes to validate tickets. Additionally, many of the commuter rail operators examined offer the majority of the ticket types available via traditional fare media in their mobile ticketing apps. The third dimension revealed a large degree of variation in the availability of additional travel-related features, such as trip planners and schedules, in mobile ticketing apps. Last, only a handful of commuter rail operators have fully integrated transfer policies between commuter rail and other nearby transit modes using mobile ticketing, which is an area that warrants further study. These findings are important for other commuter rail and transit operators that are considering deployment of mobile ticketing systems.

Objectives: To compare the services and technologies of automatic train ticketing systems. Methods: The Internet of Things (IoT), Radio Frequency Identification Technology (RFID), Quick Response Code (QR Code) and Near Field Communication (NFC) are used for automatic ticketing in the trains as well as in buses. A comparison of different machines is also done that are used for tickets-generation, ticket checking, punching machines like QR scanner, BT Services, RFID. Findings: Results are compared with manually checking (by the person) for the validity of the ticket. After the comparison of different technologies and services to find the advantages and disadvantages of each in terms of time and cost, a new method/ technology is introduced for local train ticketing system that is better and more robust than previously used technologies and services.

The world is moving towards automation. The job of conductor is one of the many that is on the wane due to automation. The most popular form of automated ticketing is using RFID. RFID based ticketing system is inexpensive to implement and reliable in operation. Cities across the world have successfully implemented this technology in metro trains and some cities like Singapore have also successfully implemented in the buses. The void that lies in all the above-mentioned places is a foolproof system. The systems rely on the promptness of the passengers for ensuring ticketless travel. This paper presents a method to avoid ticketless travel by implementing a two-way authentication for the head-count. A mismatch in the outputs of both would imply ticketless travel

E-ticketing service is one of the most popular innovations in the rail transportation service. Considering the significance of the service, the study aims to explore customer satisfaction of the e-ticketing system of the Bangladesh Railway. It also investigates the gender perspective in regards to the service quality dimensions. A cross-sectional study was conducted among 361 respondents, followed by a quantitative approach. A structured questionnaire was used to survey the passengers from different railway stations in Dhaka City. The findings showed that the service quality indicators such as well customized website (ß= .130, p value< 0.05), ease of using the website (ß = .339, p value<0.05), ticket purchasing and website crash (ß = .162, p value< 0.05), web browsing security (ß = .150, p value< 0.05), NID & credit card payment security (ß = .186, p value< 0.05) have significant impact on customer satisfaction. Both Multivariate analysis of variances and discriminant analysis revealed that male and female customers have a significant variation in perception regarding user-friendliness and fulfilment dimension. Concentrating on the findings, the study has also suggested some implications to the government and the concerned organizations, such as enforcing gender-sensitive policy (reserved compartments for women in railway service) to increase female satisfaction of the e-ticketing service of Bangladesh Railway.

Every year, the Industrial University of Ho Chi Minh City presents a cultural program in conjunction with the start of the academic year. Students from the school are given free tickets to attend this event. However, there has been an occurrence problem with fake tickets or ticket sales appearing throughout the years. To assure the validity of the ticket issued, we suggest a ticketing issuance and ticketing traceability management system by applying smart contract technology on the blockchain platform to store, manage, and trace the origin of tickets. The system is designed on the Ethereum platform to implement smart contracts and blockchain technologies using the Solidity language. A blockchain is a decentralized database that holds data in encrypted chunks that grow over time. These information blocks function independently. The benefit of block chain is that it offers great security by preventing information theft and manipulation. To evaluate the performance of the suggested system, it was carried out with real student data at the Faculty of Information Technology at the Industrial University of Ho Chi Minh City. The experimental results show that the proposed system carries out all functions well.

Nowadays, under the encouragement of national and sports competition related policies, sports industry is gradually rising. In this case, how to provide ticketing services conveniently, quickly, and accurately for sports spectators all over the country and the world and how to keep up with the times and improve traditional ticketing services are two major problems that need to be solved urgently by those who are in charge of sports events. When investigating the current traditional ticketing platform for sports competitions, it is not difficult to find that most ticketing services rely heavily on manual services. From ticket purchase to ticket checking, consumers have poor experience, there is a lot of waste of manpower and material resources, and scalpers easily sell high-priced tickets. In addition, the traditional ticket sales mode still uses old-fashioned paper tickets or cards, which are difficult to preserve, making it difficult to prevent counterfeiting and use and control information statistics. Data confusion leads to financial risks. The “paralysis” of the ticketing system of many large-scale sports competitions, especially super-large sports competitions, just proves the importance and necessity of reforming ticketing platform of sports competitions in order to keep pace with development. This paper will study a large-scale (or super-large), flexible, efficient, and excellent performance, which can meet the burst peak access requirements of sports competition ticketing platform. We will first consider the overall demand of the sports competition ticketing market, then make a general analysis according to the demand, design a sports competition ticketing platform that can effectively serve large-scale or super-large sports competitions, and raise and solve the problems encountered when building the platform.

In recent years, <underline>e</underline>vent-<underline>b</underline>ased <underline>s</underline>ocial <underline>n</underline>etworks (EBSNs), such as Meetup, Eventbrite and Douban, have emerged as a popular new type of social network on which online users can organize and register for offline social events. Existing approaches focus mainly on arrangement strategies that include users and events on an EBSN platform under an offline scenario, where all information is known in advance. However, these strategies ignore the importance of the organizer, and the offline scenarios can be impractical since all elements appear dynamically in reality. Therefore, we consider not only the user and event in the EBSN platform under the online scenario but also the importance of the organizer. In this paper, we study the complex dynamic event participant in an EBSN that considers three elements: users, events and organizers. We first formalize the event arrangement problem in the online scenario through <underline>e</underline>vent <underline>a</underline>rrangement through <underline>t</underline>hree-<underline>d</underline>imensional <underline>m</underline>atching (EATDM). Then, we propose a straightforward solution of the EATDM problem, a greedy algorithm with a competitive ratio, and further develop a basis solution, a random algorithm. We also propose a threshold-based algorithm and a weighted-threshold algorithm with a tighter competitive ratio. Finally, we verify the validity and practicability of the proposed algorithms on synthetic and real datasets, and we find that the weighted-threshold algorithm performs better than the random algorithm, the greedy algorithm and the threshold-based algorithm in terms of the total utility value.

Almost all parts in an organization both commercial and non-commercial must have held an event. No exception in the education, especially at the University. The number of events carried out on campus is a phenomenon that makes students who are members of organizations learn to organize it so that it can run well. Organizing events is not easy and requires a lot of energy, time and costs starting from marketing, registering, ticketing, participant validation, and distribution of certificates. The aim of this research is to analyze the ineffectiveness and inefficiencies that occur in running an event. Analysis and observations were made based on the processes that took place in the holding of events held by students at Universitas Atma Jaya Yogyakarta. The process will be simplified by utilizing digital media so that the process of making an event becomes more effective and efficient. This research resulted in a BPR (Business Re-engineering Process) in which the marketing, registration, and ticketing processes that were originally done manually were, converted to digital with a web-based system. It is expected that from this BPR, researchers can develop an application to digitalize marketing, online event registration, ticketing, and event validation using QR Code.   Keywords— event; marketing; promotion; QR code; registration

Information communication technologies bring the revolution into all business sectors, and transportation sector is not an exception. Ticketing system has changed from traditional to intelligent, which provides information and service to the consumer. In developed countries such systems are implemented and operate successfully, while in the developing countries electronic ticketing and other similar innovative solutions face specific challenges. These challenges are related to information era and changes in consumer behaviour, caused by the development of information and communication technologies. In these new conditions the motives of consumers to choose electronic ticketing has become an extremely important factor of success. Lack of integrity of consumer behaviour and technology acceptance (electronic ticketing in particular) was identified in previous scientific research, especially taking into consideration recent conditions of developing countries. The aim of this article is to evaluate the consumers’ behaviour and acceptance of intelligent systems, such as electronic ticketing, in order to identify factors, influencing and encouraging the customers to use electronic ticketing systems. In this study extended technology acceptance model with trust element was used to measure the consumer behaviour. The sample for this research has been taken from China and Pakistan populations and consists of 432 participants from both countries.

The demand for mobility has evolved with increasing globalization. Multimodal passenger transport has grown steeply. The ease of obtaining information on the schedule, making a ticket reservation, and making a payment is obvious. This method of e-ticketing is also more cost-efficient, simple and practical, compared to conventional ticketing system with a hardcopy ticket. Nearly all transport providers have been providing an e-ticketing service, both independently and integrated in a multimodal e-ticketing platform. This paper outlines a study on the integration of e-ticketing system of a ferry operator, and an effort to integrate it into a common e-ticketing platform. The finding shows that the biggest challenge of entering into an e-ticketing system and integrating it into an e-commerce, i.e. common e-ticketing platform lies in the core of ship operations, namely the necessity to have an access on a real-time and reliable ship scheduling.

Introduction: The Irish Paramedicine Education and Research Network (IPERN) is dedicated to collaboratively building research culture and research capacity for out-of-hospital care in Ireland and internationally. IPERN is led by an inter-professional team of paramedics, nurses, doctors, allied health professionals and scientists. IPERN supports clinicians to bridge the theory-practice gap through involvement in research training, knowledge generation, knowledge translation, evidence implementation, policy setting, research partnerships, co-production and research leadership. The aim of this study is to quantitatively describe the activity of IPERN in the first two years of it’s foundation (2021-2022). Method: This is an observational study and data was collected prospectively throughout 2021 and 2022. Data on event attendance was collated via the Event management software EventBrite and supplemented with minutes from IPERN Committee meetings. Data analysis was performed in Microsoft Excel and comprised descriptive statistics. Results: The IPERN Team comprises 14 inter-professional members of whom nine (64%) are paramedics. To date IPERN has launched seven special interest groups; Medical, Trauma, Pediatrics, Mental Health, Human Factors, Medical Logistics and Equality Diversity & Inclusion. The network hosted 14 CPD events from March 2021 to November 2022. In terms of research capacity building, the work of IPERN has been presented at six international conferences and the IPERN Team has successfully secured four grants since the foundation of the network. Conclusion: Due to the complex and multidisciplinary nature of out-of-hospital care a strategic and collaborative approach to research capacity-building is essential. Underpinning evidence-based practice is a strong research culture and it is imperative that all clinicians involved in out-of-hospital care have the opportunity to develop knowledge and expertise. IPERN takes a participatory approach to research and our events provide an open and friendly platform for members to engage in research, building a vibrant research community both in Ireland and internationally.

With the increasing demand for autonoumous ticket booking services for all kinds of events, user numbers and service performance requirements increase dramatically. Platforms such as BASH, Ticketmaster, Eventbrite, and SeatGeek, often outsource such services to third parties, but the operation cost is often high. Therefore the performance for time-critical requirements is without monitoring. Furthermore, the cloud platform is becoming a popular solution for event opganizing platforms due to its mighty computation power. In order to address these issues, this study presents a cloud-based, performance-adaptive, and time-monitored autonomous ticket booking system, utilizing a paradigm for booking as a service that can be readily included into cloud platforms. Together with the implementation itself, we also provide a variety of flexible service settings that may serve a specified number of concurrent users while consuming the least amount of resources. We carry out thorough implementations to verify our methodology.

Wellbeing is now officially acknowledged as a vital part of human societies, with the Australian government’s federal treasurer Jim Chalmers implementing a “wellbeing budget” in October 2022 that would seek to “measure what matters” and enable proactive strategies to enhance society (Wright). This could not be more welcome as we live through climate catastrophes – floods, hurricanes, and heat-waves leading to unprecedented burning, and experiences of burnout – from surviving cost-of-living, health, and housing crises, and grappling with advances in AI technologies amid escalating global uncertainty. And yet, when wellbeing is invoked without being matched, for example, by the progressive taxation of billionaires and corporations whose concentration of wealth is accelerating these crises, ever greater numbers will languish instead of flourishing.  This languishing is, of course, unevenly experienced. It has already been endured on a larger scale by inhabitants of the so-called Global South than those of the North, and by emerging, rather than established, generations whose prospects of a liveable future continue to be eroded and, as economist Alison Pennington puts it, “f’d”. How can Earth’s inhabitants be well while its lifeforms are continually degraded by extractivism? On- and offline violence, COVID-19, neofascism, and datafication present formidable challenges to wellbeing. As we move into hotter and faster worlds whose technologies amplify as much as they might mitigate historical injustices, the need to create, find, and secure wellbeing will only grow. We are therefore heartened by this issue’s case studies of wellbeing, which provide vital contributions to thinking with and beyond our present states of depletion.  With a clear understanding of what it isn’t, what is wellbeing? This issue of M/C Journal emerges from a symposium held in 2022 on Cultures of Wellbeing by the Cultural Sociology Thematic Group of The Australian Sociological Association. In response to multisystemic crises and the urgent need to re-imagine human futures, we called for papers that explore cultures of wellbeing. Beyond individual survival, we asked, what cultures, systems, and structures are needed to enable human flourishing at the crossroads of the climate emergency (with its attendant environmental and health crises), an infodemic, and growing inequality?    In designing an Eventbrite invitation for our symposium, we sought suitable pictures of wellbeing through Internet image-searches. These yielded generic, pastel, and corporatised results: wellness stock images and rainbow word clouds that poorly represented the nuances of our thinking. Our eventual choice, also this issue’s cover image, is a photograph of colourful fungi growing on Gadubanud (Katabanut) Country in the Otway rainforest of Victoria. Captured by artist CJ Conway, and reproduced with permission, this image features entangled, flourishing material life-forms that reinforce and rely on one another. In contrast to atomised, flattened neoliberal wellness paradigms, these myriad mushrooming existences cannot thrive in isolation, and depend on textured systems of nurturing.  Such systems are observed by First Nations’ issue contributors Kathleen Butler and Phoebe McIlwraith in their article “Garihma (to Care for): Examining Recent Media Coverage of Bulihm (Tea Tree) through a Cultural Lens”, in which the authors affirm their culture’s inextricable link to Country and “the guardianship of that relationship [as] a foundation for life and a key indicator of wellbeing”. Drawing on their exploration of Tea Tree oil in wellness cultures reproduced in print media and TikTok, Butler and McIlwraith highlight the role this phenomenon has played in the continued colonisation and exploitation of First Nations knowledges. As Tea Tree only grows on the lands of the Bundjalung people from North Coast New South Wales, and has been used for thousands of years as part of oral histories and spiritual governance, Butler and McIlwraith call for the structural reaffirmation and recontextualisation of First Nations ancestral medicines. Our understanding of wellbeing, therefore, comprises connectedness, the material and the immaterial, and more-than-human mutually flourishing entanglements. As Deborah Lupton, Vaughan Wozniak-O'Connor, Megan Rose, and Ash Watson accordingly reflect in their article on “More-than-Human Wellbeing: Materialising the Relations, Affects, and Agencies of Health, Kinship, and Care”, wellbeing is “a process of mark-making, realised through the reciprocal impressions we leave on each other and the world around us”. Lupton et al. present us with ways of exploring wellbeing through nature, showing how dis/connections can facilitate and inhibit this. In providing insights into the More-than-Human Wellbeing exhibition, Lupton et al. explore health and wellbeing in a digitised and datafied world. Through a series of interactive works, they attune visitors to multisensory ways of knowing that emerge from their entanglements with the digital and more-than digital things that have the potential to open capacities for health, kinship, and care.  This interconnected, entangled, reciprocal, and more-than-human quality of wellbeing is of a piece with multisystemic theories of resilience (Ungar). As Vivian Gerrand, Kim Lam, Liam Magee, Pam Nilan, Hiruni Walimunige, and David Cao write in their feature article about object-oriented wellbeing, “What Got You through Lockdown?”, socio-ecological, strengths-based approaches to resilience and their role in wellbeing go beyond mere individual attributes. The authors view resilience and wellbeing as everyday social processes that are contingent on the resources people can access (Masten). Gerrand et al. take stock of such resources in their objects-based project that worked with material things that helped people endure confinement in Melbourne’s long lockdowns (2020-21). In their collection of objects from Victorian-based participants and curation of an art exhibition that arranges and interrogates these objects, Gerrand et al. sought to extend socio-ecological conceptions of resilience and the ways in which nonhuman materialities can contribute to everyday wellbeing during times of adversity.  Wellbeing not only pertains to withstanding hardship, but also flourishing in spite of these conditions. As Juliane Roemhild and Melinda Turner explore in their article, the practice of Shared Reading, a form of creative bibliotherapy, can nurture the wellbeing of individuals and communities in our uncertain times. Flourishing entails both feeling good about one’s life, as well as being able to function inside of its paradigms, something that is enabled by shared encounters with books. This is in contrast to a sense of “stagnation” and “quiet despair” (Keyes) that might arise in unwanted isolation from others or when one feels alone in one’s experiences. To flourish in this regard reflects Aristotle’s concept of eudaimonic well-being through the way one reaches their full potential in the ecology within which they are situated. This stands in contrast to hedonistic well-being that is centred on the happiness and pleasure of the individual. Patricia Webb complicates this categorisation of wellbeing by combining both the needs of the individual and their ability to flourish in society through her article on the transformative potential of metaphors in creative writing. Through meeting one’s own potential, individuals can develop the skills to flourish. Webb explores how metaphors in creative writing expand our thinking and our practices and their effects in the world. Through the power of the metaphor we are able to better articulate our meanings and think outside the box. Webb explores how we think and act through the metaphor and relate to others through the conceptual system it offers. The latter two articles, by Jay Daniel Thompson and Bridget Mac Eochagain, consider threats to wellbeing. These appear in online spaces, where women’s wellbeing is often threatened by harassment and trolling, and in the trauma of sexual assault. Following a case study of the online harassment of Australian Journalist Lisa Millar this year, Thompson traces the contours of sexual violence and harassment and its amplification and reproduction through news reportage. In doing so, the author points to Hall’s observation of re-presentation, by which signal-boosting violence can not only raise awareness but further amplify the violence experienced through ecologies of outrage. Thompson explores and offers critical guidance for how within this context online misogyny might be ethically reported on. Bridget Mac Eochagain considers the role of theatre plays in exploring the parameters of wellbeing, through their ability to interrogate the political, legal, and social systems in which we live. Through a case study of Suzie Miller’s one-woman play Prima Facie, Mac Eochagain explores how the lived experiences and trauma of sexual assault survivors are often overlooked in structural efforts to reach “legal truths”. This compounds sexual violence. Theatrical engagements that make visible these often taken-for-granted structures, therefore, offer promising avenues for justice, empowering change, and wellbeing. For Mac Eochagain, Prima Facie presents audiences in the wake of the global pandemic and systemic unrest with a chance to begin healing through seeing themselves in the protagonists’ struggles. Our contributors’ explorations define and deepen our understanding of wellbeing in adverse times. Dreaming of mushrooming (not death caps!), we encourage you to savour their insights and be well. References Keyes, Corey L.M. “The Mental Health Continuum: From Languishing to Flourishing in Life.” Journal of Health and Social Behavior 43.2 (June 2002): 207–22. Masten, Ann S. “Ordinary Magic. Resilience Processes in Development.” American Psychologist 56.3 (2001): 227–238. Penningto

This paper examines the application of statistical methods in the management of cultural and mass events, emphasizing their strategic importance in the context of digital transformation, resource constraints, and rising expectations for event quality and accountability. Cultural events – such as festivals, concerts, exhibitions, and city-wide celebrations – are increasingly recognized as integral components of regional development strategies. They contribute to community engagement, the promotion of cultural diversity, tourism growth, and place branding. However, the effective organization of such events requires more than creative content and logistical coordination; it demands evidence-based management grounded in reliable data and analytical tools. The study presents a comprehensive framework for implementing statistical methods throughout the event management lifecycle. It explores how descriptive statistics, regression and correlation analysis, audience segmentation through cluster analysis, and real-time data visualization (including geolocation-based tools and heat maps) support data-driven decision-making. At the planning stage, statistical tools enable a better understanding of target audiences, optimal resource allocation, and the development of adaptive marketing strategies. During the implementation phase, digital technologies such as QR-code scanning, real-time crowd analytics, and mobile feedback platforms allow for continuous monitoring and situational adjustments. Post-event evaluation incorporates satisfaction surveys, regression models to assess return on investment, and benchmarking against previous events or sectoral standards. An empirical survey conducted among Ukrainian event organizers reveals that while 65–78% apply basic statistical tools (e.g., Google Forms, CRM analytics), only a minority utilize them systematically across all stages. The study highlights significant challenges – including limited digital competencies and underfunding – as well as opportunities for growth through the adoption of global best practices. Comparative analysis with international data, including sources from Eventbrite, Statista, and UNESCO, confirms the global trend toward digitization and analytics in cultural event management. The paper concludes that integrating statistical methods into the strategic framework of cultural management enhances operational efficiency, audience satisfaction, and long-term sustainability.

Drastic changes in the wake of COVID-19 have been witnessed in many sectors. The education sector has also been affected due to lockdowns. Exclusive online teaching methodologies were used due to the closing of schools. Platforms such as Zoom, Eventbrite, Microsoft Teams and many others were used for language teaching. Language teachers were trained regarding the use of digital tools, and this led to a radical shift to online language teaching. However, we are now moving back from enforced online teaching during the initial era of Covid-19 to teacher fronted classrooms. In this research, the focus will be what language teachers have learnt from that time and are using now for more effective teaching of reading skills. Data collected through interviews with 15 teachers and 10 students who have recently shifted to a hybrid way of language teaching and learning were qualitatively analyzed to demonstrate the benefits of hybrid language teaching and learning. Findings show that this methodology is becoming popular among teachers and students, because it enhances both teaching and learning and is effective in achieving teaching and learning goals. In summary, the wise and selective use of digital tools and face to face teaching for the teaching of a range of reading skills improves learning and their strategic use by the teacher should be encouraged and is discussed in this research.

Arrahmah Japan adalah komunitas muslim di Jepang yang baru saja mendirikan musala awal tahun 2022 di daerah Kanagawa, Jepang. Telah banyak kegiatan dakwah islam yang diadakan di musala ini, namun publikasinya belum dilakukan secara optimal. Sebagai pusat kegiatan Islam yang sangat jarang ditemukan di Jepang, Arrahmah saat ini membutuhkan media untuk dapat membantu menyebarkan informasi terkait kegiatan-kegiatan yang dilakukan. Tim dosen dari Fakultas Informatika Telkom University melalui program abdimas berupaya membantu dalam hal ini, yaitu dengan mengembangkan situs web komunitas sebagai media one stop service untuk menyebarkan informasi kegiatan-kegiatan yang dilakukan di Musala Arrahmah. Keseluruhan proses abdimas dilakukan dengan pendekatan PAR/CBPR (participatory action research/community based participatory research), di mana pada tahap action-nya diberikan solusi berupa pengembangan situs web komunitas arrahmah.jp. Pengembangan situs web ini dilakukan dengan metodologi pengembangan perangkat lunak Spiral yang merupakan kombinasi antara model waterfall dan prototipe. Situs web dikembangkan dengan menggunakan WordPress Content Management System (CMS) dan event management EventBrite untuk mengelola layanan pendaftaran untuk berbagai kegiatan musala, baik yang rutin maupun tidak. Berdasarkan hasil evaluasi melalui kuesioner, dapat disimpulkan bahwa situs web yang dibangun telah berhasil mencapai tujuan yang diinginkan dan mendapat penilaian cukup baik dari komunitas.

Unter dem Namen „ZUKEdigital“ (ZUKE: Zukunft Krankenhaus Einkauf) hat am 16. Juni der erste digitale Kongress von Krankenhauseinkäufer*innen für Krankenhauseinkäufer*innen stattgefunden: Mit perfekter Vorbereitung und unter Nutzung der sozialen Medien zur Werbung sowie Tools wie „Eventbrite“ zur kostenlosen Kartenvergabe. Die Resonanz war überwältigend positiv.

Networks have been widely used to represent the relations between objects such as academic networks and social networks, and learning embedding for networks has thus garnered plenty of research attention. Self-supervised network representation learning aims at extracting node embedding without external supervision. Recently, maximizing the mutual information between the local node embedding and the global summary (e.g. Deep Graph Infomax, or DGI for short) has shown promising results on many downstream tasks such as node classification. However, there are two major limitations of DGI. Firstly, DGI merely considers the extrinsic supervision signal (i.e., the mutual information between node embedding and global summary) while ignores the intrinsic signal (i.e., the mutual dependence between node embedding and node attributes). Secondly, nodes in a real-world network are usually connected by multiple edges with different relations, while DGI does not fully explore the various relations among nodes. To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way. To be more specific, we first design a joint supervision signal containing both extrinsic and intrinsic mutual information by high-order mutual information, and we propose a High-order Deep Infomax (HDI) to optimize the proposed supervision signal. Then we propose an attention based fusion module to combine node embedding from different layers of the multiplex network. Finally, we evaluate the proposed HDMI on various downstream tasks such as unsupervised clustering and supervised classification. The experimental results show that HDMI achieves state-of-the-art performance on these tasks.

ABSTRACT Fungal deconstruction of the plant cell requires a complex orchestration of a wide array of intracellular and extracellular enzymes. In Neurospora crassa, CLR-1, CLR-2, and XLR-1 have been identified as key transcription factors regulating plant cell wall degradation in response to soluble sugars. The XLR-1 regulon was defined using a constitutively active mutant allele, resulting in hemicellulase gene expression and secretion under noninducing conditions. To define genes directly regulated by CLR-1, CLR-2, and XLR-1, we performed chromatin immunoprecipitation and next-generation sequencing (ChIPseq) on epitope-tagged constructs of these three transcription factors. When N. crassa is exposed to plant cell wall material, CLR-1, CLR-2, and XLR-1 individually bind to the promoters of the most strongly induced genes in their respective regulons. These include promoters of genes encoding cellulases for CLR-1 and CLR-2 (CLR-1/CLR-2) and promoters of genes encoding hemicellulases for XLR-1. CLR-1 bound to its regulon under noninducing conditions; however, this binding alone did not translate into gene expression and enzyme secretion. Motif analysis of the bound genes revealed conserved DNA binding motifs, with the CLR-2 motif matching that of its closest paralog in Saccharomyces cerevisiae, Gal4p. Coimmunoprecipitation studies showed that CLR-1 and CLR-2 act in a homocomplex but not as a CLR-1/CLR-2 heterocomplex. IMPORTANCE Understanding fungal regulation of complex plant cell wall deconstruction pathways in response to multiple environmental signals via interconnected transcriptional circuits provides insight into fungus/plant interactions and eukaryotic nutrient sensing. Coordinated optimization of these regulatory networks is likely required for optimal microbial enzyme production. Understanding fungal regulation of complex plant cell wall deconstruction pathways in response to multiple environmental signals via interconnected transcriptional circuits provides insight into fungus/plant interactions and eukaryotic nutrient sensing. Coordinated optimization of these regulatory networks is likely required for optimal microbial enzyme production.

Dyskeratosis congenita related telomere biology disorders (DC/TBDs) are characterized by very short telomeres caused by germline pathogenic variants in telomere biology genes. Clinical presentations can affect all organs and inheritance patterns include autosomal dominant (AD), autosomal recessive (AR), X-linked (XLR) or de novo. This study examined the associations between mode of inheritance with phenotypes and long-term clinical outcomes. Two hundred thirty-one individuals with DC/TBDs [144 male, 86.6% known genotype, median age at diagnosis 19.4 years (0-71.6)], enrolled in the National Cancer Institute's Inherited Bone Marrow Failure Syndrome Study, underwent detailed clinical assessments and longitudinal follow-up [median follow-up 5.2 years (0-36.7)]. Patients were grouped by inheritance pattern, considering AD-nonTINF2, AR/XLR, and TINF2 variants separately. Severe bone marrow failure, severe liver disease and gastrointestinal telangiectasias were more prevalent in AR/XLR or TINF2 disease, whereas pulmonary fibrosis developed predominantly in adults with AD disease. After adjusting for age at DC/TBD diagnosis, we observed the highest cancer risk in AR/XLR individuals. At last follow-up, 42% of patients were deceased with a median overall survival of 52.8 years [95% confidence interval (CI) 45.5-57.6] and the hematopoietic cell or solid organ transplant-free median survival was 45.3 years (95% CI 37.4-52.1). Significantly better overall survival was present in AD versus AR/XLR/TINF2 disease (p<0.01), while patients with AR/XLR and TINF2 disease had similar survival probabilities. This long-term study of the clinical manifestations of DC/TBDs creates a foundation for incorporating the mode of inheritance into evidence-based clinical care guidelines, and risk stratification in patients with DC/TBDs. clinicaltrials.gov NCT00027274.

For all animals, the taste sense is crucial to detect and avoid ingesting toxic molecules. Many toxins are synthesized by plants as a defense mechanism against insect predation. One example of such a natural toxic molecule is L-canavanine, a nonprotein amino acid found in the seeds of many legumes. Whether and how insects are informed that some plants contain L-canavanine remains to be elucidated. In insects, the taste sense relies on gustatory receptors forming the gustatory receptor (Gr) family. Gr proteins display highly divergent sequences, suggesting that they could cover the entire range of tastants. However, one cannot exclude the possibility of evolutionarily independent taste receptors. Here, we show that L-canavanine is not only toxic, but is also a repellent for Drosophila. Using a pharmacogenetic approach, we find that flies sense food containing this poison by the DmX receptor. DmXR is an insect orphan G-protein-coupled receptor that has partially diverged in its ligand binding pocket from the metabotropic glutamate receptor family. Blockade of DmXR function with an antagonist lowers the repulsive effect of L-canavanine. In addition, disruption of the DmXR encoding gene, called mangetout (mtt), suppresses the L-canavanine repellent effect. To avoid the ingestion of L-canavanine, DmXR expression is required in bitter-sensitive gustatory receptor neurons, where it triggers the premature retraction of the proboscis, thus leading to the end of food searching. These findings show that the DmX receptor, which does not belong to the Gr family, fulfills a gustatory function necessary to avoid eating a natural toxin.

Simultaneous localization and mapping (SLAM) estimates an agent’s trajectory for all six degrees of freedom (6 DoF) and constructs a 3D map of an unknown surrounding. It is a fundamental kernel that enables head-mounted augmented/virtual reality devices and autonomous navigation of micro aerial vehicles. A noticeable recent trend in visual SLAM is to apply computation- and memory-intensive convolutional neural networks (CNNs) that outperform traditional hand-designed feature-based methods [1]. For each video frame, CNN-extracted features are matched with stored keypoints to estimate the agent’s 6-DoF pose by solving a perspective-n-points (PnP) non-linear optimization problem (Fig. 7.3.1, left). The agent’s long-term trajectory over multiple frames is refined by a bundle adjustment process (BA, Fig. 7.3.1 right), which involves a large-scale ($\sim$120 variables) non-linear optimization. Visual SLAM requires massive computation ($\gt250$ GOP/s) in the CNN-based feature extraction and matching, as well as data-dependent dynamic memory access and control flow with high-precision operations, creating significant low-power design challenges. Software implementations are impractical, resulting in 0.2s runtime with a $\sim$3 GHz CPU + GPU system with $\gt100$ MB memory footprint and $\gt100$ W power consumption. Prior ASICs have implemented either an incomplete SLAM system [2, 3] that lacks estimation of ego-motion or employed a simplified (non-CNN) feature extraction and tracking [2, 4, 5] that limits SLAM quality and range. A recent ASIC [5] augments visual SLAM with an off-chip high-precision inertial measurement unit (IMU), simplifying the computational complexity, but incurring additional power and cost overhead.

This paper reports a 60-GHz four-element beam-tapering phased-array transmitter with a lower sidelobe level (SLL). The array factor and the error vector magnitude (EVM) were calculated to explain the low sidelobe characteristic of a nonuniform amplitude array and to emphasize the importance of phase compensation, respectively. The phase-compensated radio frequency (RF) variable gain amplifiers (VGAs) were proposed to compensate the gain variance of phase shifters and, simultaneously, excite the nonuniform amplitude to each array element. The proposed VGA improves the relative phase error by 2° and the OP1dB by 2.4 dB within a gain control range of 5 dB, compared to the current steering without any compensation. Implemented in a commercial 65-nm CMOS process, the integrated array chip displays an root-mean-square (rms) gain error of <0.53 dB and an rms phase error of <8.8° in the frequency range of 57–66 GHz, while consuming a dc power of 403.2 mW and occupying a chip area of 2.88 mm2. The fabricated array was integrated with a $1 \times 4$ Teflon array antenna to verify the beamforming and beam-tapering operations. With proper gain and phase control, the integrated array decreases the SLL by up to 5.2 dB, and the minimum SLL is −15.2 dB. To the best of the authors’ knowledge, this is the first report on the implementation of CMOS beam-tapering arrays in the 60-GHz band.

This paper presents a broadband variable gain amplifier (VGA) with low phase variation in 65 nm CMOS technology. The mechanism of phase variation in CMOS VGA is analyzed. According to the analysis, the feedforward paths formed by parasitic capacitive and inductive couplings are one of the main factors that result in phase variation in CMOS VGA. In order to achieve low phase variation, a parasitic capacitor elimination technique is proposed to remove the feedforward path formed by gate-drain parasitic capacitor. Besides, an isolation enhancement layout technique is proposed to minimize the inductive coupling and parasitic capacitors in the layout of the variable gain stage. As results, the measured phase variation is 0.2° ~ 2° in 18 GHz ~ 37 GHz and 0.2° ~ 5.4° in 18 GHz ~ 45 GHz when the gain variation range is 21.5 dB. The measured peak gain is 14.5 dB with 3-dB bandwidth of 20 GHz ~ 43 GHz. The gain ripple in 3-dB bandwidth is < 2.5 dB. The noise figure and input-referred P1dB are 5.5 dB and -16.5 dBm in the maximum gain state, respectively. The chip consumes 30.8 mW from 1.1 V voltage supply and the core area is 370 um × 930 um.

A video graphics array (VGA) (640 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 480) indirect time-of-flight (ToF) CMOS image sensor has been designed with 4-tap 7-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> global-shutter pixel in 65-nm back-side illumination (BSI) process. With a 4-tap pixel structure, we achieved motion artifact-free depth map. Peak current during exposure time has been reduced by current spreading with constant delay chain in the photo-gate driver. Column fixed-pattern phase noise (FPPN) from the constant delay chain is self-compensated by the proposed time-interleaving technique with the two inversely directional clock chains in the photo-gate driver. Quantum efficiency (QE) and demodulation contrast (DC) have been optimized by using appropriate optical engineering techniques with an optimal silicon thickness. As a result, QE of 34% at 940-nm near-infrared and high DC of 86% at 100-MHz modulation frequency have been achieved. In addition, motion artifact and column FPPN are successfully removed in the depth map. The proposed ToF sensor shows depth noise less than 0.57% with 940-nm illuminator over the working distance up to 4 m, and consumes only 160 mW for VGA output at 60 frames/s.

A 40-GHz variable gain amplifier (VGA) with 4-bit digital gain control and low phase variation is designed and fabricated on the 65-nm CMOS process. The digital gain control mechanism is achieved using the presented digital version current steering topology. The measured gain of the VGA is between 22 and 6 dB at 40 GHz with 16 different linear-in-decibel gain states. Using the proposed two-stage digital version current steering VGA phase compensation technique, the measured phase variation is 3.18° within a 16-dB gain control range (GCR) and the root mean square (rms) phase error is 0.89° at 40 GHz.

A small form-factor, lightweight wireless capsule endoscope (WCE) system with body channel communication (BCC) transceiver ICs is proposed for VGA-resolution image transferring and capsule localization. The transceiver ICs are composed of capsule chip and receiver chip, implemented in a 65-nm CMOS process. In the capsule side, the proposed system provides 360° image capturing through four-camera integration to reduce the miss rate. Also, a dual-band pulse-shaping BCC transmitter is proposed to enable low-power (<1 mW), high-speed (80 Mb/s) image transmission. In the receiver side, contact attenuation compensated-received signal strength indicator (CAC-RSSI) is proposed to increase the capsule localization accuracy. The external system is composed of eight-receiver nodes that allow signal demodulation and localization of the capsule. To reduce transceiver payload and system power consumption, image encoder is implemented in the capsule. The transceiver IC is integrated into the capsule system and verified with body mimicking phantom. The proposed system satisfies compatibility with medical grade diagnosis by operating longer than 8 h with 4 fps and 12 h with 2 fps with conventional two silver oxide 55-mA · h coin batteries. The tested localization accuracy shows less than sub-centimeter range.

The XLR gene family consists of approximately 10 X-linked genes, the expression of which is regulated in lymphocyte development. Certain members of the gene family are closely linked to the murine xid immune deficiency mutation. Sequence analysis of a cDNA clone pM1 derived from the plasmacytoma MOPC167 showed an open reading frame capable of coding for a protein of 208 amino acids and mol wt 24,000. The lack of a signal peptide or transmembrane region indicates a probable cytoplasmic or nuclear localization for the predicted pM1 protein. The predicted protein shares significant homology with lamins A and C and other members of the intermediate filament family of proteins, and shares features important for the coiled-coil structure proposed for these proteins. Analysis of cDNA clones derived from a presecretory lymphoma and from adult thymus indicates that B and T lymphocytes transcribe a common major mRNA identical to pM1, while other rare transcripts were also identified by these studies. A series of clonal T lymphoma lines representing distinct stages of thymic differentiation showed that, as with B lymphoid tumors, XLR expression is correlated with the maturation of the thymomas.

In Gram-positive bacteria, a large subfamily of dual ATP-binding cassette proteins confers acquired or intrinsic resistance to macrolide, lincosamide, and streptogramin antibiotics by a far from well understood mechanism. Here, we report the first biochemical characterization of one such protein, Vga(A), which is involved in streptogramin A (SgA) resistance among staphylococci. Vga(A) is composed of two nucleotide-binding domains (NBDs), separated by a charged linker, with a C-terminal extension and without identified transmembrane domains. Highly purified Vga(A) displays a strong ATPase activity (Km = 78 μm, Vm = 6.8 min-1) that was hardly inhibited by orthovanadate. Using mutants of the conserved catalytic glutamate residues, the two NBDs of Vga(A) were shown to contribute unequally to the total ATPase activity, the mutation at NBD2 being more detrimental than the other. ATPase activity of both catalytic sites was essential for Vga(A) biological function because each single Glu mutant was unable to confer SgA resistance in the staphylococcal host. Of great interest, Vga(A) ATPase was specifically inhibited in a non-competitive manner by the SgA substrate, pristinamycin IIA (PIIA). A deletion of the last 18 amino acids of Vga(A) slightly affected the ATPase activity without modifying the PIIA inhibition values. In contrast, this deletion reduced 4-fold the levels of SgA resistance. Altogether, our results suggest a role for the C terminus in regulation of the SgA antibiotic resistance mechanism conferred by Vga(A) and demonstrate that this dual ATP-binding cassette protein interacts directly and specifically with PIIA, its cognate substrate.

This paper presents the design and measurement of an interpolation-based low noise and variable gain cell (IBA-cell) in the 60-GHz band, using a 40-nm CMOS technology. The interpolation-based gain cell is designed for an innovative analog beamforming front end, where the array pattern is not only controlled in the phase domain, but also wins the flexibility in the magnitude domain. The circuit specifications are first derived for the application at 60 GHz. Techniques to combine low noise figure (NF) with variable gain tuning are presented focusing on the NF and linearity (IIP3) on the example of a 60-GHz low noise amplifier and variable gain amplifier. Subsequently, the design and measurement of the whole gain cell (IBA-cell) integrated into a single chip are reported with the technique of a cross-coupled feedback loop to reduce the phase variations over the gain tuning states and enhance the variations of IIP3. The IBA-cell achieves 15.8-dB maximum gain and 6.5-dB NF at 57 GHz with the gain tuning range from −2 to 15.8 dB and IIP3 varying from −11.3 to −16 dBm over the gain control range. The IBA-cell consumes a dc power of maximum 54 mA from 1.1 V.

The major product of the XLR (X-chromosomal, lymphocyte-regulated) locus is found to be a 30-kD nuclear protein with a relatively short (t1/2 approximately equal to 2 h) half-life. Together with its stage- and tissue-specific pattern of expression, this suggests a role for this protein in the regulation of differentiation in T and B lymphocytes. Interestingly, the XLR protein almost completely leaches out of the nucleus after lysis of cells in low salt buffer, but is stabilized in that location by metal cations, particularly Zn++. This stabilization is reversible by chelating agents (o-phenanthroline, EDTA) which also release a number of other polypeptides in addition to XLR. These results suggest that XLR represents a novel class of nuclear proteins, and that cations such as zinc may play a role in the localization of these proteins in the nucleus.

In this paper, a wideband accurate dB-linear variable-gain amplifier (VGA) is presented, which consists of three cascading gain stages with dc-offset cancellation. Based on duplicate biasing and active load techniques, this VGA achieves merits such as simple and accurate continuous dB-linear gain control, gain robustness to process and supply voltage variation and constant bandwidth at different gain settings. The dB-linear gain control is realized based on exponential current steering. The exponential conversion can be based on bipolar transistors or MOS transistors in subthreshold region. Both ways are implemented in this design for comparison purpose. With temperature compensation circuit, this VGA is insensitive to temperature variation. All gain stages of the VGA share the same biasing, exponential V-I conversion and temperature compensation circuits to reduce design complexity. This proposed VGA is fabricated in 55-nm CMOS technology with a core area of 0.033 mm2. The measurement results show the proposed VGA has a dB-linear gain-control range from −37dB to 14 dB with an error within 0.65 dB. The −3dB bandwidth of the VGA is 740 MHz and is nearly constant when the gain varies. The core VGA circuit consumes about 2.49 mW. The proposed design features wide bandwidth, small chip area, and low power consumption.

In this letter, we propose a method for accurately estimating the 6-Degree Of Freedom (DOF) pose in an urban environment when a High Definition (HD) map is available. An HD map expresses 3D geometric data with semantic information in a compressed format and thus is more memory-efficient than point cloud maps. The small capacity of HD maps can be a significant advantage for autonomous vehicles in terms of map storage and updates within a large urban area. Unfortunately, existing approaches failed to sufficiently exploit HD maps by only estimating partial pose. In this study, we present a full 6-DOF localization against an HD map using an onboard stereo camera with semantic information from roads. We introduce an 8-bit representation for road information, which allow for effective bitwise operation when matching between query data and the HD map. For the pose estimation, we leverage a particle filter followed by a full 6-DOF pose optimization. Our experimental results show a median error of approximately 0.3 m in the lateral and longitudinal directions for a drive of approximately 11 km. These results can be used by autonomous vehicles to correct the global position without using Global Positioning System (GPS) data in highly complex urban environments. The median operation speed is approximately 60 msec supporting 10 Hz.

The X-linked lymphocyte-regulated (Xlr) protein is a 30,000 Mr nuclear protein bearing homology with meiosis-specific proteins and expressed in late stage B lymphoid cell lines. In the present study we investigated its expression in the T lymphoid lineage. In adults, a high level of expression was detected in CD4-CD8- thymocytes. Most remarkably, the peak of Xlr expression occurred early during thymus cell ontogeny, precisely on days 14-15 of gestation, and was associated with the first wave of pre-T cell differentiation. Its onset preceded the rearrangement of TCR genes, as Xlr expression was conserved in thymus cells from RAG1(0/0) mice. The lower expression of Xlr on day 13 of fetal development, the bright Thy1+ phenotype of Xlr-positive cells, their large size, and their absence from subcapsular areas suggest that Xlr expression must be turned on within the thymus and not in prethymic precursors. From day 16 of gestation, Xlr expression decreased markedly. At birth and later, Xlr(high) cells were mostly large cells scattered throughout the cortical area. As shown by confocal microscopy, expression of Xlr closely overlapped that of SATB1, which binds special AT-rich DNA sequences associated with the nuclear matrix and plays an important regulatory role for many genes. The remarkably regulated expression of Xlr in the lymphoid cell lineage and of its homologue Xmr in the germ cell lineage suggests that they might play an important role in chromatin metabolism at critical stages of differentiation during which the genome undergoes irreversible rearrangements.

Vga(A) protein variants confer different levels of resistance to lincosamides, streptogramin A, and pleuromutilins (LSAP) by displacing antibiotics from the ribosome. Here, we show that expression of vga(A) variants from Staphylococcus haemolyticus is regulated by cis-regulatory RNA in response to the LSAP antibiotics by the mechanism of ribosome-mediated attenuation. The specificity of induction depends on Vga(A)-mediated resistance rather than on the sequence of the riboregulator. ABSTRACT Vga(A) protein variants confer different levels of resistance to lincosamides, streptogramin A, and pleuromutilins (LSAP) by displacing antibiotics from the ribosome. Here, we show that expression of vga(A) variants from Staphylococcus haemolyticus is regulated by cis-regulatory RNA in response to the LSAP antibiotics by the mechanism of ribosome-mediated attenuation. The specificity of induction depends on Vga(A)-mediated resistance rather than on the sequence of the riboregulator. Fine tuning between Vga(A) activity and its expression in response to the antibiotics may contribute to the selection of more potent Vga(A) variants because newly acquired mutation can be immediately phenotypically manifested.

Extremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machine Translation from One Book (Tanzer et al., 2024) suggests that prompting long-context LLMs with one grammar book enables English-Kalamang translation, an XLR language unseen by LLMs - a noteworthy case of linguistics helping an NLP task. We investigate the source of this translation ability, finding almost all improvements stem from the book's parallel examples rather than its grammatical explanations. We find similar results for Nepali and Guarani, seen low-resource languages, and we achieve performance comparable to an LLM with a grammar book by simply fine-tuning an encoder-decoder translation model. We then investigate where grammar books help by testing two linguistic tasks, grammaticality judgment and gloss prediction, and we explore what kind of grammatical knowledge helps by introducing a typological feature prompt that achieves leading results on these more relevant tasks. We thus emphasise the importance of task-appropriate data for XLR languages: parallel examples for translation, and grammatical data for linguistic tasks. As we find no evidence that long-context LLMs can make effective use of grammatical explanations for XLR translation, we conclude data collection for multilingual XLR tasks such as translation is best focused on parallel data over linguistic description.

This letter presents a broadband digital-controlled variable gain amplifier (VGA) on 65-nm CMOS process. A body-biased digital current-steering topology is proposed to enlarge the gain control range (GCR) of the VGA under low supply voltage. For phased-array applications, a phase compensation network is added to minimize the phase shift during the gain tuning. The proposed VGA achieves a small-signal gain of 16.3/16.1 dB at 28/38 GHz with a 3-dB gain bandwidth from 25.5 to 46 GHz. The measured phase variation is 2.32°/4.8° with a GCR of 8.7/8.4 dB at 28/38 GHz. The phase variation of the VGA is less than 5° and the rms phase error is less than 1.46° over a wide frequency band of 27–43 GHz covering multiple 5G bands.

The main goal of our research was to study the possible alterations of the chemical coding of the dorsal motor vagal nucleus (DMX) neurons projecting to the porcine stomach prepyloric region following prolonged acetylsalicylic acid supplementation. Fast Blue (FB) was injected into the studied area of the stomach. Since the seventh day following the FB injection, acetylsalicylic acid (ASA) was given orally to the experimental gilts. All animals were euthanized on the 28th day after FB injection. Medulla oblongata sections were then processed for double-labeling immunofluorescence for choline acetyltransferase (ChAT), pituitary adenylate cyclase-activating peptide (PACAP), vasoactive intestinal polypeptide (VIP), nitric oxide synthase (NOS), galanin (GAL), substance P (SP), leu enkephalin (LENK), and cocaine- and amphetamine-regulated transcript (CART). In the control DMX, only PACAP was observed in 30.08 ± 1.97 % of the FB-positive neurons, while VIP, NOS, GAL, SP, LENK, and CART were found exclusively in neuronal processes running between FB-labeled perikarya. In the ASA DMX, PACAP was revealed in 49.53 ± 5.73 % of traced vagal perikarya. Moreover, we found de novo expression of VIP in 40.32 ± 7.84 %, NOS in 25.02 ± 6.08 %, and GAL in 3.37 ± 0.85 % of the FB-labeled neurons. Our results suggest that neuronal PACAP, VIP, NOS, and GAL are mediators of neural response to aspirin-induced stomach inflammatory state.

The High Definition Multimedia Interface (HDMI) is the backbone and the de-facto standard for Audio/Video interfacing between video-enabled devices. Today, almost tens of billions of HDMI devices exist in the world and are widely used to distribute A/V signals in smart homes, offices, concert halls, and sporting events making HDMI one of the most highly deployed systems in the world. An important component in HDMI is the Consumer Electronics Control (CEC) protocol, which allows for the interaction between devices within an HDMI distribution network. Nonetheless, existing network security mechanisms only protect traditional networking components, leaving CEC outside of their scope. In this work, we identify and tap into CEC protocol vulnerabilities, using them to implement realistic proof-of-work attacks on HDMI distribution networks. We study, how current insecure CEC protocol practices and carelessly implemented HDMI distributions may grant an adversary a novel attack surface for HDMI devices otherwise thought to be unreachable through traditional means. To introduce this novel attack surface, in this paper, we present HDMI-Walk, which opens a realm of remote and local CEC attacks to HDMI devices. Specifically, with HDMI-Walk, an attacker can perform malicious analysis of devices, eavesdropping, Denial of Service attacks, targeted device attacks, and even facilitate other well-known existing attacks through HDMI. With HDMI-Walk, we prove that it is feasible for an attacker to gain arbitrary control of HDMI devices. We demonstrate the implementations of both local and remote attacks with commodity HDMI devices including Smart TVs and Media Players. Our work aims to uncover vulnerabilities in a very well deployed system like HDMI distributions. The consequences of which can largely impact HDMI users as well as other systems which depend on these distributions. Finally, we discuss security mechanisms to provide impactful and comprehensive security evaluation to these real-world systems while guaranteeing deployability and providing minimal overhead, while considering the current limitations of the CEC protocol. To the best of our knowledge, this is the first work solely investigating the security of HDMI device distribution networks.

The aim of this work is to develop a video processing system with efficient hardware resource using a field programmable gate array (FPGA). The FPGA board contains several resources that may be implemented on both hardware and software platforms. Real-time video processing necessitates high-consuming logic resources, and fpga is an excellent choice for video processing applications. In this work, Omni vision (OV) 7670 and Video graphics array (VGA) interfaces with FPGA for video processing system. The tools utilized here are Xilinx Vivado and Verilog HDL to develop the system. The proposed video processing system implemented in Spartan 6 &Artix -7 FPGA which provides 530 slice registers.

Purpose: The goal of this study was to assess various recording methods, including combinations of high- versus low-cost microphones, recording interfaces, and smartphones in terms of their ability to produce commonly used time- and spectral-based voice measurements. Method: Twenty-four vowel samples representing a diversity of voice quality deviations and severities from a wide age range of male and female speakers were played via a head-and-thorax model and recorded using a high-cost, research standard GRAS 40AF (GRAS Sound & Vibration) microphone and amplification system. Additional recordings were made using various combinations of headset microphones (AKG C555 L [AKG Acoustics GmbH], Shure SM35-XLR [Shure Incorporated], AVID AE-36 [AVID Products, Inc.]) and audio interfaces (Focusrite Scarlett 2i2 [Focusrite Audio Engineering Ltd.] and PC, Focusrite and smartphone, smartphone via a TRRS adapter), as well as smartphones direct (Apple iPhone 13 Pro, Google Pixel 6) using their built-in microphones. The effect of background noise from four different room conditions was also evaluated. Vowel samples were analyzed for measures of fundamental frequency, perturbation, cepstral peak prominence, and spectral tilt (low vs. high spectral ratio). Results: Results show that a wide variety of recording methods, including smartphones with and without a low-cost headset microphone, can effectively track the wide range of acoustic characteristics in a diverse set of typical and disordered voice samples. Although significant differences in acoustic measures of voice may be observed, the presence of extremely strong correlations (rs > .90) with the recording standard implies a strong linear relationship between the results of different methods that may be used to predict and adjust any observed differences in measurement results. Conclusion: Because handheld smartphone distance and positioning may be highly variable when used in actual clinical recording situations, smartphone + a low-cost headset microphone is recommended as an affordable recording method that controls mouth-to-microphone distance and positioning and allows both hands to be available for manipulation of the smartphone device.

Enabling robust whole-body humanoid-object interaction (HOI) remains challenging due to motion data scarcity and the contact-rich nature. We present HDMI (HumanoiD iMitation for Interaction), a simple and general framework that learns whole-body humanoid-object interaction skills directly from monocular RGB videos. Our pipeline (i) extracts and retargets human and object trajectories from unconstrained videos to build structured motion datasets, (ii) trains a reinforcement learning (RL) policy to co-track robot and object states with three key designs: a unified object representation, a residual action space, and a general interaction reward, and (iii) zero-shot deploys the RL policies on real humanoid robots. Extensive sim-to-real experiments on a Unitree G1 humanoid demonstrate the robustness and generality of our approach: HDMI achieves 67 consecutive door traversals and successfully performs 6 distinct loco-manipulation tasks in the real world and 14 tasks in simulation. Our results establish HDMI as a simple and general framework for acquiring interactive humanoid skills from human videos.

A Ka-band differential variable gain amplifier (VGA) applying symmetrical unit arrays with 6-bit digital-controlled is presented. The proposed VGA, based on the conventional current steering theory, uses symmetrical unit arrays that keep the output impedance constant, while digital-controlled voltage changes. Then it combines neutralizing capacitances to counteract the Miller effect of parasitic capacitances between gain and drain of input transistors. The VGA with symmetrical unit arrays ensures the consistency of phase variation during gain fluctuation. The VGA is fabricated in a 65-nm CMOS technology, and achieves −20.2 to −4.2 dB gain varied with 1.52 dBm IP1dB as well as 8.6 dB noise figure at 36 GHz and less than 4.9° phase variation, while consuming 15.6 mW across 30.5–39 GHz frequency range.

Increasingly common usage of new technologies in information processing generates escalating threat of confidentiality loss. Utilizing current VGA analog standard based means raises many doubts. It is a widely held view that this standard is highly susceptible to electromagnetic infiltration. Digital technologies are supposed to ensure a higher security level. This also applies to data processed in graphic form, especially those being transmitted to graphic display devices, such as computer monitors using DVI and DisplayPort standards. This article presents a comparative analysis of graphic technologies (DVI and DisplayPort) in terms of the electromagnetic safety of processed data. Analyses shown are based both on simulations results and practical study. The products of recreating the data using registered sensitive emission signals for studied graphical standards are presented.

We report on a low-power VGA vision sensor embedding event-detection capabilities targeted to battery-powered vision processing at the edge. The sensor relies on an always-ON Double-Threshold dynamic background subtraction (DT-DBS) algorithm. The resulting motion bitmap is de-noised, projected along <inline-formula> <tex-math notation="LaTeX">$xy$ </tex-math></inline-formula>-axes of the array of pixels and filtered to robustly detect moving targets even in noisy outdoor scenarios. The chip operates in motion detection (MD), applied on a QQVGA sub-sampled image, looking for anomalous motion in the scene at 344 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>, and in imaging mode (IM), delivering full-resolution gray-scale images with associated local binary pattern (LBP) coding and motion bitmaps at 8 frames/s and 1.35 mW. The 4-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> pixel vision sensor is manufactured in a 110-nm 1P4M CMOS and occupies 25.4 mm².

A variable gain amplifier (VGA) featuring wide bandwidth and low phase variation is presented for the new generation phased arrays supporting multiple-frequency bands simultaneously. In this article, the mechanism of phase variation in common-source (CS) CMOS amplifier cell is investigated in detail. As revealed by this analysis, the parasitic capacitor ${C} _{\text {gd}}$ and the magnetic coupling among inductors as well as parasitic capacitors in layout inducing by interconnection lines are some of the key factors that generate feedforward signal, which leads to the phase variation. The other is the inconstant input and output admittance that result in phase deviation over a wideband region. To achieve low phase variation over broadband for CMOS CS VGA, an innovative phase compensation method based on feedforward signal suppression (FSS) and impedance-invariant techniques is proposed. Specifically, the active cross-coupling neutralization topology is employed to suppress the feedforward signal generated by ${C} _{{\text { gd}}}$ . Meanwhile, an isolation enhancement layout approach aiming at mitigating the inductive coupling and parasitic capacitors in layout is proposed. Besides, a control circuit based on the impedance-invariant technique is developed to minimize the impedance variation. As a result, a low phase variation VGA with broadband phase compensation is achieved. The measured gain control range is 23 dB, with a 3-dB bandwidth of 20–43 GHz and a peak gain of 15 dB. The measured phase variation is <2° in 18–36 GHz and <6° across 18–45 GHz. The noise figure and input-referred ${P} _{\text {1 dB}}$ are 5.5 dB and −18 dBm in the maximum gain state, respectively. The measurement also shows the proposed VGA has good robustness against the variation of supply and temperature. The VGA consumes 30.8 mW from a 1.1-V voltage supply. The chip is fabricated in a standard 65-nm CMOS process and the core area is 0.34 mm2.

In-depth understanding of the acceptor states and origins of p-type conductivity is essential and critical to overcome the great challenge for the p-type doping of ultrawide-bandgap oxide semiconductors. In this study we find that stable NO-VGa complexes can be formed with ε(0/-) transition levels significantly smaller than those of the isolated NO and VGa defects using N2 as the dopant source. Due to the defect-induced crystal-field splitting of the p orbitals of Ga, O and N atoms, and the Coulomb binding between NO(II) and VGa(I), an a' doublet state at 1.43 eV and an a'' singlet state at 0.22 eV above the valence band maximum (VBM) are formed for the β-Ga2O3:NO(II)-VGa(I) complexes with an activated hole concentration of 8.5 × 1017 cm-3 at the VBM, indicating the formation of a shallow acceptor level and the feasibility to obtain p-type conductivity in β-Ga2O3 even when using N2 as the dopant source. Considering the transition from NO(II)-V0Ga(I) + e to NO(II)-V-Ga(I), an emission peak at 385 nm with a Franck-Condon shift of 1.08 eV is predicted. These findings are of general scientific significance as well as technological application significance for p-type doping of ultrawide-bandgap oxide semiconductors.

A low-power and low noise-figure (NF) 3.1–23.7-GHz CMOS variable-gain amplifier (VGA) is presented. The VGA composes of a complimentary common-source (CCS) first stage, followed by a common-source (CS) second stage. Low power and NF and wideband <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> are achieved due to the current-reused CCS input stage with multiple inductive impedance matching and gain compensation. A large gain tuning range (with low NF) is achieved due to the concurrent gate–voltage tuning (or current steering) of the first and the second stages. The VGA consumes 5.85 mW (at <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {ctrl}}$ </tex-math></inline-formula> of 1 V) and achieves excellent 3-dB bandwidth (<inline-formula> <tex-math notation="LaTeX">$f_{3\,\mathrm {dB}}$ </tex-math></inline-formula>) of 20.6 GHz (3.1–23.7 GHz), maximum <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> of 11.9 dB, minimum NF (NFmin) of 2.53 dB, average NF (NFavg) of 3.97 dB, small group delay (GD) variation of ±16.5 ps, and input third-order intercept point (IIP3) of −6.5 dBm. Moreover, the VGA achieves a decent gain tuning range of 23.4 dB (−5.4 to 18 dB) for <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {ctrl}}$ </tex-math></inline-formula> of 0.6–1.6 V. The chip area is 0.364 mm2.

The High Definition Multimedia Interface (HDMI) is the backbone and the de-facto standard for Audio/Video connections between video-enabled devices. Today, nearly ten billion HDMI devices are used to distribute A/V signals in homes, offices, concert halls, and sporting events. An important component in HDMI is the Consumer Electronics Control (CEC) protocol, which allows HDMI devices to share an HDMI distribution to communicate and interact with each other. In this work, we identify security and privacy issues in HDMI networks by taping into CEC protocol vulnerabilities, using them to implement realistic proof-of-work attacks on HDMI distribution networks. We study how current insecure CEC protocol practices and carelessly implemented HDMI distributions may grant an adversary a novel attack surface for HDMI devices, otherwise thought to be unreachable through traditional network means. We first present HDMI-Walk, a novel attack vector, that can be used by an attacker to gain arbitrary control of HDMI devices and perform malicious analysis of devices, eavesdropping, Denial-of-Service attacks, targeted device attacks, and even facilitate other well-known existing attacks through HDMI. To defend against these new HDMI-based threats on a smart network system, we further propose HDMI-Watch, a novel intrusion detection system to detect unexpected CEC-based activity within an HDMI distribution. HDMI-Watch operates as a standalone smart intrusion detection framework within an HDMI distribution, passively monitoring and thus imposing no additional overhead to CEC communication. To test HDMI-Watch's performance, we evaluated our system in a realistic HDMI testbed with a variety of consumer HDMI-enabled devices. Our extensive evaluation results show that the proposed system achieves an average 98% accuracy in classifying unexpected CEC behavior and identifies attacks occurring without any form of modification required to existing devices in an HDMI distribution.

Introduction Synthetic cannabinoid receptor agonists (SCRAs) are a diverse class of new psychoactive substances that have been associated with multiple instances and types of toxicity. Some SCRAs appear to carry a greater toxicological burden than others, or compared to the prototypical cannabis-derived agonist Δ9-tetrahydrocannabinol (Δ9-THC), despite a common primary mechanism of action via cannabinoid 1 (CB1) receptors. “Off-target” (i.e., non-CB1 receptor) effects could underpin this differential toxicity, although there are limited data around the activity of SCRAs at such targets. Methods A selection of 7 SCRAs (AMB-FUBINACA, XLR11, PB-22, AKB-48, AB-CHMINICA, CUMYL-PINACA, and 4F-MDMB-BUTINACA), representing several distinct chemotypes and toxicological profiles, underwent a 30 μM single-point screen against 241 G protein-coupled receptor (GPCR) targets in antagonist and agonist mode using a cellular β-arrestin recruitment assay. Strong screening “hits” at specific GPCRs were followed up in detail using concentration-response assays with AMB-FUBINACA, a SCRA with a particularly notable history of toxicological liability. Results The single-point screen yielded few hits in agonist mode for any compound aside from CB1 and CB2 receptors, but many hits in antagonist mode, including a range of chemokine receptors, the oxytocin receptor, and histamine receptors. Concentration-response experiments showed that AMB-FUBINACA inhibited most off-targets only at the highest 30 μM concentration, with inhibition of only a small subset of targets, including H1 histamine and α2B adrenergic receptors, at lower concentrations (≥1 μM). AMB-FUBINACA also produced concentration-dependent CB1 receptor signaling disruption at concentrations higher than 1 μM, but did not produce overt cytotoxicity beyond CP55,940 or Δ9-THC in CB1 expressing cells. Discussion These results suggest that while some “off-targets” could possibly contribute to the SCRA toxidrome, particularly at high concentrations, CB1-mediated cellular dysfunction provides support for hypotheses concerning on-target, rather than off-target, toxicity. Further investigation of non-GPCR off-targets is warranted.

Background Conventional categorical criteria have limitations in assessing the prevalence and severity of depressive mixed state (DMX). Thus, we have developed a new scale for screening and quantification of DMX and examined the symptomatological structure and severity of DMX in individuals with major depressive episode (MDE). Methods Subjects were 154 patients with MDE (57 males and 97 females; age 13–83 years). Our original Japanese version of the self-administered 12-item questionnaire to assess DMX (DMX-12), together with the Quick Inventory of Depressive Symptomatology Self-Report Japanese version (QIDS-SR-J) and global assessment of functioning, were administered to each participant. The symptomatological structure of the DMX-12 was examined by exploratory factor analysis. Multiple regression analyses were used to analyze factors contributing to the DMX-12 scale. The relationships of this scale with categorical diagnoses (mixed depression by Benazzi and mixed features by DSM-5) were also investigated. Results A three-factor model of the DMX-12 was extracted from exploratory factor analysis, namely, “spontaneous instability”, “vulnerable responsiveness”, and “disruptive emotion/behavior”. Multiple regression analyses revealed that age was negatively correlated with total DMX-12 score, while bipolarity and the QIDS-SR-J score were positively correlated. A higher score on the disruptive emotion/behavior subscale was observed in patients with mixed depression and mixed features. Conclusion The DMX-12 seems to be useful for screening DMX in conjunction with conventional categorical diagnoses. Severely depressed younger subjects with potential bipolarity are more likely to develop DMX. The disruptive emotion/behavior subscale of the DMX-12 may be the most helpful in distinguishing patients with DMX from non-mixed patients.

While a number of O-H and O-D vibrational lines have been observed for hydrogen and deuterium in β-Ga2O3, it has been commonly reported that there is no absorption with a component of the polarization E parallel to the [010], or b, axis. This experimental result has led to O-H defect structures that involve shifted configurations of a vacancy at the tetrahedrally coordinated Ga(1) site [VGa(1)] and have ruled out structures that involve a vacancy at the octahedrally coordinated Ga(2) site [VGa(2)], because these structures are predicted to show absorption for E//[010]. In this Letter, weak O-D lines at 2475 and 2493 cm−1 with a component of their polarization with E//[010] are reported for β-Ga2O3 that had been annealed in a D2 ambient. O-D defect structures involving an unshifted VGa(2) are proposed for these centers. An estimate is made that the concentration of VGa(2) in a Czochralski-grown sample is 2–3 orders of magnitude lower than that of VGa(1) from the intensities of the IR absorption lines.

Using a Field-Programmable Gate Array, this work aims to develop a multimedia system, on-chip Block RAM (BRAM), and a Video Graphics Array (VGA) interface. The system will be centered around a VGA monitor that will display multimedia information. The BRAM will temporarily store the image data while the FPGA converts it to a format compatible with the VGA display. The system can produce 24-bit color at a resolution of 100x100. Verilog will be used to construct the hardware, whilst Xilinx will be used for simulation. To demonstrate how the system operates, it will be tested on an FPGA development board. This work aims to create a low-cost, high-performance approach for rendering and processing pictures for use in multimedia applications.

Effectively modeling relationships between distant elements in a long input sequence is an important task that remains challenging to this day. The state-of-the-art models for processing sequential data are self-attention-based transformer models. However, the computational complexity of self-attention is quadratic to the input sequence length, which often becomes the limiting factor in scaling the sequence length. Recently, state space model (SSM)-based global convolution models, which replace attention with convolution, have been found to be effective for modeling long sequences, with a sub-quadratic complexity using Fast Fourier Transform (FFT). However, they show sub-optimal performance on data-parallel accelerators like GPU, due to the regions of extremely low compute utilization with memory bandwidth-bound operations. To address this inefficiency, this paper proposes the Vandermonde matrix Generating Accelerator (VGA), a custom accelerator that performs FFT-based convolution in an area/power-efficient manner. VGA introduces Complex number Compute Units (CCUs) to fully utilize the high on-chip SRAM bandwidth, and parameters are generated on the fly to drastically reduce the required SRAM capacity. VGA achieves 76×(48×) higher area (power) efficiency than NVIDIA A100 GPU when executing the global convolution operator of H3, a state-of-the-art SSM-based model.

The widespread recreational use of synthetic cannabinoids (SCs) has become a serious health issue. Reports of life-threatening intoxications related to SC consumption have markedly increased in recent years, including neurotoxicity, cardiotoxicity, nephrotoxicity, and hepatotoxicity. We investigated the impact of acute administration of the synthetic cannabinoid XLR-11 (3 mg/kg, i.p. for 5 consecutive days) on the liver in BALB/c mouse animal model. Using real-time quantitative RT-PCR, MDA assay, and TUNEL assay, we found consistent up-regulation of a variety of genes involved in oxidative stress (NOX2, NOX4, and iNOS), inflammation (TNF-α, IL-1β, IL-6), and apoptosis (Bax) in the liver of XLR-11 treated mice compared to control mice. These finding were supported with an elevation of MDA levels and TUNEL positive cells in the liver of XLR-11 treated mice which further confirm increased oxidative stress and apoptosis, respectively. Histopathological analysis of the liver of XLR-11 treated mice confirmed pronounced hepatic necrosis associated with inflammatory cell infiltration. Furthermore, elevated ALT and AST serum levels were also identified in XLR-11 treated mice indicating possible liver damage. Overall, SC-induced hepatotoxicity seems to be mainly mediated by activated oxidative stress and inflammatory processes in the liver, but the specific mechanisms involved require further investigations. However, the present study shed light on the potential deleterious role of acute administration of SCs in the progression to acute hepatic injury which enhances our understanding of the adverse effect of SC consumption.

This paper presents a 27.7–31.2 GHz highly integrated 8-element 4-beam transmitter front end in 65-nm CMOS for phased-array applications. The proposed hybrid variable-gain amplifier (VGA) combines digital common-source (CS) VGA and analog Gilbert-cell-based VGA to realize a large tuning range and high resolution simultaneously. Furthermore, the proposed VGA scheme also integrates with an active combining network to implement a compact multi-beam structure. In the vector-modulation-based phase shifter (PS), low/high pass lumped delay lines are inserted between CS transistors and common-gate (CG) transistors to form I/Q signals which drive four pairs of Gilbert-cell based Gm-arrays for different phase states. In addition, the output element leverages a symmetrical transformer-based Doherty power amplifier (PA) and an analog adaptive bias to improve the power-back-off (PBO) efficiency with a high peak-to-average-ratio (PAPR) modulation signal. With a 1.1-V supply voltage, this front end achieves a saturated output power (Psat) of 19.2 dBm with a 20.5% channel efficiency and a 1-dB compression point (OP1dB) of 18.2 dBm. The measured maximum drain efficiency (DE) is 40.8% and 6-dB PBO DE is 28%. Compared with class-B/A PAs, the DE is improved by 40%/180%. The proposed hybrid VGA achieves a 27-dB tuning range and a 0.25-dB gain step with 0.26 dB/1.9° RMS gain/phase errors. The active PS implements 6-bit 360° phase shifting with 0.25 dB/2.5° RMS gain/phase errors.

In this research paper, we address the problem of eavesdropping on digital video displays by analyzing the electromagnetic waves that unintentionally emanate from the cables and connectors, particularly HDMI. This problem is known as TEMPEST. Compared to the analog case (VGA), the digital case is harder due to a 10-bit encoding that results in a much larger bandwidth and non-linear mapping between the observed signal and the pixel’s intensity. As a result, eavesdropping systems designed for the analog case obtain unclear and difficult-to-read images when applied to digital video. The proposed solution is to recast the problem as an inverse problem and train a deep learning module to map the observed electromagnetic signal back to the displayed image. However, this approach still requires a detailed mathematical analysis of the signal, firstly to determine the frequency at which to tune but also to produce training samples without actually needing a real TEMPEST setup. This saves time and avoids the need to obtain these samples, especially if several configurations are being considered. Our focus is on improving the average Character Error Rate in text, and our system improves this rate by over 60 percentage points compared to previous available implementations. The proposed system is based on widely available Software Defined Radio and is fully open-source, seamlessly integrated into the popular GNU Radio framework. We also share the dataset we generated for training, which comprises both simulated and over 1000 real captures. Finally, we discuss some countermeasures to minimize the potential risk of being eavesdropped by systems designed based on similar principles.

The OV7670 camera module is 8 bits data image and it has a specification image of 30 frames per second. There are many applications of research that have been done about how to access this camera module and display it to the monitor. This paper presents the interface between this camera module OV7670 and FPGA DEO-Nano-Soc and then displays the video to the monitor via a VGA port. VGA port has a minimum of 5 pins; 3 pins for RGB data images, 1 pin for horizontal synchronous, and 1 pin for vertical synchronization. Due to the FPGA DE0-Nano-Soc not having the VGA driver, so the VGA driver ADV7133 24 bits is used in this research. The result showed that this research can work well enough. The system successfully to take the image from the camera and displays it as video in real-time.

The DMX process is a second-generation CO2 capture process using a phase change solvent. The development of this process including lab scale and minipilot experimentation has been carried out by IFPEN within different projects such as OCTAVIUS & VALORCO. These studies highlighted the main advantages of the DMX process : high cyclic solvent capacity (from 0.1 to 1 mol CO2/mol solvent), low heat required for regeneration (less than 2.3 GJ/tCO2 for 90 % capture rate), high CO2 purity, no degradation of the solvent (up to 160°C) allowing to carry out the regeneration step at higher temperature and pressure, producing CO2 at higher pressure (up to 6-7 bara), no corrosion of the solvent allowing the use of Carbon Steel material for the construction of the future industrial units. First techno-economic evaluations have shown an OPEX reduction and a CO2 capture cost reduction up to 30 % compared to the first-generation processes using amine-based solvents. DMX process is very promising but it needs now to be demonstrated in an industrial scale pilot plant in order to increase its TRL from 4 to 7. This demonstration, which is the last step before the commercialisation of this process by AXENS, will be carried within the 3D (DMX Demonstration in Dunkirk) project on an industrial pilot plant to be built at the ArcelorMittal steel mill in Dunkirk (France) In this paper, are presented the design of the industrial pilot plant; first experimentation plan is also presented and explained. Planning of the demonstration and expected commercialisation will be discussed.

We demonstrate a low-power <inline-formula> <tex-math notation="LaTeX">$(P_{D})$ </tex-math></inline-formula>, low-noise, and high-gain 22.7–29.7-GHz variable-gain amplifier (VGA) in 90-nm CMOS for 28-GHz 5G systems. Low <inline-formula> <tex-math notation="LaTeX">$P_{D}$ </tex-math></inline-formula> and wide gain tuning range are achieved due to the use of the self-body-to-drain bias through body-floating resistance and the current-reused topology with auxiliary current steering. Input noise matching and body-floating techniques are used for noise figure (NF) enhancement. VGA consumes 7.4 mW and achieves <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> tuning range of 23.7 dB (19.2 to −4.5 dB) at 26 GHz. At <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {ctrl}}$ </tex-math></inline-formula> of 0 V (i.e., maximum gain), VGA attains <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> of 16.2–19.2 dB for 22.7–29.7 GHz [i.e., 3-dB bandwidth (<inline-formula> <tex-math notation="LaTeX">$f_{3\,\mathrm {dB}}$ </tex-math></inline-formula>) equal to 7 GHz], minimum noise figure (NF<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {min}}$ </tex-math></inline-formula>) of 2.39 dB at 26.5 GHz, average NF (NF<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {avg}}$ </tex-math></inline-formula>) of 2.76 dB for 22.5–29.5 GHz, small group delay (GD) variation of ± 14.7 ps for 18–30 GHz (i.e., decent phase linearity), and input third-order intercept point (IIP3) of −12.7 dBm at 28 GHz. The chip area is 0.383 mm².

Excited-State Intramolecular Proton Transfer (ESIPT) emission is associated with intense single or multiple fluorescence in the solid-state, along with enhanced photostability and sensitivity to the close environment. As a result, ESIPT probes are attractive candidates for ratiometric sensing of a variety of substrates. A new family of ESIPT fluorophores is described herein, inspired by the well-known 2-(2'hydroxyphenyl)benzazole (HBX) organic scaffold. The connection of 3,3′-dimethylindole (or 3H-indole) derivatives with phenol rings triggers the formation of novel 2-(2′-hydroxyphenyl)-3,3′-dimethylindole (HDMI) fluorophores, capable of stimuli-responsive ESIPT emission. This brand new family of dyes displays redshifted emission, as compared to HBX, along with an unprecedented acid/base-mediated stabilization of different rotamers, owing to supramolecular interactions with methyl groups. These compounds are therefore highly sensitive to external stimuli, such as the presence of acid or base, where protonated and deprotonated species have specific optical signatures. Moreover, a new pyridine-functionalized HDMI dye displays acid-sensitive AIE properties. The photophysical properties of all compounds have also been studied using ab initio calculations to support experiments in deciphering the nature of the various radiative transitions observed and the related excited rotameric species.

The transition of data and clock signals between high and low states in electronic devices creates electromagnetic radiation according to Maxwell's equations. These unintentional emissions, called emanation, may have a significant correlation with the original information-carrying signal and form an information leakage source, bypassing secure cryptographic methods at both hardware and software levels. Information extraction exploiting compromising emanations poses a major threat to information security. Shielding the devices and cables along with setting a control perimeter for a sensitive facility are the most commonly used preventive measures. These countermeasures raise the research need for the longest detection range of exploitable emanation and the efficacy of commercial shielding. In this work, using data collected from 3 types of commercial HDMI cables (unshielded, single-shielded, and double-shielded) in an office environment, we have shown that the CNN-based detection method outperforms the traditional threshold-based detection method and improves the detection range from 4 m to 22.5 m for an iso-accuracy of ~ 95%. Also, for an iso-distance of 16 m, the CNN-based method provides ~ 100% accuracy, compared to ~ 88.5% using the threshold-based method. The significant performance boost is achieved by treating the FFT plots as images and training a residual neural network (ResNet) with the data so that it learns to identify the impulse-like emanation peaks even in the presence of other interfering signals. A comparison has been made among the emanation power from the 3 types of HDMI cables to judge the efficacy of multi-layer shielding. Finally, a distinction has been made between monitor contents, i.e., still image vs video, with an accuracy of 91.7% at a distance of 16 m. This distinction bridges the gap between emanation-based image and video reconstruction algorithms.

A novel Key Quality Indicator for video delivery applications, XLR (piXel Loss Rate), is defined, characterized, and evaluated. The proposed indicator is an objective measure that captures the effects of transmission errors in the received video, has a good correlation with subjective Mean Opinion Scores, and provides comparable results with state-of-the-art Full-Reference metrics. Moreover, XLR can be estimated using only a lightweight analysis on the compressed bitstream, thus allowing a No-Reference operational method. Therefore, XLR can be used for measuring the quality of experience without latency at any network location. Thus, it is a relevant tool for network planning, specially in new high-demanding scenarios. The experiments carried out show the outstanding performance of its linear-dimension score and the reliability of the bitstream-based estimation.

Multiple imputation (MI) models can be improved with auxiliary covariates (AC), but their performance in high-dimensional data remains unclear. We aimed to develop and compare high-dimensional MI (HDMI) methods using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation with acute kidney injury as outcome and simulated 100 cohorts with a null treatment effect, incorporating creatinine labs, atrial fibrillation (AFib), and other investigator-derived confounders in the outcome generation. Missingness was imposed on creatinine based on creatinine itself and AFib. Different HDMI candidate AC were created using structured and NLP-derived features and we mimicked scenarios where AFib was unobserved by omitting it from all analyses. Using LASSO, we selected HDMI covariates for MI and propensity score models. The treatment effect was estimated after propensity score matching in MI datasets, and HDMI methods were compared to baseline imputation and complete case analysis. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency with a root-mean-squared-error of 0.173 and 94% coverage. NLP-derived AC alone did not outperform baseline MI. HDMI approaches may decrease bias in studies where confounder missingness depends on unobserved factors.

Focusing on a VGa–VN complex vacancy defect in GaN, the adiabatic potential for conformational changes of the VGa–VN is investigated using the density functional theory. There are two types of configurations due to the symmetry of the crystal, it is confirmed that there is almost no difference in their stability or electronic state. Using these configurations as the initial and final states, the defect reaction is analyzed when VGa–VN moves using the climbing‐image nudged elastic band method. The reaction barrier for the migration of VGa (VN) is found to be 2.2 (3.3) eV. It is concluded that these reactions occur frequently as GaN is annealed since the atomic diffusion potential barrier is similar to that in other semiconductors. These results suggest that VGa–VN in GaN moves through the crystal as a pair of vacancies by alternately repeating the VGa and the VN migration. These results provide important knowledge for elucidating the microscopic mechanism of the experimentally observed VGa–VN aggregation reaction.

For simultaneous screening of mixed features (MF) by DSM-5 and mixed depression (MD) by Benazzi, useful symptoms were extracted from our 12-item dimensional scale for depressive mixed state (DMX-12). Subjects were 190 consecutive cases with major depressive episode (MDE) who visited our clinic. Associations between symptomatological combinations of the DMX-12 and MF or MD were analyzed using receiver operating characteristic (ROC). The rate of MF was 4.2% while that of MD was 22.6%. Eight symptoms (overreactivity, inner tension, racing/crowded thought, impulsivity, irritability, aggression, risk-taking behavior, and dysphoria) with their AUC > 0.6 for ROC curves were specially focused on distinguishing patients with MF or MD from non-mixed patients. By using these 8 symptoms, 40.5% of the overall patients were screened as positive at the same cut-off value (≥13) for both MD and MF. The AUC of ROC curve and sensitivity/specificity were well balanced together with sufficient negative predictive values. The abovementioned 8 symptoms seem to be helpful for primary screening and negative check of DMX with considerable severity during MDE.

Cardiovascular diseases (CVDs) are the leading causes of death worldwide. CVDs have become the dominant cause of death and have been a significant health challenge since the second half of the 20th century in the Polish population. The aim of our HDMI (hospital diet medical investigation) study was to examine the quality of the hospital diets given to cardiac patients and assess how much they adhere to the European Society of Cardiology (ESC) 2021 guidelines. By comparing the diets received by patients with the recommended dietary patterns outlined in the ESC 2021 guidelines, we sought to identify discrepancies. The study was conducted in two steps: creating a 7-day model menu and comparing it with the received diets and then making comparisons with ESC 2021 guidelines. Additionally, we designed a survey to obtain the characteristics of the hospitals. The results show that the nutrition in hospitals remains substandard. None of the diets had an appropriate salt supply or predominance of plant-based food patterns. Only 1/7 diets avoided sweetened beverages, and 2/7 diets had an appropriate amount of fiber. This underscores a gap in the healthcare system to improve patients’ health by implementing dietary interventions that foster the development of healthy eating habits.

Demonstrations of electromagnetic eavesdropping attacks on digital video-interface cables usually first amplitude-demodulate the received signal, then resample the result into pixel-aligned raster images, and finally average consecutive video frames obtained this way. This non-coherent process discards useful phase information. Software-defined radio receivers allow us to coherently demodulate and average such signals, using a linear process that preserves phase information. We describe techniques for performing phase-coherent resampling and periodic averaging of compromising emanations from HDMI video cables and demonstrate how the preserved phase information enables better discrimination of on-screen colours.

This paper presents a high-linearity common-gate VGA with a new gain control technique that uses a hybrid coupling network. This new technique mitigates the drawbacks of the conventional VGAs that results in high phase distortion across gain states. This VGA is implemented in GlobalFoundries 45RFSOI and achieves an RMS phase error of 0.9°-2.8°. This low error is maintained across 8.5 GHz of bandwidth. The measured IP1dB and IIP3 at the maximum gain state is 3.5 dBm and 11.5 dBm, respectively. The NF achieved is also kept low at 4.4-5.2 dB. Application areas are in high linearity beamformers for 5G and mm-wave applications.

High-Definition Multimedia Interface (HDMI) technology is used for devices such as HDTVs, Projectors, and DVD players. One HDMI cable can only connect one device, even though, when needed, one computer or laptop can be displayed on several televisions or projectors using HDMI Splitter. HDMI Splitter is an accessory that displays content on multiple screens. HDMI Splitter has been circulated with various brands that offer different capabilities and specifications. Therefore, Therefore, thorough consideration is needed in choosing the right HDMI Splitter. This study aims to build a decision support system with the Multiple Criteria Decision Analysis (MCDA) approach using Complex Proportional Assessment (COPRAS). The COPRAS method solves the selection problem by calculating the utility level of alternatives, which shows the extent to which an alternative is better or worse than other alternatives through a comparison process. Based on existing studies, the utility values of each alternative were obtained, namely: Robot HDMI Splitter producing a value of 53.73%, Vention HDMI Splitter producing a value of 65.63%, Bafo HDMI Splitter producing a value of 97.13, and PX HDMI Splitter producing a value of 100%. Hence, based on testing through the black box, the best alternative is PX HDMI Splitter.

Use of edge computing in application of Computer Vision (CV) is an active field of research. Today, most CV applications make use of Convolutional Neural Networks (CNNs) to inference on and interpret video data. These edge devices are responsible for several CV related tasks, such as gathering, processing and enhancing, inferencing on, and displaying video data. Due to ease of reconfiguration, computation on FPGA fabric is used to achieve such complex computation tasks. Xilinx provides the PYNQ environment as a user-friendly interface that facilitates in Hardware/Software system integration. However, to the best of authors’ knowledge there is no end-to-end framework available for the PYNQ environment that allows Hardware/Software system integration and deployment of CNNs for real-time input feed from High Definition Multimedia Interface (HDMI) input to HDMI output, along with insertion of customized hardware IPs. In this work we propose an integration of reaL-time image Enancement IP with AI inferencing engine in the Pynq environment (LEAP), that integrates HDMI, AI acceleration, image enhancement in the PYNQ environment for Xilinx’s Microprocessor on Chip (MPSoC) platform. We evaluate our methodology with two well known CNN models, Resnet50 and YOLOv3. To validate our proposed methodology, LEAP, a simple image enhancement algorithm, histogram equalization, is designed and integrated in the FPGA fabric along with Xilinx’s Deep Processing Unit (DPU). Our results show successful implementation of end-to-end integration using completely open source information.

This brief develops a highly programmable and fully synthesizable large-N frequency multiplier. It is based on Time-Average-Frequency Direct Period Synthesis (TAF-DPS) and its purpose is to generate audio frequencies in HDMI (high definition multimedia interface) applications in which frequency multiplication ratio is extremely large. The proposed multiplier is a full digital feed-forward frequency control loop. The output frequency is produced by performing a fractional frequency division of the oscillator clock with TAF-DPS. The source of electrical oscillation in the TAF-DPS is a free-running inverter ring that consists of inverters. The novel structure is implemented in a TSMC 0.18 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P6M CMOS process. The implementation is fully digital HDL coding <inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> simulation <inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> synthesis <inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> place & route without any need for analog simulation. The hardware cost is equivalent to 3150 two-input NAND gates. The proposed structure can achieve fast frequency switching within two input reference clock cycles. The multiplication ratio is evaluated experimentally to be as high as 10⁶ with an input signal of 50 Hz; this is the largest ratio and lowest input frequency to be reported to the best of our knowledge. Besides generating an audio clock for HDMI applications, this frequency multiplier is very suitable for frequency measurements and system-on-a-chip applications.

The first chip I designed during my master’s degree program at Seoul National University in 1985 was a dual-modulus prescaler used for frequency synthesis in a digitally tuned automotive radio receiver. Since the incoming frequency was high, being close to an FM carrier, the chip was made mostly of CMLs using hundreds of transistors with a bipolar process. This chip was fabricated and measured, but unfortunately, it did not work. It oscillated at around 5 MHz. A bandgap reference that uses feedback was employed to compensate for the temperature variation, but the required compensation capacitance was not accurately calculated, causing instability. At that time, since a circuit simulation tool was unavailable, I could not calculate the exact loop characteristics and ended up adding an insufficient amount of capacitance. Although succeeding in the revised chip by inserting a capacitor several times larger than the value estimated by hand calculation, I became acutely aware of the need for a circuit design tool. Today, more than 40 years later, it is an incredible honor to receive the IEEE Technical Field Award, commemorating Professor Donald O. Pederson, who pioneered Simulation Program With Integrated Circuit Emphasis, which has become a standard circuit simulation tool. I entered the doctoral program at the University of California, Berkeley (UC Berkeley), and was taught by great professors, and it is unfortunate that I said hello to Prof. Pederson only a few times in the hallway.

The learning rate (LR) critically shapes the con-vergence and performance of machine learning models, yet traditional adaptive strategies often rely on manually tuned initializations and optimizer-specific designs, constraining their flexibility. This paper presents LG-XLR, a novel, fully automated, and optimizer-agnostic framework for dynamic LR adaptation, guided by real-time loss trends. Our approach hinges on two innovations: (1) loss-guided adaptation, where training and validation loss changes are monitored at each epoch to steer LR updates, and (2) an exponential LR range exploration that dynamically adjusts bounds without predefined schedules or initial guesses. By refining the LR range based on these loss-driven insights, our method exhibits stable convergence, mitigates overfitting, and eliminates task-specific tuning. Compatible with any gradient-based optimizer, it adapts seamlessly across models, datasets, and training scenarios. Experiments on CIFAR-10 image classification show consistent generalization (82-84% test accuracy) and faster convergence (25–42 epochs to 80% accuracy) across five optimizers, outperforming baselines like CLR and LRTuner in versatility and efficiency.

Background This pilot study aimed to characterize the regional distribution of genetic variants associated with autosomal recessive and X-linked recessive (AR/XLR) conditions in Fujian Province, Southeast China, to inform the development of targeted carrier screening programs. Methods An expanded carrier screening (ECS) panel utilizing next-generation sequencing (NGS) technology was designed to analyze 332 genes associated with 343 AR/XLR conditions. The panel was applied to 440 samples obtained from individuals in Fujian Province. Single nucleotide variants and copy number variations (CNVs) were identified and analyzed using a multidimensional approach that incorporated demographic characteristics, carrier frequencies, and the genetic burden of AR/XLR diseases. Results A total of 511 variants were detected among the 440 participants, including 43 CNVs (8.41%), affecting 133 genes associated with 123 conditions. The mean number of pathogenic or likely pathogenic variants per sample was 1.16. The highest genetic burden was observed in couples seeking medically assisted reproduction (MAR group), who had histories of fetal loss, second- or third-trimester abnormalities, or postnatal abnormalities. In clinical settings, the percentage of at-risk couples (ARCs) was 6.36% (n = 14), involving seven conditions, with no statistically significant difference in ARC incidence between couples undergoing genetic screening (GS group) and the MAR group. The cumulative carrier rate for 28 genes was ≥1/100. Recurrent variants in GAA, GALT, CYP1B1, and MEFV were identified, exhibiting distinct regional patterns compared to previously reported variants in the Han Chinese population. Conclusion NGS-based ECS demonstrates significant potential for assessing the genetic burden of AR/XLR conditions and identifying ARCs in Fujian Province. However, before integrating ECS into regional public health initiatives, the development of a region-specific, curated disease panel is necessary to optimize screening efficacy and clinical utility.

Abstract This study aims to explore how the abused synthetic cannabinoids in human urine can be determined qualitatively and quantitatively by using the LC-MS/MS method. As a method validation based on the current FDA guideline, we tested 55 human urine samples (5 from drug-free volunteers and 50 from drug abusers) and evaluated the presence of parent drugs (UR144 and XLR11) and their five metabolites (XLR-11-N-4-Hydroxypentyl, XLR-11-6-Hydroxyindole, UR-144-N-4-Hydroxypentyl, UR-144-N-5-hydroxypentyl, and UR-144-N-pentanoic). The experimental data showed that the values of low limit of detection (LOD) for these analytes ranged from 0.05 to 0.15 ng/mL. Their low limit of quantitation (LLOQ) value was 1.0 ng/mL. The values of linear coefficients of determination (R2) for these analytes ranged from 0.9914 to 0.9988. Among these 50 urine testing samples of drug abusers, the parent XLR-11 and UR-144 analytes were detected only in 4 samples (with a concentration of 2.14 − 4.5 ng/mL) and 3 samples (with a concentration of 3.2 − 5.4 ng/mL), respectively. At the same time, the XLR-11-N-4-Hydroxypentyl (with a concentration of 4.5 − 57.2 ng/mL) and XLR-11-6-Hydroxyindole (with a concentration of 4.8 − 51.7 ng/mL) metabolites were detected in 24 and 11 samples, respectively. In addition, the UR-144-N-4-Hydroxypentyl (with a concentration of 3.5 − 87.24 ng/mL), UR-144-N-5-hydroxypentyl (with a concentration of 14.5-83.9 ng/mL), and UR-144-N-pentanoic (with a concentration of 2.5 − 104.4 ng/mL) metabolites were found in 26, 29, and 38 samples, respectively. Graphical Abstract HIGHLIGHTS Quantification for 5 metabolites of UR-144 and XLR-11 in 50 human urinary samples with LOD of 0.05 − 0.15 ng/mL and LLOQ of 1.0 ng/mL Parent XLR-11 and UR-144 were detected in 4 and 3 samples; XLR-11-N-4-Hydroxypentyl and XLR-11-6-Hydroxyindole metabolites were detected in 24 and 11 samples; UR-144-N-4-Hydroxypentyl, UR-144-N-5-hydroxypentyl, and UR-144-N-pentanoic metabolites were found in 26, 29, and 38 samples.

In this paper, we propose the design of a high definition multimedia interface (HDMI) 2.1 connector considering signal integrity (SI) for improved electrical characteristics. We design the receptacle based on the analysis of electrical specification margins for 4 channels: D0, D1, D2, CK. The electrical characteristic includes insertion loss (IL), attenuation to crosstalk noise ratio (ACR) and time-domain reflectometry (TDR) impedance. Customized solutions for each channel are investigated considering their electrical performance margins respectively. As a result, we verify the SI performance improvement compared to the previous work through the 3D electromagnetic (EM) simulation.

HDMI (High Definition Multimedia Interface) cables could emit EM (Electromagnetic) leakage when transmitting video signals, such that it could cause sensitive information leak-age, leading to a severe security threat. However, the radio signals are too weak to be detected due to the complex electromagnetic environment. Therefore, we propose a novel non-linear detection method based on the Duffing oscillator, which is immune to noises and sensitive to periodic signals, and can even detect signals with very low SNR (Signal-to-Noise Ratio). In this paper, we first verify this method through simulation. Then this method would be applied to real signal detection in a shielded room. Overall, we achieve detecting real signals and successfully verify this method.

In this paper, the coaxial and shielded-twisted pair (STP) cables with intra-pair skew was analyzed by frequency domain analysis method by the equations in close-to-real intra-pair skew setup for high definition multimedia interface (HDMI) interface. For the high-speed interface, the intra-pair skew has become critical factor in terms of signal integrity. Heretofore, the intra-pair skew has not been commonly investigated in frequency domain for the high-speed data rate. To verify the intra-pair skew impact on the high-speed interface, the intra-pair skew location was distributed in the coaxial and STP cable assemblies for the real cable setup. Next, the skew impact was verified using frequency domain analysis and the eye-diagram. As the result, by the frequency-domain analysis, the differential loss and the mode conversion of coaxial cable assembly deteriorated due to the intra-pair skew, but STP did not deteriorate. Furthermore, the eye-diagram of the coaxial cable assembly with skew had smaller opening than without any skew, but of STP had little change.

In this paper, we propose the design of Mobile High-Definition Link (HDMI) 2.1 connector for crosstalk reduction using tabs and inverse tabs (i-tabs). We analyzed the capacitance between the HDMI differential pins, and based on this, we designed the receptacle pin using tabs and i-tabs. The i-tabs are used to reduce crosstalk between victim differential line and aggressor differential lines. The tabs are used to reduce the increased impedance as the capacitance is reduced by the i-tabs. Furthermore, the tabs are further improved crosstalk and differential insertion loss. For verification, we compared time domain reflectometry (TDR) impedance, attenuation to crosstalk ratio (ACR), and differential insertion loss between the default design, idea1 design which only reduces the mutual capacitance between differential lines using i-tabs, and the proposed design. The proposed design using tabs and i-tabs achieved all improvements in crosstalk, impedance, and insertion loss compared to the default design.

Well-known methods of measuring and analyzing sound and other signals of human organs in a wide time dimension have been reviewed, starting from the ones dated 20th century. It has been shown that even a modern expensive digital stethoscope of the Littmann CORE Digital type has a number of shortcomings - it does not allow to automatically analyze the parameters of the signals and transmit the obtained data to an external device. An experimental system that allows to continuously and for a long time automatically measure the time and frequency characteristics of the sound signals of the body and transmit the data of their analysis to an external device has been created and tested. Signals from human organs are received by microphones hidden in soundproof body capsules. Condenser microphones along with an amplifier of the MAX-9814 type, as well as the Clippy-XLR-EM272Z1 and an amplifier with an external sound card Scarlett Solo have been tested. The amplified signals are fed to the input of a Raspberry Pi Pico microcontroller or an analog-to-digital converter of the Scarlett Solo card. After digitization, the signals are input to minicomputers such as Raspberry Pi Zero or Raspberry Pi 3 via the USB port. Software named ChyPaCha was written using MicroPython, C++, and Python. The program for data analysis on Raspberry Pi 3 contains a call to the fast Fourier transform algorithm. The efficiency of the systems has been tested using compiled software tools that provided continuous processing of heart sound signals depending on the state of the load on the human body in real time and transmission of analysis results. It has been shown that the use of the Clippy-XLR-EM272Z1 and Scarlett Solo complex significantly reduced the impact of the system's own noise (amplifier-ADC) compared to the same noise of the MAX-9814 - Raspberry Pi Pico system and increased the accuracy of measuring the intensity of heart sounds.

In view of the need to use multiple remote controls to operate separately when watching HDTV, in order to reduce the unnecessary operation of users in the process of viewing the film, HDMI-CEC is generated, which just meets the needs of users. At present, all HD(High-Definition Multimedia Interface) devices produced by manufacturers have CEC(consumer electronic control)function. CEC function brings a lot of convenience to users. It allows end users to control multiple HD devices with CEC by using one remote controller, thus reducing the number of remote controls. This design is a controller based on HDMI version. The Hardware Description Language Verilog is used to realize the design of CEC bus controller IP core. VCS simulation tool is used to verify the design. The experimental results show that the design meets the requirements of hdmi-cec bus controller design. This article mainly talks about HDMI-CEC protocol, design architecture, micro architecture, and software hardware coordination design method.

Currently, the security technology applied to LED displays is focused on network security, and a problem exists with the expression security function of the final display equipment. Therefore, the development and improvement of security technology, application of standardized security protocols, security training for operators and users, and fundamental and specific approaches are required. Unintended expression accidents and security problems may occur in the display of images. Accordingly, a fundamental security plan applying a representative HDMI signal processing technology is required. Therefore, this study analyzes the security technology currently applied to digital media imaging systems and proposes a new video security technique using voice signals in videos to solve the fundamental expression problem of security methods for media images that were limited to existing network fields. To verify the proposed technique, the reliability and safety of the display security of video files were compared between the existing and the improved systems by applying it to the LED display system using HDMI. The result confirmed that the unspecified video could not be displayed depending on the security application. If the security application technique using voice frequencies proposed in this study is applied to the currently released digital media imaging system, excellent improvement can be achieved in maintaining the confidentiality of operating data, preventing external attacks and modifications, and controlling illegal content and advertisements.

Objective To determine whether a chemokine receptor type 2 antagonist, DMX-200 (repagermanium), in combination with an angiotensin receptor blocker, candesartan, improves clinical outcomes in people with COVID-19. Design Prospective, multicentre, double-blind, placebo-controlled trial. Setting Ten acute care hospitals in India. Participants Adults <65 years old intended for hospital admission with moderate/severe COVID-19 disease (respiratory rate ≥24 breaths per minute or oxygen saturation ≤93% on room air). Intervention DMX-200 120 mg two times per day, or placebo, on background of titratable candesartan commencing at 4 mg two times per day, for 28 days. Main outcome measures The primary endpoint was COVID-19 disease severity on a modified WHO Clinical Progression Scale (WHO scale) on day 14. Secondary outcomes included the WHO scale at days 28, 60, 90 and 180; intensive care unit (ICU) admission, respiratory failure or death within 28 days; length of hospitalisation; and requirement for ventilatory support or dialysis. Results Between December 2021 and August 2022, 518 people were screened, with 49 randomised to DMX-200 or placebo on a background of candesartan. The study was terminated early due to recruitment barriers, including an external requirement to restrict enrolment to adults <65 years old, contributing to a 91% screen failure rate. The median WHO Clinical Progression Scale (WHO scale) score at day 14 for both groups was 1 (IQR 1–1), indicating most participants were discharged with no limitations on activities by this time. Formal comparison was not performed due to the small sample size. One participant receiving DMX-200 died of COVID-19 disease progression. No participants required ICU admission, ventilation or dialysis. Median length of hospitalisation in both groups was 6 days (IQR 6–7 days). WHO scale scores were similar at 28, 60, 90 and 180 days. Conclusion Due to recruitment barriers, the study was unable to determine whether DMX-200 improves clinical outcomes in people with COVID-19. Trial registration number ClinicalTrials.gov NCT05122182.

The fast headway in computerized hardware has given rise to Framework System on Chip (SoC) innovation, empowering the integration of different reusable Mental Intellectual Property (IP) components, processors, memory components, and transport designs into a single chip. SoCs are progressively connected in different divisions due to their capacity to coordinated numerous functionalities, counting Web of Things (WoT) capabilities, onto a single stage. As the request for complex, multi-functional gadgets develop, plan complexity, control administration, and space limitations gotten to be significant. A various levelled plan approach, emphasizing the reuse of pre-designed and confirmed IP squares, decreases advancement costs and time. Apparatuses like the Xilinx Vivado IP Integrator encourage this by permitting creators to consistently coordinated IPs. This paper investigates an SoC engineering outlined for High-Definition interactive Media Interface (HDMI) and Ethernet flag handling, which utilizes an FPGA framework to store and transmit video information over long separations through an Ethernet organize. This plan underpins IoT gadget network and can show video on numerous screens utilizing HDMI whereas guaranteeing flag astuteness and high-quality yield.

Simple Summary Although organization of the catecholaminergic system, in the porcine vagal motor nuclei of the pig, as well as distribution and chemical nature of the parasympathetic preganglionic neurons innervating the prepyloric region of the porcine stomach in the nucleus, have been well established, the question of a possible direct regulatory interaction between both neuronal systems still remains unknown. We discovered morphological foundations for direct regulatory action of the local TH-immunoreactive neurons on vagal preganglionic parasympathetic efferent neurons supplying the prepyloric region of the porcine stomach. Abstract The vagus nerve is responsible for efferent innervation and functional control of stomach functions. The efferent fibers originate from neurons located in the dorsal motor nucleus of the vagus (DMX) and undergo functional control of the local neuroregulatory terminals. The aim of the present study was to examine the existence of morphological foundations for direct regulatory action of the local TH-immunoreactive neurons on parasympathetic efferent neurons supplying the prepyloric region of the porcine stomach. Combined injection of neuronal retrograde tracer Fast Blue into the stomach prepyloric region with TH immunostaining was used in order to visualize spatial relationship between DMX-located stomach prepyloric region supplying neuronal stomata and local TH-IR terminals. We confirmed existence of TH-immunoreactive neural terminals closely opposing the stomach prepyloric region innervating neurons at the porcine DMX area. The observed spatial relationship points out the possibility of indirect catecholaminergic control of the stomach function exerted through preganglionic parasympathetic efferent neurons in the pig.

High-definition multimedia interface (HDMI) has been widely adopted as the transmission protocol for computer monitors across various applications. However, HDMI display systems inevitably emit electromagnetic (EM) emanation during operating conditions, carrying critical image data and therefore posing threats in EM security regarding signal interception and reconstruction. Existing studies rely on sophisticated equipment to capture EM signals, which are unlikely to be encountered in civilian activities. From another viewpoint, this article proposes a novel HDMI display reconstruction approach by exploiting a protocol-based signal periodicity analysis. Specifically, the EM information is detected through the probes. Following that, the extracted HDMI signal with specific time delays is compared to that current one, enabling the pixel color classification using the degree of signal similarity. Experiments validate the capability and effectiveness of the proposed method, with a case study in black-and-white screen restoration.

We present a novel side-channel attack targeting HDMI audio transmissions via electromagnetic (EM) leakage. Our approach leverages both passive and active channels to extract information from HDMI signals without protocol-level access or physical tampering. Through the unintentional EM leakage channel, we accurately infer playback states (play vs. paused) and audio types (speech vs. music) with 100% classification accuracy. We further achieve 96.67% accuracy in fine-grained spoken digits recognition using feature fusion and bootstrapped learning. Complementing this, we demonstrate an actively modulated covert channel that encodes data into the EM emissions of HDMI audio streams. This covert channel supports reliable transmission across air-gapped, wall-separated environments with an effective throughput of 12bits per second. Our findings reveal that HDMI audio paths, traditionally considered benign, can in fact serve as powerful side-channel vectors for behavioral inference and stealthy data exfiltration, posing serious risks to both consumer and industrial systems.

Aiming to support researchers by answering their questions, this paper presents the first module of a virtual assistant for Tunisian Arabic (TA): Automatic Speech Recognition (ASR). Given the specialized lexicon used, which is characterized by a high rate of Code-Switching (CS), our primary objective is to create a new dataset, Tunacad, tailored to this context. Tunacad comprises $\mathbf{1 2. 5 6}$ hours of CS spontaneous speech in the academic domain. Additionally, we experiment with our proposed CS corpus on predefined ASR models, Whisper and Wav2vec2-XLR-S. To enhance ASR performance on our corpus, we apply several techniques, such as automatic orthographic correction, normalization, and prompt-based correction. Additionally, we incorporate human evaluation to assess transcription quality beyond surface-level accuracy. Through these experiments and CS errors analysis statistics, we highlight the persistent challenges in processing TA speech.

Wiskott–Aldrich syndrome (WAS) is a rare X-linked disorder characterized by immunodeficiency, thrombocytopenia, and eczema, with a risk of autoimmune phenomena. A 3-year-old boy presented to us with a history of recurrent skin and mucosal bleeds, itchy skin lesions, and recurrent purulent ear discharge. He had been hospitalized for pneumonia and meningitis earlier, having developed left-sided hemiplegia associated with a cerebral infarct. On examination, he was growth retarded with pallor, hepatosplenomegaly, multiple purpuric spots, and infected scalp lesions. In addition, he had spastic hemiparesis in the left side. Investigation revealed microcytic hypochromic anemia, eosinophilia, thrombocytopenia, and very high IgE titer (10,433). Coagulation profile was normal. Tuberculosis work-up was negative. The mean platelet volume was low normal (7.3) and bone marrow showed decreased number and size of megakaryocytes. Exome sequencing showed pathogenic mutation of WAS gene in exon 3 hemizygous XLR variant. The patient received weekly intravenous immunoglobulin therapy and cotrimoxazole. The eczema, thrombocytopenia, and infections improved. Later, he developed sudden-onset bilateral loss of vision. Magnetic resonance (MR) imaging brain revealed acute infarct on the left occipital region and gliosis in the right cerebral hemisphere with MR angiography showing middle and posterior cerebral artery thrombosis and narrowing of bilateral distal internal carotid arteries suggestive of moyamoya disease. Immunosuppressive agents and antiplatelets could not be administered, so the child was kept under supportive care. This case makes pediatricians aware of the possibility of cerebrovascular events as a manifestation of autoimmune vasculitis in children with WAS. Optimal treatment for such cases needs research.

We demonstrate methods to enhance the reconstruction of displayed information from the compromising emanations of HDMI or DVI video cables. Using a software-defined radio receiver, we acquire multiple recordings of such emissions for the same displayed image, at adjacent, overlapping reception bands. We first perform frequency alignment and coherent periodic averaging on each of these recordings individually. We then mutually align the resulting frames such that we can extract colour-identifying features for each displayed pixel across multiple reception bands. These features then go into a clustering algorithm to classify the signals emitted by different TMDS symbols. Finally, we build a graph data structure of the most common transitions between such symbols, and identify loops in this graph as candidates of pixel colours that cycle through multiple symbols due to the DC-balancing algorithm applied by the TMDS encoding. This can enhance the readability of eavesdropped text with some colour combinations, as we demonstrate for signals recorded at 12 metres distance.

Modern computers rely on USB and HDMI ports for connecting external peripherals and display devices. Despite their built-in security measures, these ports remain susceptible to passive power-based side-channel attacks. This paper presents a new class of attacks that exploit power consumption patterns at these ports to infer GPU activities. We develop a custom device that plugs into these ports and demonstrates that its high-resolution power measurements can drive successful inferences about GPU processes, such as neural network computations and video rendering. The ubiquitous presence of USB and HDMI ports allows for discreet placement of the device, and its non-interference with data channels ensures that no security alerts are triggered. Our findings underscore the need to reevaluate and strengthen the current generation of HDMI and USB port security defenses.

Focal segmental glomerulosclerosis (FSGS) is a major cause of glomerular disease that affects both adults and children and can rapidly progress to end-stage kidney disease. Lack of safe and effective treatment represents a significant unmet need for patients with FSGS. Change in estimated glomerular filtration rate (eGFR slope) has been used as a key measure of treatment efficacy in clinical studies. However, when using eGFR as surrogate endpoint for kidney failure, treatments that exert haemodynamic effects or have an acute eGFR reductions complicate the estimation of long-term benefit. Therefore, it is important to establish if new interventions influence blood pressure and exert acute reductions in eGFR. DMX-200 (repagermanium) is a CCR2 pathway inhibitor that is currently in a Phase 3 pivotal trial (ACTION3, NCT05183646) in patients with biopsy-proven FSGS concomitantly receiving maximal tolerated angiotensin receptor blocker (ARB). Mechanistically, the inhibition of CCR2 is not anticipated to reduce blood pressure nor have an acute effect on GFR. The aim of this investigation was to establish whether DMX-200 has an acute effect on haemodynamics or eGFR. A post-hoc review of haemodynamic, eGFR and cardiovascular safety parameters was performed using data from two double blind cross over clinical studies NCT03627715 and NCT03649152 (ACTION2). In these studies, patients with FSGS (n = 8) or DKD (n = 45) that were receiving a stable dose of irbesartan 300mg were administrated DMX-200 (120 mg BID) or placebo for up to 12- or 16-weeks respectively. Administration of DMX-200 (120 mg BID) for up to 16-weeks had no noticeable effect on systolic blood pressure compared to placebo (mean change from baseline; Δ0.1 ± 12.4 mmHg versus Δ3.7 ± 10.8 mmHg respectively, n = 8). Similarly, no acute effect on eGFR has observed following administration of DMX-200 (120 mg BID) with a mean change from baseline eGFR; Δ3.9 ± 10.0 mL/min/1.73 m2, Δ2.1 ± 8.0 mL/min/1.73 m2 and Δ0.6 ± 6.2 mL/min/1.73 m2 at 1, 8 and 16-weeks respectively, n = 8). Creatine levels were also comparable following administration of DMX-200 or placebo for up to 16-weeks (mean change from baseline; Δ2.1 ± 16.3 µmol/L versus Δ−1.9 ± 19.2 µmol/L respectively, n = 8). Comparable results for blood pressure, eGFR and creatinine were observed in patients with DKD administered DMX-200 (120 mg BID) for up to 12-weeks. Confirming the cardiac safety of DMX-200, there were no significant effects on QT/QTc following administration of DMX-200 or placebo and adverse events reflected of the underlying disease and associated comorbidities. DMX-200 is a CCR2 pathway inhibitor currently in a Phase 3 pivotal trial (ACTION3, NCT05183646) in adult patients with biopsy-proven FSGS. As anticipated, data from post-hoc analysis of Phase 2 studies indicate that DMX-200 does not induce a haemodynamic effect or acute change in eGFR. Notably, DMX-200 has a strong cardiovascular safety profile. These data support the design of ACTION3 where patients receive a stable maximally tolerated dose of an angiotensin receptor blocker prior to randomization.

Optical wireless HDMI transceiver using uncooled 12-mW violet laser diode as Tx with f-3dB=4GHz and p-i-n photodiode as Rx with f-3dB=7GHz is demonstrated to enable 26.4Gbit/s 64-QAM OFDM data and 2K ´ 1K HDMI (1080P@60fps) video transmission. © 2022 The Author(s)

CO2 capture & storage is foreseen as a necessity to limit global warming, as indicated by the recent reports from International Energy Agency. Major initiatives have to be initiated in a near future with concrete actions to get efficient results in limiting global warming. Based on its decades of experience in gas sweetening AXENS has developed an expertise in CO2 removal technologies. While conventional amine based processes can be used for some CO2 capture applications like for instance the treatment of process gas streams under pressure, other applications for low pressure gas streams like flue gases will require innovative advanced solutions. AXENS has studied various options for the removal of CO2 in SMR based hydrogen schemes, including the treatment of the process gas or the treatment of the flue gases from the SMR furnace, evaluating the respective merits of those options. For the treatment of the flue gases a new technology developed by IFPEN and AXENS based on a second generation amine solvent is considered : DMX™ DMX™ process, is foreseen as a key contributor for the removal of CO2 from all kind of low pressure gas streams. This process allows drastic reduction of CO2 capture cost in comparison to more conventional solvent such as MEA and others available solvents. The specific features of this solvent allows significant reduction of the heat requirements for the regeneration of the solvent. It also allows regenerating the solvent directly under pressure up to 6 bara, reducing the costs for downstream CO2 compression Preliminary techno-economic studies show significant advantage of DMX™ technology relatively to MEA : up to 30 % reduction in OPEX can be obtained for lower or similar CAPEX, depending on the condition. This process has been developed at the lab scale and is now going to be demonstrated in an industrial pilot unit installed in ArcelorMittal's steel mill plant in Dunkirk (France). This demonstration benefits from the support of EU's H2020 programme, under 3D project.

The present paper regards a high-resolution 3d fracture DMX modelling exercise applied to Triassic dolomites of the Ghail Fm, outcropping along Wadi Bih in the territory of Ras Al-Khaimah (UAE). The outcropping rocks show a low primary porosity, are well bedded and highly fractured (jointed) up to cm scale. They are an equivalent of the hydrocarbon producing Khuff Fm in the subsurface, and as such the exersice shows the relevance of applying new technologies to outcrop observations to confront with subsurface situations. Secondly, the exersize shows several new unique elements and related technologies not presented previously. The workflow presented which allows to arrive from observations to a 3d data driven fracture model, applying new technologies and algorithms using specific modules of the DMX protocol. Data collected comprise direct hand measurements of fractures and bedding planes, and 3d calibrated HR photographs. The scale of the model ranges from cm to meter scale fractures (core to borehole image scale). The parameters that have been analysed are size (length, height), orientation (set recognition), truncation relationships (BIXTV) between fractures and bedding planes, and spatial distribution (spacing) of the single fracture sets. The analyses and confrontation between the bedding tilt and the orientation of the 5 recognised fracture sets demonstrates that the fracture network is passively tilted with the bedding plane. A methodology is presented to arrive from in principle 1d (lineament) observations upon 2d sections (bedding planes surfaces and vertical sections) to statistical models and the usage in 3d fracture modeling. The resulting 3d model is generated as a Data Driven Model using the DMX protocol, which is directly connected to the observed features which are themselves present in the model, and comprises 5 sets with the following parameters reflecting the observed mathematical models: Specific spatial spacing relationships, size distributions (lengths and heights), relationships with modelled bedding plane surfaces, undulation of fractures, and 3d spatial truncations (XTV) between fractures and between fractures and bedding planes. This model is interrogated for 3d connectivity in different directions, P32 density (related to fracture porosity) and can be confronted with subsurface datasets from (preferably oriented) cores. The example shows for the first time how relatively simple and straightforward observations, which can be collected by hand, or with 3d stereofotogrammetrical techniques or drone technologies, can be used to build 3d analogue models that accurately mimic geological relationships.

In the present paper new original approach is presented that constitutes the link between the analyses techniques of fracture and fault (Discontinuity) networks in surface and subsurface such as obtained from borehole imaging, conventional observations and ctscan on cores, laser scan and drone observations on outcrops, and 3d seismic attribute volumes, and the novel DMX Protocol, a numerical code developed over the last 15 years to converge towards a more realistic Discontinuity Network representation in space, on the other. This new methodology uses the following flow chart: The FMX protocol analyses datasets for mathematical characterizations of distributions of parameters such as size, spacing, orientation and truncations (distribution models such as Normal, Power Law, LogNormal, etc.). These data analyses tools are linked to different type of Data Driven Modeling technologies, already presented during the last 25 years, both in 2d and 3d (FPDM statistical mapping of orientation and density, 3d deterministic surface fitting) in order to obtain realistic 2d and 3d fracture and fault networks directly based and linked to the observations. On the other hand, the DMX 3d modeling protocol (launched in 2019), includes new features such as Fracture interaction, and IXTV truncation, termination and cross cutting in 3d space, all based on newly designed collision algorithms and fracture propagation principles. The DMX protocol furthermore permits modelling at any scale range of unlimited basic 3d fracture shapes, specific 3d fracture morphology, and 3d fracture aperture types. A complete integration between classical geological/geomechanical drivers such as stress ellipse, fault zones with 3d slip vectors, and different fold models (axial plane, fold axis and bedding orientation conditioning), geological assembly modelling such as joint spacing and set dependency, offset/faulting, and probabilistic conditioning of any of the parameters and drivers. The newly presented work flow permits to integrate these technologies so that both the analyzed statistical distributions and the data driven 3d models can be successfully introduced inside the DMX 3d modeling, using its capabilities directly on the data driven models, and extending them into space towards unknown areas (e.g. inter well); I.e. the observed features are the germs of the 3d model itself. In this way the DMX protocol which has the capability of modeling an unlimited amount of combinations of geological parameters in 3d space, is now successfully integrated in the 3d modelling of the data directly observed on outcrops, seismic images and borehole Logs. Case studies of the application of the workflow will be presented of 3d models built using different sets of observations of fracture and fault networks on various scales, as examples of the application of new technologies in Artificial Intelligence to complex 3d modeling.

Background and Objectives Tandem repeats (TRs) are DNA regions of tandemly repeated nucleotide motifs. Their pathogenic expansions cause various, mainly neurologic, diseases. Methods We analyzed 65 TR loci using ExpansionHunter in individuals who underwent short-read whole-exome sequencing (WES) or whole-genome sequencing (WGS) for the diagnosis of a rare neurologic condition. Results Of 1,106 proband samples (1,053 WES, 53 WGS), we detected 232 TR expansions in the intermediate or pathogenic range in 18.7% (207/1,106). However, 51 TR expansions were revised as false positives (FPs) and 83 as nondisease-causing. Of the 98 disease-causing TR expansions, 5 were classified as causal hemizygous or heterozygous TR expansions associated with X-linked recessive (XLR) or autosomal dominant (AD) neurologic disorders in 5 probands (0.5%). The low incidence is due to the fact that individuals with typical clinical symptoms (spinocerebellar ataxia) were tested for TR expansion by conventional laboratory methods. Only 1 proband with clinical suspicion of spinal and bulbar muscular atrophy was fully explained by TR expansion in the AR gene, and in 4 others, we hypothesize the possible involvement of 2 different neurologic diseases. Another 82 causal hemizygous or heterozygous TR expansions associated with XLR or AD non-neurologic diseases (secondary findings) were identified in 81 probands (7.3%), of which 70 expansions in TCF4 were associated with Fuchs endothelial corneal dystrophy, a common eye disease in older patients. Finally, we detected 11 heterozygous TR expansions for XLR and autosomal recessive (AR) diseases in 11 probands who had no clinical symptoms of the associated TR disease. Discussion The unexpectedly high detection rate (18.7%) of TR expansions necessitates the filtration of FPs and nondisease-causing expansions, thereby underscoring the necessity of visual inspection of ExpansionHunter results. The study demonstrated that both WES and WGS diagnostics can benefit from TR expansion analysis. The secondary findings indicate that the previously published pathogenic ranges of TR expansions in RUNX2 and ZIC3 warrant further investigation.

Lignosus rhinocerus (Cooke) Ryvarden, a traditional medicinal mushroom in Southeast Asia, is utilized for various health conditions. Although there is evidence that L. rhinocerus sclerotia supplementation had decreased systolic blood pressure and improved acetylcholine-induced vasorelaxation, the underlying mechanism remains poorly understood. Therefore, this study investigated the vasorelaxant effect and possible mechanism of action of L. rhinocerus TM02® sclerotial cold water extract (xLr®) on rat-isolated aortae using an organ bath technique. Aortic rings removed from Sprague Dawley rats were pre-contracted with phenylephrine (PE) prior to the construction of the cumulative concentration-response curve to xLr®. Exposure to increasing concentrations of xLr® significantly relaxed the pre-contracted aorta [maximum tissue contraction or relaxation response (Emax): 97.70 ± 5.21%; P = 0.0005]. Furthermore, our findings revealed that the xLr®-induced vasorelaxation is independent of endothelium and muscarinic receptor-mediated pathways. The attenuation of PE-induced contractions by SKF96365 and nifedipine suggests the possible effect of xLr®-induced vasorelaxation on the inhibition of external calcium influx through membrane calcium channels. This is the first study detailing the endothelium-independent vasorelaxant effects of xLr® in isolated blood vessels. These findings have enhanced our understanding of the pharmacological effects of xLr® on vasculature.

When the camera is actually used, it is necessary to provide the user with a picture preview function on the HDMI interface, that is, to browse multiple pictures on one interface. Based on the JPEG format specification and Exif specification, combined with multi-thread decoding display technology, this paper proposes a new quick preview scheme for multiple pictures. Even if you switch and preview multiple ultra-high-definition pictures by this shceme, the refresh display speed is very fast, and it is easy to maintain later and has good stability.

In this paper, we propose the design of a HDMI 2.1 connector for 8K TV considering signal integrity (SI). Also, we firstly measure the proposed HDMI 2.1 connector. To achieve the high data rate, connector should be designed by considering not only mechanical characteristics but also electrical characteristics. We design the HDMI 2.1 connector considering SI including characteristic impedance, differential insertion loss and attenuation to crosstalk ratio (ACR). We revise the structure of metal pins and dielectric materials for improving the SI performances. Proposed HDMI 2.1 connector was verified by time-domain and frequency domain simulation using the 3D electromagnetic (EM) simulator. Proposed HDMI 2.1 connector showed improve SI performance than previous connector. Also, proposed connector was verified through measurement. With the proposed HDMI design, it shows better SI characteristics at the 24 Gbps which is expected to next generation HDMI connector’s data rate.

We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

This article conceptualizes community cultural wealth as a critical race theory (CRT) challenge to traditional interpretations of cultural capital. CRT shifts the research lens away from a deficit view of Communities of Color as places full of cultural poverty disadvantages, and instead focuses on and learns from the array of cultural knowledge, skills, abilities and contacts possessed by socially marginalized groups that often go unrecognized and unacknowledged. Various forms of capital nurtured through cultural wealth include aspirational, navigational, social, linguistic, familial and resistant capital. These forms of capital draw on the knowledges Students of Color bring with them from their homes and communities into the classroom. This CRT approach to education involves a commitment to develop schools that acknowledge the multiple strengths of Communities of Color in order to serve a larger purpose of struggle toward social and racial justice.

Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Currently available depth-based and RGB+Dbased action recognition benchmarks have a number of limitations, including the lack of training samples, distinct class labels, camera views and variety of subjects. In this paper we introduce a large-scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects. Our dataset contains 60 different action classes including daily, mutual, and health-related actions. In addition, we propose a new recurrent neural network structure to model the long-term temporal correlation of the features for each body part, and utilize them for better action classification. Experimental results show the advantages of applying deep learning methods over state-of-the-art handcrafted features on the suggested cross-subject and cross-view evaluation criteria for our dataset. The introduction of this large scale dataset will enable the community to apply, develop and adapt various data-hungry learning techniques for the task of depth-based and RGB+D-based human activity analysis.

In this work, we study 3D object detection from RGBD data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we directly operate on raw point clouds by popping up RGB-D scans. However, a key challenge of this approach is how to efficiently localize objects in point clouds of large-scale scenes (region proposal). Instead of solely relying on 3D proposals, our method leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects. Benefited from learning directly in raw point clouds, our method is also able to precisely estimate 3D bounding boxes even under strong occlusion or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection benchmarks, our method outperforms the state of the art by remarkable margins while having real-time capability.

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided with surface reconstructions, camera poses, and 2D and 3D semantic segmentations. The precise global alignment and comprehensive, diverse panoramic set of views over entire buildings enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.

We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.

Fluorescent and red light environments generate greatly different patterns of pigmentation and morphology in Fremyella diplosiphon. Most strikingly, red-illuminated cultures contain no measurable C-phycoerythrin and have a mean filament length about 10 times shorter than fluorescent-illuminated cultures. C-phycoerythrin behaves as a photoinducible constituent of this alga. Spectrophotometric and immunochemical procedures were devised so that C-phycoerythrin metabolism could be studied quantitatively with [14C]-phenylalanine pulse-chased cultures. Transfer of red-illuminated cultures to fluorescent light initiates C-phycoerythrin production by essentially de novo synthesis. C-phycoerythrin is not degraded to any significant extent in cultures continuously illuminated with fluorescent light. Transfer of fluorescent-illuminated cultures to red light causes an abrupt cessation of C-phycoerythrin synthesis. The C-phycoerythrin content of cultures adapting to red light decreases and subsequently becomes constant. Loss of C-phycoerythrin is not brought about by metabolic degradation, but rather by a decrease in mean filament length which is effected by transcellular breakage. In this experimental system, light influences intracellular C-phycoerythrin levels by regulating the rate of synthesis of the chromoprotein.

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an average precision of 37.3%, which is a 56% relative improvement over existing methods. We then focus on the task of instance segmentation where we label pixels belonging to object instances found by our detector. For this task, we propose a decision forest approach that classifies pixels in the detection window as foreground or background using a family of unary and binary tests that query shape and geocentric pose features. Finally, we use the output from our object detectors in an existing superpixel classification framework for semantic scene segmentation and achieve a 24% relative improvement over current state-of-the-art for the object categories that we study. We believe advances such as those represented in this paper will facilitate the use of perception in fields like robotics.

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding.

We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image. Limited by the nature of deep neural network, previous methods usually represent a 3D shape in volume or point cloud, and it is non-trivial to convert them to the more ready-to-use mesh model. Unlike the existing methods, our network represents 3D mesh in a graph-based convolutional neural network and produces correct geometry by progressively deforming an ellipsoid, leveraging perceptual features extracted from the input image. We adopt a coarse-to-fine strategy to make the whole deformation procedure stable, and define various of mesh related losses to capture properties of different levels to guarantee visually appealing and physically accurate 3D geometry. Extensive experiments show that our method not only qualitatively produces mesh model with better details, but also achieves higher 3D shape estimation accuracy compared to the state-of-the-art.

Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://3dmatch.cs.princeton.edu.

We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Furthermore, our method produces these results at around 10Hz, which is many times faster than the related methods. For the sake of reproducibility, we make our trained networks and detection code publicly available.

We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. DROID-SLAM is accurate, achieving large improvements over prior work, and robust, suffering from substantially fewer catastrophic failures. Despite training on monocular video, it can leverage stereo or RGB-D video to achieve improved performance at test time. The URL to our open source code is https://github.com/princeton-vl/DROID-SLAM.

Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images. In this paper, we present an approach that estimates 3D hand pose from regular RGB images. This task has far more ambiguities due to the missing depth information. To this end, we propose a deep network that learns a network-implicit 3D articulation prior. Together with detected keypoints in the images, this network yields good estimates of the 3D pose. We introduce a large scale 3D hand pose dataset based on synthetic hand models for training the involved networks. Experiments on a variety of test sets, including one on sign language recognition, demonstrate the feasibility of 3D hand pose estimation on single color images.

In order to control dispersion and dispersion slope of indexguiding photonic crystal fibers (PCFs), a new controlling technique of chromatic dispersion in PCF is reported. Moreover, our technique is applied to design PCF with both ultra-low dispersion and ultra-flattened dispersion in wide wavelength range. A full-vector finite element method with anisotropic perfectly matched layers is used to analyze the dispersion properties and the confinement losses in a PCF with finite number of air holes. It is shown from numerical results that it is possible to design a fourring PCF with flattened dispersion of 0 +/- 0.5 ps/(km.nm) from 1.19 m to 1.69 m wavelength range and a five-ring PCF with flattened dispersion of 0 +/- 0.4 ps/(km.nm) from 1.23 m to 1.72 m wavelength range.

We measured the responses of 305 neurons in striate cortex to moving sinusoidal gratings modulated in chromaticity and luminance about a fixed white point. Stimuli were represented in a 3-dimensional color space defined by 2 chromatic axes and a third along which luminance varied. With rare exceptions the chromatic properties of cortical neurons were well described by a linear model in which the response of a cell is proportional to the sum (for complex cells, the rectified sum) of the signals from the 3 classes of cones. For each cell there is a vector passing through the white point along which modulation gives rise to a maximal response. The elevation (theta m) and azimuth (phi m) of this vector fully describe the chromatic properties of the cell. The linear model also describes neurons in l.g.n. (Derrington et al., 1984), so most neurons in striate cortex have the same chromatic selectivity as do neurons in l.g.n. However, the distributions of preferred vectors differed in cortex and l.g.n.: Most cortical neurons preferred modulation along vectors lying close to the achromatic axis and those showing overt chromatic opponency did not fall into the clearly defined chromatic groups seen in l.g.n. The neurons most responsive to chromatic modulation (found mainly in layers IVA, IVC beta, and VI) had poor orientation selectivity, and responded to chromatic modulation of a spatially uniform field at least as well as they did to any grating. We encountered neurons with band-pass spatial selectivity for chromatically modulated stimuli in layers II/III and VI. Most had complex receptive fields. Neurons in layer II/III did not fall into distinct groups according to their chromatic sensitivities, and the chromatic properties of neurons known to lie within regions rich in cytochrome oxidase appeared no different from those of neurons in the interstices. Six neurons, all of which resembled simple cells, showed unusually sharp chromatic selectivity.

The use of RGB-D information for salient object detection (SOD) has been extensively explored in recent years. However, relatively few efforts have been put toward modeling SOD in real-world human activity scenes with RGB-D. In this article, we fill the gap by making the following contributions to RGB-D SOD: 1) we carefully collect a new Salient Person (SIP) data set that consists of ~1 K high-resolution images that cover diverse real-world scenes from various viewpoints, poses, occlusions, illuminations, and background s; 2) we conduct a large-scale (and, so far, the most comprehensive) benchmark comparing contemporary methods, which has long been missing in the field and can serve as a baseline for future research, and we systematically summarize 32 popular models and evaluate 18 parts of 32 models on seven data sets containing a total of about 97k images; and 3) we propose a simple general architecture, called deep depth-depurator network (D3Net). It consists of a depth depurator unit (DDU) and a three-stream feature learning module (FLM), which performs low-quality depth map filtering and cross-modal feature learning, respectively. These components form a nested structure and are elaborately designed to be learned jointly. D3Net exceeds the performance of any prior contenders across all five metrics under consideration, thus serving as a strong model to advance research in this field. We also demonstrate that D3Net can be used to efficiently extract salient object masks from real scenes, enabling effective background-changing application with a speed of 65 frames/s on a single GPU. All the saliency maps, our new SIP data set, the D3Net model, and the evaluation tools are publicly available at https://github.com/DengPingFan/D3NetBenchmark.

The cytochrome oxidase-rich patches or blobs of the monkey striate cortex have been shown to contain cells that have unoriented receptive fields, many of which are color selective. We studied the functional organization of color opponency in the blob regions of the parafoveal representation of the visual cortex. We also examined the patterns of connectivity among blob and nonblob cells by multiple electrode penetrations and cross-correlation analysis. Some of the color- selective cells in the blobs exhibited receptive fields that were similar to those found in the parvocellular layers of the lateral geniculate nucleus (LGN): one type exhibited center-surround spatial and chromatic opponency corresponding to the Type I cell found in the LGN; another had center-only chromatic opponency, corresponding to the Type II cell of the LGN. A blob color-selective cell with no LGN counterpart had center color opponency with a nonchromatically opponent surround antagonism. We termed this cell the “modified Type II” cell. Contrary to previous reports, few true double color-opponent cells were found. Some blob cells previously characterized as double opponent probably belong to our modified Type II category and, unlike true double opponent cells, do not respond well to isoluminant color boundaries. Occasional color-selective oriented cells were either intermixed or in close proximity to blob cells. Neighboring electrode penetrations within the same blob yielded cells of the same color opponency, either red versus green or blue versus yellow, suggesting that individual blobs are dedicated to processing one color opponency. Blobs dedicated to red/green color opponency were 3 times more numerous than blue/yellow blobs. Furthermore, the cells in layer 4C lying beneath blobs of a given color opponency had identical color opponency to the overlying cells in blobs. Cross-correlation analysis of pairs of nonblob, oriented cells in the superficial layers showed interactions between cells with matched orientation and eye preference, at varying horizontal separations. Such interactions are consistent with anatomically demonstrated clustered horizontal connections. Positive cross-correlograms were found between blob cells in the same and in adjacent blobs when the cells' receptive field type, color opponency, and ocular dominance matched. Correlograms also indicated monosynaptic connections from Type II to modified Type II cells of the same color opponency, suggesting that Type II cells may contribute to the construction of the modified Type II fields in the cortex.(ABSTRACT TRUNCATED AT 400 WORDS)

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization.

We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp:felk:cvut:cz/t-less.

We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence. Our tracking method combines a convolutional neural network with a kinematic 3D hand model, such that it generalizes well to unseen data, is robust to occlusions and varying camera viewpoints, and leads to anatomically plausible as well as temporally smooth hand motions. For training our CNN we propose a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network. To be more specific, we use a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images. For training this translation network we combine an adversarial loss and a cycle-consistency loss with a geometric consistency loss in order to preserve geometric properties (such as hand pose) during translation. We demonstrate that our hand tracking system outperforms the current state-of-the-art on challenging RGB-only footage.

In this Letter, we experimentally report an achromatic metalens (AML) operating over a continuous bandwidth in the visible. This is accomplished via dispersion engineering of dielectric phase shifters: titanium dioxide nanopillars tiled on a dielectric spacer layer above a metallic mirror. The AML works in reflection mode with a focal length independent of wavelength from λ = 490 to 550 nm. We also design a metalens with reverse chromatic dispersion, where the focal length increases as the wavelength increases, contrary to conventional diffractive lenses. The ability to engineer the chromatic dispersion of metalenses at will enables a wide variety of applications that were not previously possible. In particular, for the AML design, we envision applications such as imaging under LED illumination, fluorescence, and photoluminescence spectroscopy.

In this work we study the use of 3D hand poses to recognize first-person dynamic hand actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations. To obtain hand pose annotations, we used our own mo-cap system that automatically infers the 3D location of each of the 21 joints of a hand model via 6 magnetic sensors and inverse kinematics. Additionally, we recorded the 6D object poses and provide 3D object models for a subset of hand-object interaction sequences. To the best of our knowledge, this is the first benchmark that enables the study of first-person hand actions with the use of 3D hand poses. We present an extensive experimental evaluation of RGB-D and pose-based action recognition by 18 baselines/state-of-the-art approaches. The impact of using appearance features, poses, and their combinations are measured, and the different training/testing protocols are evaluated. Finally, we assess how ready the 3D hand pose estimation field is when hands are severely occluded by objects in egocentric views and its influence on action recognition. From the results, we see clear benefits of using hand pose as a cue for action recognition compared to other data modalities. Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.

Scene understanding based on image segmentation is a crucial component of autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality ( ${X}$ -modality). However, covering a wide variety of sensors with a modality-agnostic model remains an unresolved problem due to variations in sensor characteristics among different modalities. Unlike previous modality-specific methods, in this work, we propose a unified fusion framework, CMX, for RGB-X semantic segmentation. To generalize well across different modalities, that often include supplements as well as uncertainties, a unified cross-modal interaction is crucial for modality fusion. Specifically, we design a Cross-Modal Feature Rectification Module (CM-FRM) to calibrate bi-modal features by leveraging the features from one modality to rectify the features of the other modality. With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to perform sufficient exchange of long-range contexts before mixing. To verify CMX, for the first time, we unify five modalities complementary to RGB, i.e., depth, thermal, polarization, event, and LiDAR. Extensive experiments show that CMX generalizes well to diverse multi-modal fusion, achieving state-of-the-art performances on five RGB-Depth benchmarks, as well as RGB-Thermal, RGB-Polarization, and RGB-LiDAR datasets. Besides, to investigate the generalizability to dense-sparse data fusion, we establish an RGB-Event semantic segmentation benchmark based on the EventScape dataset, on which CMX sets the new state-of-the-art. The source code of CMX is publicly available at https://github.com/huaaaliu/RGBX_Semantic_ Segmentation.

We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans. The core idea of our method to jointly learn from both geometric and color signal, thus enabling accurate instance predictions. Rather than operate solely on 2D frames, we observe that most computer vision applications have multi-view RGB-D input available, which we leverage to construct an approach for 3D instance segmentation that effectively fuses together these multi-modal inputs. Our network leverages high-resolution RGB input by associating 2D images with the volumetric grid based on the pose alignment of the 3D reconstruction. For each image, we first extract 2D features for each pixel with a series of 2D convolutions; we then backproject the resulting feature vector to the associated voxel in the 3D grid. This combination of 2D and 3D feature learning allows significantly higher accuracy object detection and instance segmentation than state-of-the-art alternatives. We show results on both synthetic and real-world public benchmarks, achieving an improvement in mAP of over 13 on real-world data.

We introduce the task of 3D object localization in RGB-D scans using natural language descriptions. As input, we assume a point cloud of a scanned 3D scene along with a free-form description of a specified target object. To address this task, we propose ScanRefer, learning a fused descriptor from 3D object proposals and encoded sentence embeddings. This fused descriptor correlates language expressions with geometric features, enabling regression of the 3D bounding box of a target object. We also introduce the ScanRefer dataset, containing 51,583 descriptions of 11,046 objects from 800 ScanNet scenes. ScanRefer is the first large-scale effort to perform object localization via natural language expression directly in 3D.

Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications. However, current methods are often hampered by the non-volumetric or implicit way they represent a scene. This work introduces SplaTAM, an approach that, for the first time, leverages explicit volumetric representations, i.e., 3D Gaussians, to enable high-fidelity reconstruction from a single unposed RGB-D camera, surpassing the capabilities of existing methods. SplaTAM employs a simple online tracking and mapping system tailored to the underlying Gaussian representation. It utilizes a silhouette mask to elegantly capture the presence of scene density. This combination enables several benefits over prior representations, including fast rendering and dense optimization, quickly determining if areas have been previously mapped, and structured map expansion by adding more Gaussians. Extensive experiments show that SplaTAM achieves up to 2 x superior performance in camera pose estimation, map construction, and novel-view synthesis over existing methods, paving the way for more immersive high-fidelity SLAM applications.

This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image. Most current methods in 3D hand analysis from monocular RGB images only focus on estimating the 3D locations of hand keypoints, which cannot fully express the 3D shape of hand. In contrast, we propose a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose. To train networks with full supervision, we create a large-scale synthetic dataset containing both ground truth 3D meshes and 3D poses. When fine-tuning the networks on real-world datasets without 3D ground truth, we propose a weakly-supervised approach by leveraging the depth map as a weak supervision in training. Through extensive evaluations on our proposed new datasets and two public datasets, we show that our proposed method can produce accurate and reasonable 3D hand mesh, and can achieve superior 3D hand pose estimation accuracy when compared with state-of-the-art methods.

Estimating 3D hand pose from single RGB images is a highly ambiguous problem that relies on an unbiased training dataset. In this paper, we analyze cross-dataset generalization when training on existing datasets. We find that approaches perform well on the datasets they are trained on, but do not generalize to other datasets or in-the-wild scenarios. As a consequence, we introduce the first large-scale, multi-view hand dataset that is accompanied by both 3D hand pose and shape annotations. For annotating this real-world dataset, we propose an iterative, semi-automated `human-in-the-loop' approach, which includes hand fitting optimization to infer both the 3D pose and shape for each sample. We show that methods trained on our dataset consistently perform well when tested on other datasets. Moreover, the dataset allows us to train a network that predicts the full articulated hand shape from a single RGB image. The evaluation set can serve as a benchmark for articulated hand shape estimation.

6-DoF object pose estimation from a single RGB image is a fundamental and long-standing problem in computer vision. Current leading approaches solve it by training deep networks to either regress both rotation and translation from image directly or to construct 2D-3D correspondences and further solve them via PnP indirectly. We argue that rotation and translation should be treated differently for their significant difference. In this work, we propose a novel 6-DoF pose estimation approach: Coordinates-based Disentangled Pose Network (CDPN), which disentangles the pose to predict rotation and translation separately to achieve highly accurate and robust pose estimation. Our method is flexible, efficient, highly accurate and can deal with texture-less and occluded objects. Extensive experiments on LINEMOD and Occlusion datasets are conducted and demonstrate the superiority of our approach. Concretely, our approach significantly exceeds the state-of-the- art RGB-based methods on commonly used metrics.

Drone-based vehicle detection aims at detecting vehicle locations and categories in aerial images. It empowers smart city traffic management and disaster relief. Researchers have made a great deal of effort in this area and achieved considerable progress. However, because of the paucity of data under extreme conditions, drone-based vehicle detection remains a challenge when objects are difficult to distinguish, particularly in low-light conditions. To fill this gap, we constructed a large-scale drone-based RGB-infrared vehicle detection dataset called DroneVehicle, which contains 28, 439 RGB-infrared image pairs covering urban roads, residential areas, parking lots, and other scenarios from day to night. Cross-modal images provide complementary information for vehicle detection, but also introduce redundant information. To handle this dilemma, we further propose an uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to improve detection performance in complex environments. Specifically, we design an uncertainty-aware module using cross-modal intersection over union and illumination estimation to quantify the uncertainty of each object. Our method takes uncertainty as a weight to boost model learning more effectively while reducing bias caused by high-uncertainty objects. For more robust cross-modal integration, we further perform illumination-aware non-maximum suppression during inference. Extensive experiments on our DroneVehicle and two challenging RGB-infrared object detection datasets demonstrated the advanced flexibility and superior performance of UA-CMDet over competing methods. Our code and DroneVehicle will be available: https://github.com/VisDrone/DroneVehicle.

RGB-Infrared (IR) person re-identification is an important and challenging task due to large cross-modality variations between RGB and IR images. Most conventional approaches aim to bridge the cross-modality gap with feature alignment by feature representation learning. Different from existing methods, in this paper, we propose a novel and end-to-end Alignment Generative Adversarial Network (AlignGAN) for the RGB-IR RE-ID task. The proposed model enjoys several merits. First, it can exploit pixel alignment and feature alignment jointly. To the best of our knowledge, this is the first work to model the two alignment strategies jointly for the RGB-IR RE-ID problem. Second, the proposed model consists of a pixel generator, a feature generator and a joint discriminator. By playing a min-max game among the three components, our model is able to not only alleviate the cross-modality and intra-modality variations, but also learn identity-consistent features. Extensive experimental results on two standard benchmarks demonstrate that the proposed model performs favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve an absolute gain of 15.4% and 12.9% in terms of Rank-1 and mAP.

Semantic segmentation is a fundamental capability for autonomous vehicles. With the advancements of deep learning technologies, many effective semantic segmentation networks have been proposed in recent years. However, most of them are designed using RGB images from visible cameras. The quality of RGB images is prone to be degraded under unsatisfied lighting conditions, such as darkness and glares of oncoming headlights, which imposes critical challenges for the networks that use only RGB images. Different from visible cameras, thermal imaging cameras generate images using thermal radiations. They are able to see under various lighting conditions. In order to enable robust and accurate semantic segmentation for autonomous vehicles, we take the advantage of thermal images and fuse both the RGB and thermal information in a novel deep neural network. The main innovation of this letter is the architecture of the proposed network. We adopt the encoder–decoder design concept. ResNet is employed for feature extraction and a new decoder is developed to restore the feature map resolution. The experimental results prove that our network outperforms the state of the arts.

A key component of Simultaneous Localization and Mapping (SLAM) systems is the joint optimization of the estimated 3D map and camera trajectory. Bundle adjustment (BA) is the gold standard for this. Due to the large number of variables in dense RGB-D SLAM, previous work has focused on approximating BA. In contrast, in this paper we present a novel, fast direct BA formulation which we implement in a real-time dense RGB-D SLAM algorithm. In addition, we show that direct RGB-D SLAM systems are highly sensitive to rolling shutter, RGB and depth sensor synchronization, and calibration errors. In order to facilitate state-of-the-art research on direct RGB-D SLAM, we propose a novel, well-calibrated benchmark for this task that uses synchronized global shutter RGB and depth cameras. It includes a training set, a test set without public ground truth, and an online evaluation service. We observe that the ranking of methods changes on this dataset compared to existing ones, and our proposed algorithm outperforms all other evaluated SLAM methods. Our benchmark and our open source SLAM algorithm are available at: www.eth3d.net

Depth information has proven to be a useful cue in the semantic segmentation of RGB-D images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion to obtain better feature representations to achieve more accurate segmentation. This, however, may not lead to satisfactory results as actual depth data are generally noisy, which might worsen the accuracy as the networks go deeper. In this paper, we propose a unified and efficient Cross-modality Guided Encoder to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively. The key of the proposed architecture is a novel Separation-and-Aggregation Gating operation that jointly filters and recalibrates both representations before cross-modality aggregation. Meanwhile, a Bi-direction Multi-step Propagation strategy is introduced, on the one hand, to help to propagate and fuse information between the two modalities, and on the other hand, to preserve their specificity along the long-term propagation process. Besides, our proposed encoder can be easily injected into the previous encoder-decoder structures to boost their performance on RGB-D semantic segmentation. Our model outperforms state-of-the-arts consistently on both in-door and out-door challenging datasets. Code of this work is available at this https URL

Beginning with the origin of the four color problem in 1852, the field of graph colorings has developed into one of the most popular areas of graph theory. Introducing graph theory with a coloring theme, Chromatic Graph Theory explores connections between major topics in graph theory and graph colorings as well as emerging topics. This self-contained book first presents various fundamentals of graph theory that lie outside of graph colorings, including basic terminology and results, trees and connectivity, Eulerian and Hamiltonian graphs, matchings and factorizations, and graph embeddings. The remainder of the text deals exclusively with graph colorings. It covers vertex colorings and bounds for the chromatic number, vertex colorings of graphs embedded on surfaces, and a variety of restricted vertex colorings. The authors also describe edge colorings, monochromatic and rainbow edge colorings, complete vertex colorings, several distinguishing vertex and edge colorings, and many distance-related vertex colorings. With historical, applied, and algorithmic discussions, this text offers a solid introduction to one of the most popular areas of graph theory.

Racial trauma, a form of race-based stress, refers to People of Color and Indigenous individuals’ (POCI) reactions to dangerous events and real or perceived experiences of racial discrimination. Such experiences may include threats of harm and injury, humiliating and shaming events, and witnessing racial discrimination toward other POCI. Although similar to posttraumatic stress disorder, racial trauma is unique in that it involves ongoing individual and collective injuries due to exposure and reexposure to race-based stress. The articles in this special issue introduce new conceptual approaches, research, and healing models to challenge racial trauma. The authors encourage psychologists to develop culturally informed healing modalities and methodologically sophisticated research and urge the inclusion of public policy interventions in the area of racial trauma.

Digital signal processing (DSP) combined with a phase and polarization diverse coherent receiver is a promising technology for future optical networks. Not only can the DSP be used to remove the need for dynamic polarization control, but also it may be utilized to compensate for nonlinear and linear transmission impairments. In this paper we present results of a 42.8Gbit/s nonlinear transmission experiment, using polarization multiplexed QPSK data at 10.7GBaud, with 4 bits per symbol. The digital coherent receiver allows 107,424 ps/nm of chromatic dispersion to be compensated digitally after transmission over 6400km of standard single mode fiber.

In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem, and produce a single saliency map following a deterministic learning pipeline. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space. With the proposed saliency consensus process, we are able to generate an accurate saliency map based on these multiple predictions. Quantitative and qualitative evaluations on six challenging benchmark datasets against 18 competing algorithms demonstrate the effectiveness of our approach in learning the distribution of saliency maps, leading to a new state-of-the-art in RGB-D saliency detection.

Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic applications. While current volume-based view synthesis methods that use neural radiance fields (NeRFs) show promising results in reproducing the appearance of an object or scene, they do not reconstruct an actual surface. The volumetric representation of the surface based on densities leads to artifacts when a surface is extracted using Marching Cubes, since during optimization, densities are accumulated along the ray and are not used at a single sample point in isolation. Instead of this volumetric representation of the surface, we propose to represent the surface using an implicit function (truncated signed distance function). We show how to incorporate this representation in the NeRF framework, and extend it to use depth measurements from a commodity RGB-D sensor, such as a Kinect. In addition, we propose a pose and camera re-finement technique which improves the overall reconstruction quality. In contrast to concurrent work on integrating depth priors in NeRF which concentrates on novel view synthesis, our approach is able to reconstruct high-quality, metrical 3D reconstructions.

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. The key solution is to learn aligned features to the bridge RGB and IR modalities. However, due to the lack of correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing the distance between the entire RGB and IR sets. However, this set-level alignment may lead to misalignment of some instances, which limits the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Extensive experimental results on two standard benchmarks demonstrate that the proposed model favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and mAP. Code is available at https://github.com/wangguanan/JSIA-ReID.

Scene understanding is an active research area. Commercial depth sensors, such as Kinect, have enabled the release of several RGB-D datasets over the past few years which spawned novel methods in 3D scene understanding. More recently with the launch of the LiDAR sensor in Apple's iPads and iPhones, high quality RGB-D data is accessible to millions of people on a device they commonly use. This opens a whole new era in scene understanding for the Computer Vision community as well as app developers. The fundamental research in scene understanding together with the advances in machine learning can now impact people's everyday experiences. However, transforming these scene understanding methods to real-world experiences requires additional innovation and development. In this paper we introduce ARKitScenes. It is not only the first RGB-D dataset that is captured with a now widely available depth sensor, but to our best knowledge, it also is the largest indoor scene understanding data released. In addition to the raw and processed data from the mobile device, ARKitScenes includes high resolution depth maps captured using a stationary laser scanner, as well as manually labeled 3D oriented bounding boxes for a large taxonomy of furniture. We further analyze the usefulness of the data for two downstream tasks: 3D object detection and color-guided depth upsampling. We demonstrate that our dataset can help push the boundaries of existing state-of-the-art methods and it introduces new challenges that better represent real-world scenarios.

Advancing beyond individual-level approaches to coping with racial trauma, we introduce a new psychological framework of radical healing for People of Color and Indigenous individuals (POCI) in the United States. We begin by providing a context of race and racism in the United States and its consequences for the overall well-being of POCI. We build on existing frameworks rooted in social justice education and activism and describe a form of healing and transformation that integrates elements of liberation psychology, Black psychology, ethnopolitical psychology, and intersectionality theory. We briefly review these conceptual foundations as a prelude to introducing a psychological framework of radical healing and its components grounded in five anchors including: (a) collectivism, (b) critical consciousness, (c) radical hope, (d) strength and resistance, and (e) cultural authenticity and self-knowledge. We conclude with a discussion of the applications of radical healing to clinical practice, research, training, and social justice advocacy.

This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately-designed training process. In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF). The JL module provides robust saliency feature learning, while the latter is introduced for complementary feature discovery. Comprehensive experiments on four popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the top-1 D3Net model by an average of ~1.9% (S-measure) across six challenging datasets, showing that the proposed framework offers a potential solution for real-world applications and could provide more insight into the cross-modality complementarity task. The code will be available at https://github.com/kerenfu/JLDCF/.

Semantic segmentation is a fundamental task in computer vision, and it has various applications in fields such as robotic sensing, video surveillance, and autonomous driving. A major research topic in urban road semantic segmentation is the proper integration and use of cross-modal information for fusion. Here, we attempt to leverage inherent multimodal information and acquire graded features to develop a novel multilabel-learning network for RGB-thermal urban scene semantic segmentation. Specifically, we propose a strategy for graded-feature extraction to split multilevel features into junior, intermediate, and senior levels. Then, we integrate RGB and thermal modalities with two distinct fusion modules, namely a shallow feature fusion module and deep feature fusion module for junior and senior features. Finally, we use multilabel supervision to optimize the network in terms of semantic, binary, and boundary characteristics. Experimental results confirm that the proposed architecture, the graded-feature multilabel-learning network, outperforms state-of-the-art methods for urban scene semantic segmentation, and it can be generalized to depth data.

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity task.

Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assisted) person perception, (semantic) free space detection, (semantic) mapping, and (semantic) navigation. In this paper, we propose an efficient and robust RGB-D segmentation approach that can be optimized to a high degree using NVIDIA TensorRT and, thus, is well suited as a common initial processing step in a complex system for scene analysis on mobile robots. We show that RGB-D segmentation is superior to processing RGB images solely and that it can still be performed in real time if the network architecture is carefully designed. We evaluate our proposed Efficient Scene Analysis Network (ESANet) on the common indoor datasets NYUv2 and SUNRGB-D and show that we reach state-of-the-art performance while enabling faster inference. Furthermore, our evaluation on the outdoor dataset Cityscapes shows that our approach is suitable for other areas of application as well. Finally, instead of presenting benchmark results only, we also show qualitative results in one of our indoor application scenarios.

We describe a learning-based system that estimates the camera position and orientation from a single input image relative to a known environment. The system is flexible w.r.t. the amount of information available at test and at training time, catering to different applications. Input images can be RGB-D or RGB, and a 3D model of the environment can be utilized for training but is not necessary. In the minimal case, our system requires only RGB images and ground truth poses at training time, and it requires only a single RGB image at test time. The framework consists of a deep neural network and fully differentiable pose optimization. The neural network predicts so called scene coordinates, i.e., dense correspondences between the input image and 3D scene space of the environment. The pose optimization implements robust fitting of pose parameters using differentiable RANSAC (DSAC) to facilitate end-to-end training. The system, an extension of DSAC++ and referred to as DSAC*, achieves state-of-the-art accuracy on various public datasets for RGB-based re-localization, and competitive accuracy for RGB-D based re-localization.

Saliency detection on RGB-D images is receiving more and more research interests recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input RGB and depth data or their saliency maps, which incur the problem of distribution gap or information loss. Some other models use the feature fusion scheme but are limited by the linear feature fusion methods. In this paper, we propose to fuse attention learned in both modalities. Inspired by the Non-local model, we integrate the self-attention and each other's attention to propagate long-range contextual dependencies, thus incorporating multi-modal information to learn attention and propagate contexts more accurately. Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term. We embed the proposed attention module in a two-stream CNN for RGB-D saliency detection. Furthermore, we also propose a residual fusion module to fuse the depth decoder features into the RGB stream. Experimental results on seven benchmark datasets demonstrate the effectiveness of the proposed model components and our final saliency model. Our code and saliency maps are available at https://github.com/nnizhang/S2MA.

The wide range of functions that are possible with lithium niobate (LN) waveguide devices, including phase and intensity modulation, second-harmonic generation, and difference-frequency generation, makes it attractive as a potential microcomb material. LN microcombs would combine essential comb self-referencing and control functions with the pulse generation process in a single microresonator device. Here, we demonstrate a soliton microcomb in a monolithic high-Q LN resonator. Direct frequency doubling of the soliton spectrum is observed inside the same cavity. The LN soliton mode-locking process also self-starts and allows bi-directional switching of soliton states, effects that are shown to result from the LN photorefractive effect. The Kerr solitons exhibit a self-frequency shift resulting from the Raman effect of LN. This microcomb platform can dramatically simplify miniature time keeping, frequency synthesis/division, and spectroscopy systems. Moreover, direct generation of femtosecond timescale pulses within LN microresonators can benefit quantum photonics and signal processing systems.

Existing RGB-D Salient Object Detection (SOD) methods take advantage of depth cues to improve the detection accuracy, while pay insufficient attention to the quality of depth information. In practice, a depth map is often with uneven quality and sometimes suffers from distractors, due to various factors in the acquisition procedure. In this article, to mitigate distractors in depth maps and highlight salient objects in RGB images, we propose a Hierarchical Alternate Interactions Network (HAINet) for RGB-D SOD. Specifically, HAINet consists of three key stages: feature encoding, cross-modal alternate interaction, and saliency reasoning. The main innovation in HAINet is the Hierarchical Alternate Interaction Module (HAIM), which plays a key role in the second stage for cross-modal feature interaction. HAIM first uses RGB features to filter distractors in depth features, and then the purified depth features are exploited to enhance RGB features in turn. The alternate RGB-depth-RGB interaction proceeds in a hierarchical manner, which progressively integrates local and global contexts within a single feature scale. In addition, we adopt a hybrid loss function to facilitate the training of HAINet. Extensive experiments on seven datasets demonstrate that our HAINet not only achieves competitive performance as compared with 19 relevant state-of-the-art methods, but also reaches a real-time processing speed of 43 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://github.com/MathLee/HAINet.

A bstractThe search for a theory of the S-Matrix over the past five decades has revealed surprising geometric structures underlying scattering amplitudes ranging from the string worldsheet to the amplituhedron, but these are all geometries in auxiliary spaces as opposed to the kinematical space where amplitudes actually live. Motivated by recent advances providing a reformulation of the amplituhedron and planar N$$ \mathcal{N} $$ = 4 SYM amplitudes directly in kinematic space, we propose a novel geometric understanding of amplitudes in more general theories. The key idea is to think of amplitudes not as functions, but rather as differential forms on kinematic space. We explore the resulting picture for a wide range of massless theories in general spacetime dimensions. For the bi-adjoint ϕ3 scalar theory, we establish a direct connection between its “scattering form” and a classic polytope — the associahedron — known to mathematicians since the 1960’s. We find an associahedron living naturally in kinematic space, and the tree level amplitude is simply the “canonical form” associated with this “positive geometry”. Fundamental physical properties such as locality and unitarity, as well as novel “soft” limits, are fully determined by the combinatorial geometry of this polytope. Furthermore, the moduli space for the open string worldsheet has also long been recognized as an associahedron. We show that the scattering equations act as a diffeomorphism between the interior of this old “worldsheet associahedron” and the new “kinematic associahedron”, providing a geometric interpretation and simple conceptual derivation of the bi-adjoint CHY formula. We also find “scattering forms” on kinematic space for Yang-Mills theory and the Non-linear Sigma Model, which are dual to the fully color-dressed amplitudes despite having no explicit color factors. This is possible due to a remarkable fact—“Color is Kinematics”— whereby kinematic wedge products in the scattering forms satisfy the same Jacobi relations as color factors. Finally, all our scattering forms are well-defined on the projectivized kinematic space, a property which can be seen to provide a geometric origin for color-kinematics duality.

We present Neural Head Avatars, a novel neural representation that explicitly models the surface geometry and appearance of an animatable human avatar that can be used for teleconferencing in AR/VR or other applications in the movie or games industry that rely on a digital human.11philgras.github.io/neural_head_avatars/neural_head_avatars.html Our representation can be learned from a monocular RGB portrait video that features a range of different expressions and views. Specifically, we propose a hybrid representation consisting of a morphable model for the coarse shape and expressions of the face, and two feed-forward networks, predicting vertex offsets of the underlying mesh as well as a view- and expression-dependent texture. We demonstrate that this representation is able to accurately extrapolate to unseen poses and view points, and generates natural expressions while providing sharp texture details. Compared to previous works on head avatars, our method provides a disentangled shape and appearance model of the complete human head (including hair) that is compatible with the standard graphics pipeline. Moreover, it quantitatively and qualitatively outperforms current state of the art in terms of reconstruction quality and novel-view synthesis.

The key to address clothes-changing person re-identification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works mainly focus on modeling body shape from multi-modality information (e.g., silhouettes and sketches), but do not make full use of the clothes-irrelevant information in the original RGB images. In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w.r.t. clothes. Extensive experiments demonstrate that using RGB images only, CAL outperforms all state-of-the-art methods on widely-used clothes-changing person re-id benchmarks. Besides, compared with images, videos contain richer appearance and additional temporal information, which can be used to model proper spatiotemporal patterns to assist clothes-changing re-id. Since there is no publicly available clothes-changing video re-id dataset, we contribute a new dataset named CCVID and show that there exists much room for improvement in modeling spatiotemporal information. The code and new dataset are available at: h t t $p$ s: //github.com/guxinqian/Simple-CCReID.

We theoretically study physical properties of the most promising color center candidates for the recently observed single-photon emissions in hexagonal boron nitride (h-BN) monolayers. Through our group theory analysis combined with density functional theory (DFT) calculations we provide several pieces of evidence that the electronic properties of the color centers match the characters of the experimentally observed emitters. We calculate the symmetry-adapted multielectron wave functions of the defects using group theory methods and analyze the spin–orbit and spin–spin interactions in detail. We also identify the radiative and nonradiative transition channels for each color center. An advanced ab initio DFT method is then used to compute energy levels of the color centers and their zero-phonon-line (ZPL) emissions. The computed ZPLs, the profile of excitation and emission dipole polarizations, and the competing relaxation processes are discussed and matched with the observed emission lines. By providing e...

We construct perturbative classical solutions of the Yang-Mills equations coupled to dynamical point particles carrying color charge. By applying a set of color to kinematics replacement rules first introduced by Bern, Carrasco and Johansson, these are shown to generate solutions of d-dimensional dilaton gravity, which we also explicitly construct. Agreement between the gravity result and the gauge theory double copy implies a correspondence between non-Abelian particles and gravitating sources with dilaton charge. When the color sources are highly relativistic, dilaton exchange decouples, and the solutions we obtain match those of pure gravity. We comment on possible implications of our findings to the calculation of gravitational waveforms in astrophysical black hole collisions, directly from computationally simpler gluon radiation in Yang-Mills theory.

Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal fusion between the RGB stream and the depth stream. They do not deeply explore the effect of the depth map itself. In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion between RGB and depth, which saves the feature encoder of the depth stream and achieves a lightweight and real-time model. We tactfully utilize depth information from two perspectives: (1) Overcoming the incompatibility problem caused by the great difference between modalities, we build a single stream encoder to achieve the early fusion, which can take full advantage of ImageNet pre-trained backbone model to extract rich and discriminative features. (2) We design a novel depth-enhanced dual attention module (DEDA) to efficiently provide the fore-/back-ground branches with the spatially filtered features, which enables the decoder to optimally perform the middle fusion. Besides, we put forward a pyramidally attended feature extraction module (PAFE) to accurately localize the objects of different scales. Extensive experiments demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics. Furthermore, this model is 55.5\% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $384 \times 384$ image.

Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of research has been considerably hindered by the noise and ambiguity that prevail in raw depth images. To address the aforementioned issues, we propose a Depth Calibration and Fusion (DCF) framework that contains two novel components: 1) a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance; 2) a simple yet effective cross reference module to fuse features from both RGB and depth modalities. Extensive empirical experiments demonstrate that the proposed approach achieves superior performance against 27 state-of-the-art methods. Moreover, our depth calibration strategy alone can work as a preprocessing step; empirically it results in noticeable improvements when being applied to existing cutting-edge RGB-D SOD models. Source code is available at https://github.com/jiwei0921/DCF.

We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene; the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detection and description problems, we propose Scan2Cap, an end-to-end trained method, to detect objects in the input scene and describe them in natural language. We use an attention mechanism that generates descriptive tokens while referring to the related components in the local context. To reflect object relations (i.e. relative spatial relations) in the generated captions, we use a message passing graph module to facilitate learning object relation features. Our method can effectively localize and describe 3D objects in scenes from the ScanRefer dataset, outperforming 2D baseline methods by a significant margin (27.61% CiDEr@0.5IoU improvement).

Under ideal environmental conditions, RGB-based deep convolutional neural networks can achieve high performance for salient object detection (SOD). In scenes with cluttered backgrounds and many objects, depth maps have been combined with RGB images to better distinguish spatial positions and structures during SOD, achieving high accuracy. However, under low-light and uneven lighting conditions, RGB and depth information may be insufficient for detection. Thermal images are insensitive to lighting and weather conditions, being able to capture important objects even during nighttime. By combining thermal images and RGB images, we propose an effective and consistent feature fusion network (ECFFNet) for RGB-T SOD. In ECFFNet, an effective cross-modality fusion module fully fuses features of corresponding sizes from the RGB and thermal modalities. Then, a bilateral reversal fusion module performs bilateral fusion of foreground and background information, enabling the full extraction of salient object boundaries. Finally, a multilevel consistent fusion module combines features across different levels to obtain complementary information. Comprehensive experiments on three RGB-T SOD datasets show that the proposed ECFFNet outperforms 12 state-of-the-art methods under different evaluation indicators.

Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, existing works either rely on RGB-D sensors or require a separate monocular SLAM approach for camera tracking, and fail to produce high-fidelity 3D dense reconstructions. To address these shortcomings, we present NICER-SLAM, a dense RGB SLAM system that simultaneously optimizes for camera poses and a hierarchical neural implicit map representation, which also allows for high-quality novel view synthesis. To facilitate the optimization process for mapping, we integrate additional supervision signals including easy-to-obtain monocular geometric cues and optical flow, and also introduce a simple warping loss to further enforce geometric consistency. Moreover, to further boost performance in complex large-scale scenes, we also propose a local adaptive transformation from signed distance functions (SDFs) to density in the volume rendering equation. On multiple challenging indoor and outdoor datasets, NICER-SLAM demonstrates strong performance in dense mapping, novel view synthesis, and tracking, even competitive with recent RGB-D SLAM systems. Project page: https://nicer-slam.github.io/.

The identification and mapping of trees via remotely sensed data for application in forest management is an active area of research. Previously proposed methods using airborne and hyperspectral sensors can identify tree species with high accuracy but are costly and are thus unsuitable for small-scale forest managers. In this work, we constructed a machine vision system for tree identification and mapping using Red–Green–Blue (RGB) image taken by an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN). In this system, we first calculated the slope from the three-dimensional model obtained by the UAV, and segmented the UAV RGB photograph of the forest into several tree crown objects automatically using colour and three-dimensional information and the slope model, and lastly applied object-based CNN classification for each crown image. This system succeeded in classifying seven tree classes, including several tree species with more than 90% accuracy. The guided gradient-weighted class activation mapping (Guided Grad-CAM) showed that the CNN classified trees according to their shapes and leaf contrasts, which enhances the potential of the system for classifying individual trees with similar colours in a cost-effective manner—a useful feature for forest management.

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras and LiDAR, have significant potential in precision agriculture, including object detection. Tassel detection in maize is an essential trait given its relevance as the beginning of the reproductive stage of growth and development of the plants. However, compared with general object detection, tassel detection based on RGB imagery acquired by UAVs is more challenging due to the small size, time-dependent variable shape, and complexity of the objects of interest. A novel algorithm referred to as YOLOv5-tassel is proposed to detect tassels in UAV-based RGB imagery. A bidirectional feature pyramid network is adopted for the path-aggregation neck to effectively fuse cross-scale features. The robust attention module of SimAM is introduced to extract the features of interest before each detection head. An additional detection head is also introduced to improve small-size tassel detection based on the original YOLOv5. Annotation is performed with guidance from center points derived from CenterNet to improve the selection of the bounding boxes for tassels. Finally, to address the issue of limited reference data, transfer learning based on the VisDrone dataset is adopted. Testing results for our proposed YOLOv5-tassel method achieved the mAP value of 44.7%, which is better than well-known object detection approaches, such as FCOS, RetinaNet, and YOLOv5.

The chromatic polynomial of a graph G counts the number of proper colorings of G. We give an affirmative answer to the conjecture of Read and Rota-Heron-Welsh that the absolute values of the coefficients of the chromatic polynomial form a log-concave sequence. We define a sequence of numerical invariants of projective hypersurfaces analogous to the Milnor number of local analytic hypersurfaces. Then we give a characterization of correspondences between projective spaces up to a positive integer multiple which includes the conjecture on the chromatic polynomial as a special case. As a byproduct of our approach, we obtain an analogue of Kouchnirenko's theorem relating the Milnor number with the Newton polytope.

Significance Semantic typology documents and explains how languages vary in their structuring of meaning. Information theory provides a formal model of communication that includes a precise definition of efficient compression. We show that color-naming systems across languages achieve near-optimal compression and that this principle explains much of the variation across languages. These findings suggest a possible process for color category evolution that synthesizes continuous and discrete aspects of previous accounts. The generality of this principle suggests that it may also apply to other semantic domains. We derive a principled information-theoretic account of cross-language semantic variation. Specifically, we argue that languages efficiently compress ideas into words by optimizing the information bottleneck (IB) trade-off between the complexity and accuracy of the lexicon. We test this proposal in the domain of color naming and show that (i) color-naming systems across languages achieve near-optimal compression; (ii) small changes in a single trade-off parameter account to a large extent for observed cross-language variation; (iii) efficient IB color-naming systems exhibit soft rather than hard category boundaries and often leave large regions of color space inconsistently named, both of which phenomena are found empirically; and (iv) these IB systems evolve through a sequence of structural phase transitions, in a single process that captures key ideas associated with different accounts of color category evolution. These results suggest that a drive for information-theoretic efficiency may shape color-naming systems across languages. This principle is not specific to color, and so it may also apply to cross-language variation in other semantic domains.

Salient object detection (SOD) in RGB and depth images has attracted increasing research interest. Existing RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities, while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, the specificity-preserving network (SPNet), which improves SOD performance by exploring both the shared information and modality-specific properties. Specifically, we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and propagate the fused feature to the next layer to integrate cross-level information. Moreover, to capture rich complementary multi-modal information to boost SOD performance, we use a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using skip connections between encoder and decoder layers, hierarchical features can be fully combined. Extensive experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at https://github.com/taozh2017/SPNet .

We exploit the inherent dispersion in diffractive optics to demonstrate planar chromatic-aberration-corrected lenses. Specifically, we designed, fabricated and characterized cylindrical diffractive lenses that efficiently focus the entire visible band (450 nm to 700 nm) onto a single line. These devices are essentially pixelated, multi-level microstructures. Experiments confirm an average optical efficiency of 25% for a three-wavelength apochromatic lens whose chromatic focus shift is only 1.3 μm and 25 μm in the lateral and axial directions, respectively. Super-achromatic performance over the continuous visible band is also demonstrated with averaged lateral and axial focus shifts of only 1.65 μm and 73.6 μm, respectively. These lenses are easy to fabricate using single-step grayscale lithography and can be inexpensively replicated. Furthermore, these devices are thin (<3 μm), error tolerant, has low aspect ratio (<1:1) and offer polarization-insensitive focusing, all significant advantages compared to alternatives that rely on metasurfaces. Our design methodology offers high design flexibility in numerical aperture and focal length and is readily extended to 2D.

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene components by means of a graph neural network. We also propose a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario. Although our proposed method is designed to run on submaps of the scene, we show it also transfers to entire 3D scenes. Experiments show that our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35Hz.

An electrically modulated single‐/dual‐color imaging photodetector with fast response speed is developed based on a small molecule (COi8DFIC)/perovskite (CH3NH3PbBr3) hybrid film. Owing to the type‐I heterojunction, the device can facilely transform dual‐color images to single‐color images by applying a small bias voltage. The photodetector exhibits two distinct cut‐off wavelengths at ≈544 nm (visible region) and ≈920 nm (near‐infrared region), respectively, without any power supply. Its two peak responsivities are 0.16 A W−1 at ≈525 nm and 0.041 A W−1 at ≈860 nm with a fast response speed (≈102 ns). Under 0.6 V bias, the photodetector can operate in a single‐color mode with a peak responsivity of 0.09 A W−1 at ≈475 nm, showing a fast response speed (≈102 ns). A physical model based on band energy theory is developed to illustrate the origin of the tunable single‐/dual‐color photodetection. This work will stimulate new approaches for developing solution‐processed multifunctional photodetectors for imaging photodetection in complex circumstances.

Diffraction gratings disperse light in a rainbow of colors with the opposite order than refractive prisms, a phenomenon known as negative dispersion [1, 2]. While refractive dispersion can be controlled via material refractive index, diffractive dispersion is fundamentally an interference effect dictated by geometry. Here we show that this fundamental property can be altered using dielectric metasurfaces [3-5], and we experimentally demonstrate diffractive gratings and focusing mirrors with positive, zero, and hyper negative dispersion. These optical elements are implemented using a reflective metasurface composed of dielectric nano-posts that provide simultaneous control over phase and its wavelength derivative. In addition, as a first practical application, we demonstrate a focusing mirror that exhibits a five fold reduction in chromatic dispersion, and thus an almost three times increase in operation bandwidth compared to a regular diffractive element. This concept challenges the generally accepted dispersive properties of diffractive optical devices and extends their applications and functionalities.

RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality retrieval problem, which aims at matching the person-of-interest over visible and infrared camera views. Most existing works achieve performance gains through manually-designed feature selection modules, which often require significant domain knowledge and rich experience. In this paper, we study a general paradigm, termed Neural Feature Search (NFS), to automate the process of feature selection. Specifically, NFS combines a dual-level feature search space and a differentiable search strategy to jointly select identity-related cues in coarse-grained channels and fine-grained spatial pixels. This combination allows NFS to adaptively filter background noises and concentrate on informative parts of human bodies in a data-driven manner. Moreover, a cross-modality contrastive optimization scheme further guides NFS to search features that can minimize modality discrepancy whilst maximizing inter-class distance. Extensive experiments on mainstream benchmarks demonstrate that our method outperforms state-of-the-arts, especially achieving better performance on the RegDB dataset with significant improvement of 11.20% and 8.64% in Rank-1 and mAP, respectively.

Most recent methods for RGB (red–green–blue)-thermal salient object detection (SOD) involve several floating-point operations and have numerous parameters, resulting in slow inference, especially on common processors, and impeding their deployment on mobile devices for practical applications. To address these problems, we propose a lightweight spatial boosting network (LSNet) for efficient RGB-thermal SOD with a lightweight MobileNetV2 backbone to replace a conventional backbone (e.g., VGG, ResNet). To improve feature extraction using a lightweight backbone, we propose a boundary boosting algorithm that optimizes the predicted saliency maps and reduces information collapse in low-dimensional features. The algorithm generates boundary maps based on predicted saliency maps without incurring additional calculations or complexity. As multimodality processing is essential for high-performance SOD, we adopt attentive feature distillation and selection and propose semantic and geometric transfer learning to enhance the backbone without increasing the complexity during testing. Experimental results demonstrate that the proposed LSNet achieves state-of-the-art performance compared with 14 RGB-thermal SOD methods on three datasets while improving the numbers of floating-point operations (1.025G) and parameters (5.39M), model size (22.1 MB), and inference speed (9.95 fps for PyTorch, batch size of 1, and Intel i5-7500 processor; 93.53 fps for PyTorch, batch size of 1, and NVIDIA TITAN V graphics processor; 936.68 fps for PyTorch, batch size of 20, and graphics processor; 538.01 fps for TensorRT and batch size of 1; and 903.01 fps for TensorRT/FP16 and batch size of 1). The code and results can be found from the link of https://github.com/zyrant/LSNet.

RGB-D semantic segmentation has attracted increasing attention over the past few years. Existing methods mostly employ homogeneous convolution operators to consume the RGB and depth features, ignoring their intrinsic differences. In fact, the RGB values capture the photometric appearance properties in the projected image space, while the depth feature encodes both the shape of a local geometry as well as the base (whereabout) of it in a larger context. Compared with the base, the shape probably is more inherent and has a stronger connection to the semantics, and thus is more critical for segmentation accuracy. Inspired by this observation, we introduce a Shape-aware Convolutional layer (ShapeConv) for processing the depth feature, where the depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components. ShapeConv is model-agnostic and can be easily integrated into most CNNs to replace vanilla convolutional layers for semantic segmentation. Extensive experiments on three challenging indoor RGB-D semantic segmentation benchmarks, i.e., NYU-Dv2(-13,-40), SUN RGB-D, and SID, demonstrate the effectiveness of our ShapeConv when employing it over five popular architectures. Moreover, the performance of CNNs with ShapeConv is boosted without introducing any computation and memory increase in the inference phase. The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.

Maintaining local interactions in the quantum simulation of gauge field theories relegates most states in the Hilbert space to be unphysical -- theoretically benign, but experimentally difficult to avoid. Reformulations of the gauge fields can modify the ratio of physical to gauge-variant states often through classically preprocessing the Hilbert space and modifying the representation of the field on qubit degrees of freedom. This paper considers the implications of representing SU(3) Yang-Mills gauge theory on a lattice of irreducible representations in both a global basis of projected global quantum numbers and a local basis in which controlled-plaquette operators support efficient time evolution. Classically integrating over the internal gauge space at each vertex (e.g., color isospin and color hypercharge) significantly reduces both the qubit requirements and the dimensionality of the unphysical Hilbert space. Initiating tuning procedures that may inform future calculations at scale, the time evolution of one- and two-plaquettes are implemented on one of IBM's superconducting quantum devices, and early benchmark quantities are identified. The potential advantages of qudit environments, with either constrained 2D hexagonal or 1D nearest-neighbor internal state connectivity, are discussed for future large-scale calculations.

Compared with traditional handcrafted features, deep learning has greatly improved the performance of scene parsing. However, it remains challenging under various environmental conditions caused by imaging limitations. Thermal imaging cameras have several advantages over cameras for the visible spectrum, such as operation in total darkness, robustness to shadow effects, insensitivity to illumination variations, and strong ability to penetrate smog and haze. These advantages of thermal imaging cameras make them ideal for the scene parsing of semantic objects in daytime and nighttime. In this paper, we propose a novel multiscale feature fusion and enhancement network (MFFENet) for accurate parsing of RGB–thermal urban road scenes even when the quality of the available RGB data is compromised. The proposed MFFENet consists of two encoders, a feature fusion layer, and a multi-label supervision layer. We concatenate the multi-scale features with the features that contain global semantic information. Furthermore, we explore the cross-modal fusion of RGB and thermal features at multiple stages, rather than fusing them once at the low or high stage. Then, we propose a spatial attention mechanism module that provides a higher weight to (focuses more on) the foreground area, allowing MFFENet to emphasize foreground objects. Finally, multi-label supervision is introduced to optimize parameters of the proposed MFFENet. Experimental results confirm that the proposed MFFENet outperforms similar high-performing methods.

Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weighted summation, to fuse features from different modalities. These strategies, unfortunately, overlook the modality differences due to different imaging mechanisms, so that they suffer from the reduced discriminability of the fused features. To address such an issue, we propose, for the first time, the strategy of bridging-then-fusing, where the innovation lies in a novel Adaptive-weighted Bi-directional Modality Difference Reduction Network (ABMDRNet). Concretely, a Modality Difference Reduction and Fusion (MDRF) subnetwork is designed, which first employs a bi-directional image-to-image translation based method to reduce the modality differences between RGB features and thermal features, and then adaptively selects those discriminative multi-modality features for RGB-T semantic segmentation in a channel-wise weighted fusion way. Furthermore, considering the importance of contextual information in semantic segmentation, a Multi-Scale Spatial Context (MSC) module and a Multi-Scale Channel Context (MCC) module are proposed to exploit the interactions among multi-scale contextual information of cross-modality features together with their long-range dependencies along spatial and channel dimensions, respectively. Comprehensive experiments on MFNet dataset demonstrate that our method achieves new state-of-the-art results.

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGB-D feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. More-over, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.

RGB-D salient object detection (SOD) recently has attracted increasing research interest and many deep learning methods based on encoder-decoder architectures have emerged. However, most existing RGB-D SOD models conduct feature fusion either in the single encoder or the decoder stage, which hardly guarantees sufficient cross-modal fusion ability. In this paper, we make the first attempt in addressing RGB-D SOD through 3D convolutional neural networks. The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams. Specifically, RD3D first conducts pre-fusion across RGB and depth modalities through an inflated 3D encoder, and later provides in-depth feature fusion by designing a 3D decoder equipped with rich back-projection paths (RBPP) for leveraging the extensive aggregation ability of 3D convolutions. With such a progressive fusion strategy involving both the encoder and decoder, effective and thorough interaction between the two modalities can be exploited and boost the detection accuracy. Extensive experiments on six widely used benchmark datasets demonstrate that RD3D performs favorably against 14 state-of-the-art RGB-D SOD approaches in terms of four key evaluation metrics. Our code will be made publicly available: https://github.com/PPOLYpubki/RD3D.

RGB salient object detection (SOD) has made great progress. However, the performance of this single-modal salient object detection will be significantly decreased when encountering some challenging scenes, such as low light or darkness. To deal with the above challenges, thermal infrared (T) image is introduced into the salient object detection. This fused modal is called RGB-T salient object detection. To achieve deep mining of the unique characteristics of single modal and the full integration of cross-modality information, a novel Cross-Guided Fusion Network (CGFNet) for RGB-T salient object detection is proposed. Specifically, a Cross-Scale Alternate Guiding Fusion (CSAGF) module is proposed to mine the high-level semantic information and provide global context support. Subsequently, we design a Guidance Fusion Module (GFM) to achieve sufficient cross-modality fusion by using single modal as the main guidance and the other modal as auxiliary. Finally, the Cross-Guided Fusion Module (CGFM) is presented and serves as the main decoding block. And each decoding block is consists of two parts with two modalities information of each being the main guidance, i.e., cross-shared Cross-Level Enhancement (CLE) and Global Auxiliary Enhancement (GAE). The main difference between the two parts is that the GFM using different modalities as the main guide. The comprehensive experimental results prove that our method achieves better performance than the state-of-the-art salient detection methods. The source code has released at: https://github.com/wangjie0825/CGFNet.git.

We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric feature grid representing the scene; this feature grid is then decoded into an implicit 3D scene representation. Key to our approach is the transformer architecture that enables the network to learn to attend to the most relevant image frames for each 3D location in the scene, supervised only by the scene reconstruction task. Features are fused in a coarse-to-fine fashion, storing fine-level features only where needed, requiring lower memory storage and enabling fusion at interactive rates. The feature grid is then decoded to a higher-resolution scene reconstruction, using an MLP-based surface occupancy prediction from interpolated coarse-to-fine 3D features. Our approach results in an accurate surface reconstruction, outperforming state-of-the-art multi-view stereo depth estimation methods, fully-convolutional 3D reconstruction approaches, and approaches using LSTM- or GRU-based recurrent networks for video sequence fusion.

Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively. The code and results can be found from the link of https://rmcong.github.io/proj_CIRNet.html.

Salient object detection is the pixel-level dense prediction task which can highlight the prominent object in the scene. Recently U-Net framework is widely used, and continuous convolution and pooling operations generate multi-level features which are complementary with each other. In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers. It is the first to use three transformer encoders with shared weights to enhance multi-level features. By further designing scale adjustment module to process the input, devising three-stream decoder to process the output and attaching depth features to color features for the multi-modal fusion, the proposed triplet transformer embedding network (TriTransNet) achieves the state-of-the-art performance in RGB-D salient object detection, and pushes the performance to a new level. Experimental results demonstrate the effectiveness of the proposed modules and the competition of TriTransNet.

Group-IV -- Vacancy color centers in diamond are fast emerging qubits that can be harnessed in quantum communication and sensor applications. There is an immediate quest for understanding their magneto-optical properties, in order to select the appropriate qubits for varying needs of particular quantum applications. Here we present results from cutting edge \emph{ab initio} calculations about the charge state stability, zero-phonon-line energies, spin-orbit and electron-phonon couplings for Group-IV -- Vacancy color centers. Based on the analysis of our results, we develop a novel spin Hamiltonian for these qubits which incorporates the interaction of the electron spin and orbit coupled with phonons beyond perturbation theory. Our results are in good agreement with previous data and predict a new defect for qubit applications with thermally initialized ground state spin and long spin coherence time.

Semantic segmentation is important for scene understanding. To address the scenes of adverse illumination conditions of natural images, thermal infrared (TIR) images are introduced. Most existing RGB-T semantic segmentation methods follow three cross-modal fusion paradigms, i. e., encoder fusion, decoder fusion, and feature fusion. Some methods, unfortunately, ignore the properties of RGB and TIR features or the properties of features at different levels. In this paper, we propose a novel feature fusion-based network for RGB-T semantic segmentation, named LASNet, which follows three steps of location, activation, and sharpening. The highlight of LASNet is that we fully consider the characteristics of cross-modal features at different levels, and accordingly propose three specific modules for better segmentation. Concretely, we propose a Collaborative Location Module (CLM) for high-level semantic features, aiming to locate all potential objects. We propose a Complementary Activation Module for middle-level features, aiming to activate exact regions of different objects. We propose an Edge Sharpening Module (ESM) for low-level texture features, aiming to sharpen the edges of objects. Furthermore, in the training phase, we attach a location supervision and an edge supervision after CLM and ESM, respectively, and impose two semantic supervisions in the decoder part to facilitate network convergence. Experimental results on two public datasets demonstrate that the superiority of our LASNet over relevant state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/LASNet.

The emergence and future of mammalian synthetic biology depends on technologies for orchestrating and custom tailoring complementary gene expression and signaling processes in a predictable manner. Here, we demonstrate for the first time multi-chromatic expression control in mammalian cells by differentially inducing up to three genes in a single cell culture in response to light of different wavelengths. To this end, we developed an ultraviolet B (UVB)-inducible expression system by designing a UVB-responsive split transcription factor based on the Arabidopsis thaliana UVB receptor UVR8 and the WD40 domain of COP1. The system allowed high (up to 800-fold) UVB-induced gene expression in human, monkey, hamster and mouse cells. Based on a quantitative model, we determined critical system parameters. By combining this UVB-responsive system with blue and red light-inducible gene control technology, we demonstrate multi-chromatic multi-gene control by differentially expressing three genes in a single cell culture in mammalian cells, and we apply this system for the multi-chromatic control of angiogenic signaling processes. This portfolio of optogenetic tools enables the design and implementation of synthetic biological networks showing unmatched spatiotemporal precision for future research and biomedical applications.

. We study stationary optical solitons for the case of Lakshmanan– Porsezian–Daniel model with nonlinear chromatic dispersion and a Kerr law of nonlinear refractive index. The solution is expressed in terms of a special function and its structure is described in details.

Understanding urban scenes is a fundamental ability requirement for assisted driving and autonomous vehicles. Most of the available urban scene understanding methods use red-green-blue (RGB) images; however, their segmentation performances are prone to degradation under adverse lighting conditions. Recently, many effective artificial neural networks have been presented for urban scene understanding and have shown that incorporating RGB and thermal (RGB-T) images can improve segmentation accuracy even under unsatisfactory lighting conditions. However, the potential of multimodal feature fusion has not been fully exploited because operations such as simply concatenating the RGB and thermal features or averaging their maps have been adopted. To improve the fusion of multimodal features and the segmentation accuracy, we propose a multitask-aware network (MTANet) with hierarchical multimodal fusion (multiscale fusion strategy) for RGB-T urban scene understanding. We developed a hierarchical multimodal fusion module to enhance feature fusion and built a high-level semantic module to extract semantic information for merging with coarse features at various abstraction levels. Using the multilevel fusion module, we exploited low-, mid-, and high-level fusion to improve segmentation accuracy. The multitask module uses boundary, binary, and semantic supervision to optimize the MTANet parameters. Extensive experiments were performed on two benchmark RGB-T datasets to verify the improved performance of the proposed MTANet compared with state-of-the-art methods.1

Psychophysical sensitivity to isoluminant chromatic modulation declines at temporal frequencies beyond 4 Hz, whereas chromatically opponent cells of the afferent visual pathway (long- to middle-wavelength (L-M) cone-opponent or short-wavelength (S) cone cells) show responses at much higher temporal frequencies, indicating a central limitation in temporal processing capacity. Here, we sought to localize this limit in cortical retinotopic visual areas. We used fMRI to investigate responses of lateral geniculate nucleus and cortical visual areas in humans to isoluminant chromatic modulation as a function of temporal frequency (2-12 Hz). Our results suggest that L-M cone-opponent and S-cone signals are processed in LGN up to 12 Hz. In all visual areas except MT (middle temporal) and V3a, S-cone responses declined steeply with temporal frequency, implying that psychophysical sensitivity loss to blue-yellow modulation might occur early within these areas. While V1 showed robust L-M responses up to 12 Hz, there was a progressive falloff of responses with temporal frequency as information is transferred from V1 to higher areas (V2, V3, and V4), suggesting that, in humans, temporal limitation in perception of red-green chromatic modulation likely results from limited processing capacity of higher ventral extrastriate areas.

In recent years, various neural network architectures for computer vision have been devised, such as the visual transformer and multilayer perceptron (MLP). A transformer based on an attention mechanism can outperform a traditional convolutional neural network. Compared with the convolutional neural network and transformer, the MLP introduces less inductive bias and achieves stronger generalization. In addition, a transformer shows an exponential increase in the inference, training, and debugging times. Considering a wave function representation, we propose the WaveNet architecture that adopts a novel vision task-oriented wavelet-based MLP for feature extraction to perform salient object detection in RGB (red–green–blue)-thermal infrared images. In addition, we apply knowledge distillation to a transformer as an advanced teacher network to acquire rich semantic and geometric information and guide WaveNet learning with this information. Following the shortest-path concept, we adopt the Kullback–Leibler distance as a regularization term for the RGB features to be as similar to the thermal infrared features as possible. The discrete wavelet transform allows for the examination of frequency-domain features in a local time domain and time-domain features in a local frequency domain. We apply this representation ability to perform cross-modality feature fusion. Specifically, we introduce a progressively cascaded sine–cosine module for cross-layer feature fusion and use low-level features to obtain clear boundaries of salient objects through the MLP. Results from extensive experiments indicate that the proposed WaveNet achieves impressive performance on benchmark RGB-thermal infrared datasets. The results and code are publicly available at https://github.com/nowander/WaveNet.

Object detection on visible (RGB) and infrared (IR) images, as an emerging solution to facilitate robust detection for around-the-clock applications, has received extensive attention in recent years. With the help of IR images, object detectors have been more reliable and robust in practical applications by using RGB-IR combined information. However, existing methods still suffer from modality miscalibration and fusion imprecision problems. Since transformer has the powerful capability to model the pairwise correlations between different features, in this article, we propose a novel Calibrated and Complementary Transformer called C2Former to address these two problems simultaneously. In C2Former, we design an intermodality cross-attention (ICA) module to obtain the calibrated and complementary features by learning the cross-attention relationship between the RGB and IR modality. To reduce the computational cost caused by computing the global attention in ICA, an adaptive feature sampling (AFS) module is introduced to decrease the dimension of feature maps. Because C2Former performs in the feature domain, it can be embedded into existing RGB-IR object detectors via the backbone network. Thus, one single-stage and one two-stage object detector both incorporating our C2Former are constructed to evaluate its effectiveness and versatility. With extensive experiments on the DroneVehicle and KAIST RGB-IR datasets, we verify that our method can fully utilize the RGB-IR complementary information and achieve robust detection results. The code is available at https://github.com/yuanmaoxun/C2Former.git.

Human action recognition (HAR) in RGB-D videos has been widely investigated since the release of affordable depth sensors. Currently, unimodal approaches (e.g., skeleton-based and RGB video-based) have realized substantial improvements with increasingly larger datasets. However, multimodal methods specifically with model-level fusion have seldom been investigated. In this article, we propose a model-based multimodal network (MMNet) that fuses skeleton and RGB modalities via a model-based approach. The objective of our method is to improve ensemble recognition accuracy by effectively applying mutually complementary information from different data modalities. For the model-based fusion scheme, we use a spatiotemporal graph convolution network for the skeleton modality to learn attention weights that will be transferred to the network of the RGB modality. Extensive experiments are conducted on five benchmark datasets: NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, Northwestern-UCLA Multiview, and Toyota Smarthome. Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR. We also tested our MMNet on an RGB video dataset Kinetics 400 that contains more outdoor actions, which shows consistent results with those of RGB-D video datasets.

The three areas of realistic forward rendering, per-pixel inverse rendering, and generative image synthesis may seem like separate and unrelated sub-fields of graphics and vision. However, recent work has demonstrated improved estimation of per-pixel intrinsic channels (albedo, roughness, metallicity) based on a diffusion architecture; we call this the RGB → X problem. We further show that the reverse problem of synthesizing realistic images given intrinsic channels, X → RGB, can also be addressed in a diffusion framework. Focusing on the image domain of interior scenes, we introduce an improved diffusion model for RGB → X, which also estimates lighting, as well as the first diffusion X → RGB model capable of synthesizing realistic images from (full or partial) intrinsic channels. Our X → RGB model explores a middle ground between traditional rendering and generative models: We can specify only certain appearance properties that should be followed, and give freedom to the model to hallucinate a plausible version of the rest. This flexibility allows using a mix of heterogeneous training datasets that differ in the available channels. We use multiple existing datasets and extend them with our own synthetic and real data, resulting in a model capable of extracting scene properties better than previous work and of generating highly realistic images of interior scenes.

We present a family of finite unit-distance graphs in the plane that are not 4-colourable, thereby improving the lower bound of the Hadwiger-Nelson problem. The smallest such graph that we have so far discovered has 1581 vertices.

Recent years have witnessed the prevalence of using the Siamese network for RGB-T tracking because of its remarkable success in RGB object tracking. Despite their faster than real-time speeds, existing RGB-T Siamese trackers suffer from low accuracy and poor robustness, compared to other state-of-the-art RGB-T trackers. To address such issues, a new complementarity- and distractor-aware RGB-T tracker based on Siamese network (referred to as SiamCDA) is developed in this paper. To this end, several modules are presented, where the feature pyramid network (FPN) is incorporated into the Siamese network to capture the cross-level information within unimodal features extracted from the RGB or the thermal images. Next, a complementarity-aware multi-modal feature fusion module (CA-MF) is specially designed to capture the cross-modal information between RGB features and thermal features. In the final bounding box selection phase, a distractor-aware region proposal selection module (DAS) further enhances the robustness of our tracker. On top of the technical modules, we also build a large-scale, diverse synthetic RGB-T tracking dataset, containing more than 4831 pairs of synthetic RGB-T videos and 12K synthetic RGB-T images. Extensive experiments on three RGB-T tracking benchmark datasets demonstrate the outstanding performance of our proposed tracker with a tracking speed over 37 frames per second (FPS).

Like most mammals, mice feature dichromatic color vision based on short (S) and middle (M) wavelength-sensitive cone types. It is thought that mammals share a retinal circuit that in dichromats compares S- and M-cone output to generate blue/green opponent signals, with bipolar cells (BCs) providing separate chromatic channels. Although S-cone-selective ON-BCs (type 9 in mouse) have been anatomically identified, little is known about their counterparts, the M-cone-selective OFF-BCs. Here, we characterized cone connectivity and light responses of selected mouse BC types using immunohistochemistry and electrophysiology. Our anatomical data indicate that four (types 2, 3a/b, and 4) of the five mouse OFF-BCs indiscriminately contact both cone types, whereas type 1 BCs avoid S-cones. Light responses showed that the chromatic tuning of the BCs strongly depended on their position along the dorsoventral axis because of the coexpression gradient of M- and S-opsin found in mice. In dorsal retina, where coexpression is low, most type 2 cells were green biased, with a fraction of cells (≈14%) displaying strongly blue-biased responses, likely reflecting S-cone input. Type 1 cells were also green biased but did not comprise blue-biased “outliers,” consistent with type 1 BCs avoiding S-cones. We therefore suggest that type 1 represents the green OFF pathway in mouse. In addition, we confirmed that type 9 BCs display blue-ON responses. In ventral retina, all BC types studied here displayed similar blue-biased responses, suggesting that color vision is hampered in ventral retina. In conclusion, our data support an antagonistically organized blue/green circuit as the common basis for mammalian dichromatic color vision.

A bstractWe perturbatively calculate classical radiation in Yang-Mills theory and dilaton gravity, to next-to-leading order in couplings. The radiation is sourced by the scattering of two relativistic massive scalar sources with the dynamical effect taken into account, corresponding to the post-Minkowskian regime in gravity. We show how to arrange the Yang-Mills radiation such that the duality between color and kinematics is manifest, including the three-term Jacobi identity. The search for duality-satisfying expressions exploits an auxiliary bi-adjoint scalar theory as a guide for locality. The double copy is obtained by replacing the color factors in Yang-Mills with kinematic counterparts, following Bern-Carrasco-Johansson construction in S-matrix. On the gravity side, the radiation is directly computed at the third post-Minkowskian order with massive sources. We find perfect agreement between observables in dilaton gravity and the Yang-Mills double copy. This non-trivially generalizes the leading-order rules by Goldberger and Ridgway. For the first time, the kinematic Jacobi identity appears beyond field-theory S-matrix, suggesting that the color-kinematics duality holds more generally. Our results offer a path for simplifying analytical calculations in post-Minkowskian regime.

How to explore useful information from depth is the key success of the RGB-D saliency detection methods. While the RGB and depth images are from different domains, a modality gap will lead to unsatisfactory results for simple feature concatenation. Towards better performance, most methods focus on bridging this gap and designing different cross-modal fusion modules for features, while ignoring explicitly extracting some useful consistent information from them. To overcome this problem, we develop a simple yet effective RGB-D saliency detection method by learning discriminative cross-modality features based on the deep neural network. The proposed method first learns modality-specific features for RGB and depth inputs. And then we separately calculate the correlations of every pixel-pair in a cross-modality consistent way, i.e., the distribution ranges are consistent for the correlations calculated based on features extracted from RGB (RGB correlation) or depth inputs (depth correlation). From different perspectives, color or spatial, the RGB and depth correlations end up at the same point to depict how tightly each pixel-pair is related. Secondly, to complemently gather RGB and depth information, we propose a novel correlation-fusion to fuse RGB and depth correlations, resulting in a cross-modality correlation. Finally, the features are refined with both long-range cross-modality correlations and local depth correlations to predict salient maps. In which, the long-range cross-modality correlation provides context information for accurate localization, and the local depth correlation keeps good subtle structures for fine segmentation. In addition, a lightweight DepthNet is designed for efficient depth feature extraction. We solve the proposed network in an end-to-end manner. Both quantitative and qualitative experimental results demonstrate the proposed algorithm achieves favorable performance against state-of-the-art methods.

RGB-T tracking aims to leverage the mutual enhancement and complement ability of RGB and TIR modalities for improving the tracking process in various scenarios, where cross-modal interaction is the key component. Some previous methods concatenate the RGB and TIR search region features directly to perform a coarse interaction process with redundant background noises introduced. Many other methods sample candidate boxes from search frames and conduct various fusion approaches on isolated pairs of RGB and TIR boxes, which limits the cross-modal interaction within local regions and brings about inadequate context modeling. To alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions by gathering and distributing target-relevant object and environment contexts. Original templates are also updated with enriched multimodal contexts from the template medium. Our TBSI module is inserted into a ViT backbone for joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three popular RGB-T tracking benchmarks demonstrate our method achieves new state-of-the-art performances. Code is available at https://github.com/RyanHTR/TBSI.

We consider a linear relation which expresses Stanley's chromatic symmetric function for a poset in terms of the chromatic symmetric functions of some closely related posets, which we call the modular law. By applying this in the context of (3+1)-free posets, we are able to reduce Stanley and Stembridge's conjecture that the chromatic symmetric functions of all (3+1)-free posets are e-positive to the case of (3+1)-and-(2+2)-free posets, also known as unit interval orders. In fact, our reduction can be pushed further to a much smaller class of posets, for which we have no satisfying characterization. We also obtain a new proof of the fact that all 3-free posets have e-positive chromatic symmetric functions.