Publications

2020

• Epitaxial quantum dot lasers on silicon with high thermal stability and strong resistance to optical feedback
  
  • Huang H.
  • Duan J.
  • Dong B.
  • Norman J.
  • Jung D.
  • Bowers J. E
  • Grillot F.
  APL Photonics, AIP Publishing LLC, 2020, 5 (1), pp.016103. (10.1063/1.5120029)
  DOI : 10.1063/1.5120029
• An Indirect Determination of the Polarization Anisotropy in a Quantum Cascade Laser Under Strong Cross-Polarization Feedback
  
  • Spitz O
  • Herdt A
  • Carras M
  • Maisons G
  • Elsässer W
  • Grillot F
  , 2020. This work demonstrates that a non TM-polarized wave can be generated by a quantum cascade laser subjected to strong cross-polarization optical feedback. This finding is used to determine the anisotropy between the two existing polarizations Acknowledgments: this work is supported by the French Defense Agency (DGA), the French ANR program under grant ANR-17-ASMA-0006
• Optical noise of dual-state lasing quantum dot lasers
  
  • Zhou Yueguang
  • Duan Jianan
  • Grillot Frederic
  • Wang Cheng
  IEEE Journal of Quantum Electronics, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. (10.1109/JQE.2020.3026090)
  DOI : 10.1109/JQE.2020.3026090
• Constructions of optimal locally recoverable codes via Dickson polynomials.
  
  • Liu J.
  • Mesnager Sihem
  • Tang D.
  Journal of Designs, Codes, and Cryptography, 2020.
• An Experimental Study of State-of-the-Art Entity Alignment Approaches
  
  • Zhao Xiang
  • Zeng Weixin
  • Tang Jiuyang
  • Wang​ Wei
  • Suchanek Fabian
  IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2020. Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research. (10.1109/TKDE.2020.3018741)
  DOI : 10.1109/TKDE.2020.3018741
• Constructions of self-orthogonal codes from hulls of BCH codes and their parameters
  
  • Du Z.
  • Li C.
  • Mesnager Sihem
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020.
• Recent results and problems on constructions of linear codes from cryptographic functions
  
  • Li N.
  • Mesnager Sihem
  Journal of Cryptography and Communications- Discrete Structures, Boolean Functions, and Sequences, 2020.
• Processing Simple Geometric Attributes with Autoencoders
  
  • Newson Alasdair
  • Almansa Andrés
  • Gousseau Yann
  • Ladjal Saïd
  Journal of Mathematical Imaging and Vision, Springer Verlag, 2020, 62 (3), pp.293-312. Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and Generative Adversarial networks produce spectacular results on highly complex data, such as images of faces or landscapes. While these results open up a wide range of new, advanced synthesis applications, there is also a severe lack of theoretical understanding of how these networks work. This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems. In this paper, we propose to analyse the ability of the simplest generative network, the autoencoder, to encode and decode two simple geometric attributes : size and position. We believe that, in order to understand more complicated tasks, it is necessary to first understand how these networks process simple attributes. For the first property, we analyse the case of images of centred disks with variable radii. We explain how the autoencoder projects these images to and from a latent space of smallest possible dimension, a scalar. In particular, we describe a closed-form solution to the decoding training problem in a network without biases, and show that during training, the network indeed finds this solution. We then investigate the best regularisation approaches which yield networks that generalise well. For the second property, position, we look at the encoding and decoding of Dirac delta functions, also known as `one-hot' vectors. We describe a hand-crafted filter that achieves encoding perfectly, and show that the network naturally finds this filter during training. We also show experimentally that the decoding can be achieved if the dataset is sampled in an appropriate manner. (10.1007/s10851-019-00924-w)
  DOI : 10.1007/s10851-019-00924-w
• A lightweight ECC-based authentication scheme for Internet of Things (IoT)
  
  • Hammi Badis
  • Fayad Achraf
  • Khatoun Rida
  • Zeadally Sherali
  IEEE Systems Journal, IEEE, 2020. (10.1109/JSYST.2020.2970167)
  DOI : 10.1109/JSYST.2020.2970167
• Discrete and stochastic coalitional storage games
  
  • Kiedanski Diego
  • Orda Ariel
  • Kofman Daniel
  , 2020. To achieve a fully decarbonized power grid, a massive deployment of renewable energy resources will be needed, but because of the intermittent nature of their generation, their full potential will not be unleashed unless demand side flexibility plays a bigger role than today. Introducing energy storage at the residential level enables increasing load flexibility, as it allows end-customers to easily change their consumption profile and adapt to the grid requirements. As of today, energy storage for residential consumers represents a considerable investment that is not guaranteed to be profitable. Shared investment models in which a group of consumers jointly acquires energy storage have been proposed in the literature to increase the attractiveness of these devices. Such models naturally employ concepts of cooperative game theory. In this paper, we extend the state-of-the-art cooperative game for modeling the shared investment in storage by adding two crucial extensions: stochasticity of the load and discreetness of the storage device capacity. As our goal is to increase storage capacity in the grid, the number of devices that would be acquired by a group of players that cooperate according to our proposed scheme is compared to the number of devices that would be bought by consumers acting individually. Under the same criteria of customer profitability , simulations using real data reveal that our proposed scheme can increase the deployed storage capacity between 100% and 250%. (10.1145/3396851.3397729)
  DOI : 10.1145/3396851.3397729
• Minimal linear codes from characteristic functions
  
  • Mesnager Sihem
  • Qi Y.
  • Ru H.
  • Tang C.
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020.
• From Interferometric to Tomographic Synthetic Aperture Radar. Scatterer unmixing in urban areas: A review of synthetic aperture radar tomography-processing techniques
  
  • Rambour Clement
  • Budillon Alessandra
  • Johnsy Angel
  • Denis Loïc
  • Tupin Florence
  • Schirinzi Gilda
  IEEE geoscience and remote sensing magazine, IEEE, 2020, 8 (2).
• La transmission « naturelle » des savoirs
  
  • Dessalles Jean-Louis
  , 2020, pp.49-58. Les sociétés de chasseurs-cueilleurs n'ont pas d'écoles. Elles accumulent pourtant des savoirs, elles possèdent des langues et des cultures sophistiquées. Si l'on compare notre espèce aux autres primates, tout est différent. Les cultures animales existent, mais elles sont si restreintes qu'elles sont longtemps passées inaperçues aux yeux des éthologues. Pourquoi existe-t-il tant de « savoirs » dans notre espèce ? Et pourquoi les transmettons-nous ? Si la question semble saugrenue, c'est parce que nous avons perdu de vue le caractère apparemment contre-nature de ce comportement. Un comportement contre-nature Les chimpanzés étudiés par l'éthologue Tetsuro Matsuzawa [1994] dans la forêt de Bossou, en Guinée, cassent spontanément des noix très dures en posant la noix sur une pierre (l'enclume) et en frappant avec une autre pierre (le marteau). Leur expertise demande des années d'imitation des congénères. Les jeunes l'acquièrent en explorant un peu au hasard des centaines de combinaisons de paramètres : choix des pierres, position de l'enclume, face et angle de frappe, etc. Les adultes sont très bienveillants vis-à-vis des jeunes qui les observent, mais on ne les voit jamais leur montrer activement le bon geste et encore moins intervenir lorsqu'ils font des erreurs à répétition. L'espèce humaine offre un contraste saisissant. Prenons l'exemple des forums techniques. Imaginons un étudiant qui apprend le langage informatique Python et cherche à savoir comment inverser une liste. Il tape « python reverse list » sur son moteur de recherche et arrive sur un forum comme Stackoverflow. Il constate que sa question a déjà été posée par un autre débutant et qu'elle a obtenu 29 réponses qui ont elles-mêmes reçu 34 commentaires. Les personnes qui ont pris la peine de répondre ne connaissent pas l'étudiant. Elles font partie d'une communauté, celle des programmeurs Python, au sein de laquelle l'entraide spontanée est considérée comme normale. Paru dans : P. Pion & N. Schlanger (Eds.), Apprendre-Archéologie de la transmission des savoirs, pp. 49-58. Paris: La Découverte, 2020.
• « Je dois y aller ». Analyses de séquences de clôtures entre humains et robot
  
  • Licoppe Christian
  • Rollet Nicolas
  Réseaux : communication, technologie, société, Lavoisier, La Découverte, 2020, N°220-221 (2), pp.151. (10.3917/res.220.0151)
  DOI : 10.3917/res.220.0151
• Complexity of voting procedures
  
  • Hudry Olivier
  , 2020.
• The Compared Costs of Domination, Location-Domination and Identification
  
  • Hudry Olivier
  • Lobstein Antoine
  Discussiones Mathematicae Graph Theory, University of Zielona Góra, 2020, 40 (1), pp.127-147. Let G = (V, E) be a finite graph and r ≥ 1 be an integer. For v ∈ V , let B r (v) = {x ∈ V : d(v, x) ≤ r} be the ball of radius r centered at v. A set C ⊆ V is an r-dominating code if for all v ∈ V , we have B r (v) ∩ C = ∅; it is an r-locating-dominating code if for all v ∈ V , we have B r (v) ∩ C = ∅, and for any two distinct non-codewords x ∈ V \ C, y ∈ V \ C, we have B r (x) ∩ C = B r (y) ∩ C; it is an r-identifying code if for all v ∈ V , we have B r (v) ∩ C = ∅, and for any two distinct vertices x ∈ V , y ∈ V , we have B r (x) ∩ C = B r (y) ∩ C. We denote by γ r (G) (respectively, ld r (G) and id r (G)) the smallest possible cardinality of an r-dominating code (respectively, an r-locating-dominating code and an r-identifying code). We study how small and how large the three differences id r (G)−ld r (G), id r (G)−γ r (G) and ld r (G) − γ r (G) can be. (10.7151/dmgt.2129)
  DOI : 10.7151/dmgt.2129
• phiflow: A Differentiable PDE Solving Framework for Deep Learning via Physical Simulations
  
  • Holl Philipp
  • Koltun Vladlen
  • Um Kiwon
  • Thuerey Nils
  , 2020.
• Real-Time Deformation with Coupled Cages and Skeletons
  
  • Corda F
  • Thiery J M
  • Livesu M
  • Puppo E
  • Boubekeur T
  • Scateni R
  Computer Graphics Forum, Wiley, 2020. Skeleton-based and cage-based deformation techniques represent the two most popular approaches to control real-time deformations of digital shapes and are, to a vast extent, complementary to one another. Despite their complementary roles, high-end modelling packages do not allow for seamless integration of such control structures, thus inducing a considerable burden on the user to maintain them synchronized. In this paper, we propose a framework that seamlessly combines rigging skeletons and deformation cages, granting artists with a real-time deformation system that operates using any smooth combination of the two approaches. By coupling the deformation spaces of cages and skeletons, we access a much larger space, containing poses that are impossible to obtain by acting solely on a skeleton or a cage. Our method is oblivious to the specific techniques used to perform skinning and cage-based deformation, securing it compatible with pre-existing tools. We demonstrate the usefulness of our hybrid approach on a variety of examples. (10.1111/cgf.13900)
  DOI : 10.1111/cgf.13900
• Donsker's theorem in {Wasserstein}-1 distance
  
  • Coutin Laure
  • Decreusefond Laurent
  Electronic Communications in Probability, Institute of Mathematical Statistics (IMS), 2020, 25, pp.1--13. We compute the Wassertein-1 (or Kantorovitch-Rubinstein) distance between a random walk in $R^d$ and the Brownian motion. The proof is based on a new estimate of the Lipschitz modulus of the solution of the Stein's equation. As an application, we can evaluate the rate of convergence towards the local time at 0 of the Brownian motion. (10.1214/20-ECP308)
  DOI : 10.1214/20-ECP308
• Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations
  
  • Pirovano A.
  • Heuberger H.
  • Berlemont S.
  • Ladjal S.
  • Bloch Isabelle
  , 2020, LNCS 12446, pp.43-53.
• 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
• Codebooks from generalized bent Z4-valued quadratic forms
  
  • Qi Y.
  • Mesnager Sihem
  • Tang C.
  Discrete Mathematics, Elsevier, 2020.
• 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.
• Time Series Source Separation with Slow Flows
  
  • Pineau Edouard
  • Razakarivony Sébastien
  • Bonald Thomas
  , 2020. In this paper, we show that slow feature analysis (SFA), a common time series decomposition method, naturally fits into the flow-based models (FBM) framework, a type of invertible neural latent variable models. Building upon recent advances on blind source separation, we show that such a fit makes the time series decomposition identifiable.
• 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.