Publications

2022

• New decoding techniques for modified product code used in critical applications
  
  • Freitas David C.C.
  • Marcon César
  • Silveira Jarbas A.N.
  • Naviner Lirida A.B.
  • Mota João C.M.
  Microelectronics Reliability, Elsevier, 2022, 128, pp.114444. The shrinking of memory devices increased the probability of system failures due to the higher sensitivity to electromagnetic radiation. Critical memory systems employ fault-tolerant techniques like Error Correction Code (ECC) to mitigate these failures. This work explores error correction techniques and algorithms employing the Line Product Code (LPC), a product-like ECC. We propose to decode LPC codewords using a single error correction algorithm (AlgSE) followed by a double error correction algorithm (AlgDE). Both algorithms explore the LPC characteristics to attain greater decoding efficiency. AlgSE is implemented with an iterative technique associated with a correction heuristic, while AlgDE is an innovative proposal that allows increasing correction effectiveness through the inference of errors. AlgDE allows increasing the efficiency of the LPC decoder significantly when used together with AlgSE. It corrects 100% of the cases up to three bitflips as well as 98% and 92%, respectively, for four and five upsets in exhaustive tests. Besides, we present tradeoffs concerning the error correction potential versus the costs of implementing the correction algorithms. (10.1016/j.microrel.2021.114444)
  DOI : 10.1016/j.microrel.2021.114444
• Lyrics segmentation via bimodal text–audio representation
  
  • Fell Michael
  • Nechaev Yaroslav
  • Meseguer-Brocal Gabriel
  • Cabrio Elena
  • Gandon Fabien
  • Peeters Geoffroy
  Natural Language Engineering, Cambridge University Press (CUP), 2022, 28 (3), pp.317 - 336. Song lyrics contain repeated patterns that have been proven to facilitate automated lyrics segmentation, with the final goal of detecting the building blocks (e.g., chorus, verse) of a song text. Our contribution in this article is twofold. First, we introduce a convolutional neural network (CNN)-based model that learns to segment the lyrics based on their repetitive text structure. We experiment with novel features to reveal different kinds of repetitions in the lyrics, for instance based on phonetical and syntactical properties. Second, using a novel corpus where the song text is synchronized to the audio of the song, we show that the text and audio modalities capture complementary structure of the lyrics and that combining both is beneficial for lyrics segmentation performance. For the purely text-based lyrics segmentation on a dataset of 103k lyrics, we achieve an F-score of 67.4%, improving on the state of the art (59.2% F-score). On the synchronized text–audio dataset of 4.8k songs, we show that the additional audio features improve segmentation performance to 75.3% F-score, significantly outperforming the purely text-based approaches. (10.1017/S1351324921000024)
  DOI : 10.1017/S1351324921000024
• LAKE DETECTION WITH SENTINEL-1 DATA USING A GRAB-CUT METHOD AND ITS MULTI-TEMPORAL EXTENSION
  
  • Gasnier Nicolas
  • Denis Loïc
  • Fjørtoft Roger
  • Liege Frédéric
  • Tupin Florence
  , 2022. This paper presents a semi-guided method to detect lakes in Sentinel-1 SAR data. The proposed approach is an adaptation of the grab-cut framework developed in [1]. Starting from a coarse bounding box around the lake, an accurate segmentation is extracted using a Conditional Random Field formalism and a graph-cut based optimization. Then an extension of this approach to process jointly a stack of multi-temporal data is presented. A temporal regularization term is introduced to control the joint segmentation. The proposed approach is evaluated on Sentinel-1 datasets. Qualitative and quantitative results demonstrate the interest of the proposed framework and its robustness to the initialization polygon of the lake. (10.1109/IGARSS46834.2022.9883219)
  DOI : 10.1109/IGARSS46834.2022.9883219
• High-capacity free-space optical link in the midinfrared thermal atmospheric windows using unipolar quantum devices
  
  • Didier Pierre
  • Dely Hamza
  • Bonazzi Thomas
  • Spitz Olivier
  • Awwad Elie
  • Rodriguez Étienne
  • Vasanelli Angela
  • Sirtori Carlo
  • Grillot Frédéric
  Advanced photonics, SPIE, 2022, 4 (5), pp.056004. Free-space optical communication is a very promising alternative to fiber communication systems, in terms of ease of deployment and costs. Midinfrared light has several features of utter relevance for free-space applications: low absorption when propagating in the atmosphere even under adverse conditions, robustness of the wavefront during long-distance propagation, and absence of regulations and restrictions for this range of wavelengths. A proof-of-concept of high-speed transmission taking advantage of intersubband devices has recently been demonstrated, but this effort was limited by the short-distance optical path (up to 1 m). In this work, we study the possibility of building a long-range link using unipolar quantum optoelectronics. Two different detectors are used: an uncooled quantum cascade detector and a nitrogen-cooled quantum well-infrared photodetector. We evaluate the maximum data rate of our link in a back-to-back configuration before adding a Herriott cell to increase the length of the light path up to 31 m. By using pulse shaping, pre- and post-processing, we reach a record bitrate of 30 Gbit s − 1 for both two-level (OOK) and four-level (PAM-4) modulation schemes for a 31-m propagation link and a bit error rate compatible with error-correction codes. (10.1117/1.AP.4.5.056004)
  DOI : 10.1117/1.AP.4.5.056004
• Handwriting and Drawing Features for Detecting Personality Traits: An Analysis on Big Five Sub-dimensions
  
  • Esposito Anna
  • Amorese Terry
  • Buonanno Michele
  • Cuciniello Marialucia
  • Esposito Antonietta
  • Faundez-Zanuy Marcos
  • Likforman-Sulem Laurence
  • Riviello Maria Teresa
  • Spagnuolo Carmine
  • Troncone Alda
  • Cordasco Gennaro
  Acta Polytechnica Hungarica, Óbuda University, 2022, 19 (11), pp.65-84. : Handwriting and Drawing are functional tasks involving physical and cognitive processes. Recently they have been investigated for detecting cognitive and motor disorders. In this work, handwriting/drawing features are investigated for identifying connections with personality traits. For this purpose, an experiment comprising seven handwriting/drawing tasks has been administrated to 78 young adults (mean age=24.6 ± 2.4 years) equally balanced by gender. Handwriting and Drawing activities - both on and close to the paper – had been recorded online through a digitizing tablet able to measure handwriting and drawing features such as pressure, speed, dimension, and inclination of each pen-stroke on the paper. Participants were asked to fill the Big Five Personality Questionnaire (BFQ) and according to the scores obtained for each of the 5 dimensions and 10 Big Five sub-dimensions, were partitioned into three categories: low, typical, and high. To evaluate whether the recorded handwriting/drawing features are connected with personality traits ANOVA repeated measures have been performed with gender and group category (low, typical, and high) as between and the listed handwriting/drawing features as within factors. The analyses show significant differences among low, typical and, high BFQ scores for the main Big Five dimensions and the ten Big Five sub-dimensions, indicating that personality traits can be revealed by a quantitative analysis of the proposed handwriting/drawing features. (10.12700/APH.19.11.2022.11.4)
  DOI : 10.12700/APH.19.11.2022.11.4
• Combining Embeddings and Rules for Fact Prediction
  
  • Boschin Armand
  • Jain Nitisha
  • Keretchashvili Gurami
  • Suchanek Fabian M.
  , 2022. Knowledge bases are typically incomplete, meaning that they are missing information that we would expect to be there. Recent years have seen two main approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as "If two people are married, they most likely live in the same city". These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of neuro-symbolic works that combine embeddings and rule mining approaches for fact prediction. (10.4230/OASIcs.AIB.2022.4)
  DOI : 10.4230/OASIcs.AIB.2022.4
• (Complex) natural gradient optimization for optical quantum circuit design
  
  • Yao Yuan
  • Cussenot Pierre
  • Wolf Richard A.
  • Miatto Filippo M.
  Physical Review A, American Physical Society, 2022, 105 (5), pp.052402. Few-mode optical quantum circuits can be simulated and optimized using gradient-descent methods, as optical gates can be parametrized by continuous parameters. However, the parameter space as seen by a nonholomorphic cost function is not Euclidean, which means that the Euclidean gradient does not generally point in the direction of steepest ascent. In order to retrieve the true steepest-ascent direction, one must take into account the local metric in what is known as the natural-gradient (NG) method. In this work we implement the NG for the optimization of optical quantum circuits. Specifically, we adapt the NG approach to a complex-valued parameter space, allowing us to apply the NG method directly within the Bargmann formalism, which we rely on for transforming between phase space and Fock space. We then compare the NG approach to vanilla gradient descent and to Adam over two standard state-preparation benchmarks: a single-photon source and a Gottesman-Kitaev-Preskill state source. We observe that the NG approach converges significantly faster (due in part to the possibility of using larger learning rates) and with a smoother decay of the cost function throughout the optimization. This result further improves our ability to design quantum circuits via classical optimization methods. (10.1103/PhysRevA.105.052402)
  DOI : 10.1103/PhysRevA.105.052402
• Enhanced four-wave mixing dynamics in epitaxial quantum dot laser on silicon
  
  • Ding Shihao
  • Dong Bozhang
  • Chow Weng
  • Bowers John
  • Grillot Frédéric
  , 2022, pp.NpTh3D.1. The four-wave mixing conversion efficiency of quantum dot laser is much higher than that of quantum well. These results are important for self-mode-locked pulse production and high-bandwidth optical frequency comb generation. (10.1364/NP.2022.NpTh3D.1)
  DOI : 10.1364/NP.2022.NpTh3D.1
• A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
  
  • Leluc Rémi
  • Portier François
  • Zhuman Aigerim
  • Segers Johan
  Advances in Neural Information Processing Systems, Morgan Kaufmann Publishers, 2022, 35, pp.11842--11853. Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration. However, standard methods do not allow the distribution of the particles to evolve during the algorithm, as is the case in sequential simulation methods. Within the standard adaptive importance sampling framework, a simple weighted least squares approach is proposed to improve the procedure with control variates. The procedure takes the form of a quadrature rule with adapted quadrature weights to reflect the information brought in by the control variates. The quadrature points and weights do not depend on the integrand, a computational advantage in case of multiple integrands. Moreover, the target density needs to be known only up to a multiplicative constant. Our main result is a non-asymptotic bound on the probabilistic error of the procedure. The bound proves that for improving the estimate's accuracy, the benefits from adaptive importance sampling and control variates can be combined. The good behavior of the method is illustrated empirically on synthetic examples and real-world data for Bayesian linear regression.
• Post-actes de la Conférence Nationale en Intelligence Artificielle (CNIA 2018-2020)
  
  • Bloch Isabelle
  • Euzenat Jérôme
  • Lang Jérôme
  • Schwarzentruber François
  Revue Ouverte d'Intelligence Artificielle, Association pour la diffusion de la recherche francophone en intelligence artificielle, 2022, 3, pp.193-413. no abstract
• Compositional Equivalences Based on Open pNets
  
  • Ameur-Boulifa Rabéa
  • Henrio Ludovic
  • Madelaine Eric
  Journal of Logical and Algebraic Methods in Programming, Elsevier, 2022, 131, pp.100842. Establishing equivalences between programs is crucial both for verifying correctness of programs and for justifying optimisations and program transformations. There exist several equivalence relations for programs, and bisimulations are among the most versatile of these equivalences. Among bisimulations one distinguishes strong bisimulation that requires that each action of a program is simulated by a single action of the equivalent program, and weak bisimulation that allows some of the actions to be invisible, and thus not simulated. pNet is a generalisation of automata that model open systems. They feature variables and hierarchical composition. Open pNets are pNets with holes, i.e. placeholders that can be filled later by subsystems. However, there is no standard tool for defining the semantics of an open system in this context. This article first defines open automata that are labelled transition systems with parameters and holes. Relying on open automata, it then defines bisimilarity relations for the comparison of systems specified as pNets. We first present a strong bisimilarity for open pNets called FH-bisimilarity. Next we offer an equivalence relation similar to the classical weak bisimulation equivalence, and study its properties. Among these properties we are interested in compositionality: if two systems are proven equivalent they will be indistinguishable by their context, and they will also be indistinguishable when their holes are filled with equivalent systems. We identify sufficient conditions to ensure compositionality of strong and weak bisimulation. The contributions of this article are illustrated using a transport protocol as running example. (10.1016/j.jlamp.2022.100842)
  DOI : 10.1016/j.jlamp.2022.100842
• Unsupervised Audio Source Separation Using Differentiable Parametric Source Models
  
  • Schulze-Forster Kilian
  • Doire Clement S J
  • Richard Gael
  • Badeau Roland
  Computing Research Repository, ACM / ArXiv, 2022. Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical mixtures. This raises a need for unsupervised methods. We propose a novel unsupervised modelbased deep learning approach to musical source separation. Each source is modelled with a differentiable parametric sourcefilter model. A neural network is trained to reconstruct the observed mixture as a sum of the sources by estimating the source models' parameters given their fundamental frequencies. At test time, soft masks are obtained from the synthesized source signals. The experimental evaluation on a vocal ensemble separation task shows that the proposed method outperforms learning-free methods based on nonnegative matrix factorization and a supervised deep learning baseline. Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio. This work makes powerful deep learning based separation usable in scenarios where training data with ground truth is expensive or nonexistent.
• Introduction
  
  • Bloch Isabelle
  • Euzenat Jérôme
  • Lang Jérôme
  • Schwarzentruber François
  Revue Ouverte d'Intelligence Artificielle, Association pour la diffusion de la recherche francophone en intelligence artificielle, 2022, 3, pp.193-199. no abstract (10.5802/roia.28fr)
  DOI : 10.5802/roia.28fr
• Selected Topics in Malliavin Calculus
  
  • Decreusefond Laurent
  , 2022, 10. (10.1007/978-3-031-01311-9)
  DOI : 10.1007/978-3-031-01311-9
• Conditional and Relevant Common Information
  
  • Graczyk Robert
  • Lapidoth Amos
  • Wigger Michèle M
  Information and Inference, Oxford University Press (OUP), 2022, 11 (2), pp.679 - 737. Two variations on Wyner's common information are proposed: conditional common information and relevant common information. These are shown to have operational meanings analogous to those of Wyner's common information in appropriately defined distributed problems of compression, simulation and channel synthesis. For relevant common information, an additional operational meaning is identified: on a multiple-access channel with private and common messages, it is the minimal common-message rate that enables communication at the maximum sum-rate under a weak coordination constraint on the inputs and output. En route, the weak-coordination problem over a Gray-Wyner network is solved under the no-excess-rate constraint. (10.1093/imaiai/iaab021)
  DOI : 10.1093/imaiai/iaab021
• Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments
  
  • Sekiguchi Kouhei
  • Nugraha Aditya Arie
  • Du Yicheng
  • Bando Yoshiaki
  • Fontaine Mathieu
  • Yoshii Kazuyoshi
  , 2022. This paper describes the practical response-and performance-aware development of online speech enhancement for an augmented reality (AR) headset that helps a user understand conversations made in real noisy echoic environments (e.g., cocktail party). One may use a state-of-the-art blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) that works well in various environments thanks to its unsupervised nature. Its heavy computational cost, however, prevents its application to real-time processing. In contrast, a supervised beamforming method that uses a deep neural network (DNN) for estimating spatial information of speech and noise readily fits real-time processing, but suffers from drastic performance degradation in mismatched conditions. Given such complementary characteristics, we propose a dual-process robust online speech enhancement method based on DNN-based beamforming with FastMNMF-guided adaptation. FastMNMF (back end) is performed in a mini-batch style and the noisy and enhanced speech pairs are used together with the original parallel training data for updating the direction-aware DNN (front end) with backpropagation at a computationally-allowable interval. This method is used with a blind dereverberation method called weighted prediction error (WPE) for transcribing the noisy reverberant speech of a speaker, which can be detected from video or selected by a user's hand gesture or eye gaze, in a streaming manner and spatially showing the transcriptions with an AR technique. Our experiment showed that the word error rate was improved by more than 10 points with the runtime adaptation using only twelve minutes observation.
• Optimizing Higher-Order Correlation Analysis Against Inner Product Masking Scheme
  
  • Ming Jingdian
  • Zhou Yongbin
  • Cheng Wei
  • Li Huizhong
  IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, 2022, 17, pp.3555-3568. (10.1109/TIFS.2022.3209890)
  DOI : 10.1109/TIFS.2022.3209890
• Vibration Detection and Localization in Buried Fiber Cable after 80km of SSMF using Digital Coherent Sensing System with Co-Propagating 600Gb/s WDM Channels
  
  • Guerrier Sterenn
  • Benyahya Kaoutar
  • Dorize Christian
  • Awwad Elie
  • Mardoyan Haik
  • Renaudier Jérémie
  , 2022, pp.M2F.3. (10.1364/OFC.2022.M2F.3)
  DOI : 10.1364/OFC.2022.M2F.3
• Somme et produit, couple star
  
  • Zayana Karim
  • Rabiet Victor
  CultureMath, ENS, 2022. Relation entre somme et produit de deux nombres. Résolution des équations du second degré sans utiliser le discriminant
• Variations on a Theme by Massey
  
  • Rioul Olivier
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2022, 68 (5), pp.2813-2828. In 1994, Jim Massey proposed the guessing entropy as a measure of the difficulty that an attacker has to guess a secret used in a cryptographic system, and established a well-known inequality between entropy and guessing entropy. Over 15 years before, in an unpublished work, he also established a well-known inequality for the entropy of an integer-valued random variable of given variance. In this paper, we establish a link between the two works by Massey in the more general framework of the relationship between discrete (absolute) entropy and continuous (differential) entropy. Two approaches are given in which the discrete entropy (or Rényi entropy) of an integer-valued variable can be upper bounded using the differential (Rényi) entropy of some suitably chosen continuous random variable. As an application, lower bounds on guessing entropy and guessing moments are derived in terms of entropy or Rényi entropy (without side information) and conditional entropy or Arimoto conditional entropy (when side information is available) (10.1109/TIT.2022.3141264)
  DOI : 10.1109/TIT.2022.3141264
• Free-Space Communication With Directly Modulated Mid-Infrared Quantum Cascade Devices
  
  • Spitz Olivier
  • Didier Pierre
  • Durupt Lauréline
  • Andrés Díaz-Thomas Daniel
  • Baranov Alexei N
  • Cerutti Laurent
  • Grillot Frédéric
  IEEE Journal of Selected Topics in Quantum Electronics, Institute of Electrical and Electronics Engineers, 2022. This study deals with the communication capabilities of two kinds of semiconductor lasers emitting in one of the atmosphere transparency windows, around 4 µm. One of these two lasers is a quantum cascade laser and the other one is an interband cascade laser. With the quantum cascade laser, a subsequent attenuation is added to the optical path in order to mimic the attenuation of free-space transmission of several kilometers. Direct electrical modulation is used to transmit the message and two-level formats, non-return-to-zero and return-to-zero, are used and compared in terms of maximum transmission data rate. The sensitivity to optical feedback is also analyzed, as well as the evolution of the error rate when reducing the optical power at the level of the detector. This work provides a novel insight into the development of future secure free-space optical communication links based on midinfrared semiconductor lasers and sheds the light on improvements required to achieve multi-Gbits/s communication with off-the-shelf components. Index Terms-Quantum cascade laser, interband cascade laser, mid-infrared photonics, free-space communication. I. INTRODUCTION T HE development of semiconductor laser technology was considerably boosted with inventing quantum cascade lasers (QCLs) in early 90s [1] and interband cascade lasers (ICLs) shortly afterwards [2]. At the early stages of QCLs, free-space optical transmissions were envisioned [3] alongside Manuscript (10.1109/JSTQE.2021.3096316)
  DOI : 10.1109/JSTQE.2021.3096316
• Characterization of the majority matrices of profiles of equivalence relations
  
  • Hudry Olivier
  , 2022.
• Participation de l’équipe TGV à DEFT 2022 : Prédiction automatique de notes d’étudiants à des questionnaires en fonction du type de question
  
  • Gaudray Bouju Vanessa
  • Guettier Margot
  • Lerus Gwennola
  • Guibon Gaël
  • Labeau Matthieu
  • Lefeuvre Luce
  , 2022, pp.23-35. Cet article présente l’approche de l’équipe TGV lors de sa participation à la tâche de base de DEFT 2022, dont l’objectif était de prédire automatiquement les notes obtenues par des étudiants sur la base de leurs réponses à des questionnaires. Notre stratégie s’est focalisée sur la mise au point d’une méthode de classification des questions en fonction du type de réponse qu’elles attendent, de manière à pouvoir mener une approche différenciée pour chaque type. Nos trois runs ont consisté en une approche non différenciée, servant de référence, et deux approches différenciées, la première se basant sur la constitution d’un jeu de caractéristiques et la seconde sur le calcul de TF-IDF et de la fonction de hashage. Notre objectif premier était ainsi de vérifier si des approches dédiées à chaque type de questions sont préférables à une approche globale.
• Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
  
  • Gonthier Nicolas
  • Ladjal Saïd
  • Gousseau Yann
  Computer Vision and Image Understanding, Elsevier, 2022, 214. Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed with a domain-specific solution focused on natural images, we show that a simple multiple instance approach applied on pre-trained deep features yields excellent performances on non-photographic datasets, possibly including new classes. The approach does not include any fine-tuning or cross-domain learning and is therefore efficient and possibly applicable to arbitrary datasets and classes. We investigate several flavors of the proposed approach, some including multi-layers perceptron and polyhedral classifiers. Despite its simplicity, our method shows competitive results on a range of publicly available datasets, including paintings (People-Art, IconArt), watercolors, cliparts and comics and allows to quickly learn unseen visual categories. (10.1016/j.cviu.2021.103299)
  DOI : 10.1016/j.cviu.2021.103299
• From Coherent Systems Technology to Advanced Fiber Sensing for Smart Network Monitoring
  
  • Dorize Christian
  • Guerrier Sterenn
  • Awwad Elie
  • Mardoyan Haik
  • Renaudier Jeremie
  Journal of Lightwave Technology, Institute of Electrical and Electronics Engineers (IEEE)/Optical Society of America(OSA), 2022, pp.1-10. (10.1109/JLT.2022.3221552)
  DOI : 10.1109/JLT.2022.3221552