Publications

2019

• New Bernstein and Hoeffding type inequalities for regenerative Markov chains
  
  • Bertail Patrice
  • Ciołek Gabriela
  ALEA : Latin American Journal of Probability and Mathematical Statistics, Instituto Nacional de Matemática Pura e Aplicada (Rio de Janeiro, Brasil) [2006-....], 2019, 16 (1), pp.259. (10.30757/ALEA.v16-09)
  DOI : 10.30757/ALEA.v16-09
• Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering
  
  • Charpentier Bertrand
  • Bonald Thomas
  , 2019. We introduce the tree sampling divergence (TSD), an information-theoretic metric for assessing the quality of the hierarchical clustering of a graph. Any hierarchical clustering of a graph can be represented as a tree whose nodes correspond to clusters of the graph. The TSD is the Kullback-Leibler divergence between two probability distributions over the nodes of this tree: those induced respectively by sampling at random edges and node pairs of the graph. A fundamental property of the proposed metric is that it is interpretable in terms of graph reconstruction. Specifically, it quantifies the ability to reconstruct the graph from the tree in terms of information loss. In particular, the TSD is maximum when perfect reconstruction is feasible , i.e., when the graph has a complete hierarchical structure. Another key property of TSD is that it applies to any tree, not necessarily binary. In particular , the TSD can be used to compress a binary tree while minimizing the information loss in terms of graph reconstruction, so as to get a compact representation of the hierarchical structure of a graph. We illustrate the behavior of TSD compared to existing metrics on experiments based on both synthetic and real datasets.
• Documenting Supermarkets: Contemporary Efforts To Support Intellectually Disturbing Organizations Food Coop (2016) -Tom Boothe Unplugged -Voices
  
  • Ouahab Alban
  M@n@gement, AIMS (Association internationale de management stratégique), 2019, 22, pp.671 - 702.
• Anytime Large-Scale Analytics of Linked Open Data
  
  • Soulet Arnaud
  • Suchanek Fabian
  , 2019. Analytical queries are queries with numerical aggregators: computing the average number of objects per property, identifying the most frequent subjects, etc. Such queries are essential to monitor the quality and the content of the Linked Open Data (LOD) cloud. Many analytical queries cannot be executed directly on the SPARQL endpoints, because the fair use policy cuts off expensive queries. In this paper, we show how to rewrite such queries into a set of queries that each satisfy the fair use policy. We then show how to execute these queries in such a way that the result provably converges to the exact query answer. Our algorithm is an anytime algorithm, meaning that it can give intermediate approximate results at any time point. Our experiments show that the approach converges rapidly towards the exact solution, and that it can compute even complex indicators at the scale of the LOD cloud.
• On the Capacity of MIMO Optical Wireless Channels
  
  • Li Longguang
  • Moser Stefan M
  • Wang Ligong
  • Wigger Michèle
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2019. This paper studies the capacity of a general multiple-input multiple-output (MIMO) free-space optical intensity channel under a per-input-antenna peak-power constraint and a total average-power constraint over all input antennas. The main focus is on the scenario with more transmit than receive antennas. In this scenario, different input vectors can yield identical distributions at the output, when they result in the same image vector under multiplication by the channel matrix. We first determine the most energy-efficient input vectors that attain each of these image vectors. Based on this, we derive an equivalent capacity expression in terms of the image vector, and establish new lower and upper bounds on the capacity of this channel. The bounds match when the signal-to-noise ratio (SNR) tends to infinity, establishing the high-SNR asymptotic capacity. We also characterize the low-SNR slope of the capacity of this channel. (10.1109/ITW.2018.8613496)
  DOI : 10.1109/ITW.2018.8613496
• Merit-guided dynamic feature selection filter for data streams
  
  • Barddal Jean Paul
  • Enembreck Fabrício
  • Gomes Heitor Murilo
  • Bifet Albert
  • Pfahringer Bernhard
  Expert Syst. Appl., 2019, 116, pp.227-242. (10.1016/j.eswa.2018.09.031)
  DOI : 10.1016/j.eswa.2018.09.031
• A 3D Beamforming Scheme Based on The Spatial Distribution of User Locations
  
  • Rachad Jalal
  • Nasri Ridha
  • Decreusefond Laurent
  , 2019. Multi-antenna technologies such as massive Multiple-Input Multiple-Output (massive MIMO) and beamforming are key features to enhance performance, in terms of capacity and coverage, by using a large number of antennas intelligently. With the upcoming 5G New Radio (NR), FD-MIMO (Full Dimension MIMO) will play a major key role. FD-MIMO consists in arranging a large number of antennas in a 2D array, which enables to use 3D beamforming i.e., beamforming in both horizontal and vertical dimensions. The present paper provides a 3D beamforming model where beam steering depends on the random spatial distribution of users. We attempt to derive some analytical results regarding the probability distribution of antenna beamforming radiation pattern. Also, through system level simulations, we show how 3D beamforming can reduce interference impact, compared to the traditional 2D beamforming, and enhances system performance in terms of the coverage probability and users throughput.
• The $f$-divergence expectation iteration scheme
  
  • Daudel Kamélia
  • Douc Randal
  • Portier François
  • Roueff François
  , 2019. This paper introduces the $f$-EI$(\phi)$ algorithm, a novel iterative algorithm which operates on measures and performs $f$-divergence minimisation in a Bayesian framework. We prove that for a rich family of values of $(f,\phi)$ this algorithm leads at each step to a systematic decrease in the $f$-divergence and show that we achieve an optimum. In the particular case where we consider a weighted sum of Dirac measures and the $\alpha$-divergence, we obtain that the calculations involved in the $f$-EI$(\phi)$ algorithm simplify to gradient-based computations. Empirical results support the claim that the $f$-EI$(\phi)$ algorithm serves as a powerful tool to assist Variational methods.
• Several new classes of self-dual bent functions derived from involutions
  
  • Mesnager Sihem
  • Luo G.
  • Cao X.
  Journal of Cryptography and Communications- Discrete Structures, Boolean Functions, and Sequences, 2019.
• On q-ary plateaued functions over Fq and their explicit characterizations.
  
  • Mesnager Sihem
  • Özbudak Ferruh
  • Sinak A.
  • Cohen Gerard
  European Journal of Combinatorics, Elsevier, 2019.
• Three-Weight Minimal Linear Codes and Their Applications
  
  • Mesnager Sihem
  • Sinak A.
  • Yayla O.
  , 2019.
• Unique (Optimal) Solutions: Complexity Results for Identifying and Locating-Dominating Codes
  
  • Hudry Olivier
  • Lobstein Antoine
  Theoretical Computer Science, Elsevier, 2019, 767, pp.83-102. We investigate the complexity of four decision problems dealing with the uniqueness of a solution in a graph: “Uniqueness of an r-Locating–Dominating Code with bounded size” (U-LDCr), “Uniqueness of an Optimal r-Locating–Dominating Code” (U-OLDCr), “Uniqueness of an r-Identifying Code with bounded size” (U-IdCr), “Uniqueness of an Optimal r-Identifying Code” (U-OIdCr), for any fixed integer r ≥ 1 In particular, we describe a polynomial reduction from “Unique Satisfiability of a Boolean formula” (U-SAT) to U-OLDCr, and from U-SAT to U-OIdCr; for U-LDCr and U-IdCr, we can do even better and prove that their complexity is the same as that of U-SAT, up to polynomials. Consequently, all these problems are NP-hard, and U-LDCr and U-IdCr belong to the class DP. (10.1016/j.tcs.2018.09.034)
  DOI : 10.1016/j.tcs.2018.09.034
• Deep learning approaches for electrical vehicular mobility management
  
  • Dridi Aicha
  • Boucetta Chérifa
  • Alhassan Abubakar Yau
  • Moungla Hassine
  • Afifi Hossam
  • Labiod Houda
  , 2019, pp.1-6. Electrical vehicular (EV) energy management is a promising trend. Forecasting vehicular trajectories and delay is crucial for EV energy management. The presented work is devoted to the study and the application of deep learning techniques on specific road trajectories. First, exhaustive deep learning algorithms are considered. Second, road traces are converted to time series. Then, delays and road trajectories are analyzed. In fact, we consider two Recurrent Neural Networks (RNN): LSTM (Long Short Term Memory) and GRU (Gated Recurrent Units). Neural Networks are adapted and trained on 60 days of real urban traffic of Rome in Italy. We calculate the Loss function for both machine learning techniques which is defined by mean square error (MSE) and Root mean square error (RMSE). Experimental results demonstrate that both LSTM and GRU are adequate for the context of EV in terms of route trajectory and delay prediction. (10.1109/WINCOM47513.2019.8942569)
  DOI : 10.1109/WINCOM47513.2019.8942569
• Channel Impulsive Noise Mitigation for Linear Video Coding Schemes
  
  • Zheng Shuo
  • Cagnazzo Marco
  • Kieffer Michel
  IEEE Transactions on Circuits and Systems for Video Technology, Institute of Electrical and Electronics Engineers, 2019. (10.1109/TCSVT.2019.2937451)
  DOI : 10.1109/TCSVT.2019.2937451
• Procédé de gestion de contenus multimédia et dispositif pour la mise en œuvre du procédé
  
  • Lucas Gregory
  • Le Feuvre J.
  • Toullec Eric
  , 2019.
• Procédé de gestion de contenus multimédia et dispositif pour la mise en œuvre du procédé
  
  • Grégory Lucas
  • Le Feuvre J.
  • Toullec Eric
  , 2019.
• A Conditional Gradient-Based Augmented Lagrangian Framework
  
  • Yurtsever Alp
  • Fercoq Olivier
  • Cevher Volkan
  , 2019, 97. This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing conditional gradient methods either do not apply to our template or are too slow in practice. To this end, we propose a new conditional gradient method, based on a unified treatment of smoothing and augmented Lagrangian frameworks. The proposed method maintains favorable properties of the classical conditional gradient method, such as cheap linear minimization oracle calls and sparse representation of the decision variable. We prove $\mathcal{O}(1/\sqrt{k})$ convergence rate of our method in the objective residual and the feasibility gap. This rate is essentially the same as the state of the art CG-type methods for our problem template, but the proposed method is significantly superior to existing methods in various semidefinite programming applications.
• New characterization and parametrization of LCD codes.
  
  • Mesnager Sihem
  • Carlet C.
  • Tang C.
  • Qi Y.
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2019.
• On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction
  
  • Ben Youssef Atef
  • Varni Giovanna
  • Essid Slim
  • Clavel Chloé
  International Journal of Social Robotics, 2019. In this paper, we address the detection of engagement decrease of users spontaneously interacting with a socially assistive robot in a public space. We first describe the UE-HRI dataset that collects spontaneous Human-Robot Interactions following the guidelines provided by the Affective Computing research community to collect data "in-the-wild". We then analyze the users' behaviors focusing on proxemics, gaze, head motion, facial expressions and speech during interactions with the robot. Engaged behaviors versus signs of engagement decrease exhibited by the users were annotated and analyzed. Finally, we investigate the use of deep leaning techniques (Recurrent and Deep Neural Networks) to detect user engagement decrease in real-time. The results of this work particularly highlight the relevance of taking into account temporal dynamics of the user's behavior. Allowing 1 to 2 seconds as buffer delay improves the performance of taking a decision on user engagement.
• Evaluation of cortical segmentation pipelines on clinical neonatal MRI data
  
  • Tor-Díez Carlos
  • Pham Chi-Hieu
  • Meunier Hélène
  • Faisan Sylvain
  • Bloch Isabelle
  • Bednarek Nathalie
  • Passat Nicolas
  • Rousseau François
  , 2019, pp.6553-6556. Magnetic Resonance Imaging (MRI) can provide 3D morphological information on brain structures. Such information is particularly relevant for carrying out morphometric brain analysis, especially in the newborn and in the case of prematurity. However, 3D neonatal MRI acquired in clinical environments are low-resolution, anisotropic images, making segmentation a challenging task. In this context, preprocessing techniques aim to increase the image resolution. Interpolation techniques were classically used; super-resolution (SR) techniques have recently appeared as an emerging alternative. In this paper, we evaluate the performance of different SR methods against the classical interpolation in the application of neonatal cortex segmentation. Additionally, we assess the robustness of different segmentation methods for each estimation of high resolution MRI input. Results are evaluated both qualitatively and quantitatively with neonatal clinical MRI. (10.1109/EMBC.2019.8856795)
  DOI : 10.1109/EMBC.2019.8856795
• Medical imaging and AI
  
  • Bloch Isabelle
  , 2019.
• Distributed Approximate k-Core Decomposition and Min-Max Edge Orientation: Breaking the Diameter Barrier
  
  • Chan T-H Hubert
  • Sozio Mauro
  • Sun Bintao
  , 2019. We design distributed algorithms to compute approximate solutions for several related graph optimization problems. All our algorithms have round complexity being logarithmic in the number of nodes of the underlying graph and in particular independent of the graph diameter. By using a primal-dual approach, we develop a 2(1 +)-approximation algorithm for computing the coreness values of the nodes in the underlying graph, as well as a 2(1 +)-approximation algorithm for the min-max edge orientation problem, where the goal is to orient the edges so as to minimize the maximum weighted in-degree. We provide lower bounds showing that the aforementioned algorithms are tight both in terms of the approximation guarantee and the round complexity. Finally, motivated by the fact that the densest subset problem has an inherent dependency on the diameter of the graph, we study a weaker version that does not suffer from the same limitation. (10.1109/IPDPS.2019.00044)
  DOI : 10.1109/IPDPS.2019.00044
• On the Optimal Input of the Nondispersive Optical Fiber
  
  • Fahs Jihad
  • Tchamkerten Aslan
  • Yousefi Mansoor
  , 2019.
• The joint weight enumerator of an LCD code and its dual
  
  • Alahmadi Adel
  • Deza Michel
  • Dutour-Sikirić Mathieu
  • Sole Patrick
  Discrete Applied Mathematics, Elsevier, 2019. A binary linear code is called LCD if it intersects its dual trivially. We show that the coefficients of the joint weight enumerator of such a code with its dual satisfy linear constraints, leading to a new linear programming bound on the size of an LCD code of given length and minimum distance. In addition, we show that this polynomial is, in general, an invariant of a matrix group of dimension 4 and order 12. Also, we sketch a Gleason formula for this weight enumerator. (10.1016/j.dam.2018.10.032)
  DOI : 10.1016/j.dam.2018.10.032
• Trace codes over Z 4 , and Boolean functions
  
  • Shi Minjia
  • Liu Yan
  • Randriambololona Hugues
  • Sok Lin
  • Sole Patrick
  Designs, Codes and Cryptography, Springer Verlag, 2019, 87, pp.1447 - 1455. We construct trace codes over Z 4 based on Boolean functions and their support. The Lee weight distribution of these codes is studied by using the Walsh-Hadamard transform of the Boolean functions, and exponential character sums. We obtain few weights codes. In particular, bent and semi-bent functions give three-weight codes. (10.1007/s10623-018-0542-x)
  DOI : 10.1007/s10623-018-0542-x