Publications

2020

• A Proof of the Beierle-Kranz-Leander Conjecture related to Lightweight Multiplication in $F_2^n$
  
  • Mesnager Sihem
  • Kim K. H.
  • Jo D.
  • Choe J.
  • Han M.
  • Lee D. N.
  Journal of Designs, Codes, and Cryptography, 2020.
• Low Complexity MIMO Detection for CDL Mitigation in Multi-Core Fiber Transmission
  
  • Abouseif Akram
  • Rekaya-Ben Othman Ghaya
  • Jaouën Yves
  , 2020.
• 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
• 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
• 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
• 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.
• 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.
• 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.
• Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
  
  • Şimşekli Umut
  • Sener Ozan
  • Deligiannidis George
  • Erdogdu Murat A.
  , 2020. Despite its success in a wide range of applications, characterizing the generalization properties of stochastic gradient descent (SGD) in non-convex deep learning problems is still an important challenge. While modeling the trajectories of SGD via stochastic differential equations (SDE) under heavy-tailed gradient noise has recently shed light over several peculiar characteristics of SGD, a rigorous treatment of the generalization properties of such SDEs in a learning theoretical framework is still missing. Aiming to bridge this gap, in this paper, we prove generalization bounds for SGD under the assumption that its trajectories can be well-approximated by a \emph{Feller process}, which defines a rich class of Markov processes that include several recent SDE representations (both Brownian or heavy-tailed) as its special case. We show that the generalization error can be controlled by the \emph{Hausdorff dimension} of the trajectories, which is intimately linked to the tail behavior of the driving process. Our results imply that heavier-tailed processes should achieve better generalization; hence, the tail-index of the process can be used as a notion of "capacity metric". We support our theory with experiments on deep neural networks illustrating that the proposed capacity metric accurately estimates the generalization error, and it does not necessarily grow with the number of parameters unlike the existing capacity metrics in the literature.
• 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
• Stein's method for rough paths
  
  • Coutin Laure
  • Decreusefond Laurent
  Potential Analysis, Springer Verlag, 2020, 53, pp.387--406. The original Donsker theorem says that a standard random walk converges in distribution to a Brownian motion in the space of continuous functions. It has recently been extended to enriched random walks and enriched Brownian motion. We use the Stein-Dirichlet method to precise the rate of this convergence in the topology of fractional Sobolev spaces. (10.1007/s11118-019-09773-z)
  DOI : 10.1007/s11118-019-09773-z
• Discrepancies of Measured SAR between Traditional and Fast Measuring Systems
  
  • Liu Zicheng
  • Allal Djamel
  • Cox Maurice
  • Wiart Joe
  International Journal of Environmental Research and Public Health, MDPI, 2020. Human exposure to mobile devices is traditionally measured by a system in which the human body (or head) is modelled by a phantom and the energy absorbed from the device is estimated based on the electric fields measured with a single probe. Such a system suffers from low efficiency due to repeated volumetric scanning within the phantom needed to capture the absorbed energy throughout the volume. To speed up the measurement, fast SAR (specific absorption rate) measuring systems have been developed. However, discrepancies of measured results are observed between traditional and fast measuring systems. In this paper, the discrepancies in terms of post-processing procedures after the measurement of electric field (or its amplitude) are investigated. Here, the concerned fast measuring system estimates SAR based on the reconstructed field of the region of interest while the amplitude and phase of the electric field are measured on a single plane with a probe array. The numerical results presented indicate that the fast SAR measuring system has the potential to yield more accurate estimations than the traditional system, but no conclusion can be made on which kind of system is superior without knowledge of the field-reconstruction algorithms and the emitting source. (10.3390/ijerph17062111)
  DOI : 10.3390/ijerph17062111
• Stein's method for diffusive limit of queueing processes
  
  • Besançon Eustache
  • Decreusefond Laurent
  • Moyal Pascal
  Queueing Systems, Springer Verlag, 2020, 95, pp.173--201. Donsker Theorem is perhaps the most famous invariance principle result for Markov processes. It states that when properly normalized, a random walk behaves asymptotically like a Brownian motion. This approach can be extended to general Markov processes whose driving parameters are taken to a limit, which can lead to insightful results in contexts like large distributed systems or queueing networks. The purpose of this paper is to assess the rate of convergence in these so-called diffusion approximations, in a queueing context. To this end, we extend the functional Stein method introduced for the Brownian approximation of Poisson processes, to two simple examples: the single-server queue and the infinite-server queue. By doing so, we complete the recent applications of Stein's method to queueing systems, with results concerning the whole trajectory of the considered process, rather than its stationary distribution. (10.1007/s11134-020-09658-8)
  DOI : 10.1007/s11134-020-09658-8
• A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data
  
  • Cheng Xi
  • Henry Clément
  • Andriulli Francesco
  • Person Christian
  • Wiart Joe
  International Journal of Environmental Research and Public Health, MDPI, 2020. This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient's scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed. (10.3390/ijerph17072586)
  DOI : 10.3390/ijerph17072586
• An analysis of the transfer learning of convolutional neural networks for artistic images
  
  • Gonthier Nicolas
  • Gousseau Yann
  • Ladjal Saïd
  , 2020. Transfer learning from huge natural image datasets, fine-tuning of deep neural networks and the use of the corresponding pre-trained networks have become de facto the core of art analysis applications. Nevertheless, the effects of transfer learning are still poorly understood. In this paper, we first use techniques for visualizing the network internal representations in order to provide clues to the understanding of what the network has learned on artistic images. Then, we provide a quantitative analysis of the changes introduced by the learning process thanks to metrics in both the feature and parameter spaces, as well as metrics computed on the set of maximal activation images. These analyses are performed on several variations of the transfer learning procedure. In particular, we observed that the network could specialize some pre-trained filters to the new image modality and also that higher layers tend to concentrate classes. Finally, we have shown that a double fine-tuning involving a medium-size artistic dataset can improve the classification on smaller datasets, even when the task changes. (10.1007/978-3-030-68796-0_39)
  DOI : 10.1007/978-3-030-68796-0_39
• GAME-ON: A Multimodal Dataset for Cohesion and Group Analysis
  
  • Maman Lucien
  • Ceccaldi Eleonora
  • Lehmann-Willenbrock Nale
  • Likforman-Sulem Laurence
  • Chetouani Mohamed
  • Volpe Gualtiero
  • Varni Giovanna
  IEEE Access, IEEE, 2020, 8, pp.124185-124203. (10.1109/ACCESS.2020.3005719)
  DOI : 10.1109/ACCESS.2020.3005719
• The Need to Move beyond Triples
  
  • Suchanek Fabian
  , 2020. Almost all major knowledge bases are concerned mainly with binary relationships between entities. In this vision paper, we argue that it is time to broaden this view: first to relations of higher arity, complex objects, and events, and then also to knowledge about knowledge: We should be able to represent why something is true, that something is not true, that something happened before something else, or that something is mainly believed. While this idea is as old as Artificial Intelligence itself, we argue that only now we have the tools to achieve it: a better understanding of our use-cases and large amounts of data. We survey relevant approaches, and point out avenues of research.
• On the boomerang uniformity of quadratic permutations
  
  • Mesnager Sihem
  • Tang C.
  • Xiong M.
  Journal of Designs, Codes, and Cryptography, 2020.
• Solving $x+x^{2^l}+\cdots+x^{2^{ml}}=a$ over $\mathbb{F}_{2^n}$.
  
  • Mesnager Sihem
  • Kim K.H.
  • Choe J.H.
  • Lee D.N.
  • Go D.S.
  Cryptography and Communications-Discrete Structures, Boolean Functions, and Sequences (CCDS), 2020.
• Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
  
  • Wallach Hanna M.
  • Larochelle Hugo
  • Beygelzimer Alina
  • d'Alché-Buc Florence
  • Fox Emily B.
  • Garnett Roman
  , 2020.
• QFib: Fast and Efficient Brain Tractogram Compression
  
  • Mercier Corentin
  • Rousseau S.
  • Gori P.
  • Bloch Isabelle
  • Boubekeur T.
  Neuroinformatics, Springer, 2020, 18, pp.627-640. Diffusion MRI fiber tracking datasets can contain millions of 3D streamlines, and their representation can weight tens of gigabytes of memory. These sets of streamlines are called tractograms and are often used for clinical operations or research. Their size makes them difficult to store, visualize, process or exchange over the network. We propose a new compression algorithm well-suited for trac-tograms, by taking advantage of the way streamlines are obtained with usual tracking algorithms. Our approach is based on unit vector quantization methods combined with a spatial transformation which results in low compression and decompression times, as well as a high compression ratio. For instance, a 11.5GB tractogram can be compressed to a 1.02GB file and decompressed in 11.3 seconds. Moreover, our method allows for the compression and decompression of individual streamlines, reducing the need for a costly out-of-core algorithm with heavy datasets. Last, we open a way toward on-the-fly compression and decompression for handling larger datasets without needing a load of RAM (i.e. in-core handling), faster network exchanges and faster loading times for visualization or processing.
• Fidelity metrics between curves and surfaces: currents, varifolds, and normal cycles
  
  • Charon Nicolas
  • Charlier Benjamin
  • Glaunès Joan
  • Gori Pietro
  • Roussillon Pierre
  , 2020, pp.441-477. This chapter provides an overview of some mathematical and computational models that have been proposed over the past few years for defining data attachment terms on shape spaces of curves or surfaces. In all these models shapes are seen as elements of a space of generalized distributions, such as currents or varifolds. Then norms are defined through reproducing kernel Hilbert spaces (RKHS), which lead to shape distances that can be conveniently computed in practice. These were originally introduced in conjunction with diffeomorphic methods in computational anatomy and have indeed proved to be very efficient in this field. We provide a basic description of these different models and their practical implementation, then discuss the respective properties and potential advantages or downsides of each of them in diffeomorphic registration problems. (10.1016/B978-0-12-814725-2.00021-2)
  DOI : 10.1016/B978-0-12-814725-2.00021-2
• Matrix Factorization for High Frequency Non Intrusive Load Monitoring
  
  • Henriet Simon
  • Fuentes Benoît
  • Şimşekli Umut
  • Richard Gael
  , 2020, pp.20-24. Non Intrusive Load Monitoring has been introduced 30 years ago in order to monitor the electric consumption of specific equipments inside a building without the need of installing multiples sensors. During three decades, researchers and industrials have described the NILM problems according to the electric data available, the desired quantity to be monitored and the application it was used for. As a consequence of the multitude of choices, a lot of different formulations can be found in the literature. This diversity makes it difficult for researchers from general domains such as machine learning to tackle the NILM problem. In this paper we aim at defining the NILM problem as a Matrix Factorization task using high frequency measurements and also to review methods to solve this problem. We start by defining the general concepts driving the NILM problem and then show how to cast high frequency NILM into a Matrix Factorization problem. Once casted as a machine learning problem, we will review general purposes algorithms applicable to this problem such as Independent Component Analysis, Sparse Coding or Semi Non-negative Matrix Factorization and specific NILM methods such as BOLT and IVMF. (10.1145/3427771.3427847)
  DOI : 10.1145/3427771.3427847
• Introducing coherent MIMO sensing, a fading-resilient, polarization-independent approach to ϕ-OTDR
  
  • Guerrier Sterenn
  • Dorize Christian
  • Awwad Elie
  • Renaudier Jeremie
  Optics Express, Optical Society of America - OSA Publishing, 2020, 28 (14), pp.21081. (10.1364/OE.396460)
  DOI : 10.1364/OE.396460