Publications

2020

• Motion Correction for brain PET using a Real Time Motion Capture System
  
  • Chemli Y.
  • Tétrault M.-A.
  • Marin T.
  • Toussaint M.
  • Bloch Isabelle
  • El Fakhri G.
  • Normandin M.
  • Ouyang J.
  • Petibon Y.
  , 2020.
• MR based PET motion correction for irregular respiratory motion
  
  • Djebra Y.
  • Marin T.
  • Han P.
  • Chemli Y.
  • Bloch Isabelle
  • El Fakhri G.
  • Ouyang J.
  • Petibon Y.
  • Ma C.
  , 2020.
• KClist++: A Simple Algorithm for Finding k-Clique Densest Subgraphs in Large Graphs
  
  • Sun Bintao
  • Danisch Maximilien
  • Chan T-H Hubert
  • Sozio Mauro
  Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2020. The problem of finding densest subgraphs has received increasing attention in recent years finding applications in biology , finance, as well as social network analysis. The k-clique densest subgraph problem is a generalization of the densest subgraph problem, where the objective is to find a subgraph maximizing the ratio between the number of k-cliques in the subgraph and its number of nodes. It includes as a special case the problem of finding subgraphs with largest average number of triangles (k = 3), which plays an important role in social network analysis. Moreover, algorithms that deal with larger values of k can effectively find quasi-cliques. The densest subgraph problem can be solved in polynomial time with algorithms based on maximum flow, linear programming or a recent approach based on convex optimization. In particular, the latter approach can scale to graphs containing tens of billions of edges. While finding a densest subgraph in large graphs is no longer a bottleneck , the k-clique densest subgraph remains challenging even when k = 3. Our work aims at developing near-optimal and exact algorithms for the k-clique densest subgraph problem on large real-world graphs. We give a surprisingly simple procedure that can be employed to find the maximal k-clique densest subgraph in large-real world graphs. By leveraging appealing properties of existing results, we combine it with a recent approach for listing all k-cliques in a graph and a sampling scheme, obtaining the state-of-the-art approaches for the aforementioned problem. Our theoretical results are complemented with an extensive experimental evaluation showing the effectiveness of our approach in large real-world graphs.
• New Characterizations for the Multi-output Correlation- Immune Boolean Functions
  
  • Chai J.
  • Mesnager Sihem
  • Wang Z.
  Discrete Mathematics, Elsevier, 2020.
• Ultra-flat supercontinuum from 1.95 to 2.65 µm in a nanosecond pulsed Thulium-doped fiber laser
  
  • Romano Clément
  • Jaouën Yves
  • Tench Robert E
  • Delavaux Jean-Marc
  Optical Fiber Technology, Elsevier, 2020, 54, pp.102113. (10.1016/j.yofte.2019.102113)
  DOI : 10.1016/j.yofte.2019.102113
• The POTUS Corpus, a database of weekly addresses for the study of stance in politics and virtual agents
  
  • Janssoone Thomas
  • Bailly Kevin
  • Richard Gael
  • Clavel Chloé
  , 2020, pp.11 - 16. One of the main challenges in the field of Embodied Conversational Agent (ECA) is to generate socially believable agents. The common strategy for agent behaviour synthesis is to rely on dedicated corpus analysis. Such a corpus is composed of multimedia files of socio-emotional behaviors which have been annotated by external observers. The underlying idea is to identify interaction information for the agent's socio-emotional behavior by checking whether the intended socio-emotional behavior is actually perceived by humans. Then, the annotations can be used as learning classes for machine learning algorithms applied to the social signals. This paper introduces the POTUS Corpus composed of high-quality audio-video files of political addresses to the American people. Two protagonists are present in this database. First, it includes speeches of former president Barack Obama to the American people. Secondly, it provides videos of these same speeches given by a virtual agent named Rodrigue. The ECA reproduces the original address as closely as possible using social signals automatically extracted from the original one. Both are annotated for social attitudes, providing information about the stance observed in each file. It also provides the social signals automatically extracted from Obama's addresses used to generate Rodrigue's ones.
• A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments
  
  • Catanese Clara
  • Ayassi Reda
  • Pincemin Erwan
  • Jaouën Yves
  , 2020, pp.SpTh3I.1. (10.1364/SPPCOM.2020.SpTh3I.1)
  DOI : 10.1364/SPPCOM.2020.SpTh3I.1
• Analyse de représentations spatiales de la musique par des opérateurs simples de morphologie mathématique
  
  • Lascabettes P.
  • Bloch Isabelle
  • Agon C.
  , 2020.
• Solving $x^{2^k+1}+x+a=0$ in $\mathbb{F}_{2^n}$ with $\gcd(n,k)=1$
  
  • Kim K. H.
  • Mesnager Sihem
  Finite Fields and Their Applications, Elsevier, 2020.
• Several classes of minimal linear codes with few weights from weakly regular plateaued function
  
  • Mesnager Sihem
  • Sinak A.
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020.
• Popularity-Based Full Replica Caching For Erasure-Coded Distributed Storage Systems
  
  • Ruty Guillaume
  • Baccouch Hana
  • Nguyen Victor
  • Surcouf André
  • Rougier Jean Louis
  • Boukhatem Nadia
  Cluster Computing, Springer Verlag, 2020.
• Explicit Regularisation in Gaussian Noise Injections
  
  • Camuto Alexander
  • Willetts Matthew
  • Şimşekli Umut
  • Roberts Stephen
  • Holmes Chris
  , 2020. We study the regularisation induced in neural networks by Gaussian noise injections (GNIs). Though such injections have been extensively studied when applied to data, there have been few studies on understanding the regularising effect they induce when applied to network activations. Here we derive the explicit regulariser of GNIs, obtained by marginalising out the injected noise, and show that it penalises functions with high-frequency components in the Fourier domain; particularly in layers closer to a neural network's output. We show analytically and empirically that such regularisation produces calibrated classifiers with large classification margins.
• Magnetic Tunnel Junction Applications
  
  • Maciel Nilson
  • Marques Elaine
  • Naviner Lirida
  • Zhou Yongliang
  • Cai Hao
  Sensors, MDPI, 2020, 20 (1), pp.121. Spin-based devices can reduce energy leakage and thus increase energy efficiency. They have been seen as an approach to overcoming the constraints of CMOS downscaling, specifically, the Magnetic Tunnel Junction (MTJ) which has been the focus of much research in recent years. Its nonvolatility, scalability and low power consumption are highly attractive when applied in several components. This paper aims at providing a survey of a selection of MTJ applications such as memory and analog to digital converter, among others. (10.3390/s20010121)
  DOI : 10.3390/s20010121
• Towards Phase Balancing using Energy Storage
  
  • Hashmi Md Umar
  • Horta José
  • Pereira Lucas
  • Lee Zachary
  • Bušić Ana
  • Kofman Daniel
  , 2020. Ad-hoc growth of single-phase-connected distributed energy resources, such as solar generation and electric vehicles, can lead to network unbalance with negative consequences on the quality and efficiency of electricity supply. Case-studies are presented for a substation in Madeira, Portugal and an EV charging facility in Pasadena, California. These case studies show that phase imbalance can happen due to a large amount of distributed generation (DG) and electric vehicle (EV) integration. We conducted stylized load-flow analysis on a radial distribution network using an openDSS-based simulator to understand such negative effects of phase imbalance on neutral and phase conductor losses, and in voltage drop/rise. We evaluate the integration of storage in the distribution network as a possible solution for mitigating effects caused by imbalance. We present control architectures of storage operation for phase balancing. Numerically we show that relatively small-sized storage (compared to unbalance magnitude) can significantly reduce network imbalance. We identify the end node of the feeder as the best location to install storage. (10.48550/arXiv.2002.04177)
  DOI : 10.48550/arXiv.2002.04177
• A Fundamental Storage-Communication Tradeoff for Distributed Computing with Straggling Nodes
  
  • Yan Qifa
  • Wigger Michèle
  • Yang Sheng
  • Tang Xiaohu
  IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2020. Placement delivery arrays for distributed computing (Comp-PDAs) have recently been proposed as a framework to construct universal computing schemes for MapReduce-like systems. In this work, we extend this concept to systems with straggling nodes, i.e., to systems where a subset of the nodes cannot accomplish the assigned map computations in due time. Unlike most previous works that focused on computing linear functions, our results are universal and apply for arbitrary map and reduce functions. Our contributions are as follows. Firstly, we show how to construct a universal coded computing scheme for MapReduce-like systems with straggling nodes from any given Comp-PDA. We also characterize the storage and communication loads of the resulting scheme in terms of the Comp-PDA parameters. Then, we prove an information-theoretic converse bound on the storage-communication (SC) tradeoff achieved by universal computing schemes with straggling nodes. We show that the information-theoretic bound matches the performance achieved by the coded computing schemes with straggling nodes corresponding to the Maddah-Ali and Niesen (MAN) PDAs, i.e., to the Comp-PDAs describing Maddah-Ali and Niesen's coded caching scheme. Interestingly, the MAN-PDAs are optimal for any number of straggling nodes. This implies that the map phase of optimal coded computing schemes does not need to be adapted to the number of stragglers in the system. We show that the points that lie exactly on the fundamental SC tradeoff cannot be achieved with Comp-PDAs that require smaller number of files than the MAN-PDAs. This is however possible for some of the points that lie close to the SC tradeoff. For these latter points, the decrease in the requested number of files can be exponential in the number of nodes of the system. We also model the total execution time, and numerically show that the active set size should be chosen to balance the duration of the map phase and the durations of the shuffle and reduce phases. (10.1109/TCOMM.2020.3020549)
  DOI : 10.1109/TCOMM.2020.3020549
• Estimation of the Ricean K Factor in the presence of shadowing
  
  • Leturc Xavier
  • Ciblat Philippe
  • Le Martret Christophe J.
  IEEE Communications Letters, Institute of Electrical and Electronics Engineers, 2020, 24 (1), pp.108-112. We address the estimation of the Ricean K factor when the available complex channel samples are noisy and subject to Nakagami-m shadowing, i.e., the line-of-sight component is modeled as a Nakagami-m random variable. We propose two estimators: one based on the expectation-maximization (EM) procedure, and a second one based on the method of moment (MoM). The MoM estimator can be used to initialize the EM procedure. We show by simulations that the two proposed estimators outperform the existing ones. (10.1109/LCOMM.2019.2950027)
  DOI : 10.1109/LCOMM.2019.2950027
• Multi-Layer HARQ with Delayed Feedback
  
  • Khreis Alaa
  • Bassi Francesca
  • Ciblat Philippe
  • Duhamel Pierre
  IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers, 2020. In order to improve the transmission reliability in current wireless communication systems, the Hybrid Automatic ReQuest (HARQ) protocol is employed to manage the unknown time-varying channel. The acknowledgments are fed back with delay on the return link. To fill up the idle time between a transmission and its acknowledgment, parallel HARQ streams associated with different messages are carried out. In this paper we improve on parallel HARQ by proposing a multi-layer HARQ protocol (also called superposition coding or multi-packet HARQ), where a single transmission may carry information on multiple messages. The multi-layer HARQ protocol works in presence of delay on the return link as parallel HARQ does, and does not require additional feedback such as the channel state information. It aims at improving the accuracy as well as the user's delay distribution, thus achieving throughput increase. Assuming capacity-achieving codes, we show that the proposed protocol outperforms parallel HARQ in throughput, message error rate, and delay distribution. Using practical codes and decoding algorithms the gains are as well significant, at the expense of the receiver's complexity. (10.1109/TWC.2020.3001420)
  DOI : 10.1109/TWC.2020.3001420
• Low Complexity MIMO Detection for CDL Mitigation in Multi-Core Fiber Transmission
  
  • Abouseif Akram
  • Rekaya-Ben Othman Ghaya
  • Jaouën Yves
  , 2020.
• SPECTRAL EMBEDDING OF REGULARIZED BLOCK MODELS
  
  • de Lara Nathan
  • Bonald Thomas
  , 2020. Spectral embedding is a popular technique for the representation of graph data. Several regularization techniques have been proposed to improve the quality of the embedding with respect to downstream tasks like clustering. In this paper, we explain on a simple block model the impact of the complete graph regularization, whereby a constant is added to all entries of the adjacency matrix. Specifically, we show that the regularization forces the spectral embedding to focus on the largest blocks, making the representation less sensitive to noise or outliers. We illustrate these results on both on both synthetic and real data, showing how regularization improves standard clustering scores.
• Monadic Datalog, Tree Validity, and Limited Access Containment
  
  • Benedikt Michael
  • Bourhis Pierre
  • Gottlob Georg
  • Senellart Pierre
  ACM Transactions on Computational Logic, Association for Computing Machinery, 2020, 21 (1), pp.6:1-6:45. We reconsider the problem of containment of monadic datalog (MDL) queries in unions of conjunctive queries (UCQs). Prior work has dealt with special cases of the problem, but has left the precise complexity characterization open. In addition, the complexity of one important special case, that of containment under access patterns, was not known before. We start by revisiting the connection between MDL/UCQ containment and containment problems involving regular tree languages. We then present a general approach for getting tighter bounds on the complexity of query containment, based on analysis of the number of mappings of queries into tree-like instances. We give two applications of the machinery. We first give an important special case of the MDL/UCQ containment problem that is in EXPTIME, and use this bound to show an EXPTIME bound on containment under access patterns. Secondly we show that the same technique can be used to get a new tight upper bound for containment of tree automata in UCQs. We finally show that the new MDL/UCQ upper bounds are tight. We establish a 2EXPTIME lower bound on the MDL/UCQ containment problem, resolving an open problem from the early 1990s. This bound holds for the MDL/CQ containment problem as well. We also show that changes to the conditions given in our special cases can not be eliminated, and that in particular slight variations of the problem of containment under access patterns become 2EXPTIME-complete. (10.1145/3344514)
  DOI : 10.1145/3344514
• Scikit-network: Graph Analysis in Python
  
  • Bonald Thomas
  • de Lara Nathan
  • Lutz Quentin
  • Charpentier Bertrand
  Journal of Machine Learning Research, Microtome Publishing, 2020. Scikit-network is a Python package inspired by scikit-learn for the analysis of large graphs. Graphs are represented by their adjacency matrix in the sparse CSR format of SciPy. The package provides state-of-the-art algorithms for ranking, clustering, classifying, embedding and visualizing the nodes of a graph. High performance is achieved through a mix of fast matrix-vector products (using SciPy), compiled code (using Cython) and parallel processing. The package is distributed under the BSD license, with dependencies limited to NumPy and SciPy. It is compatible with Python 3.6 and newer. Source code, documentation and installation instructions are available online.
• A Model-Based Combination Language for Scheduling Verification
  
  • Zhao Hui
  • Apvrille Ludovic
  • Mallet Frédéric
  , 2020. Cyber-Physical Systems (CPSs) are built upon discrete software and hardware components, as well as continuous physical components. Such heterogeneous systems involve numerous domains with competencies and expertise that go far beyond traditional software engineering: systems engineering. In this paper , we explore a model-based approach for systems engineering that advocates the composition of several heterogeneous artifacts (called views) into a sound and consistent system model. A model combination Language is proposed for this purpose. Thus, rather than trying to build the universal language able to capture all possible aspects of systems, the proposed language proposes to relate small subsets of languages in order to offer specific analysis capabilities while keeping a global consistency between all joined models. We demonstrate the interest of our approach through an industrial process based on Capella, which provides (among others) a large support for functional analysis from requirements to components deployment. Even though Capella is already quite expressive, it lacks support for schedulability analysis. AADL is also a language dedicated to system analysis. If it is backed with advanced schedulability tools, it lacks support for functional analysis. Thus, instead of proposing ways to add missing aspects in either Capella or AADL, we rather extract a relevant subset of both languages to build a view adequate for conducting schedulability analysis of Capella functional models. Finally, our combination language is generic enough to extract pertinent subsets of languages and combine them to build views for different experts. It also helps maintaining a global consistency between different modeling views.
• Multimodal Analysis of Cohesion in Multi-party Interactions
  
  • B Kantharaju Reshmashree
  • Langlet Caroline
  • Barange Mukesh
  • Clavel Chloé
  • Pelachaud Catherine I
  , 2020. Group cohesion is an emergent phenomenon that describes the tendency of the group members' shared commitment to group tasks and the interpersonal attraction among them. This paper presents a multimodal analysis of group cohesion using a corpus of multi-party interactions. 16 two-minute segments annotated with cohesion data is used. We define three layers of modalities: non-verbal social cues, dialogue acts and interruptions. The initial analysis is performed at the individual level and later, we combine the different modalities to observe their impact on perceived level of cohesion. Results indicate that occurrence of laughter and interruption are higher in high cohesive segments. We also observed that, dialogue acts and head nods did not have an impact on the level of cohesion by itself. However, when combined there was an impact on the perceived level of cohesion. Overall, the analysis shows that multimodal cues are crucial for accurate analysis of group cohesion.
• Uniform convergence rates for the approximated halfspace and projection depth
  
  • Nagy Stanislav
  • Dyckerhoff Rainer
  • Mozharovskyi Pavlo
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2020, 14 (2). (10.1214/20-EJS1759)
  DOI : 10.1214/20-EJS1759
• Quantitative Propagation of Chaos for SGD in Wide Neural Networks
  
  • de Bortoli Valentin
  • Durmus Alain
  • Fontaine Xavier
  • Şimşekli Umut
  , 2020. In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Stochastic Gradient Descent (SGD) algorithm applied to two-layer overparameterized neural networks, as the number or neurons (ie, the size of the hidden layer) $N \to +\infty$. Following a probabilistic approach, we show 'propagation of chaos' for the particle system defined by this continuous-time dynamics under different scenarios, indicating that the statistical interaction between the particles asymptotically vanishes. In particular, we establish quantitative convergence with respect to $N$ of any particle to a solution of a mean-field McKean-Vlasov equation in the metric space endowed with the Wasserstein distance. In comparison to previous works on the subject, we consider settings in which the sequence of stepsizes in SGD can potentially depend on the number of neurons and the iterations. We then identify two regimes under which different mean-field limits are obtained, one of them corresponding to an implicitly regularized version of the minimization problem at hand. We perform various experiments on real datasets to validate our theoretical results, assessing the existence of these two regimes on classification problems and illustrating our convergence results.