Publications

2021

• 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
• 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.
• 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.
• 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.
• 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.
• 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
• 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.
• 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
• Exogenous coordination in multi-scale systems: How information flows and timing affect system properties
  
  • Diaconescu Ada
  • Di Felice Louisa Jane
  • Mellodge Patricia
  Future Generation Computer Systems, Elsevier, 2021.
• Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems
  
  • Tohidi Ehsan
  • Gesbert David
  • Ciblat Philippe
  , 2021. We address the problem of resource allocation (RA) in a cognitive radio (CR) communication system with multiple secondary operators sharing spectrum with an incumbent primary operator. The key challenge of the RA problem is the inter-operator coordination arising in the optimization problem so that the aggregated interference at the primary users (PUs) does not exceed the target threshold. While this problem is easily solvable if a centralized unit could access information of all secondary operators, it becomes challenging in a realistic scenario. In this paper, considering a satellite setting, we alleviate this problem by proposing two approaches to reduce the information exchange level among the secondary operators. In the first approach, we formulate an RA scheme based on a partial information sharing method which enables distributed optimization across secondary operators. In the second approach, instead of exchanging secondary users (SUs) information, the operators only exchange their contributions of the interference-level and RA is performed locally across secondary operators. These two approaches, for the first time in this context, provide a trade-off between performance and level of inter-operator information exchange. Through the numerical simulations, we explain this trade-off and illustrate the penalty resulting from partial information exchange.
• The Deep Learning Revolution in MIR: The Pros and Cons, the Needs and the Challenges 2021
  
  • Peeters Geoffroy
  , 2021.
• Convergence and Dynamical Behavior of the Adam Algorithm for Non Convex Stochastic Optimization
  
  • Barakat Anas
  • Bianchi Pascal
  SIAM Journal on Optimization, Society for Industrial and Applied Mathematics, 2021. Adam is a popular variant of the stochastic gradient descent for finding a local minimizer of a function. The objective function is unknown but a random estimate of the current gradient vector is observed at each round of the algorithm. Assuming that the objective function is differentiable and non-convex, we establish the convergence in the long run of the iterates to a stationary point. The key ingredient is the introduction of a continuous-time version of Adam, under the form of a non-autonomous ordinary differential equation. The existence and the uniqueness of the solution are established, as well as the convergence of the solution towards the stationary points of the objective function. The continuous-time system is a relevant approximation of the Adam iterates, in the sense that the interpolated Adam process converges weakly to the solution to the ODE.
• Anomalies Detection Using Isolation in Concept-Drifting Data Streams
  
  • Togbe Maurras Ulbricht
  • Chabchoub Yousra
  • Boly Aliou
  • Barry Mariam
  • Chiky Raja
  • Bahri Maroua
  Computers, MDPI, 2021, 10 (1), pp.13. Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency. (10.3390/computers10010013)
  DOI : 10.3390/computers10010013
• SAR2SAR: a semi-supervised despeckling algorithm for SAR images
  
  • Dalsasso Emanuele
  • Denis Loïc
  • Tupin Florence
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2021, pp.1-1. Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. Multi-temporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field. (10.1109/JSTARS.2021.3071864)
  DOI : 10.1109/JSTARS.2021.3071864
• An ontology for multi-paradigm modelling
  
  • Giese Holger
  • Blouin Dominique
  • Al-Ali Rima
  • Mkaouar Hana
  • Bandyopadhyay Soumyadip
  • Iacono Mauro
  • Amrani Moussa
  • Klikovits Stefan
  • Erata Ferhat
  , 2021, pp.67-122. (10.1016/B978-0-12-819105-7.00009-X)
  DOI : 10.1016/B978-0-12-819105-7.00009-X
• MUSIC GENRE DESCRIPTOR FOR CLASSIFICATION BASED ON TONNETZ TRAJECTORIES
  
  • Karystinaios Emmanouil
  • Guichaoua Corentin
  • Andreatta Moreno
  • Bigo Louis
  • Bloch Isabelle
  , 2021. Dans cet article, nous présentons un nouveau descripteur pour la classification automatique du style musical. Notre méthode consiste à définir une trajectoire harmonique dans un espace géométrique, le Tonnetz, puis à la résumer à ses valeurs de centralité, qui constituent les descripteurs. Ceux-ci, associés à des descripteurs classiques, sont utilisés comme caractéristiques pour la classification. Les résultats montrent des scores F 1 supérieurs à 0,8 avec une méthode classique de forêts aléatoires pour 8 classes (une par compositeur), et supérieurs à 0,9 pour une classification en 4 classes de style ou période de composition.
• Data&Musée : de nouveaux usages sémantiques du big data culturel en France
  
  • Moissinac Jean-Claude Jc
  • Wadhera Piyush
  Histoire de l'art, Association des professeurs d'archéologie et d'histoire de l'art des universités – APAHAU [1988-....], 2021.
• Learnable Descriptors for Visual Search
  
  • Migliorati Andrea
  • Fiandrotti Attilio
  • Francini Gianluca
  • Leonardi Riccardo
  IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2021, 30, pp.80 - 91. This work proposes LDVS, a learnable binary local descriptor devised for matching natural images within the MPEG CDVS framework. LDVS descriptors are learned so that they can be sign-quantized and compared using the Hamming distance. The underlying convolutional architecture enjoys a moderate parameters count for operations on mobile devices. Our experiments show that LDVS descriptors perform favorably over comparable learned binary descriptors at patch matching on two different datasets. A complete pair-wise image matching pipeline is then designed around LDVS descriptors, integrating them in the reference CDVS evaluation framework. Experiments show that LDVS descriptors outperform the compressed CDVS SIFT-like descriptors at pair-wise image matching over the challenging CDVS image dataset. (10.1109/tip.2020.3031216)
  DOI : 10.1109/tip.2020.3031216
• An ontological foundation for multi-paradigm modelling for cyber-physical systems
  
  • Blouin Dominique
  • Al-Ali Rima
  • Iacono Mauro
  • Tekinerdogan Bedir
  • Giese Holger
  , 2021, pp.9-43. (10.1016/B978-0-12-819105-7.00007-6)
  DOI : 10.1016/B978-0-12-819105-7.00007-6
• Playable Video Generation
  
  • Menapace Willi
  • Lathuilière Stéphane
  • Tulyakov Sergey
  • Siarohin Aliaksandr
  • Ricci Elisa
  , 2021. This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety. Further details, code and examples are available on our project page willi-menapace.github.io/playable-video-generation-website.