Publications

2018

• Taking Apart Autoencoders: How do They Encode Geometric Shapes ?
  
  • Newson Alasdair
  • Almansa Andrés
  • Gousseau Yann
  • Ladjal Saïd
  , 2018. We study the precise mechanisms which allow autoencoders to encode and decode a simple geometric shape, the disk. In this carefully controlled setting, we are able to describe the specific form of the optimal solution to the minimisation problem of the training step. We show that the autoencoder indeed approximates this solution during training. Secondly, we identify a clear failure in the generali-sation capacity of the autoencoder, namely its inability to interpolate data. Finally, we explore several regularisation schemes to resolve the generalisation problem. Given the great attention that has been recently given to the generative capacity of neural networks, we believe that studying in depth simple geometric cases sheds some light on the generation process and can provide a minimal requirement experimental setup for more complex architectures.
• Estimation d'un circuit électrique équivalent, à résistances et capacités thermiques, d'un bâtiment pour le contrôle optimal du chauffage du bâtiment
  
  • Nabil Tahar
  • Jicquel Jean-Marc
  • Girard Alexandre
  • Roueff François
  , 2018, pp.https://permalink.orbit.com/RenderStaticFirstPage?XPN=S5GmjW98%252BeXWqxLm4QBXD3fDUqlXTJ5uwQdFuycu4uk%3D%26n%3D1&id=0&base=FAMPAT. The invention relates to a method for determining a thermal model of a building equipped with a heating installation, in particular for energy diagnosis or optimization of the heating of said building, wherein: - An overall energy consumption load curve (CDC) is obtained from at least one energy consumption meter (COC), In predefined time steps, said load curve being capable of containing a consumption payload (Qu) for heating the building by said installation as well as a load for consumption needs not linked to the heating of the building, - And of one or more connected objects associated with respective appliances, which consume energy and are not controlled for a heat supply (ACN), at least one item of information on the switching on or off of said appliances (ACN), And time intervals are detected in the load curve (CDC) during which the connected objects inform of a stop state of the respective apparatuses, in order to obtain a first estimate of said payload (Qu), which makes it possible to iteratively correct the model for its optimization.
• Generalization Bounds for Minimum Volume Set Estimation based on Markovian Data, ISAIM, International Symposium on Artificial Intelligence and Mathematics proceedings, 1-7
  
  • Bertail Patrice
  • Ciołek Gabriela
  • Clémençon Stéphan
  , 2018.
• An In-depth Comparison of Group Betweenness Centrality Estimation Algorithms
  
  • Chehreghani Mostafa Haghir
  • Bifet Albert
  • Abdessalem Talel
  , 2018, pp.2104-2113.
• Ultra-low noise dual-frequency VECSEL at telecom wavelength using fully correlated pumping
  
  • Liu Hui
  • Gredat Grégory
  • De Syamsundar
  • Fsaifes Ihsan
  • Ly Aliou
  • Vatré Rémy
  • Baili Ghaya
  • Bouchoule Sophie
  • Goldfarb Fabienne
  • Bretenaker Fabien
  Optics Letters, Optical Society of America - OSA Publishing, 2018, 43 (8), pp.1794. An ultra-low intensity and beatnote phase noise dual-frequency vertical-external-cavity surface-emitting laser is built at telecom wavelength. The pump laser is realized by polarization combining two single-mode fibered laser diodes in a single-mode fiber, leading to a 100% in-phase correlation of the pump noises for the two modes. The relative intensity noise is lower than −140 dB∕Hz, and the beatnote phase noise is suppressed by 30 dB, getting close to the spontaneous emission limit. The role of the imperfect cancellation of the thermal effect resulting from unbalanced pumping of the two modes in the residual phase noise is evidenced. (10.1364/OL.43.001794)
  DOI : 10.1364/OL.43.001794
• Identifier Randomization: An Efficient Protection Against CAN-Bus Attacks
  
  • Danger Jean-Luc
  • Karray Khaled
  • Guilley Sylvain
  • Elaabid M. Abdelaziz
  , 2018, pp.219-254. (10.1007/978-3-319-98935-8_11)
  DOI : 10.1007/978-3-319-98935-8_11
• Adding Missing Words to Regular Expressions
  
  • Rebele Thomas
  • Tzompanaki Aikaterini
  • Suchanek Fabian M.
  , 2018.
• Assessing Locator/Identifier Separation Protocol interworking performance through RIPE Atlas
  
  • Li Yue
  • Iannone Luigi
  , 2018.
• Statistical Inference with Ensemble of Clustered Desparsified Lasso
  
  • Chevalier Jérôme-Alexis
  • Salmon Joseph
  • Thirion Bertrand
  , 2018. Medical imaging involves high-dimensional data, yet their acquisition is obtained for limited samples. Multivariate predictive models have become popular in the last decades to fit some external variables from imaging data, and standard algorithms yield point estimates of the model parameters. It is however challenging to attribute confidence to these parameter estimates, which makes solutions hardly trustworthy. In this paper we present a new algorithm that assesses parameters statistical significance and that can scale even when the number of predictors p ≥ 10^5 is much higher than the number of samples n ≤ 10^3 , by lever-aging structure among features. Our algorithm combines three main ingredients: a powerful inference procedure for linear models –the so-called Desparsified Lasso– feature clustering and an ensembling step. We first establish that Desparsified Lasso alone cannot handle n p regimes; then we demonstrate that the combination of clustering and ensembling provides an accurate solution, whose specificity is controlled. We also demonstrate stability improvements on two neuroimaging datasets.
• Operations research and voting theory
  
  • Hudry Olivier
  , 2018, pp.20-41.
• Perception of Emotions and Body Movement in the Emilya Database
  
  • Fourati Nesrine
  • Pelachaud Catherine
  IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers, 2018, 9 (1), pp.90-101. In this paper, we examine the perception of emotions as well as the characterization and the classification of emotional body expressions based on perceptual body cues ratings. Emilya (EMotional body expression In daILY Actions), a database of body expressions of 8 emotions (including Neutral) in 7 daily actions performed by 11 actors, is used for these purposes. A perceptual study is conducted to explore four issues: 1) how expressed emotions are perceived by humans, 2) how emotion recognition by humans differs across daily actions, 3) how expressed emotions are characterized by humans through body cues, and 4) how emotions are automatically classified based on human rating of body cues. Across all the actions, most of the expressed emotions were correctly identified, but some were confused (e.g. Shame and Sadness). Confusions occurring at the level of emotion perception may be due to a lack of contextual factors (Emilya contains body movement of daily actions without reference to a context), to a similarity of bodily expressions, but also to the lack of other modalities that may contribute to a better recognition of bodily expression of these emotions (e.g. facial expressions). In the paper, we detail and discuss the results from these different studies. (10.1109/TAFFC.2016.2591039)
  DOI : 10.1109/TAFFC.2016.2591039
• Audio-Visual Analysis of Music Performances
  
  • Duan Zhiyao
  • Essid Slim
  • Liem Cynthia
  • Richard Gael
  • Sharma Gaurav
  IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2018.
• A Survey on Data-driven Dictionary-based Methods for 3D Modeling
  
  • Lescoat Thibault
  • Ovsjanikov Maks
  • Memari Pooran
  • Thiery Jean-Marc
  • Boubekeur Tamy
  Computer Graphics Forum, Wiley, 2018. Dictionaries are very useful objects for data analysis, as they enable a compact representation of large sets of objects through the combination of atoms. Dictionary-based techniques have also particularly benefited from the recent advances in machine learning, which has allowed for data-driven algorithms to take advantage of the redundancy in the input dataset and discover relations between objects without human supervision or hard-coded rules. Despite the success of dictionary-based techniques on a wide range of tasks in geometric modeling and geometry processing, the literature is missing a principled state-of-the-art of the current knowledge in this field. To fill this gap, we provide in this survey an overview of data-driven dictionary-based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary-based methods suitable for 3D data synthesis, with applications in geometric modeling and design. Our ultimate goal is to enlight the fact that these techniques can be used to combine the data-driven paradigm with design intent to synthesize new plausible objects with minimal human intervention. This is the main motivation to restrict the scope of the present survey to techniques handling point clouds and meshes, making use of dictionaries whose definition depends on the input data, and enabling shape reconstruction or synthesis through the combination of atoms.
• Mean value coordinates for quad cages in 3D
  
  • Thiery Jean-Marc
  • Memari Pooran
  • Boubekeur Tamy
  ACM Transactions on Graphics, Association for Computing Machinery, 2018.
• Mathematical models for very high resolution SAR data and their applications
  
  • Deledalle Charles-Alban
  • Denis L.
  • Ferraioli G.
  • Tupin Florence
  , 2018.
• DyBED: An Efficient Algorithm for Updating Betweenness Centrality in Directed Dynamic Graphs
  
  • Chehreghani Mostafa Haghir
  • Bifet Albert
  • Abdessalem Talel
  , 2018, pp.2114-2123.
• Quarante ans d’imagerie satellitaire radar
  
  • Nicolas Jean-Marie
  • Tupin Florence
  Revue Française de Photogrammétrie et de Télédétection, Société Française de Photogrammétrie et de Télédétection, 2018.
• Managing 'proto-ecosystems' - two smart mobility case studies
  
  • Marcocchia Giulia
  • Maniak Rémi
  International Journal of Automotive Technology and Management, Inderscience, 2018, 18 (3), pp.209-228. This paper considers how ecosystem-based research projects can be managed for a successful deployment of systemic and disruptive innovation. Such projects are defined as assignments in which heterogeneous organisations must invest upfront, aiming at co-constructing a systemic offer with shared interest, shared uncertainty and high economic, environmental and social impacts. Innovation management, ecosystem, and public-private partnership literatures have been investigated, as well as two European Commission funded research projects aimed at smart mobility infrastructure development. Results show these projects are both critical and disappointing for each player. We explain this contradiction of value perception showing that partners need such ecosystem projects to go forward and update their competences and roadmaps, but that the observed project management approach hampers the collectively built learning and the evolution of the strategic agenda of each partner. In conclusion, we define the concept of proto-ecosystem as an intermediary 'management object' for innovation management, and point out implications to manage such projects in order to unfold their whole potential. (10.1504/IJATM.2018.093413)
  DOI : 10.1504/IJATM.2018.093413
• Method, device and computer program for encapsulating media data into a media file
  
  • Denoual Franck
  • Mazé Frédéric
  • Le Feuvre J.
  • Ouedraogo Nael
  , 2018.
• Musical Descriptions Based on Formal Concept Analysis and Mathematical Morphology
  
  • Agon Carlos
  • Andreatta Moreno
  • Atif Jamal
  • Bloch Isabelle
  • Mascarade Pierre
  , 2018, pp.105-119. In the context of mathematical and computational representations of musical structures, we propose algebraic models for formalizing and understanding the harmonic forms underlying musical compositions. These models make use of ideas and notions belonging to two algebraic approaches: Formal Concept Analysis (FCA) and Mathematical Morphology (MM). Concept lattices are built from interval structures whereas mathematical morphology operators are subsequently defined upon them. Special equivalence relations preserving the ordering structure of the lattice are introduced in order to define musically relevant quotient lattices modulo congruences. We show that the derived descrip-tors are well adapted for music analysis by taking as a case study Ligeti's String Quartet No. 2. (10.1007/978-3-319-91379-7_9)
  DOI : 10.1007/978-3-319-91379-7_9
• Profitable Bandits
  
  • Achab Mastane
  • Clémençon Stéphan
  • Garivier Aurélien
  Proceedings of Machine Learning Research, PMLR, 2018, 95, pp.694-709. Originally motivated by default risk management applications, this paper investigates a novel problem, referred to as the profitable bandit problem here. At each step, an agent chooses a subset of the K ≥ 1 possible actions. For each action chosen, she then respectively pays and receives the sum of a random number of costs and rewards. Her objective is to maximize her cumulated profit. We adapt and study three well-known strategies in this purpose, that were proved to be most efficient in other settings: kl-UCB, Bayes-UCB and Thompson Sampling. For each of them, we prove a finite time regret bound which, together with a lower bound we obtain as well, establishes asymptotic optimality in some cases. Our goal is also to compare these three strategies from a theoretical and empirical perspective both at the same time. We give simple, self-contained proofs that emphasize their similarities, as well as their differences. While both Bayesian strategies are automatically adapted to the geometry of information, the numerical experiments carried out show a slight advantage for Thompson Sampling in practice.
• Mass volume curves and anomaly ranking
  
  • Clémençon Stéphan
  • Thomas Albert
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (2), pp.2806-2872. (10.1214/18-EJS1474)
  DOI : 10.1214/18-EJS1474
• Une approche par patchs, multi-atlas, itérative pour la segmentation du cortex cérébral en IRM néonatale
  
  • Tor-Díez Carlos
  • Passat Nicolas
  • Bloch Isabelle
  • Faisan Sylvain
  • Bednarek Nathalie
  • Rousseau François
  , 2018. L’analyse des structures cérébrales chez le nouveau-né constitue un enjeu de santé majeur, notamment en cas de prématurité, afin de disposer d’informations prédictives sur le développement de l’enfant. Le cortex est, en particulier, une structure d’intérêt, observable en IRM (imagerie par résonance magnétique). Les données IRM néonatales présentent toutefois des spécificités qui les rendent complexes à traiter. Dans ce contexte, les approches multi-atlas constituent une stratégie efficace, tirant parti de données traitées préalablement. La méthode proposée dans cet article repose sur une telle stratégie multi-atlas. Elle s’appuie notamment sur deux paradigmes : l’utilisation d’un modèle non local à base de patchs, et l’utilisation d’un schéma d’optimisation itératif. L’usage couplé de ces deux concepts permet notamment de considérer des patchs liés à l’image ainsi qu’à sa segmentation courante. Cette stratégie, comparée à de précédentes méthodes multi-atlas de la littérature, aboutit à des résultats de segmentation corticale robustes.
• Segmentation of pelvic vessels in pediatric MRI using a patch based learning approach
  
  • Virzi Alessio
  • Gori Pietro
  • Muller Cécile
  • Mille Eva
  • Peyrot Quoc
  • Berteloot Laureline
  • Boddaert Nathalie
  • Sarnacki Sabine
  • Bloch Isabelle
  , 2018, pp.617.
• Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks
  
  • Granell Emilio
  • Chammas Edgard
  • Likforman-Sulem Laurence
  • Martínez-Hinarejos Carlos-D
  • Mokbel Chafic
  • Cirstea Bogdan
  Journal of Imaging, MDPI, 2018, 4 (1), pp.22.