Publications

2017

• A Methodology for the Automatic Extraction and Generation of Non-Verbal Signals Sequences Conveying Interpersonal Attitudes
  
  • Chollet Mathieu
  • Ochs Magalie
  • Pelachaud Catherine
  IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers, 2017, XX, pp.1 - 1. —Depending on their application, Embodied Conversational Agents (ECAs) must be able to express various affects or social constructs such as emotions or social attitudes. Non-verbal signals, such as smiles or gestures, contribute to the expression of attitudes. Social attitudes affect the whole behavior of a person: as Scherer puts it, they are " characteristic of an affective style that colors the entire interaction " [1]. Moreover, recent findings have demonstrated that non-verbal signals are not interpretated in isolation but along with surrounding signals: for instance, a smile followed by a gaze aversion and a head aversion may signal embarassment rather than amusement [2]. Non-verbal behavior planning models designed to allow ECAs to express attitudes should thus consider complete sequences of non-verbal signals and not only signals independently of one another. However, existing models do not take this into account, or in a limited manner. The contribution of this paper is a methodology for the automatic extraction of sequences of non-verbal signals characteristic of a social phenomenon from a multimodal corpus, and a non-verbal behavior planning model that takes into account sequences of non-verbal signals rather than signals independently. This methodology is applied to design a virtual recruiter capable of expressing social attitudes, which is then evaluated in and out of an interaction context. (10.1109/TAFFC.2017.2753777)
  DOI : 10.1109/TAFFC.2017.2753777
• An adaptive perceptual quantization method for HDR video coding
  
  • Liu Yi
  • Sidaty Naty
  • Hamidouche Wassim
  • Déforges Olivier
  • Valenzise Giuseppe
  • Zerman Emin
  , 2017. This paper presents a new adaptive perceptual quantization method for the High Dynamic Range (HDR) content. This method considers the luminance distribution of the HDR image as well as the Minimum Detectable Contrast (MDC) thresholds to preserve the contrast information during quantization. Base on this method, we develop a mapping function for HDR video compression and apply it to a HEVC Main 10 Profile-based video coding chain. Our experiments show that the proposed mapping function can efficiently improve the quality of the reconstructed HDR video in both objective and subjective assessments. (10.1109/icip.2017.8296437)
  DOI : 10.1109/icip.2017.8296437
• Fabry-Perot Filtered Emission for 25 Gbit/s Single-Side Band NRZ and ODB Transmissions in C-band up to 20 km
  
  • Zhicheng Y
  • Jing D
  • Shengping L
  • Konopacki Justine
  • Saliou Fabienne
  • Le Guyader Bertrand
  • Barthomeuf Sylvain
  • Chanclou Philippe
  • Erasme Didier
  , 2017. VSB-NRZ and VSB-ODB emitters based on an etalon Fabry-Perot filter is combined to a 12 GHz photodiode and electrical post-equalization to allow transmission performances at 25 Gbit/s in C-band up to 30 km.
• Real-Time 20 and 25 Gbit/s Pre-Equalized C-band Transmission With Electrical Duo-binary Detection
  
  • Konopacki Justine
  • Le Guyader Bertrand
  • Genay Naveena
  • Anet Neto Luiz
  • Saliou Fabienne
  • Barthomeuf Sylvain
  • Chanclou Philippe
  • Erasme Didier
  , 2017. We experimentally demonstrate real-time C-band transmissions with up to 29 dB optical budget after 25 km propagation at 20 Gbit/s and 25 Gbit/s. Our solution uses pre-equalization and electro-optical components dedicaded for 10 Gbit/s transmissions.
• Motion-consistent video inpainting
  
  • Le Thuc Trinh
  • Almansa Andrés
  • Gousseau Yann
  • Masnou Simon
  , 2017. We propose a fast and automatic inpainting technique for high-definition videos which works under many challenging conditions such as a moving camera, a dynamic background or a long lasting occlusion. Built upon the previous work by Newson et al. [1] which optimizes a global patch-based function, our method makes a significant improvement, especially in motion preservation, by incorporating the optical flow in several stages of the algorithm. Moreover, code par-allelization and a modification in the process of patches pair-wise matching yield a significant reduction of computation time. Experimental results on both classical and challenging datasets show that our algorithm outperforms other state-of-the-art approaches. (10.1109/ICIP.2017.8296651)
  DOI : 10.1109/ICIP.2017.8296651
• Characterization of the 3F4 – 3H6 Transition in Thulium-doped Silica Fibres and Simulation of a 2µm Single Clad Amplifier
  
  • Romano Clément
  • Tench Robert E
  • Delavaux Jean-Marc
  • Jaouën Yves
  , 2017. We report measurements of absorption, gain and lifetime for two commercially available Tm-doped single clad silica fibres for the transition 3H6 – 3F4. Comparison of simulation and experimental results for a single stage amplifier at 1952 nm yields good agreement.
• JOINT DENOISING AND DECOMPRESSION: A PATCH-BASED BAYESIAN APPROACH
  
  • Preciozzi Javier
  • González Mario
  • Almansa Andrés
  • Musé Pablo
  , 2017. JPEG and Wavelet compression artifacts leading to Gibbs effects and loss of texture are well known and many restoration solutions exist in the literature. So is denoising, which has occupied the image processing community for decades. However, when a noisy image is compressed, the noisy wavelet coefficients can be assigned to the " wrong " quantization interval, generating artifacts that can have dramatic consequences in products derived from satellite image pairs such as sub-pixel stereo vision and digital terrain elevation models. Despite the fact that the importance of such artifacts in very high resolution satellite imaging has recently been recognized, this restoration problem has been rarely addressed in the literature. In this work we present a thorough probabilistic analysis of the wavelet outliers phenomenon, and conclude that their probabilistic nature is characterized by a single parameter related to the ratio q/σ of the compression rate and the instrumental noise. This analysis provides the conditional probability for a Bayesian MAP estimator, whereas a patch-based local Gaussian prior model is learnt from the corrupted image iteratively, like in state of the art patch-based de-noising algorithms, albeit with the additional difficulty of dealing with non-Gaussian noise during the learning process. The resulting joint denoising and decompression algorithm is experimentally evaluated under realistic conditions. The results show its ability to simultaneously denoise, decompress and remove wavelet outliers better than the available alternatives, both from a quantitative and a qualitative point of view. As expected, the advantage of our method is more evident for large values of q/σ.
• Rules for Computing Resistance of Transitions of Learning Algorithms in Games
  
  • Ali Mohammed Shabbir
  • Coucheney Pierre
  • Coupechoux Marceau
  , 2017, 212, pp.97-107. (10.1007/978-3-319-67540-4_9)
  DOI : 10.1007/978-3-319-67540-4_9
• White Matter Fiber Segmentation Using Functional Varifolds
  
  • Kumar Kuldeep
  • Gori Pietro
  • Charlier Benjamin
  • Durrleman Stanley
  • Colliot Olivier
  • Desrosiers Christian
  , 2017, 10551, pp.92-100. The extraction of fibers from dMRI data typically produces a large number of fibers, it is common to group fibers into bundles. To this end, many specialized distance measures, such as MCP, have been used for fiber similarity. However, these distance based approaches require point-wise correspondence and focus only on the geometry of the fibers. Recent publications have highlighted that using microstructure measures along fibers improves tractography analysis. Also, many neurodegenerative diseases impacting white matter require the study of microstructure measures as well as the white matter geometry. Motivated by these, we propose to use a novel computational model for fibers, called functional varifolds, characterized by a metric that considers both the geometry and microstructure measure (e.g. GFA) along the fiber pathway. We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.
• A User-Centric System for Verified Identities on the Bitcoin Blockchain
  
  • Augot Daniel
  • Chabanne Hervé
  • Chenevier Thomas
  • George William
  • Lamber Laurent
  , 2017. We present an identity management scheme built into the Bitcoin blockchain, allowing for identities that are as indelible as the blockchain itself. Moreover, we take advantage of Bitcoin's decentralized nature to facilitate a shared control between users and identity providers, allowing users to directly manage their own identities, fluidly coordinating identities from different providers, even as identity providers can revoke identities and impose controls.
• Towards trusted and secure communications in a vehicular environment
  
  • Tan Heng Chuan
  , 2017. Routing and key management are the biggest challenges in vehicular networks. Inappropriate routing behaviour may affect the effectiveness of communications and affect the delivery of safety-related applications. On the other hand, key management, especially due to the use of PKI certificate management, can lead to high latency, which may not be suitable for many time-critical applications. For this reason, we propose two trust models to assist the routing protocol in selecting a secure end-to-end path for forwarding. The first model focusses on detecting selfish nodes, including reputation-based attacks, designed to compromise the “true” reputation of a node. The second model is intended to detect forwarders that modify the contents of a packet before retransmission. In key management, we have developed a Secure and Authentication Key Management Protocol (SA-KMP) scheme that uses symmetric cryptography to protect communication, including eliminating certificates during communication to reduce PKI-related delays.
• Dual-rotation C-arm cone-beam computed tomography to increase low-contrast detection
  
  • Reshef Aymeric
  • Riddell Cyril
  • Trousset Yves
  • Ladjal Saïd
  • Bloch Isabelle
  Medical Physics : The international journal of medical physics research and practice, American Association of Physicists in Medicine, 2017, 44 (9), pp.e164-e173. Purpose: This paper investigates the capabilities of a dual-rotation C-arm cone-beam computed tomography (CBCT) framework to improve non-contrast-enhanced low-contrast detection for full volume or volume-of-interest (VOI) brain imaging. Method: The idea is to associate two C-arm short-scan rotational acquisitions (spins): one over the full detector field of view (FOV) at low dose, and one collimated to deliver a higher dose to the central densest parts of the head. The angular sampling performed by each spin is allowed to vary in terms of number of views and angular positions. Collimated data is truncated and does not contain measurement of the incoming X-ray intensities in air (air calibration). When targeting full volume reconstruction, the method is intended to act as a virtual bow-tie. When targeting VOI imaging, the method is intended to provide the minimum full detector FOV data that sufficiently corrects for truncation artifacts. A single dedicated iterative algorithm is described that handles all proposed sampling configurations despite truncation and absence of air calibration. Results: Full volume reconstruction of dual-rotation simulations and phantom acquisitions are shown to have increased low-contrast detection for less dose, with respect to a single-rotation acquisition. High CNR values were obtained on 1% inserts of the Catphanmath formula 515 module in 0.94 mm thick slices. Image quality for VOI imaging was preserved from truncation artifacts even with less than 10 non-truncated views, without using the sparsity a priori common to such context. Conclusion: A flexible dual-rotation acquisition and reconstruction framework is proposed that has the potential to improve low-contrast detection in clinical C-arm brain soft-tissue imaging. (10.1002/mp.12247)
  DOI : 10.1002/mp.12247
• Formal Specification of Security Guidelines for Program Certification
  
  • Zhioua Zeineb
  • Roudier Yves
  • Ameur-Boulifa R.
  , 2017.
• High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects
  
  • Cai Hao
  • Kang Wang
  • Wang You
  • Naviner Lirida
  • Yang Jun
  • Zhao Weisheng
  Applied Sciences, Multidisciplinary digital publishing institute (MDPI), 2017, 7 (9), pp.1-13. The Internet of Things (IoTs) relies on efficient node memories to process data among sensors, cloud and RF front-end. Both mainstream and emerging memories have been developed to achieve this energy efficiency target. Spin transfer torque magnetic tunnel junction (STT-MTJ)-based nonvolatile memory (NVM) has demonstrated great performance in terms of zero standby power, switching power efficiency, infinite endurance and high density. However, it still has a big performance gap; e.g., high dynamic write energy, large latency, yield and reliability. Recently, voltage-controlled magnetic anisotropy (VCMA) has been introduced to achieve improved energy-delay efficiency and robust non-volatile writing control with an electric field or a switching voltage. VCMA-MTJ-based MRAM could be a promising candidate in IoT node memory for high-performance, ultra-low power consumption targets. (10.3390/app7090929)
  DOI : 10.3390/app7090929
• Relating Brain Structures To Open-Ended Descriptions Of Cognition
  
  • Dockès Jérôme
  • Grisel Olivier
  • Massich Joan
  • Suchanek Fabian
  • Thirion Bertrand
  • Varoquaux Gaël
  , 2017, 10 (2), pp.59 - 63. Finding correspondences between mental processes and brain structures is a central goal in cognitive neuroscience. Neuroimaging provides brain-activity maps associated with cognitive task, however each study explores only a handful of mental processes. Here we use literature mining to bridge results across studies, establishing a bidirectional mapping between brain and mind. Our goal is to work on an open-ended set of terms describing cognitive processes. Moreover we introduce a validation framework using information retrieval met-rics to ensure the accuracy of such correspondences, with a clear focus on relative frequencies, ignored in previous studies , to capture the relative importance of cognitive concepts. We show that this approach enables open-ended encoding and decoding. (10.1016/j.tics.2005.12.004)
  DOI : 10.1016/j.tics.2005.12.004
• Reconstruction tomographique 2D : une nouvelle méthode de rétro-projection filtrée
  
  • Reshef Aymeric
  • Riddell Cyril
  • Trousset Yves
  • Ladjal Saïd
  • Bloch Isabelle
  , 2017. En reconstruction tomographique 2D, le terme de « rétro-projection filtrée » est ambigu, car les projections sont d’abord filtrées puis rétro-projetées dans le plan image. Si l’inversion des opérations est possible mathématiquement, elle n’est jamais appliquée en pratique. Cet article présente un moyen de calculer la rétro-projection filtrée avec la même précision numérique quel que soit l’ordre des opérations. Une illustration dans le cadre de la reconstruction de régions d’intérêt démontre la versatilité de la méthode.
• Ondelettes analytiques, application à l'analyse des processus multivariés à longue mémoire
  
  • Gannaz Irène
  • Achard Sophie
  • Clausel Marianne
  • Roueff François
  , 2017. Dans de nombreux domaines tels que la finance, la géophysique ou les neurosciences, les données se présentent sous la forme de séries temporelles multivariées. Un enjeu de l'analyse statistique est de prendre en compte cet aspect multivarié, notamment en raison de phénomènes de phase pouvant être induits par la présence de longue mémoire. Les ondelettes analytiques sont un outil adapté à ce cadre d'étude. Nous nous intéressons ainsi aux ondelettes quasi-analytiques introduites par Selesnick. Nous montrons tout d'abord leur existence et nous explicitons ensuite leur qualité analytique. Nous illustrons enfin l'avantage de l'utilisation de ces ondelettes dans un cas simple de processus multivariés à longue mémoire.
• Histoire de la transformée de Mellin
  
  • Nicolas Jean-Marie
  • Badeau Roland
  , 2017. La transformée de Mellin est probablement la transformation intégrale la plus méconnue mais aussi une des plus fondamentales dans de nombreux domaines. Sa genèse a été fort longue, et il est difficile de donner une référence précise de son introduction dans les ouvrages scientifiques. Longtemps réduite à jouer un rôle secondaire vis à vis des transformées de Fourier et de Laplace, il est peut être temps de lui redonner la place qu’elle mérite dans les outils de traitement de signal “modernes”.
• Clustering en haute dimension pour le débruitage d'image
  
  • Houdard Antoine
  • Bouveyron Charles
  • Delon Julie
  , 2017. Dans cet article, nous présentons l'algorithme HDMI de débruitage par patchs. HDMI est fondé sur l'apprentissage d'un modèle de mélange de gaussiennes en grande dimension sur l'ensemble des patchs bruités. La méthode proposée estime une dimension intrinsèque pour chaque groupe puis les patchs débruités sont estimés par une espérance conditionnelle.
• Lévy NMF : un modèle robuste de séparation de sources non-négatives
  
  • Magron Paul
  • Badeau Roland
  • Liutkus Antoine
  , 2017. Dans cet article, nous nous intéressons à la séparation robuste de sources non-négatives. Nous introduisons les distributions Positives α-stables (PαS), un sous-ensemble de la famille des lois stables, qui modélisent les variables latentes non-négatives. Comme ces distributions sont à queue lourde, elles possèdent naturellement une propriété de robustesse aux valeurs aberrantes. En étudiant plus particulièrement la loi de Lévy, la seule loi PαS dont la densité s’exprime sous forme analytique simple, nous mettons au point un modèle de mélange dans lequel nous structurons par un modèle de factorisations en matrices non-négatives (NMF) les paramètres de dispersion des variables de Lévy. Ce modèle, appelé Lévy NMF, est estimé au sens du maximum de vraisemblance. Nous obtenons également un estimateur des sources qui généralise le filtrage de Wiener aux distributions PαS. Des expériences conduites sur des spectrogrammes musicaux et des spectres de fluorescence démontrent le potentiel de ce modèle pour décomposer des données non-négatives.
• Séparation de sources audio en milieu réverbérant : Factorisation en matrices non-négatives et représentation temporelle du mélange convolutif
  
  • Leglaive Simon
  • Badeau Roland
  • Richard Gael
  , 2017. Cet article traite du problème de séparation de sources audio sous-déterminé pour les mélanges réverbérants multi- canaux. Nous visons une application semi-aveugle où les filtres de mélange sont connus. La méthode proposée consiste à travailler directement avec les signaux temporels du mélange. Cette approche permet de représenter de façon exacte le processus de mélange convolutif, elle est donc adaptée pour la séparation de mélanges fortement réverbérants. Les signaux sources sont quant à eux représentés dans le domaine de la transformée en cosinus discrète modifiée, en utilisant un modèle gaussien basé sur la factorisation en matrices non-négatives. L'inférence des sources repose sur un algorithme espérance-maximisation variationnel. Nous montrons expérimentalement l'intérêt d'utiliser conjointement une représentation temporelle du mélange convolutif et un modèle de source basé sur la factorisation en matrices non-négatives.
• Accélération de la méthode de microscopie à illumination structurée à l'aide de l'acquisition comprimée
  
  • Meiniel William
  • Spinicelli Piernicola
  • Angelini Elsa
  • Fragola Alexandra
  • Loriette Vincent
  • Orieux François
  • Sepulveda Eduardo
  • Olivo-Marin Jean-Christophe
  , 2017. Dans cet article, nous proposons une méthode pour appliquer la théorie de l'Acquisition Comprimée (Compressed Sensing-CS) à une technique de super-résolution : la microscopie à illumination structurée (SIM). Nous commençons par définir le cadre d'application du CS en domaine direct, en exploitant la parcimonie dans le domaine de Fourier d'images en microscopie à fluorescence. Ensuite, nous proposons d'appliquer cette méthode à la reconstruction SIM à 4 images, ce qui permet de reconstruire des images en microscopie à fluorescence super-résolues, en n'utilisant que 25% des pixels de la caméra pour chaque acquisition.
• Méthode de séparation de sources appliquée aux données géoradar
  
  • Terrasse Guillaume
  • Nicolas Jean Marie
  • Trouvé Emmanuel
  , 2017.
• Convergence d'un algorithme du gradient proximal stochastique à pas constant et généralisation aux opérateurs monotones aléatoires
  
  • Salim Adil
  • Bianchi Pascal
  • Hachem Walid
  , 2017. – L'algorithme du gradient proximal permet de trouver les minimiseurs d'une somme F + G de deux fonctions convexes propres et fermées, l'une étant supposée dérivable. Cet article introduit une version stochastique de cet algorithme. Les itérations font intervenir une suite iid de deux fonctions aléatoires, dont les espérances coïncident respectivement avec F et G, ainsi que des projections aléatoires sur des ensembles convexes fermés. L'objectif est de fournir une analyse de convergence, dans un contexte adaptatif où le pas de l'algorithme est supposé constant. On montre que, en moyenne de Césaro, la probabilité pour que les itérées soient hors d'un voisinage des minimiseurs souhaités est arbitrairement faible lorsque le nombre d'itérations tend vers l'infini, et dans la limite de pas faibles. Le comportement ergodique des itérées est également étudié. Enfin, l'algorithme est étendu au contexte plus général des opérateurs maximaux monotones aléatoires. Abstract – The proximal gradient algorithm allows to find the minimizers of a sum F + G of two proper closed convex functions, one of them being differentiable. This paper introduces a stochastic version of the proximal gradient algorithm. The iterations involve an iid sequence of two random functions, whose expectations coincide with F and G respectively, as well as random projections onto closed convex sets. The aim is to provide a convergence analysis in an adaptive context where the step size of the algorithm is constant. We prove that, in Césaro mean, the probability that the iterates are away from the sought minimizers is small when the number of iterations tends to infinity and in the limit of small step sizes. The ergodic behavior is studied as well. Finally, the algorithm is extended to the context of random maximal monotone operators.
• Performance et simplicité d'implantation des constellations tournées: une approche pragmatique
  
  • Arbi Tarak
  • Yang Jianxiao
  • Abdel Nour Charbel
  • Rioul Olivier
  • Geller Benoit
  , 2017, pp.ID29. La norme DVB-T2 est la première à utiliser les constellations QAM tournées dont les performances théoriques sont meilleures que celles des constellations QAM conventionnelles sur des canaux à évanouissements. Toutefois, leur complexité de démodulation constitue un frein pour une diffusion plus large de cette technique. Dans cet article, nous proposons une série d'angles de rotation particulière afin de mettre en oeuvre une détection de faible complexité. Les résultats obtenus montrent que la méthode proposée simplifie non seulement le récepteur mais également l'émetteur, avec souvent des performances meilleures que pour des angles de la norme DVB-T2. Le démappeur proposé réduit la complexité globale de plus de 60% par rapport à la solution la plus simple utilisée actuellement en DVB-T2.