Gotowa bibliografia na temat „Discriminative Encoding”

PurposeThe purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.Design/methodology/approachTo promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.FindingsThe experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.Originality/valueHashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.

Critical diagnostic information inferred using state of the artradiology techniques helps radiologists in determining the severity of diseases and hence suggest suitable treatment procedures. As a result, dealing with medical image compression necessitates a trade-off between good perceptual quality and high compression rate. The objective of this work is twofold, i) to investigate the effect of increasing the number of encoding loops on medical image compression parameters, and ii) to determine the most suitable wavelet for medical image compression. Haar, Daubechies, Biorthogonal Demeyer, Coifletand Symlet wavelets are used for comparison. Six different sets of medical images are used for testing and from the results obtained it is observed that increasing the number of encoding loops results in better compression parameters but increasing beyond 9 has no significant effect on compression parameters and thus the optimum choice for the number of encoding loops is 9. From the second analysis it is observed that changing the type of wavelets used has no significant effect on the compression parameters.

Multi-object tracking aims to estimate the complete trajectories of objects in a scene. Distinguishing among objects efficiently and correctly in complex environments is a challenging problem. In this paper, a Siamese network with an auto-encoding constraint is proposed to extract discriminative features from detection responses in a tracking-by-detection framework. Different from recent deep learning methods, the simple two layers stacked auto-encoder structure enables the Siamese network to operate efficiently only with small-scale online sample data. The auto-encoding constraint reduces the possibility of overfitting during small-scale sample training. Then, the proposed Siamese network is improved to extract the previous-appearance-next vector from tracklet for better association. The new feature integrates the appearance, previous, and next stage motions of an element in a tracklet. With the new features, an online incremental learned tracking framework is established. It contains reliable tracklet generation, data association to generate complete object trajectories, and tracklet growth to deal with missing detections and to enhance the new feature for tracklet. Benefiting from discriminative features, the final trajectories of objects can be achieved by an efficient iterative greedy algorithm. Feature experiments show that the proposed Siamese network has advantages in terms of both discrimination and correctness. The system experiments show the improved tracking performance of the proposed method.

Vein recognition has been drawing more attention recently because it is highly secure and reliable for practical biometric applications. However, underlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. Recent successes of convolutional neural networks (CNNs) for large-scale image recognition tasks motivate us to replace the traditional hand-engineered features with the superior CNN to design a robust and discriminative vein recognition system. To address the difficulty of direct training or fine-tuning of a CNN with existing small-scale vein databases, a new knowledge transfer approach is formulated using pre-trained CNN models together with a training dataset (e.g., ImageNet) as a robust descriptor generation machine. With the generated deep residual descriptors, a very discriminative model, namely deep residual vector encoding (DRVE), is proposed by a hierarchical design of dictionary learning, coding, and classifier training procedures. Rigorous experiments are conducted with a high-quality hand-dorsa vein database, and superior recognition results compared with state-of-the-art models fully demonstrate the effectiveness of the proposed models. An additional experiment with the PolyU multispectral palmprint database is designed to illustrate the generalization ability.

Recording anomalous traces in business processes diminishes an event log?s quality. The abnormalities may represent bad execution, security issues, or deviant behavior. Focusing on mitigating this phenomenon, organizations spend efforts to detect anomalous traces in their business processes to save resources and improve process execution. However, in many real-world environments, reference models are unavailable, requiring expert assistance and increasing costs. The con15 siderable number of techniques and reduced availability of experts pose an additional challenge for particular scenarios. In this work, we combine the representational power of encoding with a Meta-learning strategy to enhance the detection of anomalous traces in event logs towards fitting the best discriminative capability be tween common and irregular traces. Our approach creates an event log profile and recommends the most suitable encoding technique to increase the anomaly detetion performance. We used eight encoding techniques from different families, 80 log descriptors, 168 event logs, and six anomaly types for experiments. Results indicate that event log characteristics influence the representational capability of encodings. Moreover, we investigate the process behavior?s influence for choosing the suitable encoding technique, demonstrating that traditional process mining analysis can be leveraged when matched with intelligent decision support approaches.

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.

Five experiments explored the effect of conditioning AB and CD compounds on responding to transfer AD and BC compounds and to elements. These experiments used several conditioning procedures: flavour aversion and instrumental discriminative learning in rats and autoshaping in pigeons. All of the experiments found greater responding to the trained AB and CD than to the transfer AD and BC compounds, a result that agrees with some configural models, but not with an elemental model. All experiments also found greater responding to the transfer AD and BC compounds than to the elements, a result that agrees with elemental, but not configural, models.

Chez les mammifères, le néocortex constitue une structure remarquablement plastique assurant leurs multiples capacités d’adaptation et d’apprentissage. Par exemple, l’apprentissage associatif permet à chaque individu d’apprendre les relations entre un événement particulier (un danger par exemple) et les signaux environnementaux qui y sont associés, afin d’en anticiper les conséquences s’il se reproduit dans le futur. Dans le cas de la peur conditionnée, l'apprentissage associatif améliore les capacités de discrimination des signaux de menace et de sécurité, garantissant ainsi une représentation précise de l'environnement. Ce processus comportemental est en partie dépendant de l'interaction entre deux structures cérébrales: le cortex préfrontal (PFC) et le complexe basolatéral de l'amygdale (BLA). Bien que le PFC puisse encoder à la fois les mémoires de menace et de sécurité qui seraient recrutées préférentiellement après l'apprentissage, on ignore toujours si une telle représentation discriminative existe réellement, et si oui, les mécanismes neuronaux et synaptiques qui en sont à l'origine. Au cours de mon travail de thèse, j'ai démontré que l'activité des neurones excitateurs du PFC est nécessaire à la discrimination entre les signaux de menace et de sécurité grâce à la formation d'ensembles spécifiques de neurones. Au cours de l'apprentissage, les neurones pyramidaux sont potentialisés et recrutés au sein de ses ensembles grâce à l'association au niveau dendritique d'événements synaptiques non-linéaires issus des entrées sensorielles avec des entrées synaptiques provenant de la BLA. En conclusion, nos données fournissent la preuve d'un nouveau mécanisme synaptique qui associe, pendant l'apprentissage, l'expérience perçue avec l’état émotionnel transmis par la BLA permettant ainsi la formation d'ensembles neuronaux dans le cortex préfrontal
The ability of an organism to predict forthcoming events is crucial for survival, and depends on the repeated contingency and contiguity between sensory cues and the events (i.e. danger) they must predict. The resulting learned association provides an accurate representation of the environment by increasing discriminative skills between threat and safety signals, most likely as a result of the interaction between the prefrontal cortex (PFC) and the basolateral amygdala (BLA). Although it suggests that local neuronal networks in the PFC might encode opposing memories that are preferentially selected during recall by recruiting specific cortical or subcortical structures, whether such a discriminative representation is wired within discrete prefrontal circuits during learning and by which synaptic mechanisms remain unclear. Here, the work at issue demonstrates that discrimination learning of both safe and fear-conditioned stimuli depends on full activity of the frontal association cortex, and is associated with the formation of cue-specific neuronal assemblies therein. During learning, prefrontal pyramidal neurons were potentiated through sensory-driven dendritic non-linearities supported by the activation of long-range inputs from the basolateral amygdala (BLA). Taken together, these data provide evidence for a new synaptic level mechanism that coincidently link (or meta-associate) during learning features of perceived experience with BLA mediated emotional state into prefrontal memory assemblies

The human sense of touch integrates feedback from a multitude of touch receptors, but how this information is represented in the neural responses such that it can be extracted quickly and reliably is still largely an open question. At the same time, dexterous robots equipped with touch sensors are becoming more common, necessitating better methods for representing sequentially updated information and new control strategies that aid in extracting relevant features for object manipulation from the data. This thesis uses information theoretic methods for two main aims: First, the neural code for tactile processing in humans is analyzed with respect to how much information is transmitted about tactile features. Second, machine learning approaches are used in order to influence both what data is gathered by a robot and how it is represented by maximizing information theoretic quantities. The first part of this thesis contains an information theoretic analysis of data recorded from primary tactile neurons in the human peripheral somatosensory system. We examine the differences in information content of two coding schemes, namely spike timing and spike counts, along with their spatial and temporal characteristics. It is found that estimates of the neurons’ information content based on the precise timing of spikes are considerably larger than for spikes counts. Moreover, the information estimated based on the timing of the very first elicited spike is at least as high as that provided by spike counts, but in many cases considerably higher. This suggests that first spike latencies can serve as a powerful mechanism to transmit information quickly. However, in natural object manipulation tasks, different tactile impressions follow each other quickly, so we asked whether the hysteretic properties of the human fingertip affect neural responses and information transmission. We find that past stimuli affect both the precise timing of spikes and spike counts of peripheral tactile neurons, resulting in increased neural noise and decreased information about ongoing stimuli. Interestingly, the first spike latencies of a subset of afferents convey information primarily about past stimulation, hinting at a mechanism to resolve ambiguity resulting from mechanical skin properties. The second part of this thesis focuses on using machine learning approaches in a robotics context in order to influence both what data is gathered and how it is represented by maximizing information theoretic quantities. During robotic object manipulation, often not all relevant object features are known, but have to be acquired from sensor data. Touch is an inherently active process and the question arises of how to best control the robot’s movements so as to maximize incoming information about the features of interest. To this end, we develop a framework that uses active learning to help with the sequential gathering of data samples by finding highly informative actions. The viability of this approach is demonstrated on a robotic hand-arm setup, where the task involves shaking bottles of different liquids in order to determine the liquid’s viscosity from tactile feedback only. The shaking frequency and the rotation angle of shaking are optimized online. Additionally, we consider the problem of how to better represent complex probability distributions that are sequentially updated, as approaches for minimizing uncertainty depend on an accurate representation of that uncertainty. A mixture of Gaussians representation is proposed and optimized using a deterministic sampling approach. We show how our method improves on similar approaches and demonstrate its usefulness in active learning scenarios. The results presented in this thesis highlight how information theory can provide a principled approach for both investigating how much information is contained in sensory data and suggesting ways for optimization, either by using better representations or actively influencing the environment.

Many items encoded into episodic memory are highly similar – seeing a stranger’s car may result in a memory representation which overlaps in many features with the memory of your friend’s car. To avoid falsely recognising the novel but similar car, it is important for the representations to be distinguished in memory. Even in healthy young adults failures of this mnemonic discrimination lead relatively often to false recognition, and such errors become substantially more frequent in older age. Whether an item’s representation is discriminated from similar memory representations depends critically on how it is encoded. However, the precise encoding mechanisms involved remain poorly understood. Establishing the determinants of successful mnemonic discrimination is essential for future research into strategies or interventions to prevent recognition errors, particularly in the context of age-related decline. A fuller understanding of age-related decline in mnemonic discrimination can also inform basic models of memory. This thesis evaluated the contribution of encoding processes to mnemonic discrimination both in young adults and in ageing, within the framework of two prominent accounts of recognition memory, the pattern separation account (Wilson et al., 2006) and Fuzzy Trace Theory (FTT; Brainerd & Reyna, 2002). Firstly, a functional magnetic resonance imaging study in young adults found evidence for differences in regions engaged at encoding of images according to the accuracy of later mnemonic discrimination, consistent with both pattern separation and FTT. Evidence of functional overlap between regions showing activity consistent with pattern separation, and activity associated with later accurate recognition was consistent with a role of cortical pattern separation in successful encoding, but there was no direct evidence that cortical pattern separation contributed to mnemonic discrimination. This first evidence of cortical pattern separation in humans was supported by findings that in the majority of pattern separation regions, response functions to stimuli varied in their similarity to previous items were consistent with predictions of computational models. Regional variation in the dimension(s) of similarity (conceptual/perceptual) driving pattern separation was indicative of variation in the type of mnemonic interference minimised by cortical pattern separation. Further evidence of encoding contributions to mnemonic discrimination was provided by an event-related potential study in young and older adults. Older adults showed less distinct waveforms than young adults at encoding of items whose similar lures were later correctly rejected compared to those falsely recognised, supporting the proposal that age-related encoding impairments contribute to the decline in mnemonic discrimination. Finally, a set of behavioural studies found that older adults’ mnemonic discrimination deficit is increased by conceptual similarity, supporting previous findings and consistent with FTT’s account of greater emphasis by older adults on gist processing. However, older adults required greater reduction in perceptual or conceptual similarity in order to successfully reject lures, as uniquely predicted by the pattern separation account. Together, the findings support the notion that encoding processes contribute directly to mnemonic discrimination and its age-related decline. An integrated view of the pattern separation account and FTT is discussed and developed in relation to the current findings.

This thesis reports an investigation into dual-energy X-ray discrimination techniques. These techniques are designed to provide colour-coded materials discrimination information in a sequence of perspective images exhibiting sequential parallax. The methods developed are combined with a novel 3D imaging technique employing depth from motion or kinetic depth effect (KDE). This technique when applied to X-ray images is termed KDEX imaging and was developed previously by the university team for luggage screening applications at security checkpoints. A primary motivation for this research is that the dual-energy X-ray techniques, which are routinely incorporated into ‘standard’ 2D luggage scanners, provide relatively crude materials discrimination information. In this work it was critical that robust materials discrimination and colour encoding process was implemented as the sequential parallax exhibited by the KDEX imagery may introduce colour changes, due to the different X-ray beam paths associated with each perspective image. Any introduction of ‘colour noise’ into the resultant image sequences could affect the perception of depth and hinder the ongoing assessment of the potential utility of the dual-energy KDEX technique. Two dual-energy discrimination methods have been developed, termed K-II and W-E respectively. Employing the total amount of attenuation measured at each energy level and the weight fraction of layered structures, a combination of the K-II and the W-E techniques enables the computation and extraction of a target objects’ effective atomic number (Zeff) and its surface density (ρS) in the presence of masking layers. These material parameters (Zeff and ρS) together with laminographic layer thickness estimation enable mass density extraction. A series of experiments investigated the computation of Zeff and ρS as a function of system noise and repeatability. The estimation of thickness depends on the depth increment provided by the image capture geometry and the laminographic processing. Within the atomic number range of 6 to 30 and with up to 4 masking layers, the investigated techniques produce an accuracy of Zeff and ρS up to 97% and 95% respectively. The thickness estimation technique provided a relatively high accuracy for object thickness’ greater than 4cm. Although, the measurement accuracy for relatively thin layers is inherently limited by the minimum resolvable depth increment of the image collection geometry. The mass density extracted had an overall accuracy of greater than 90% for well-estimated thicknesses. Implementing a new four colours scheme highlights the presence of potential threat materials in the resultant KDEX imagery. This thesis forms part of a larger programme of activity in collaboration with and funded by the UK Home Office Scientific Development Branch, and the US Department of Homeland Security and the EPSRC.

Pendant longtemps, le voyage mental dans le temps a été considéré comme unique à l’humain. Selon des auteurs, les animaux ne pourraient pas se projeter dans le passé ou le futur parce qu’ils sont ancrés dans le présent. Néanmoins, pendant les 30 dernières années les chercheurs ont apporté des connaissances considérables sur les capacités des animaux à se souvenir de leur passé et à anticiper leur futur. Même si les opinions ont évolué, le débat sur l’unicité du voyage mental dans le temps est toujours d’actualité. Le but de ma thèse est d’apporter de nouvelles données sur les capacités des animaux à se souvenir du passé et à anticiper le futur. Plus particulièrement, je me suis intéressée à la mémoire de la source, qui est la capacité de retrouver l’origine d’un souvenir, chez deux espèces animales très éloignées, la seiche commune Sepia officinalis, et le geai des chênes, Garrulus glandarius. Les résultats ont montré que les seiches étaient capables de résoudre une tâche de discrimination perceptive, montrant qu’elles pouvaient discriminer et retenir leurs propres perceptions après un délai de 3 heures. Les geais, eux, ont révélé des différences mâles/femelles inattendues concernant leur capacité d’encoder et de retrouver une information contextuelle non-intentionnelle (source contextuelle). Une étude sur le comportement orienté vers le futur a montré que les seiches étaient capables de prendre une décision dans le présent en fonction de ce qu’elles avaient appris dans le passé, et en fonction des conditions expérimentales futures. Une étude préliminaire sur la planification a également apporté des résultats prometteurs sur la capacité des seiches à anticiper leurs besoins futurs. Enfin, nous avons pu explorer et mettre en lumière pour la première fois les substrats neuroanatomiques de la mémoire de type épisodique chez la seiche. Ces résultats permettent d’enrichir nos connaissances sur le voyage mental dans le temps chez la seiche et chez le geai, suggérant que cette capacité cognitive complexe peut avoir évolué sous différentes contraintes environnementales
Some authors support that mental time travel is unique to humans. To their point of view, animals are not able to project themselves into the past of the future because they are bound into the present. Nevertheless, during the last 30 years, researchers have brought considerable knowledge on animals’ capacities to travel mentally through time. Even though opinions have evolved, the debate concerning the unicity of mental time travel is still on. My PhD thesis aimed at bringing further knowledge on this matter by focusing on an innovative aspect of episodic cognition in common cuttlefish, Sepia officinalis and Eurasian jay, Garrulus glandarius, namely, source-memory. Source-memory is the capacity to retrieve the origin of an episodic memory. Results showed that cuttlefish were able to perform a source-discrimination study, revealing that they were able to discriminate and retrieve their own perceptions after 3-hours delay. A study on jays’ capacity to encode incidentally a contextual information (contextual source) revealed unexpected differences between males and females. Investigation of future-oriented behaviour in cuttlefish showed that they were able to take a decision in the present according to previous encoded knowledge and according to future experimental conditions. A preliminary study also revealed promising results on cuttlefish capacity to anticipate their future needs. To finish, we explored and revealed for the first time the neuronal substrates of episodic-like memory in cuttlefish. Alltogether, these results provide new knowledge on mental time travel in cuttlefish and in jays, suggesting that this capacity would have evolved under different environmental contraints

The desire to enhance and make ourselves better is not a new one and it has continued to intrigue throughout the ages. Individuals have continued to seek ways to improve and enhance their well-being for example through nutrition, physical exercise, education and so on. Crucial to this improvement of their well-being is improving their ability to remember. Hence, people interested in improving their well-being, are often interested in memory as well. The rationale being that memory is crucial to our well-being. The desire to improve one’s memory then is almost certainly as old as the desire to improve one’s well-being. Traditionally, people have used different means in an attempt to enhance their memories: for example in learning through storytelling, studying, and apprenticeship. In remembering through practices like mnemonics, repetition, singing, and drumming. In retaining, storing and consolidating memories through nutrition and stimulants like coffee to help keep awake; and by external aids like notepads and computers. In forgetting through rituals and rites. Recent scientific advances in biotechnology, nanotechnology, molecular biology, neuroscience, and information technologies, present a wide variety of technologies to enhance many different aspects of human functioning. Thus, some commentators have identified human enhancement as central and one of the most fascinating subject in bioethics in the last two decades. Within, this period, most of the commentators have addressed the Ethical, Social, Legal and Policy (ESLP) issues in human enhancements as a whole as opposed to specific enhancements. However, this is problematic and recently various commentators have found this to be deficient and called for a contextualized case-by-case analysis to human enhancements for example genetic enhancement, moral enhancement, and in my case memory enhancement (ME). The rationale being that the reasons for accepting/rejecting a particular enhancement vary depending on the enhancement itself. Given this enormous variation, moral and legal generalizations about all enhancement processes and technologies are unwise and they should instead be evaluated individually. Taking this as a point of departure, this research will focus specifically on making a case for ME and in doing so assessing the ESLP implications arising from ME. My analysis will draw on the already existing literature for and against enhancement, especially in part two of this thesis; but it will be novel in providing a much more in-depth analysis of ME. From this perspective, I will contribute to the ME debate through two reviews that address the question how we enhance the memory, and through four original papers discussed in part three of this thesis, where I examine and evaluate critically specific ESLP issues that arise with the use of ME. In the conclusion, I will amalgamate all my contribution to the ME debate and suggest the future direction for the ME debate.

Contexte La connectomique, ou la cartographie des connexions neuronales, est un champ de recherche des neurosciences évoluant rapidement, promettant des avancées majeures en ce qui concerne la compréhension du fonctionnement cérébral. La formation de circuits neuronaux en réponse à des stimuli environnementaux est une propriété émergente du cerveau. Cependant, la connaissance que nous avons de la nature précise de ces réseaux est encore limitée. Au niveau du cortex visuel, qui est l’aire cérébrale la plus étudiée, la manière dont les informations se transmettent de neurone en neurone est une question qui reste encore inexplorée. Cela nous invite à étudier l’émergence des microcircuits en réponse aux stimuli visuels. Autrement dit, comment l’interaction entre un stimulus et une assemblée cellulaire est-elle mise en place et modulée? Méthodes En réponse à la présentation de grilles sinusoïdales en mouvement, des ensembles neuronaux ont été enregistrés dans la couche II/III (aire 17) du cortex visuel primaire de chats anesthésiés, à l’aide de multi-électrodes en tungstène. Des corrélations croisées ont été effectuées entre l’activité de chacun des neurones enregistrés simultanément pour mettre en évidence les liens fonctionnels de quasi-synchronie (fenêtre de ± 5 ms sur les corrélogrammes croisés corrigés). Ces liens fonctionnels dévoilés indiquent des connexions synaptiques putatives entre les neurones. Par la suite, les histogrammes peri-stimulus (PSTH) des neurones ont été comparés afin de mettre en évidence la collaboration synergique temporelle dans les réseaux fonctionnels révélés. Enfin, des spectrogrammes dépendants du taux de décharges entre neurones ou stimulus-dépendants ont été calculés pour observer les oscillations gamma dans les microcircuits émergents. Un indice de corrélation (Rsc) a également été calculé pour les neurones connectés et non connectés. Résultats Les neurones liés fonctionnellement ont une activité accrue durant une période de 50 ms contrairement aux neurones fonctionnellement non connectés. Cela suggère que les connexions entre neurones mènent à une synergie de leur inter-excitabilité. En outre, l’analyse du spectrogramme dépendant du taux de décharge entre neurones révèle que les neurones connectés ont une plus forte activité gamma que les neurones non connectés durant une fenêtre d’opportunité de 50ms. L’activité gamma de basse-fréquence (20-40 Hz) a été associée aux neurones à décharge régulière (RS) et l’activité de haute fréquence (60-80 Hz) aux neurones à décharge rapide (FS). Aussi, les neurones fonctionnellement connectés ont systématiquement un Rsc plus élevé que les neurones non connectés. Finalement, l’analyse des corrélogrammes croisés révèle que dans une assemblée neuronale, le réseau fonctionnel change selon l’orientation de la grille. Nous démontrons ainsi que l’intensité des relations fonctionnelles dépend de l’orientation de la grille sinusoïdale. Cette relation nous a amené à proposer l’hypothèse suivante : outre la sélectivité des neurones aux caractères spécifiques du stimulus, il y a aussi une sélectivité du connectome. En bref, les réseaux fonctionnels «signature » sont activés dans une assemblée qui est strictement associée à l’orientation présentée et plus généralement aux propriétés des stimuli. Conclusion Cette étude souligne le fait que l’assemblée cellulaire, plutôt que le neurone, est l'unité fonctionnelle fondamentale du cerveau. Cela dilue l'importance du travail isolé de chaque neurone, c’est à dire le paradigme classique du taux de décharge qui a été traditionnellement utilisé pour étudier l'encodage des stimuli. Cette étude contribue aussi à faire avancer le débat sur les oscillations gamma, en ce qu'elles surviennent systématiquement entre neurones connectés dans les assemblées, en conséquence d’un ajout de cohérence. Bien que la taille des assemblées enregistrées soit relativement faible, cette étude suggère néanmoins une intrigante spécificité fonctionnelle entre neurones interagissant dans une assemblée en réponse à une stimulation visuelle. Cette étude peut être considérée comme une prémisse à la modélisation informatique à grande échelle de connectomes fonctionnels.
Background ‘Connectomics’— the mapping of neural connections, is a rapidly advancing field in neurosciences and it promises significant insights into the brain functioning. The formation of neuronal circuits in response to the sensory environment is an emergent property of the brain; however, the knowledge about the precise nature of these sub-networks is still limited. Even at the level of the visual cortex, which is the most studied area in the brain, how sensory inputs are processed between its neurons, is a question yet to be completely explored. Heuristically, this invites an investigation into the emergence of micro-circuits in response to a visual input — that is, how the intriguing interplay between a stimulus and a cell assembly is engineered and modulated? Methods Neuronal assemblies were recorded in response to randomly presented drifting sine-wave gratings in the layer II/III (area 17) of the primary visual cortex (V1) in anaesthetized cats using tungsten multi-electrodes. Cross-correlograms (CCGs) between simultaneously recorded neural activities were computed to reveal the functional links between neurons that were indicative of putative synaptic connections between them. Further, the peristimulus time histograms (PSTH) of neurons were compared to divulge the epochal synergistic collaboration in the revealed functional networks. Thereafter, perievent spectrograms were computed to observe the gamma oscillations in emergent microcircuits. Noise correlation (Rsc) was calculated for the connected and unconnected neurons within these microcircuits. Results The functionally linked neurons collaborate synergistically with augmented activity in a 50-ms window of opportunity compared with the functionally unconnected neurons suggesting that the connectivity between neurons leads to the added excitability between them. Further, the perievent spectrogram analysis revealed that the connected neurons had an augmented power of gamma activity compared with the unconnected neurons in the emergent 50-ms window of opportunity. The low-band (20-40 Hz) gamma activity was linked to the regular-spiking (RS) neurons, whereas the high-band (60-80 Hz) activity was related to the fast-spiking (FS) neurons. The functionally connected neurons systematically displayed higher Rsc compared with the unconnected neurons in emergent microcircuits. Finally, the CCG analysis revealed that there is an activation of a salient functional network in an assembly in relation to the presented orientation. Closely tuned neurons exhibited more connections than the distantly tuned neurons. Untuned assemblies did not display functional linkage. In short, a ‘signature’ functional network was formed between neurons comprising an assembly that was strictly related to the presented orientation. Conclusion Indeed, this study points to the fact that a cell-assembly is the fundamental functional unit of information processing in the brain, rather than the individual neurons. This dilutes the importance of a neuron working in isolation, that is, the classical firing rate paradigm that has been traditionally used to study the encoding of a stimulus. This study also helps to reconcile the debate on gamma oscillations in that they systematically originate between the connected neurons in assemblies. Though the size of the recorded assemblies in the current investigation was relatively small, nevertheless, this study shows the intriguing functional specificity of interacting neurons in an assembly in response to a visual input. One may form this study as a premise to computationally infer the functional connectomes on a larger scale.

Traditionally, communication scholars have been most concerned with how, when, where, and with whom individuals choose to communicate. While investigating communication events from an encoder perspective is important, it is equally important to investigate communication from a decoder perspective. Many researchers agree that gaining insight into the listening process—how individuals perceive, process, remember and understand oral messages—should enhance our understanding of communication events substantially. There appears to be a good deal of theoretical support for the notion that listening is a multidimensional concept. For example, descriptions of listening constructs such as “appreciative,” “critical,” “discriminative,” and therapeutic” appear throughout the literature. Furthermore, empirical evidence provided by broadly administered listening performance tests highlights considerable individual differences across divergent constructs such as content, relational, and emotional listening. Differences in listening styles reflect attitudes, beliefs, and predispositions about the how, where, when, who, and what of information reception and encoding. Several examples illustrate the diversity of listening styles. Some people prefer listening to factual information or statistics while others favor personal examples and illustrations. Some are more willing to linger on content while others prefer concise and to the point presentations. The Listening Styles Profile (LSP-16) was developed to identify an individual’s predominant listening style (Watson, Barker, & Weaver, 1995). The Listening Styles Profile is a sixteen item inventory designed to assess four distinct listening preferences labeled people, action, content, and time.

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID is an emerging open topic. Existing methods either adopt hand-crafted features or learn gait features by traditional supervised learning paradigms. Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner. Specifically, we first propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, which facilitates learning richer high-level semantics and better gait representations. Second, inspired by the fact that motion's continuity endows temporally adjacent skeletons with higher correlations (“locality”), we propose a locality-aware attention mechanism that encourages learning larger attention weights for temporally adjacent skeletons when reconstructing current skeleton, so as to learn locality when encoding gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are built using context vectors learned by locality-aware attention, as final gait representations. AGEs are directly utilized to realize effective person Re-ID. Our approach typically improves existing skeleton-based methods by 10-20% Rank-1 accuracy, and it achieves comparable or even superior performance to multi-modal methods with extra RGB or depth information.

Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted features or model skeleton dynamics from the trajectory of body joints, while they rarely explore valuable relation information contained in body structure or motion. To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID. Specifically, considering that structurally-connected body components are highly correlated in a skeleton, we first propose a multi-head structural relation layer to learn different relations of neighbor body-component nodes in graphs, which helps aggregate key correlative features for effective node representations. Second, inspired by the fact that body-component collaboration in walking usually carries recognizable patterns, we propose a cross-level collaborative relation layer to infer collaboration between different level components, so as to capture more discriminative skeleton graph features. Finally, to enhance graph dynamics encoding, we propose a novel self-supervised sparse sequential prediction task for model pre-training, which facilitates encoding high-level graph semantics for person Re-ID. MG-SCR outperforms state-of-the-art skeleton-based methods, and it achieves superior performance to many multi-modal methods that utilize extra RGB or depth features. Our codes are available at https://github.com/Kali-Hac/MG-SCR.

Label distribution learning (LDL) is a newly arisen learning paradigm to deal with label ambiguity problems, which can explore the relative importance of different labels in the description of a particular instance. Although some existing LDL algorithms have achieved better effectiveness in real applications, most of them typically emphasize on improving the learning ability by manipulating the label space, while ignoring the fact that irrelevant and redundant features exist in most practical classification learning tasks, which increase not only storage requirements but also computational overheads. Furthermore, noises in data acquisition will bring negative effects on the generalization performance of LDL algorithms. In this paper, we propose a novel algorithm, i.e., Latent Semantics Encoding for Label Distribution Learning (LSE-LDL), which learns the label distribution and implements feature selection simultaneously under the guidance of latent semantics. Specifically, to alleviate noise disturbances, we seek and encode discriminative original physical/chemical features into advanced latent semantic features, and then construct a mapping from the encoded semantic space to the label space via empirical risk minimization. Empirical studies on 15 real-world data sets validate the effectiveness of the proposed algorithm.