Publications

2020

• Holarchic structures for decentralized deep learning: a performance analysis
  
  • Pournaras Evangelos
  • Yadhunathan Srivatsan
  • Diaconescu Ada
  Cluster Computing, Springer Verlag, 2020.
• Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data
  
  • Cífka Ondřej
  • Şimşekli Umut
  • Richard Gael
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, 28, pp.2638-2650. Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match the style of a given example. Even though the task has interesting practical applications within the music industry, it has so far received little attention from the audio and music processing community. In this paper, we present Groove2Groove, a one-shot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz. We propose an encoder-decoder neural network for the task, along with a synthetic data generation scheme to supply it with parallel training examples. This synthetic parallel data allows us to tackle the style transfer problem using end-to-end supervised learning, employing powerful techniques used in natural language processing. We experimentally demonstrate the performance of the model on style transfer using existing and newly proposed metrics, and also explore the possibility of style interpolation. (10.1109/TASLP.2020.3019642)
  DOI : 10.1109/TASLP.2020.3019642
• Artifical Intelligence and Pattern Recognition, Vision, Learning
  
  • Bloch Isabelle
  • Clouard Régis
  • Revenu Marinette
  • Sigaud Olivier
  , 2020, III, pp.337-364.
• 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
• Lattice Codes for C-RAN Based Sectored Cellular Networks
  
  • Gelincik Samet
  • Rekaya-Ben Othman Ghaya
  , 2020.
• Precoding in Massive MU-MIMO Systems Based on New CSI Accuracy Indicator Reporting
  
  • Askri Aymen
  • Rekaya-Ben Othman Ghaya
  , 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
• 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
• 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.
• SENSITIVITY ANALYSIS FOR STOCHASTIC SIMULATORS USING DIFFERENTIAL ENTROPY
  
  • Azzi Soumaya
  • Sudret Bruno
  • Wiart Joe
  International Journal for Uncertainty Quantification, Begell House Publishers, 2020, 10 (1), pp.25-33. (10.1615/Int.J.UncertaintyQuantification.2020031610)
  DOI : 10.1615/Int.J.UncertaintyQuantification.2020031610
• 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.
• 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
• 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.
• Advanced Optical Communications and Networking
  
  • Gallion Philippe
  Proceeding of International Conference on Optical & Wireless Technologies , OWT 2019, 2020, Éditeurs : Ghanshyam Singh, Manish Tiwari, Tawfik Ismail, Vijay Janyani.
• New Characterizations for the Multi-output Correlation- Immune Boolean Functions
  
  • Chai J.
  • Mesnager Sihem
  • Wang Z.
  Discrete Mathematics, Elsevier, 2020.
• Analyse de représentations spatiales de la musique par des opérateurs simples de morphologie mathématique
  
  • Lascabettes P.
  • Bloch Isabelle
  • Agon C.
  , 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.
• A new parametrization for the Rician distribution
  
  • Nicolas Jean Marie
  • Tupin Florence
  IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2020. The Rician distribution is widely used in SAR imagery to model the backscattering of a strong target inside a resolution cell. Nevertheless the computation of the parameters of the Rice distribution remains a difficult task. In this paper, a new parametrization to model the Rice distribution is introduced. Thanks to the introduction of a new variable defined by the ratio of the target contribution to the speckle, the relationship between the coefficient of variation and this new parameter can be derived. An efficient numerical method is proposed to evaluate it from the coefficient of variation and a discussion on the variance of this estimator is led. A comparison with other methods of estimation showed that the proposed approach is a good compromise between the variance of the estimate and the computation time. At last, a link between permanent scatterers and Rice distributed targets is proposed through this new parametrization. (10.1109/LGRS.2019.2957240)
  DOI : 10.1109/LGRS.2019.2957240
• Resolution of a Routing and Wavelength Assignment Problem by Independent Sets in Conflict Graphs
  
  • Hudry Olivier
  , 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.
• 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.
• 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
• Query Rewriting On Path Views Without Integrity Constraints
  
  • Romero Julien
  • Preda Nicoleta
  • Suchanek Fabian
  , 2020. A view with a binding pattern is a parameterised query on a database. Such views are used, e.g., to model Web services. To answer a query on such views, one has to orchestrate the views together in execution plans. The goal is usually to nd equivalent rewritings, which deliver precisely the same results as the query on all databases. However, such rewritings are usually possible only in the presence of integrity constraints and not all databases have such constraints. In this paper, we describe a class of plans that give practical guarantees about their result even if there are no integrity constraints. We provide a characterisation of such plans and a complete and correct algorithm to enumerate them. Finally, we show that our method can nd plans on real-world Web Services.