Publications

2021

• Distributed Learning assisted Fronthaul Compression for Multi-Antenna Uplink C-RAN
  
  • Askri Aymen
  • Zhang Chao
  • Rekaya-Ben Othman Ghaya
  IEEE Access, IEEE, 2021.
• River: machine learning for streaming data in Python
  
  • Montiel Jacob
  • Halford Max
  • Mastelini Saulo Martiello
  • Bolmier Geoffrey
  • Sourty Raphaël
  • Vaysse Robin
  • Zouitine Adil
  • Gomes Heitor Murilo
  • Read Jesse
  • Abdessalem Talel
  • Bifet Albert
  Journal of Machine Learning Research, Microtome Publishing, 2021, 22, pp.1-8. River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikitmultiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River’s ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river. (10.48550/arXiv.2012.04740)
  DOI : 10.48550/arXiv.2012.04740
• Power Allocation for Uplink Multiband Satellite Communications with Nonlinear Impairments
  
  • Louchart Arthur
  • Ciblat Philippe
  • Poulliat Charly
  IEEE Communications Letters, Institute of Electrical and Electronics Engineers, 2021, 25 (8), pp.2713-2717. In this letter, we develop some generic power allocation strategies in an uplink multiband satellite communications system when nonlinear impairments on the High-Power Amplifier onboard satellite occur. Based on the capacity closed-form expression related to receivers seeing nonlinear interference as a noise, we propose practical and scalable algorithms for three power allocation problems: i) sum-power minimization, ii) maximization of minimum per-user data rate, iii) sum-rate maximization. We show that the solutions mainly rely on Geometric Programming and/or Successive Convex Approximation approaches. The proposed solutions outperform naive approaches while enabling user scalability contrary to optimal brute-force grid search algorithms. (10.1109/LCOMM.2021.3087408)
  DOI : 10.1109/LCOMM.2021.3087408
• Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement
  
  • Guibon Gaël
  • Labeau Matthieu
  • Flamein Hélène
  • Lefeuvre Luce
  • Clavel Chloé
  , 2021, pp.199-208. Dans cet article nous reproduisons un scénario d’apprentissage selon lequel les données cibles ne sont pas accessibles et seules des données connexes le sont. Nous utilisons une approche par méta-apprentissage afin de déterminer si les méta-informations apprises à partir de messages issus de médias sociaux, finement annotés en émotions, peuvent produire de bonnes performances une fois utilisées sur des messages issus de conversations, étiquetés en émotions avec une granularité différente. Nous mettons à profit l’apprentissage sur quelques exemples (few-shot learning) pour la mise en place de ce scénario. Cette approche se montre efficace pour capturer les méta-informations d’un jeu d’étiquettes émotionnelles pour prédire des étiquettes jusqu’alors inconnues au modèle. Bien que le fait de varier le type de données engendre une baisse de performance, notre approche par méta-apprentissage atteint des résultats décents comparés au référentiel d’apprentissage supervisé.
• Dual Optimization for Kolmogorov Model Learning Using Enhanced Gradient Descent
  
  • Duan Qiyou
  • Ghauch Hadi
  • Kim Taejoon
  IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2021. Data representation techniques have made a substantial contribution to advancing data processing and machine learning (ML). Improving predictive power was the focus of previous representation techniques, which unfortunately perform rather poorly on the interpretability in terms of extracting underlying insights of the data. Recently, Kolmogorov model (KM) was studied, which is an interpretable and predictable representation approach to learning the underlying probabilistic structure of a set of random variables. The existing KM learning algorithms using semi-definite relaxation with randomization (SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility to big data applications because they do not scale well computationally. In this paper, we propose a computationally scalable KM learning algorithm, based on the regularized dual optimization combined with enhanced gradient descent (GD) method. To make our method more scalable to large-dimensional problems, we propose two acceleration schemes, namely, eigenvalue decomposition (EVD) elimination strategy and proximal EVD algorithm. When applied to big data applications, it is demonstrated that the proposed method can achieve compatible training/prediction performance with significantly reduced computational complexity; roughly two orders of magnitude improvement in terms of the time overhead, compared to the existing KM learning algorithms. Furthermore, it is shown that the accuracy of logical relation mining for interpretability by using the proposed KM learning algorithm exceeds 80%.
• Approximate Inference and Learning of State Space Models with Laplace Noise
  
  • Neri Julian
  • Depalle Philippe
  • Badeau Roland
  IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2021, 69, pp.3176 - 3189. State space models have been extensively applied to model and control dynamical systems in disciplines including neuroscience, target tracking, and audio processing. A common modeling assumption is that both the state and data noise are Gaussian because it simplifies the estimation of the system's state and model parameters. However, in many real-world scenarios where the noise is heavy-tailed or includes outliers, this assumption does not hold, and the performance of the model degrades. In this aper, we present a new approximate inference algorithm for state space models with Laplace-distributed multivariate data that is robust to a wide range of non-Gaussian noise. Exact inference is combined with an expectation propagation algorithm, leading to filtering and smoothing that outperforms existing approximate inference methods for Laplace-distributed data, while retaining a fast speed similar to the Kalman filter. Further, we present a maximum posterior expectation-maximization (EM) algorithm that learns the parameters of the model in an unsupervised way, automatically avoids over-fitting the data, and provides better model estimation than existing methods for the Gaussian model. The quality of the inference and learning algorithms are exemplified through a diverse set of experiments and an application to non-linear tracking of audio frequency. (10.1109/tsp.2021.3075146)
  DOI : 10.1109/tsp.2021.3075146
• The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord
  
  • Blair Gordon
  • Bassett Richard
  • Bastin Louis
  • Beevers L.
  • Borrajo Garcia Maribel
  • Brown Mike
  • Dance Sarah L
  • Diaconescu Ada
  • Edwards Elizabeth
  • Ferrario Maria Angela
  • Fraser Robert
  • Harriet Fraser
  Patterns, Cell Press Elsevier, 2021.
• Infinite-dimensional gradient-based descent for alpha-divergence minimisation
  
  • Daudel Kamélia
  • Douc Randal
  • Portier François
  Annals of Statistics, Institute of Mathematical Statistics, 2021, 49 (4), pp.2250 - 2270. This paper introduces the $(\alpha, \Gamma)$-descent, an iterative algorithm which operates on measures and performs $\alpha$-divergence minimisation in a Bayesian framework. This gradient-based procedure extends the commonly-used variational approximation by adding a prior on the variational parameters in the form of a measure. We prove that for a rich family of functions $\Gamma$, this algorithm leads at each step to a systematic decrease in the $\alpha$-divergence and derive convergence results. Our framework recovers the Entropic Mirror Descent algorithm and provides an alternative algorithm that we call the Power Descent. Moreover, in its stochastic formulation, the $(\alpha, \Gamma)$-descent allows to optimise the mixture weights of any given mixture model without any information on the underlying distribution of the variational parameters. This renders our method compatible with many choices of parameters updates and applicable to a wide range of Machine Learning tasks. We demonstrate empirically on both toy and real-world examples the benefit of using the Power descent and going beyond the Entropic Mirror Descent framework, which fails as the dimension grows.
• A Latent Transformer for Disentangled Face Editing in Images and Videos
  
  • Yao Xu
  • Newson Alasdair
  • Gousseau Yann
  • Hellier Pierre
  , 2021, pp.13789-13798.
• Sum-capacity of Uplink Multiband Satellite Communications with Nonlinear Impairments
  
  • Louchart Arthur
  • Ciblat Philippe
  • Poulliat Charly
  , 2021. A compact and closed-form expression of capacity is derived for a uplink multiband satellite system in the presence of nonlinear interference. The nonlinear effect comes from the satellite high-power amplifier modeled by a Volterra series expansion. The derivations reveal that the nonlinear interference can provide a constructive power contribution that could be used to increase the transmission rate. Consequently, decoders designed by viewing this interference as only an additional noise are suboptimal. Numerical results confirm this claim and also shows that an appropriate power allocation amongst the subbands may be of interest.
• Screening Rules and its Complexity for Active Set Identification
  
  • Ndiaye Eugene
  • Fercoq Olivier
  • Salmon Joseph
  Journal of Convex Analysis, Heldermann, 2021, 28 (4), pp.1053--1072. Screening rules were recently introduced as a technique for explicitly identifying active structures such as sparsity, in optimization problem arising in machine learning. This has led to new methods of acceleration based on a substantial dimension reduction. We show that screening rules stem from a combination of natural properties of subdifferential sets and optimality conditions, and can hence be understood in a unified way. Under mild assumptions, we analyze the number of iterations needed to identify the optimal active set for any converging algorithm. We show that it only depends on its convergence rate. (10.48550/arXiv.2009.02709)
  DOI : 10.48550/arXiv.2009.02709
• Resolution of a Routing and Wavelength Assignment Problem by Independent Sets in Conflict Graphs
  
  • Hudry Olivier
  , 2021.
• Sequence-to-Sequence Predictive Model: From Prosody To Communicative Gestures
  
  • Yunus Fajrian
  • Clavel Chloé
  • Pelachaud Catherine
  , 2021. Communicative gestures and speech acoustic are tightly linked. Our objective is to predict the timing of gestures according to the acoustic. That is, we want to predict when a certain gesture occurs. We develop a model based on a recurrent neural network with attention mechanism. The model is trained on a corpus of natural dyadic interaction where the speech acoustic and the gesture phases and types have been annotated. The input of the model is a sequence of speech acoustic and the output is a sequence of gesture classes. The classes we are using for the model output is based on a combination of gesture phases and gesture types. We use a sequence comparison technique to evaluate the model performance. We find that the model can predict better certain gesture classes than others. We also perform ablation studies which reveal that fundamental frequency is a relevant feature for gesture prediction task. In another sub-experiment, we find that including eyebrow movements as acting as beat gesture improves the performance. Besides, we also find that a model trained on the data of one given speaker also works for the other speaker of the same conversation. We also perform a subjective experiment to measure how respondents judge the naturalness, the time consistency, and the semantic consistency of the generated gesture timing of a virtual agent. Our respondents rate the output of our model favorably.
• Adaptation Mechanisms in Human-Agent Interaction: Effects on User's Impressions and Engagement
  
  • Biancardi Beatrice
  • Dermouche Soumia
  • Pelachaud Catherine
  Frontiers in Computer Science, Lausanne: Frontiers Media SA, 2021. Adaptation is a key mechanism in human-human interaction. In our work, we aim at endowing embodied conversational agents with the ability to adapt their behaviour when interacting with a human interlocutor. With the goal to better understand what are the main challenges concerning adaptive agents, we investigated the effects on user's experience of three adaptation models for a virtual agent. The adaptation mechanisms performed by the agent take into account user's reaction and learn how to adapt on the fly during the interaction. Agent's adaptation is realised at several levels (i.e., at behavioural, conversational and signal level) and focuses on improving user's experience along different dimensions (i.e., user's impressions and engagement). In our first two studies, we aim to learn agent's multi-modal behaviours and conversational strategies to optimise dynamically user's engagement and impressions of the agent, by taking them as input during the learning process. In our third study, our model takes as input both the user's and the agent's past behaviour and predicts the agent's next behaviour. Our adaptation models have been evaluated through experimental studies sharing the same interacting scenario, with the agent playing the role of a virtual museum guide. These studies showed an impact of the adaptation mechanisms on user's experience of the interaction and their perception of the agent. Interacting with an adaptive agent vs a non-adaptive agent tended to be more positively perceived. Finally, the effects of people's a-priori about virtual agents found in our studies highlight the importance to take into account user's expectancies in human-agent interaction.
• Linewidth enhancement factor measurement by using phase modulation method for epitaxial quantum dot laser on silicon
  
  • Ding Shihao
  • Dong Bozhang
  • Huang Heming
  • Bowers John E
  • Grillot Frédéric
  , 2021.
• Identification of Rayleigh fading induced phase artifacts in coherent differential ϕ-OTDR
  
  • Dorize Christian
  • Guerrier Sterenn
  • Awwad Elie
  • Renaudier Jérémie
  Optics Letters, Optical Society of America - OSA Publishing, 2021, 46 (11), pp.2754. (10.1364/OL.427944)
  DOI : 10.1364/OL.427944
• Stimulating polarization switching dynamics in mid-infrared quantum cascade lasers
  
  • Spitz Olivier
  • Herdt Andreas
  • Elsässer Wolfgang
  • Grillot Frédéric
  Journal of the Optical Society of America B, Optical Society of America, 2021, 38 (8), pp.B35. (10.1364/JOSAB.425097)
  DOI : 10.1364/JOSAB.425097
• EXPERIMENTAL COMPARISON OF REGISTRATION METHODS FOR MULTISENSOR SAR-OPTICAL DATA
  
  • Pinel-Puysségur Béatrice
  • Maggiolo Luca
  • Roux Michel
  • Gasnier Nicolas
  • Solarna David
  • Moser Gabriele
  • Serpico Sebastiano B
  • Tupin Florence
  , 2021. Synthetic aperture radar (SAR) and optical satellite image registration is a field that developed in the last decades and gave rise to a great number of approaches. The registration process is composed of several steps: feature definition, feature comparison and optimization of a geometric transformation between the images. Feature definition can be done using simple traditional filtering or more complex deep learning (DL) methods. In this paper, two traditional approaches and a DL approach are compared. One can then wonder if the complexity of DL is worth to address the registration task. The aim of this paper is to quantitatively compare approaches rooted in distinct methodological areas on two common datasets with different resolutions. The comparison suggests that, although more complex, the DL approach is more precise than traditional methods.
• Uplink Dimensioning Over Log-Normal Shadowing for OMA and NOMA Schemes
  
  • Liu Bin
  • Martins Philippe
  • Decreusefond Laurent
  • Gomez Jean-Sebastien
  • Song Rongfang
  IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2021, 70 (5), pp.5126--5130. This paper investigates the uplink dimensioning problem for OMA (Orthogonal Multiple Access) and NOMA (Non-Orthogonal Multiple Access) schemes. Dimensioning is to make radio resource provision for a service area to fulfill an outage constraint. The radio resource limit and outage in dimensioning make classical inhomogeneous Poisson assumption of uplink served user point process questionable. In this paper, we first prove that this process admits a homogeneous Poisson distribution in the limiting regime. As a consequence, uplink coverage probabilities over log-normal shadowing for both schemes are derived. Then, tractable stochastic geometry models for two schemes are proposed to obtain numbers of total required radio blocks. Their upper bounds under an outage constraint are also given to reduce computing overhead. Finally, the simulations confirm accuracy of derivations and demonstrate the effectiveness of our models. (10.1109/TVT.2021.3073982)
  DOI : 10.1109/TVT.2021.3073982
• A Hitchhiker's Guide to Ontology
  
  • Suchanek Fabian
  , 2021. A knowledge base (KB) is a computer-processable collection of knowledge about the world. In its simplest variant, a KB takes the form of a labeled graph, where the nodes are entities (such as people, organizations, and geographical locations), and the edges represent the links between these entities in the real world (such as who was born where, which organization is headed by whom, which city is the capital of which country etc.). Knowledge bases provide the background knowledge for different artificial intelligence applications, ranging from personal assistants to Web search, question answering, and text analysis. In particular, KBs are useful in information retrieval (IR), where they serve for structured search and entity disambiguation. Research has made extraordinary progress in the automated construction of KBs, and today's KBs contain billions of entities [1]. Nevertheless, KBs are still far from perfect. In this keynote talk, I outline several challenges in the construction and maintenance of KBs, and show how our research group approached them.
• Modeling Imprecise and Bipolar Algebraic and Topological Relations using Morphological Dilations
  
  • Bloch Isabelle
  Mathematical Morphology - Theory and Applications, De Gruyter, 2021, 5 (1), pp.1-20. (10.1515/mathm-2020-0107)
  DOI : 10.1515/mathm-2020-0107
• Mathematical Morphology and Spatial Reasoning: Fuzzy and Bipolar Setting
  
  • Bloch Isabelle
  TWMS Journal of Pure and Applied Mathematics, TWMS : Turkic World Mathematical Society, 2021, 12 (1), pp.104-125.
• Joint Europa Mission (JEM): A Multiscale, Multi-Platform Mission to Characterize Europa's Habitability and Search for Extant Life. A White Paper prepared for the NAS 2023-2032 Decadal Survey for Planetary Science and Astrobiology August 15th, 2020
  
  • Blanc Michel
  • Prieto-Ballesteros Olga
  • André Nicolas
  • Gomez-Elvira Javier
  • Jones Geraint
  • Sterken Veerle
  • Desprats William
  • Gurvits Leonid I.
  • Khurana Krishan
  • Balmino Georges
  • Blöcker Aljona
  • Broquet Renaud
  • Bunce Emma
  • Cavel Cyril
  • Choblet Gael
  • Colins Geoffrey
  • Coradini Marcello
  • Cooper John
  • Dirkx Dominic
  • Fontaine D.
  • Garnier Philippe
  • Gaudin David
  • Hartogh Paul
  • Hussmann Hauke
  • Genova Antonio
  • Iess Luciano
  • Jäggi Adrian
  • Kempf Sascha
  • Krupp Norbert
  • Lara Luisa
  • Lasue Jérémie
  • Lainey Valéry
  • Leblanc François
  • Lebreton Jean-Pierre
  • Longobardo Andrea
  • Lorenz Ralph
  • Martins Philippe
  • Martins Zita
  • Marty Jean-Charles
  • Masters Adam
  • Mimoun David
  • Palumba Ernesto
  • Parro Victor
  • Regnier Pascal
  • Saur Joachim
  • Schutte Adriaan
  • Sittler Edward C.
  • Spohn Tilman
  • Srama Ralf
  • Stephan Katrin
  • Szegő Károly
  • Tosi Federico
  • Vance Steve
  • Wagner Roland
  • Hoolst Tim Van
  • Volwerk Martin
  • Wahlund Jan-Erik
  • Westall Frances
  • Wurz Peter
  Bulletin of the American Astronomical Society, American Astronomical Society, 2021, 53 (4), pp.e-id. 380. In this White Paper we propose that NASA works with ESA and other potentially interested international partners to design and fly jointly an ambitious and exciting planetary mission to characterize Europa's habitability and search for bio-signatures in the environment of Europa (surface, subsurface and exosphere). A White Paper prepared for the NAS 2023-2032 Decadal Survey for Planetary Science and Astrobiology August 15th, 2020 (10.3847/25c2cfeb.a4c47358)
  DOI : 10.3847/25c2cfeb.a4c47358
• Slicing-Based Offloading in Vehicular Edge Computing
  
  • Berri Sara
  • Hejja Khaled
  • Labiod Houda
  , 2021.
• KM Learning for Millimeter-Wave Beam Alignment and Tracking: Predictability and Interpretability
  
  • Ghauch Hadi
  • Duan Qiyou
  • Kim Taejoon
  IEEE Access, IEEE, 2021.