Publications

2018

• Building identification within a connected object ecosystem
  
  • Nabil Tahar
  , 2018. This thesis is devoted to the problem of the identification of a thermal model of a smart building, whose connected objects alleviate the lack of measurements of the physical quantities of interest. The first algorithm deals with the estimation of the open-loop building system, despite its actual exploitation in closed loop. This algorithm is then modified to account for the uncertainty of the data. We suggest a closedloop estimation of the building system as soon as the indoor temperature is not measured. Then, we return to open-loop approaches. The different algorithms enable respectively to reduce the possible bias contained in a connected outdoor air temperature sensor, to replace the costly solar flux sensor by another connected temperature sensor, and finally to directly use the total load curve, without disaggregation, by making the most of the On/Off signals of the connected objects.
• 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.
• Adding Missing Words to Regular Expressions
  
  • Rebele Thomas
  • Tzompanaki Aikaterini
  • Suchanek Fabian M.
  , 2018.
• An In-depth Comparison of Group Betweenness Centrality Estimation Algorithms
  
  • Chehreghani Mostafa Haghir
  • Bifet Albert
  • Abdessalem Talel
  , 2018, pp.2104-2113.
• 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.
• Assessing Locator/Identifier Separation Protocol interworking performance through RIPE Atlas
  
  • Li Yue
  • Iannone Luigi
  , 2018.
• 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.
• 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.
• 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
• Operations research and voting theory
  
  • Hudry Olivier
  , 2018, pp.20-41.
• 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
• 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
• On the optimality and practicability of mutual information analysis in some scenarios
  
  • de Chèrisey Èloi
  • Guilley Sylvain
  • Heuser Annelie
  • Rioul Olivier
  Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences, Springer, 2018, 10 (1), pp.101-121. The best possible side-channel attack maximizes the success rate and would correspond to a maximum likelihood (ML) distinguisher if the leakage probabilities were totally known or accurately estimated in a profiling phase. When profiling is unavailable, however, it is not clear whether Mutual Information Analysis (MIA), Correlation Power Analysis (CPA), or Linear Regression Analysis (LRA) would be the most successful in a given scenario. In this paper, we show that MIA coincides with the maximum likelihood expression when leakage probabilities are replaced by online estimated probabilities. Moreover, we show that the calculation of MIA is lighter that the computation of the maximum likelihood. We then exhibit two case-studies where MIA outperforms CPA. One case is when the leakage model is known but the noise is not Gaussian. The second case is when the leakage model is partially unknown and the noise is Gaussian. In the latter scenario MIA is more efficient than LRA of any order. (10.1007/s12095-017-0241-x)
  DOI : 10.1007/s12095-017-0241-x
• Prediction of weakly locally stationary processes by auto-regression
  
  • Roueff François
  • Sanchez-Perez Andres
  ALEA : Latin American Journal of Probability and Mathematical Statistics, Instituto Nacional de Matemática Pura e Aplicada (Rio de Janeiro, Brasil) [2006-....], 2018, 15, pp.1215–1239. In this contribution we introduce weakly locally stationary time series through the local approximation of the non-stationary covariance structure by a stationary one. This allows us to define autoregression coefficients in a non-stationary context, which, in the particular case of a locally stationary Time Varying Autoregressive (TVAR) process, coincide with the generating coefficients. We provide and study an estimator of the time varying autoregression coefficients in a general setting. The proposed estimator of these coefficients enjoys an optimal minimax convergence rate under limited smoothness conditions. In a second step, using a bias reduction technique, we derive a minimax-rate estimator for arbitrarily smooth time-evolving coefficients, which outperforms the previous one for large data sets. In turn, for TVAR processes, the predictor derived from the estimator exhibits an optimal minimax prediction rate. (10.30757/ALEA.v15-45)
  DOI : 10.30757/ALEA.v15-45
• Pas de probas, pas de chocolat !
  
  • Zayana Karim
  Au fil des maths, APMEP, 2018. Expériences aléatoires, lois discrètes et continues, approximation des unes par les autres, intervalles de confiance, fluctuations d’échantillonnage, tests statistiques, paradoxes probabilistes
• Introduction to the issue on physics and applications of laser dynamics (IS-PALD 2017)
  
  • Grillot F.
  • Sciamanna Marc
  • Chan S.-C.
  Optics Express, Optical Society of America - OSA Publishing, 2018, 26 (16), pp.21375-21378. In this paper, we introduce the Optics Express feature issue of the 7th International Symposium on Physics and Applications of Laser Dynamics (IS-PALD). This issue consists of expanded papers related to oral and poster presentations. Selected papers represent the best of IS-PALD 2017. © 2018 Optical Society of America (10.1364/OE.26.021375)
  DOI : 10.1364/OE.26.021375
• Adaptive random forests for data stream regression
  
  • Gomes Heitor Murilo
  • Barddal Jean Paul
  • Ferreira Luis Eduardo Boiko
  • Bifet Albert
  , 2018.
• Building a coverage hole-free communication tree
  
  • Vergne Anais
  • Decreusefond Laurent
  • Martins Philippe
  , 2018. Wireless networks are present everywhere but their management can be tricky since their coverage may contain holes even if the network is fully connected. In this paper we propose an algorithm that can build a communication tree between nodes of a wireless network with guarantee that there is no coverage hole in the tree. We use simplicial homology to compute mathematically the coverage, and Prim's algorithm principle to build the communication tree. Some simulation results are given to study the performance of the algorithm and compare different metrics. In the end, we show that our algorithm can be used to create coverage hole-free communication groups with a limited number of hops.
• High-Dimensional Mixture Models For Unsupervised Image Denoising (HDMI)
  
  • Houdard Antoine
  • Bouveyron Charles
  • Delon Julie
  SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2018. This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture models on the noisy patches. The model, named hereafter HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimension-alities. This parsimonious modeling allows in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a blind denoising algorithm that demonstrates state-of-the-art performance, both when the noise level is known and unknown.
• Caching Encrypted Content via Stochastic Cache Partitioning
  
  • Araldo Andrea
  • Dan Gyorgy
  • Rossi Dario
  IEEE/ACM Transactions on Networking, IEEE/ACM, 2018, 26 (1), pp.548-561. In-network caching is an appealing solution to cope with the increasing bandwidth demand of video, audio and data transfer over the Internet. Nonetheless, in order to protect consumer privacy and their own business, Content Providers (CPs) increasingly deliver encrypted content, thereby preventing Internet Service Providers (ISPs) from employing traditional caching strategies, which require the knowledge of the objects being transmitted. To overcome this emerging tussle between security and effi- ciency, in this paper we propose an architecture in which the ISP partitions the cache space into slices, assigns each slice to a different CP, and lets the CPs remotely manage their slices. This architecture enables transparent caching of encrypted content, and can be deployed in the very edge of the ISP’s network (i.e., base stations, femtocells), while allowing CPs to maintain exclusive control over their content. We propose an algorithm, called SDCP, for partitioning the cache storage into slices so as to maximize the bandwidth savings provided by the cache. A distinctive feature of our algorithm is that ISPs only need to measure the aggregated miss rates of each CP, but they need not know of the individual objects that are requested. We prove that the SDCP algorithm converges to a partitioning that is close to the optimal, and we bound its optimality gap. We use simulations to evaluate SDCP’s convergence rate under stationary and non-stationary content popularity. Finally, we show that SDCP significantly outperforms traditional reactive caching techniques, considering both CPs with perfect and with imperfect knowledge of their content popularity.
• A contrario comparison of local descriptors for change detection in Very High spatial Resolution (VHR) satellite images of urban areas
  
  • Tupin Florence
  • Liu Gang
  • Gousseau Yann
  IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2018. Change detection is a key problem for many remote sensing applications. In this paper, we present a novel unsupervised method for change detection between two high resolution remote sensing images possibly acquired by two different sensors. This method is based on keypoints matching, evaluation and grouping, and does not require any image co-registration. It consists of two main steps. First, global and local mapping functions are estimated through keypoints extraction and matching. Secondly, based on these mappings, keypoint matchings are used to detect changes and then grouped to extract regions of changes. Both steps are defined through an {\it a contrario} framework, simplifying the parameter setting and providing a robust pipeline. The proposed approach is evaluated on synthetic and real data from different optic sensors with different resolutions, incidence angles and illumination conditions. (10.1109/TGRS.2018.2888985)
  DOI : 10.1109/TGRS.2018.2888985
• Gaussian Priors for Image denoising
  
  • Delon Julie
  • Houdard Antoine
  , 2018. This chapter is dedicated to the study of Gaussian priors for patch-based image denoising. In the last twelve years, patch priors have been widely used for image restoration. In a Bayesian framework, such priors on patches can be used for instance to estimate a clean patch from its noisy version, via classical estimators such as the conditional expectation or the maximum a posteriori. As we will recall, in the case of Gaussian white noise, simply assuming Gaussian (or Mixture of Gaussians) priors on patches leads to very simple closed-form expressions for some of these estimators. Nevertheless, the convenience of such models should not prevail over their relevance. For this reason, we also discuss how these models represent patches and what kind of information they encode. The end of the chapter focuses on the different ways in which these models can be learned on real data. This stage is particularly challenging because of the curse of dimensionality. Through these different questions, we compare and connect several denoising methods using this framework.
• Online Learning with Reoccurring Drifts: The Perspective of Case-Based Reasoning
  
  • Al-Ghossein Marie
  • Murena Pierre-Alexandre
  • Cornuéjols Antoine
  • Abdessalem Talel
  , 2018.