Publications

2020

• 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
• 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
• Template-Based Graph Clustering
  
  • Riva M.
  • Yger F.
  • Gori P.
  • Cesar R.
  • Bloch Isabelle
  , 2020.
• 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
• 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.
• 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.
• Statistical and Topological Properties of Sliced Probability Divergences
  
  • Nadjahi Kimia
  • Durmus Alain
  • Chizat Lénaïc
  • Kolouri Soheil
  • Shahrampour Shahin
  • Şimşekli Umut
  , 2020. The idea of slicing divergences has been proven to be successful when comparing two probability measures in various machine learning applications including generative modeling, and consists in computing the expected value of a `base divergence' between one-dimensional random projections of the two measures. However, the computational and statistical consequences of such a technique have not yet been well-established. In this paper, we aim at bridging this gap and derive some properties of sliced divergence functions. First, we show that slicing preserves the metric axioms and the weak continuity of the divergence, implying that the sliced divergence will share similar topological properties. We then precise the results in the case where the base divergence belongs to the class of integral probability metrics. On the other hand, we establish that, under mild conditions, the sample complexity of the sliced divergence does not depend on the dimension, even when the base divergence suffers from the curse of dimensionality. We finally apply our general results to the Wasserstein distance and Sinkhorn divergences, and illustrate our theory on both synthetic and real data experiments.
• 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.
• 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.
• Uncovering reflection insensitive semiconductor lasers for silicon photonic integration (invited)
  
  • Grillot Frédéric
  , 2020, pp.M4H.4. (10.1364/OFC.2020.M4H.4)
  DOI : 10.1364/OFC.2020.M4H.4
• 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
• 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
• 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.
• 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
• Motion correction for PET data using subspace-based real-time MR imaging in simultaneous PET/MR
  
  • Marin Thibault
  • Djebra Y.
  • Han P.
  • Chemli Y.
  • Bloch Isabelle
  • El Fakhri G.
  • Ouyang J.
  • Petibon Y.
  • Ma C.
  Physics in Medicine and Biology, IOP Publishing, 2020, 65.
• 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
• 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
• 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
• 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
• On the boomerang uniformity of quadratic permutations
  
  • Mesnager Sihem
  • Tang C.
  • Xiong M.
  Journal of Designs, Codes, and Cryptography, 2020.
• 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
• Degrees-of-Freedom in Multi-Cloud Based Sectored Cellular Networks
  
  • Gelincik Samet
  • Rekaya-Ben Othman Ghaya
  Entropy, MDPI, 2020.
• Space-Time Coding Performance Analysis for CDL-impaired Multi-Core Fiber Transmission
  
  • Abouseif Akram
  • Rekaya-Ben Othman Ghaya
  • Jaouën Yves
  , 2020.
• Combined Vector Perturbation for Adaptive Modulation in MU-MIMO
  
  • Askri Aymen
  • Rekaya-Ben Othman Ghaya
  , 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.