Publications

2020

• Estimated whole-brain and lobe-specific radiofrequency electromagnetic fields doses and brain volumes in preadolescents
  
  • Cabré-Riera Alba
  • El Marroun Hanan
  • Muetzel Ryan
  • van Wel Luuk
  • Liorni Ilaria
  • Thielens Arno
  • Birks Laura
  • Pierotti Livia
  • Huss Anke
  • Joseph Wout
  • Wiart Joe
  • Capstick Myles
  • Hillegers Manon
  • Vermeulen Roel
  • Cardis Elisabeth
  • Vrijheid Martine
  • White Tonya
  • Röösli Martin
  • Tiemeier Henning
  • Guxens Mònica
  Environment International, Elsevier, 2020. Objective: To assess the association between estimated whole-brain and lobe-specific radiofrequency electromagnetic fields (RF-EMF) doses, using an improved integrated RF-EMF exposure model, and brain volumes in preadolescents at 9-12 years old. Methods: Cross-sectional analysis in preadolescents aged 9-12 years from the Generation R Study, a population-based birth cohort set up in Rotterdam, The Netherlands (n = 2592). An integrated exposure model was used to estimate whole-brain and lobe-specific RF-EMF doses (mJ/kg/day) from different RF-EMF sources including mobile and Digital Enhanced Cordless Telecommunications (DECT) phone calls, other mobile phone uses than calling, tablet use, laptop use, and far-field sources. Whole-brain and lobe-specific RF-EMF doses were estimated for all RF-EMF sources together (i.e. overall) and for three groups of RF-EMF sources that lead to a different pattern of RF-EMF exposure. Information on brain volumes was extracted from magnetic resonance imaging scans. Results: Estimated overall whole-brain RF-EMF dose was 84.3 mJ/kg/day. The highest overall lobe-specific dose was estimated in the temporal lobe (307.1 mJ/kg/day). Whole-brain and lobe-specific RF-EMF doses from all RF-EMF sources together, from mobile and DECT phone calls, and from far-field sources were not associated with global, cortical, or subcortical brain volumes. However, a higher whole-brain RF-EMF dose from mobile phone use for internet browsing, e-mailing, and text messaging, tablet use, and laptop use while wirelessly connected to the internet was associated with a smaller caudate volume. Conclusions: Our results suggest that estimated whole-brain and lobe-specific RF-EMF doses were not related to brain volumes in preadolescents at 9-12 years old. Screen activities with mobile communication devices while https://doi. T wirelessly connected to the internet lead to low RF-EMF dose to the brain and our observed association may thus rather reflect effects of social or individual factors related to these specific uses of mobile communication devices. However, we cannot discard residual confounding, chance finding, or reverse causality. Further studies on mobile communication devices and their potential negative associations with brain development are warranted, regardless whether associations are due to RF-EMF exposure or to other factors related to their use. (10.1016/j.envint.2020.105808)
  DOI : 10.1016/j.envint.2020.105808
• Spectral Mesh Simplification
  
  • Lescoat Thibault
  • Liu Hsueh-Ti Derek -
  • Thiery Jean-Marc
  • Jacobson Alec
  • Boubekeur Tamy
  • Ovsjanikov Maks
  Computer Graphics Forum, Wiley, 2020. The spectrum of the Laplace-Beltrami operator is instrumental for a number of geometric modeling applications, from processing to analysis. Recently, multiple methods were developed to retrieve an approximation of a shape that preserves its eigenvectors as much as possible, but these techniques output a subset of input points with no connectivity, which limits their potential applications. Furthermore, the obtained Laplacian results from an optimization procedure, implying its storage alongside the selected points. Focusing on keeping a mesh instead of an operator would allow to retrieve the latter using the standard cotangent formulation, enabling easier processing afterwards. Instead, we propose to simplify the input mesh using a spectrum-preserving mesh decimation scheme, so that the Laplacian computed on the simplified mesh is spectrally close to the one of the input mesh. We illustrate the benefit of our approach for quickly approximating spectral distances and functional maps on low resolution proxies of potentially high resolution input meshes.
• Private free-space communications based on chaos synchronization of mid-infrared quantum cascade laser light
  
  • Spitz O
  • Herdt A
  • Wu J
  • Wong C.-W
  • Elsässer W
  • Grillot F
  , 2020. Free Space Optics (FSO) is a growing technology offering higher bandwidth with fast and cost-effective deployment compared to fiber technology. This work demonstrates private free-space communication with quantum cascade lasers (QCLs). The secret message is encoded into a chaotic waveform so that the information is hard for an eavesdropper to extract [1]. Chaos-based transmissions in FSO are fundamentally restricted by atmospheric phenomena (e.g., turbulence, fog or scattering). Thus, the operating wavelength is a key parameter that has to be chosen wisely to reduce the impact of the environmental parameters. In this context, QCLs are relevant semiconductor lasers because their optical wavelength lies within mid-infrared domains where the atmosphere is highly transparent [2]. The simplest way to generate a chaotic optical carrier from a QCL is to feed back part of its emitted light into the device after a certain time delay [3], beyond which chaos synchronization between the drive and the response QCLs occurs.
• La théorie de l'information
  
  • Rioul Olivier
  , 2020, pp.7.
• Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
  
  • Pilzer Andrea
  • Lathuilière Stéphane
  • Xu Dan
  • Puscas Mihai Marian
  • Ricci Elisa
  • Sebe Nicu
  IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2020, 42 (10), pp.2380-2395. Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps. We introduce a new network architecture, named Progressive Fusion Network (PFN), that is specifically designed for binocular stereo depth estimation. This network is based on a multi-scale refinement strategy that combines the information provided by both stereo views. In addition, we propose to stack twice this network in order to form a cycle. This cycle approach can be interpreted as a form of data-augmentation since, at training time, the network learns both from the training set images (in the forward half-cycle) but also from the synthesized images (in the backward half-cycle). The architecture is jointly trained with adversarial learning. Extensive experiments on the publicly available datasets KITTI, Cityscapes and ApolloScape demonstrate the effectiveness of the proposed model which is competitive with other unsupervised deep learning methods for depth prediction. (10.1109/TPAMI.2019.2942928)
  DOI : 10.1109/TPAMI.2019.2942928
• On linguistic descriptions of image content
  
  • Bloch Isabelle
  , 2020, pp.57-64.
• On generalized hyper-bent functions
  
  • Mesnager Sihem
  Cryptography and Communications-Discrete Structures, Boolean Functions, and Sequences (CCDS), 2020.
• DNN Based Beam Selection in mmW Heterogeneous Networks
  
  • Jagyasi Deepa
  • Coupechoux Marceau
  , 2020. We consider a heterogeneous cellular network wherein multiple small cell millimeter wave (mmW) base stations (BSs) coexist with legacy sub-6GHz macro BSs. In the mmW band, small cells use multiple narrow beams to ensure sufficient coverage and User Equipments (UEs) have to select the best small cell and the best beam in order to access the network. This process usually based on exhaustive search may introduce unacceptable latency. In order to address this issue, we rely on the sub-6GHz macro BS support and propose a deep neural network (DNN) architecture that utilizes basic components from the Channel State Information (CSI) of sub-6GHz network as input features. The output of the DNN is the mmW BS and beam selection that can provide the best communication performance. In the set of features, we avoid using the UE location, which may not be readily available for every device. We formulate a mmW BS selection and beam selection problem as a classification and regression problem respectively and propose a joint solution using a branched neural network. The numerical comparison with the conventional exhaustive search results shows that the proposed design demonstrate better performance than exhaustive search in terms of la-tency with at least 85% accuracy.
• Codebooks from generalized bent Z4-valued quadratic forms
  
  • Qi Y.
  • Mesnager Sihem
  • Tang C.
  Discrete Mathematics, Elsevier, 2020.
• Tracking hundreds of people in densely crowded scenes with particle filtering supervising deep convolutional neural networks
  
  • Franchi G.
  • Aldea Emanuel
  • Dubuisson Séverine
  • Bloch Isabelle
  , 2020. (10.1109/ICIP40778.2020.9190953)
  DOI : 10.1109/ICIP40778.2020.9190953
• DYNAMIC-TDD INTERFERENCE TRACTABILITY APPROACHES AND PERFORMANCE ANALYSIS IN MACRO-CELL AND SMALL-CELL DEPLOYMENTS
  
  • Rachad J
  • Nasri R.
  • Decreusefond Laurent
  Annals of Telecommunications - annales des télécommunications, Springer, 2020. Meeting the continued growth in data traffic volume, Dynamic Time Division Duplex (D-TDD) has been introduced as a solution to deal with the uplink (UL) and downlink (DL) traffic asymmetry, mainly observed for dense heterogeneous network deployments, since it is based on instantaneous traffic estimation and provide more flexibility in resource assignment. However, the use of this feature requires new interference mitigation schemes capable to handle two additional types of interference between cells in opposite transmission direction: DL to UL and UL to DL interference. The aim of this work is to provide a complete analytical approach to model inter-cell interference in macro-cell and dense small-cell networks. We derive the explicit expressions of Interference to Signal Ratio (ISR) at each position of the network , in both DL and UL, to quantify the impact of each type of interference on the system performance. Also, we provide the explicit expressions of the coverage probability as functions of different system parameters by covering different scenarios. Finally, through system level simulations, we analyze the feasibility of D-TDD implementation in both deployments and we compare its performance to the static-TDD (S-TDD) configuration. (10.1007/s12243-020-00781-4)
  DOI : 10.1007/s12243-020-00781-4
• New characterizations and construction methods of bent and hyper-bent Boolean functions
  
  • Mesnager Sihem
  • Mandal B.
  • Tang C.
  Discrete Mathematics, Elsevier, 2020.
• High resolution face age editing
  
  • Yao Xu
  • Newson Alasdair
  • Puy Gilles
  • Gousseau Yann
  • Hellier Pierre
  , 2020.
• On the number of the rational zeros of linearized polynomials and the second-order nonlinearity of cubic Boolean functions.
  
  • Mesnager Sihem
  • Kim K.H.
  • Jo. M.S.
  Cryptography and Communications- Discrete Structures, Boolean Functions, and Sequences (CCDS),, 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.
• 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
• 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.
• 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
• Computing and Illustrating Query Rewritings on Path Views with Binding Patterns
  
  • Romero Julien
  • Preda Nicoleta
  • Amarilli Antoine
  • Suchanek Fabian
  , 2020. In this system demonstration, we study views with binding patterns, which are a formalization of REST Web services. Such views are database queries that can be evaluated using the service, but only if values for the input variables are provided. We investigate how to use such views to answer a complex user query, by rewriting it as an execution plan, i.e., an orchestration of calls to the views. In general, it is undecidable to determine whether a given user query can be answered with the available views. In this demo, we illustrate a particular scenario studied in our earlier work [11], where the problem is not only decidable, but has a particularly intuitive graphical solution. Our demo allows users to play with views defined by real Web services, and to animate the construction of execution plans visually. (10.1145/3340531.3417431)
  DOI : 10.1145/3340531.3417431
• 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
• Template-Based Graph Clustering
  
  • Riva M.
  • Yger F.
  • Gori P.
  • Cesar R.
  • Bloch Isabelle
  , 2020.
• 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
• 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.
• 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.