Integrating Conflict Driven Clause Learning to Local Search

This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach for propositional satisfiability. It combines local search and conflict driven clause learning (CDCL) scheme. Each time the local search part reaches a local minimum, the CDCL is launched. For SAT problems it behaves like a tabu list, whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable sub-formula (MUS). Experimental results show good performances on many classes of SAT instances from the last SAT competitions

Adaptive multi-class Bayesian sparse regression - An application to brain activity classification

International audienceIn this article we describe a novel method for regularized regression and apply it to the prediction of a behavioural variable from brain activation images. In the context of neuroimaging, regression or classification techniques are often plagued with the curse of dimensionality, due to the extremely high number of voxels and the limited number of activation maps. A commonly-used solution is the regularization of the weights used in the parametric prediction function. It entails the difficult issue of introducing an adapted amount of regularization in the model; this question can be addressed in a Bayesian framework, but model specification needs a careful design to balance adaptiveness and sparsity. Thus, we introduce an adaptive multi-class regularization to deal with this cluster-based structure of the data. Based on a hierarchical model and estimated in a Variational Bayes framework, our algorithm is robust to overfit and more adaptive than other regularization methods. Results on simulated data and preliminary results on real data show the accuracy of the method in the context of brain activation images

Annotation automatique en syllabes d'un dialogue oral spontané

International audienceThis paper proposes a solution to identify automatically syllable boundaries in the particular context of spontaneous speech. The main goal consists in identifying syllables from a continuous stream of phonemes. At first, phoneme classes are defined to be as well-suited as possible to reduce the problem complexity. Secondly, a few number of general rules are defined. Finally, some exception rules allows to adapt the problem to the specific context of spontaneous speech. The proposed system is evaluated and compares favorably to the only two existing other systems, for French, with significant improvements. Keywords:syllable, phoneme, segmentation, rules.Cet article propose une méthode pour identifier automatiquement les frontières de syllabes dans le contexte particulier de la parole spontanée. Le principe est d'identifier les syllabes à partir d'un flux de phonèmes. Dans un premier temps, nous proposons de regrouper les phonèmes dans des classes. Nous proposons ensuite des règles de segmentation selon les suites de classes rencontrées.Cette méthode a été appliquée sur le CID, corpus conversationnel français. Les évaluations montrent que notre proposition est plus proche d'une segmentation manuelle que les 3 outils qui existent déjà

La démocratie participative dans le système institutionnel de l’Union européenne

Avec la création de l’initiative citoyenne, la démocratie participative fait son entrée dans le droit de l’Union européenne. Si ses règles de fonctionnement étaient initialement peu favorables, le déficit démocratique dont souffre l’Union ainsi que la volonté de donner davantage de substance à la citoyenneté européenne expliquent cette innovation. Toutefois, l’initiative citoyenne n’est pas exempte de limites et ne pourra pas réellement permettre une participation effective des citoyens de l’Union au processus décisionnel. En effet, il s’agit de permettre à un groupe de citoyens de proposer un texte. Mais l’adoption de celui-ci restera entièrement entre les mains des institutions de l’Union.Direct democracy made its entry into the European Union’s legal system with the creation of the citizen initiative. Although at first hedged with encumbering rules, this innovation was seen as a corrective to the Union’s lack of democratic process as well as a means to give more political voice to its citizenry. Nonetheless, the citizen initiative retains limits that prevent it from providing effective participation by Union citizens in decision-making processes. While it does permit a group of citizens to propose a law, its adoption remains entirely in the hands of Union institutions

Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

Inverse inference has recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, called Multiclass Sparse Bayesian Regression (MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features

A polynomial texture extraction with application in dynamic texture classification

International audienceGeometry and texture image decomposition is an important paradigm in image processing. Following to Yves Meyer works based on Total Variation (VT), the decomposition model has known a renewed interest. In this paper , we propose an algorithm which decomposes color image into geometry and texture component by projecting the image in a bivariate polynomial basis and considering the geometry component as the partial reconstruction and the texture component as the remaining part. The experimental results show the adequacy of using our method as a texture extraction tool. Furthermore, we integrate it into a dynamic texture classification process