Publications

2017

• Prioritized network coding scheme for multi-layer video streaming
  
  • Baccouch Hana
  • Ageneau Paul-Louis
  • Tizon Nicolas
  • Boukhatem Nadia
  , 2017.
• Foreword to Radio Science for Humanity: URSI-France 2017 Workshop
  
  • Tanzi Tullio
  • Hamelin Joel
  Radio Science Bulletin, Union Radio-Scientifique Internationale (URSI), 2017 (360), pp.60-61.
• Règles d'Associations Temporelles de signaux sociaux pour la synthèse d'Agents Conversationnels Animés : Application aux attitudes sociales
  
  • Janssoone Thomas
  • Clavel Chloé
  • Bailly Kevin
  • Richard Gael
  Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2017. Afin d'améliorer l'interaction entre des humains et des agents conversationnels animés (ACA), l'un des enjeux majeurs du domaine est de générer des agents crédibles socialement. Dans cet article, nous présentons une méthode, intitulée SMART pour social multimodal association rules with timing, capable de trouver automatiquement des associations temporelles entre l'utilisation de signaux sociaux (mouvements de tête, expressions faciales, prosodie. . .) issues de vidéos d'interactions d'humains exprimant différents états affectifs (comportement, attitude, émotions,. . .). Notre système est basé sur un algorithme de fouille de séquences qui lui permet de trouver des règles d'associations temporelles entre des signaux sociaux extraits automatiquement de flux audio-vidéo. SMART va également analyser le lien de ces règles avec chaque état affectif pour ne conserver que celles qui sont pertinentes. Finalement, SMART va les enrichir afin d'assurer une animation facile d'un ACA pour qu'il exprime l'état voulu. Dans ce papier, nous formalisons donc l'implémentation de SMART et nous justifions son inté-rêt par plusieurs études. Dans un premier temps, nous montrons que les règles calculées sont bien en accord avec la littérature en psychologie et sociologie. Ensuite, nous présentons les résultats d'évaluations perceptives que nous avons conduites suite à des études de corpus pro-posant l'expression d'attitudes sociales marquées. ABSTRACT. In the field of Embodied Conversational Agent (ECA) one of the main challenges is to generate socially believable agents. The long run objective of the present study is to infer rules for the multimodal generation of agents' socio-emotional behaviour. In this paper, we introduce the Social Multimodal Association Rules with Timing (SMART) algorithm. It proposes to Revue d'intelligence artificielle-n o 4/2017, 511-537 512 RIA. Volume 31-n o 4/2017 learn the rules from the analysis of a multimodal corpus composed by audio-video recordings of human-human interactions. The proposed methodology consists in applying a Sequence Mining algorithm using automatically extracted Social Signals such as prosody, head movements and facial muscles activation as an input. This allows us to infer Temporal Association Rules for the behaviour generation. We show that this method can automatically compute Temporal Association Rules coherent with prior results found in the literature especially in the psychology and sociology fields. The results of a perceptive evaluation confirms the ability of a Temporal Association Rules based agent to express a specific stance. (10.3166/RIA.31.511-537)
  DOI : 10.3166/RIA.31.511-537
• CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration
  
  • Deledalle Charles-Alban
  • Papadakis Nicolas
  • Salmon Joseph
  • Vaiter Samuel
  SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2017, 10 (1), pp.243-284. In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a ``twicing'' flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks. (10.1137/16M1080318)
  DOI : 10.1137/16M1080318
• Trends in Social Network Analysis - Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment
  
  • Missaoui Rokia
  • Abdessalem Talel
  • Latapy Mathieu
  , 2017, pp.255. <p>The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field.</p> <p> </p> (10.1007/978-3-319-53420-6)
  DOI : 10.1007/978-3-319-53420-6
• La fabrique des données brutes. Le travail en coulisses de l'open data
  
  • Denis Jérôme
  • Goëta Samuel
  , 2017. Depuis quelques années, les initiatives d’open data se sont multipliées à travers le monde. Présentées jusque dans la presse grand public comme une ressource inexploitée, le « pétrole » sur lequel le monde serait assis, les données publiques sont devenues objet de toutes les attentions et leur ouverture porteuse de toutes les promesses, à la fois terreau d’un renouveau démocratique et moteur d’une innovation distribuée. Comme dans les sciences, qui ont connu un mouvement de focalisation similaire sur les données et leur partage, l’injonction à l’ouverture opère une certaine mise en invisibilité. Le vocabulaire de la « libération », de la « transparence » et plus encore celui de la « donnée brute » efface toute trace des conditions de production des données, des contextes de leurs usages initiaux et pose leur universalité comme une évidence. Ce chapitre explore les coulisses de l’open data afin de retrouver les traces de cette production et d’en comprendre les spécificités. À partir d’une série d’entretiens ethnographiques dans diverses institutions, il décrit la fabrique des données brutes, dont l’ouverture ne se résume jamais à une mise à disponibilité immédiate, évidente et universelle. Il montre que trois aspects sont particulièrement sensibles dans le processus d’ouverture : l’identification, l'extraction et la « brutification » des données. Ces trois séries d’opérations donnent à voir l’épaisseur sociotechnique des données brutes dont la production mêle dimensions organisationnelles, politiques et techniques.
• Closed-form expressions of the eigen decomposition of 2 x 2 and 3 x 3 Hermitian matrices
  
  • Deledalle Charles-Alban
  • Denis Loic
  • Tabti Sonia
  • Tupin Florence
  , 2017. The eigen decomposition of covariance matrices is at the core of many data analysis techniques. The study of 2-components or 3-components vector fields typically requires computing numerous eigen decompositions of 2 x 2 or 3 x 3 matrices. This is, for example, the case in the analysis of interferometric or polarimetric SAR images, see MuLoG algorithm (https://hal.archives-ouvertes.fr/hal-01388858). The closed-form expression of eigen-values and eigenvectors then provides a way to derive faster data processing algorithms. This note gives these expressions in the general case (special cases where some coefficients are zero, or the eigenvalues are not separated may not be covered and then require either to introduce a small perturbation of the initial matrix or to derive other expressions).
• Towards building 3D individual models from MRI segmentation and tractography to enhance surgical planning for pediatric pelvic tumors and malformations
  
  • Muller Cécile
  • Virzi Alessio
  • Marret Jean-Baptiste
  • Mille Eva
  • Berteloot Laureline
  • Grevent David
  • Blanc Thomas
  • Garcelon Nicolas
  • Buffet Isabelle
  • Hullier-Ammard Elisabeth
  • Gori Pietro
  • Boddaert Nathalie
  • Bloch Isabelle
  • Sarnacki Sabine
  , 2017, pp.113-115.
• Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape prior
  
  • Evain Timothée
  • Ripoche Xavier
  • Atif J.
  • Bloch Isabelle
  , 2017, pp.1197-1200. We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images for which a reference segmentation was available, with an overall Dice coefficient above 0.95 and a global consistency error less than 0.005.
• Hyperparameter optimization of deep neural networks: combining Hperband with Bayesian model selection
  
  • Bertrand Hadrien
  • Ardon Roberto
  • Perrot Matthieu
  • Bloch Isabelle
  , 2017. One common problem in building deep learning architectures is the choice of the hyper-parameters. Among the various existing strategies, we propose to combine two complementary ones. On the one hand, the Hyperband method formalizes hyper-parameter optimization as a resource allocation problem, where the resource is the time to be distributed between many configurations to test. On the other hand, Bayesian optimization tries to model the hyper-parameter space as efficiently as possible to select the next model to train. Our approach is to model the space with a Gaussian process and sample the next group of models to evaluate with Hyperband. Preliminary results show a slight improvement over each method individually, suggesting the need and interest for further experiments.
• Nonequilibrium Green's functions theory for the alpha factor of quantum cascade lasers
  
  • Pereira Mauro
  • Winge David
  • Wacker Andreas
  • Jumpertz Louise
  • Michel Florian
  • Pawlus Robert
  • Elsassaer Wolfgang
  • Schires Kevin
  • Carras Mathieu
  • Grillot Frédéric
  , 2017.
• Étude des perturbations des systèmes de positionnement magnéto-inductifs en intérieur, » Journées nationales micorondes
  
  • Gharat Vighnesh
  • Colin Elizabeth
  • Baudoin Geneviève
  • Richard Damien Richard
  , 2017.
• Beat Gesture Prediction using Prosodic Features
  
  • Jain Varun
  • Clavel Chloé
  • Pelachaud Catherine I
  , 2017.
• Method, device, and computer program for transmitting portions of encapsulated media content
  
  • Denoual Franck
  • Mazé Frédéric
  • Ruellan Hervé
  • Le Feuvre J.
  • Ouedraogo Nael
  , 2017.
• Autoreject: Automated artifact rejection for MEG and EEG data
  
  • Jas Mainak
  • Engemann Denis A
  • Bekhti Yousra
  • Raimondo Federico A
  • Gramfort Alexandre
  NeuroImage, Elsevier, 2017. We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience. (10.1016/j.neuroimage.2017.06.030)
  DOI : 10.1016/j.neuroimage.2017.06.030
• Cournot-Nash Equilibria for Bandwidth Allocation under Base-Station Cooperation
  
  • Gomez J S
  • Vergne A
  • Martins P
  • Decreusefond Laurent
  • Chen Wei
  , 2017. —In this paper, a novel resource allocation scheme based on discrete Cournot-Nash equilibria and optimal transport theory is proposed. The originality of this framework lies in the joint optimization of downlink bandwidth allocation and cooperation between base stations. A tractable formalization is given in the form of a quadratic optimization problem. A low complexity approximate solution is derived and theoretically characterized. Simulations highlight the existence of an optimal working point, that maximizes user satisfaction ratio and network load. The impact of the network deployment on the optimum is numerically investigated, thanks to the β-Ginibre model. Indeed, base stations are assumed to be drawn according to β-Ginibre point processes. Numerical analysis shows that the network performance increases with β going to one.
• Large Scale Density-friendly Graph Decomposition via Convex Programming
  
  • Danisch Maximilien
  • Chan T-H. Hubert
  • Sozio Mauro
  , 2017.
• Robust dynamic range computation for high dynamic range content
  
  • Hulusic Vedad
  • Valenzise Giuseppe
  • Debattista Kurt
  • Dufaux Frederic
  , 2017. High dynamic range (HDR) imaging has become an important topic in both academic and industrial domains. Nevertheless, the concept of dynamic range (DR), which underpins HDR, and the way it is measured are still not clearly understood. The current approach to measure DR results in a poor correlation with perceptual scores (r ≈ 0.6). In this paper, we analyze the limitations of the existing DR measure, and propose several options to predict more accurately subjective DR judgments. Compared to the traditional DR estimates, the proposed measures show significant improvements in Spearman's and Pearson's correlations with subjective data (up to r ≈ 0.9). Despite their straightforward nature, these improvements are particularly evident in specific cases, where the scores obtained by using the classical measure have the highest error compared to the perceptual mean opinion score.
• Very high resolution and interferometric SAR: Markovian and patch-based non-local mathematical models
  
  • Deledalle Charles-Alban
  • Denis Loïc
  • Ferraioli Giampaolo
  • Pascazio Vito
  • Schirinzi Gilda
  • Tupin Florence
  , 2017. This chapter is dedicated to very high resolution (VHR) SAR imagery, including interferometric applications. First, the principles of SAR data acquisition are presented as well as the different types of configurations. The widely adopted Gaussian complex model of fully developed speckle is described as well as more advanced statistical models for VHR SAR data that account for textures. The following two parts are devoted to SAR image estimation and to image denoising within two different frameworks. First, Markovian modeling is introduced and the associated optimization approaches are presented, including graph-cut based optimization. The second framework is the patch-based non-local modeling of SAR complex data. Both frameworks are adapted to SAR images through the use of statistical models specific to SAR imagery. Their applications to amplitude data, interferometry, and fusion with optical data are illustrated. A special focus is given to phase unwrapping applied to single and multi- channel interferometry, showing the usefulness of local and global contextual models. (10.1007/978-3-319-66330-2)
  DOI : 10.1007/978-3-319-66330-2
• Parallel Combining: Making Use of Free Cycles
  
  • Aksenov Vitaly
  • Kuznetsov Petr
  Computing Research Repository, ACM / ArXiv, 2017, abs/1710.07588.
• Optimal two-step prediction in regression
  
  • Chételat Didier
  • Lederer Johannes
  • Salmon Joseph
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11 (1), pp.2519-2546.
• Demonstration of 16QAM-OFDM UDWDM Transmission Using a Tunable Optical Flat Comb Source
  
  • Hraghi Abir
  • Chaibi Mohamed E.
  • Menif Mourad
  • Erasme Didier
  Journal of Lightwave Technology, Institute of Electrical and Electronics Engineers (IEEE)/Optical Society of America(OSA), 2017, 35 (2), pp.238-245. A new approach for designing broad and flattened spectrum multicarriers optical sources is presented leading to a 32 spectral lines source using a dual-arm Mach-Zehnder modulator (MZM) and a 41 spectral lines source from two-stage MZM. A modified simulated annealing-based optimization method is applied to derive the necessary settings allowing the optical flat comb source (OFCS) to be ultraflat. The OFCS is mooted as a technology to enhance the overall capacity of an access optical network by increasing the number of WDM channels. Here, we demonstrate an ultra-dense WDM (UDWDM) trans- mission for application to passive optical networks (PON) with (11x12.5Gbps) Quadrature Amplitude Modulation (QAM) based on a 4b/s/Hz spectral efficiency orthogonal frequency division multiplex (16QAM-OFDM) transmitter and direct detection. We use an OFCS to generate the 11 subcarriers spaced by 6.25GHz, made of a two-stage MZM. We study the performance of 3 filtered channels in terms of error vector magnitude (EVM) in back-to-back (B-to-B) conditions and after propagation through 25km and 100km standard single mode fiber (SSMF). (10.1109/JLT.2016.2636442)
  DOI : 10.1109/JLT.2016.2636442
• Parametric models of phase-amplitude coupling in neural time series
  
  • Dupré La Tour Tom
  • Grenier Yves
  • Gramfort Alexandre
  , 2017.
• Physical attacks
  
  • El Mrabet Nadia
  • Goubin Louis
  • Fournier Jacques Jean-Alain
  • Jauvart Damien
  • Guilley Sylvain
  • Moreau Martin
  • Rauzy Pablo
  • Rondepierre Franck
  , 2017.
• Classification of MRI data using deep learning and Gaussian process-based model selection
  
  • Bertrand Hadrien
  • Perrot Matthieu
  • Ardon Roberto
  • Bloch Isabelle
  , 2017, pp.745-748. The classification of MRI images according to the anatomical field of view is a necessary task to solve when faced with the increasing quantity of medical images. In parallel, advances in deep learning makes it a suitable tool for computer vision problems. Using a common architecture (such as AlexNet) provides quite good results, but not sufficient for clinical use. Improving the model is not an easy task, due to the large number of hyper-parameters governing both the architecture and the training of the network, and to the limited understanding of their relevance. Since an exhaustive search is not tractable, we propose to optimize the network first by random search, and then by an adaptive search based on Gaussian Processes and Probability of Improvement. Applying this method on a large and varied MRI dataset, we show a substantial improvement between the baseline network and the final one (up to 20% for the most difficult classes).