Le rôle de l’orientation régulatrice chronique sur les représentations du lien santé-alimentation et les comportements alimentaires: une étude exploratoire

International audienceL’objectif de cette recherche exploratoire est de mettre au jour l’influence de l’orientation régulatrice chronique sur les représentations du lien santé/alimentation et les stratégies de consommation alimentaire. Aujourd’hui, le consommateur fait un lien très fort entre la recherche de santé et son alimentation, est soumis à de nombreux messages nutritionnels, doit faire face périodiquement à des crises alimentaires qui peuvent remettre en cause ses habitudes, bien qu’il attache une grande importance au goût des aliments. Nos résultats montrent que l’orientation régulatrice influence les représentations et comportements alimentaires. Nous décrivons les représentations et les comportements alimentaires au sein des deux groupes identifiés, l’un constitué d’individus orientés prévention et l’autre de consommateurs orientés promotion

A multimodal pattern recognition framework for speaker detection

Speaker detection is an important component of a speech-based user interface. Audiovisual speaker detection, speech and speaker recognition or speech synthesis for example find multiple applications in human-computer interaction, multimedia content indexing, biometrics, etc. Generally speaking, any interface which relies on speech for communication requires an estimate of the user's speaking state (i.e. whether or not he/she is speaking to the system) for its reliable functioning. One needs therefore to identify the speaker and discriminate from other users or background noise. A human observer would perform such a task very easily, although this decision results from a complex cognitive process referred to as decision-making. Generally speaking, this process starts with the acquisition by the human being of information about the environment, through each of its five senses. The brain then integrates these multiple information. An amazing property of this multi-sensory integration by the brain, as pointed out by cognitive sciences, is the perception of stimuli of different modalities as originating from a single source, provided they are synchronized in space and time. A speaker is a bimodal source emitting jointly an auditory signal and a visual signal (the motion of the articulators during speech production). The two signals are obviously co-occurring spatio-temporally. This interesting property allows us – as human observers – to discriminate between a speaking mouth and a mouth whose motion is not related with the auditory signal. This dissertation deals with the modelling of such a complex decision-making, using a pattern recognition procedure. A pattern recognition process comprises all the stages of an investigation, from data acquisition to classification and assessment of the results. In the audiovisual speaker detection problem, tackled more specifically in this thesis, the data are acquired using only one microphone and camera. The pattern recognizer integrates and combines these two modalities to perform and is therefore denoted as "multimodal". This multimodal approach is expected to increase the performance of the system. But it also raises many questions such as what should be fused, when in the decision process this fusion should take place, and how is it to be achieved. This thesis provides answers to each of these issues through the proposition of detailed solutions for each step of the classification process. The basic principle is to evaluate the synchrony between the audio and video features extracted from potentially speaking mouths, in order to classify each mouth as speaking or not. This synchrony is evaluated through a mutual information based function. A key to success is the extraction of suitable features. The audiovisual data are then processed through an information theoretic feature extraction framework after having been acquired and represented in a tractable way. This feature extraction framework uses jointly the two modalities in a feature-level fusion scheme. This way, the information originating from the common source is recovered while the independent noise is discarded. This approach is shown to minimize the probability of committing an error on the source estimate. These optimal features are put as inputs of the classifier, defined through a hypothesis testing approach. Using jointly the two modalities, it outputs a single decision about the class label of each candidate mouth region ("speaker" or "non-speaker"). Therefore, the acoustic and visual information are combined at both the feature and the decision levels, so that we can talk about a hybrid fusion method. The hypothesis testing approach gives means for evaluating the performance of the classifier itself but also of the whole pattern recognition system. In particular, the added-value offered by the feature extraction step can be assessed. The framework is applied in a first time with a particular emphasis on the audio modality: the information theoretic feature extraction addresses the optimization of the audio features using jointly the video information. As a result, audio features specific to speech production are produced. The system evaluation framework establishes that putting these features at input of the classifier increases its discrimination power with respect to equivalent non-optimized features. Then the enhancement of the video content is addressed more specifically. The mouth motion is obviously the suitable video representation for handling a task such as speaker detection. However, only an estimate of this motion, the optical flow, can be obtained. This estimation relies on the intensity gradient of the image sequence. Graph theory is used to establish a probabilistic model of the relationships between the audio, the motion and the image intensity gradient, in the particular case of a speaking mouth. The interpretation of this model leads back to the optimization function defined for the information theoretic feature extraction. As a result, a scale-space approach is proposed for estimating the optical flow, where the strength of the smoothness constraint is controlled via a mutual information based criterion involving both the audio and the video information. First results are promising even if more extensive tests should be carried out, in noisy conditions in particular. As a conclusion, this thesis proposes a complete pattern recognition framework dedicated to audiovisual speaker detection and minimizing the probability of misclassifying a mouth as "speaker" or "non-speaker". The importance of fusing the audio and video content as soon as at the feature level is demonstrated through the system evaluation stage included in the pattern recognition process

Hypothesis testing for evaluating a multimodal pattern recognition framework applied to speaker detection

Background: Speaker detection is an important component of many human-computer interaction applications, like for example, multimedia indexing, or ambient intelligent systems. This work addresses the problem of detecting the current speaker in audio-visual sequences. The detector performs with few and simple material since a single camera and microphone meets the needs. Method: A multimodal pattern recognition framework is proposed, with solutions provided for each step of the process, namely, the feature generation and extraction steps, the classification, and the evaluation of the system performance. The decision is based on the estimation of the synchrony between the audio and the video signals. Prior to the classification, an information theoretic framework is applied to extract optimized audio features using video information. The classification step is then defined through a hypothesis testing framework in order to get confidence levels associated to the classifier outputs, allowing thereby an evaluation of the performance of the whole multimodal pattern recognition system. Results: Through the hypothesis testing approach, the classifier performance can be given as a ratio of detection to false-alarm probabilities. Above all, the hypothesis tests give means for measuring the whole pattern recognition process effciency. In particular, the gain offered by the proposed feature extraction step can be evaluated. As a result, it is shown that introducing such a feature extraction step increases the ability of the classifier to produce good relative instance scores, and therefore, the performance of the pattern recognition process. Conclusion: The powerful capacities of hypothesis tests as an evaluation tool are exploited to assess the performance of a multimodal pattern recognition process. In particular, the advantage of performing or not a feature extraction step prior to the classification is evaluated. Although the proposed framework is used here for detecting the speaker in audiovisual sequences, it could be applied to any other classification task involving two spatio-temporal co-occurring signals

A Comprehensive Model of Audiovisual Perception: Both Percept and Temporal Dynamics

The sparse information captured by the sensory systems is used by the brain to apprehend the environment, for example, to spatially locate the source of audiovisual stimuli. This is an ill-posed inverse problem whose inherent uncertainty can be solved by jointly processing the information, as well as introducing constraints during this process, on the way this multisensory information is handled. This process and its result - the percept - depend on the contextual conditions perception takes place in. To date, perception has been investigated and modeled on the basis of either one of two of its dimensions: the percept or the temporal dynamics of the process. Here, we extend our previously proposed audiovisual perception model to predict both these dimensions to capture the phenomenon as a whole. Starting from a behavioral analysis, we use a data-driven approach to elicit a Bayesian network which infers the different percepts and dynamics of the process. Context-specific independence analyses enable us to use the model's structure to directly explore how different contexts affect the way subjects handle the same available information. Hence, we establish that, while the percepts yielded by a unisensory stimulus or by the non-fusion of multisensory stimuli may be similar, they result from different processes, as shown by their differing temporal dynamics. Moreover, our model predicts the impact of bottom-up (stimulus driven) factors as well as of top-down factors (induced by instruction manipulation) on both the perception process and the percept itself

A new mouse model for the trisomy of the Abcg1–U2af1 region reveals the complexity of the combinatorial genetic code of down syndrome

Mental retardation in Down syndrome (DS), the most frequent trisomy in humans, varies from moderate to severe. Several studies both in human and based on mouse models identified some regions of human chromosome 21 (Hsa21) as linked to cognitive deficits. However, other intervals such as the telomeric region of Hsa21 may contribute to the DS phenotype but their role has not yet been investigated in detail. Here we show that the trisomy of the 12 genes, found in the 0.59 Mb (Abcg1–U2af1) Hsa21 sub-telomeric region, in mice (Ts1Yah) produced defects in novel object recognition, open-field and Y-maze tests, similar to other DS models, but induces an improvement of the hippocampal-dependent spatial memory in the Morris water maze along with enhanced and longer lasting long-term potentiation in vivo in the hippocampus. Overall, we demonstrate the contribution of the Abcg1–U2af1 genetic region to cognitive defect in working and short-term recognition memory in DS models. Increase in copy number of the Abcg1–U2af1 interval leads to an unexpected gain of cognitive function in spatial learning. Expression analysis pinpoints several genes, such as Ndufv3, Wdr4, Pknox1 and Cbs, as candidates whose overexpression in the hippocampus might facilitate learning and memory in Ts1Yah mice. Our work unravels the complexity of combinatorial genetic code modulating different aspect of mental retardation in DS patients. It establishes definitely the contribution of the Abcg1–U2af1 orthologous region to the DS etiology and suggests new modulatory pathways for learning and memory

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements