Publications

2022

• Codes in the q-ary Lee Hypercube
  
  • Hudry Olivier
  • Charon Irène
  • Lobstein Antoine
  WSEAS Transactions on Mathematics, World Scientific and Engineering Academy and Society (WSEAS), 2022, 21, pp.173-186. (10.37394/23206.2022.21.24)
  DOI : 10.37394/23206.2022.21.24
• Delaunay Painting: Perceptual image coloring from raster contours with gaps
  
  • Parakkat Amal Dev
  • Memari Pooran
  • Cani Marie-Paule
  Computer Graphics Forum, Wiley, 2022. We introduce Delaunay Painting, a novel and easy-to-use method to flat-color contour-sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colors, thanks to the dynamic update of flow values calculated from color hints. Aesthetic finish is then achieved, through energy minimisation of contour curves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our color diffusion framework which automatically extends coloring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any coloring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the coloring strategy for quick segmentation of contours images, that we illustrate with an application to medical imaging.
• Online Unsupervised Domain Adaptation for Person Re-identification
  
  • Rami Hamza
  • Ospici Matthieu
  • Lathuilière Stéphane
  , 2022. Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network's cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.
• Playable Environments: Video Manipulation in Space and Time
  
  • Menapace Willi
  • Lathuilière Stéphane
  • Siarohin Aliaksandr
  • Theobalt Christian
  • Tulyakov Sergey
  • Golyanik Vladislav
  • Ricci Elisa
  , 2021, 97, pp.116366. We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a video by providing a sequence of desired actions. The actions are learnt in an unsupervised manner. The camera can be controlled to get the desired viewpoint. Our method builds an environment state for each frame, which can be manipulated by our proposed action module and decoded back to the image space with volumetric rendering. To support diverse appearances of objects, we extend neural radiance fields with style-based modulation. Our method trains on a collection of various monocular videos requiring only the estimated camera parameters and 2D object locations. To set a challenging benchmark, we introduce two large scale video datasets with significant camera movements. As evidenced by our experiments, playable environments enable several creative applications not attainable by prior video synthesis works, including playable 3D video generation, stylization and manipulation. Further details, code and examples are available at https://willi-menapace.github.io/playable-environments-website
• Tunnel Magnetoresistance Based Passive Resistance Replacement in Hybrid MTJ-CMOS Integration
  
  • Wu Yu-Ang
  • Xie Xinshu
  • Tong Xinfang
  • Di Yantong
  • Naviner Lirida
  • Liu Bo
  • Xiao Jian
  • Cai Hao
  IEEE Transactions on Nanotechnology, Institute of Electrical and Electronics Engineers, 2022, 21, pp.638-647. Previous theoretical and experimental works revealed the novel factors that Magnetic tunnel junction (MTJ) can be integrated into novel hybrid circuits except for memory applications. This paper makes exploitation of tunnel magnetoresistacne based replacement in diminishing layout penalty of on-chip passive component during circuit design and takes sigma-delta analog-to-digital converter (SD-ADC), resistor-based temperature sensor (-55 ∘ C ∼125∘ C) as two case study where large resistance is needed and restricts the scaling down. Considering the application in MRAM, the mainstream field of MTJ process and dealing with the problems of MRAM in wide temperature write operation, two temperature adaptive write schemes of MRAM are also proposed as the further applications of the proposed MTJ-based temperature sensor. The research of these circuits covers major characteristics of MTJ as the passive component, including area, variation and temperature characteristics. Large CMOS resistance in SD-ADC and bridge transducer in resistor-based temperature sensor are replaced by MTJ-based resistors. Simulation results reveal that the layout area of passive resistors in resistor-capacitor (RC) integrator was greatly reduced by 94.52% in comparison with fully 28nm CMOS design or 94.13% for wide temperature use when other performance is almost unchanged. In addition, the MTJ based bridge transducer in resistor-based temperature sensor can reduce the resistance layout area by over 90% with better linearity comparing with general CMOS resistor-based temperature sensor designs. Based on the MTJ-based temperature sensor, the two different adaptive write circuits help reduce write power consumption and delay of MRAM respectively for wide temperature use. (10.1109/TNANO.2022.3216778)
  DOI : 10.1109/TNANO.2022.3216778
• Robust and Scalable Content-and-Structure Indexing
  
  • Wellenzohn Kevin
  • Böhlen Michael H.
  • Helmer Sven
  • Pietri Antoine
  • Zacchiroli Stefano
  The VLDB Journal, Springer, 2022. Frequent queries on semi-structured hierarchical data are Content-and-Structure (CAS) queries that filter data items based on their location in the hierarchical structure and their value for some attribute. We propose the Robust and Scalable Content-and-Structure (RSCAS) index to efficiently answer CAS queries on big semi-structured data. To get an index that is robust against queries with varying selectivities we introduce a novel dynamic interleaving that merges the path and value dimensions of composite keys in a balanced manner. We store interleaved keys in our triebased RSCAS index, which efficiently supports a wide range of CAS queries, including queries with wildcards and descendant axes. We implement RSCAS as a log-structured merge (LSM) tree to scale it to data-intensive applications with a high insertion rate. We illustrate RSCAS's robustness and scalability by indexing data from the Software Heritage (SWH) archive, which is the world's largest, publiclyavailable source code archive.
• Vector-Valued Least-Squares Regression under Output Regularity Assumptions
  
  • Brogat-Motte Luc
  • Rudi Alessandro
  • Brouard Celine
  • Rousu Juho
  • d'Alché-Buc Florence
  Journal of Machine Learning Research, Microtome Publishing, 2022. We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output. We derive learning bounds for our method, and study under which setting statistical performance is improved in comparison to full-rank method. Our analysis extends the interest of reduced-rank regression beyond the standard low-rank setting to more general output regularity assumptions. We illustrate our theoretical insights on synthetic least-squares problems. Then, we propose a surrogate structured prediction method derived from this reduced-rank method. We assess its benefits on three different problems: image reconstruction, multi-label classification, and metabolite identification.
• On the Complexity of Determining Whether there is a Unique Hamiltonian Cycle or Path
  
  • Hudry Olivier
  • Lobstein Antoine
  WSEAS Transactions on Mathematics, World Scientific and Engineering Academy and Society (WSEAS), 2022, 21, pp.433-446. The decision problems of the existence of a Hamiltonian cycle or of a Hamiltonian path in a given graph, and of the existence of a truth assignment satisfying a given Boolean formula C, are well-known NPcomplete problems. Here we study the problems of the uniqueness of a Hamiltonian cycle or path in an undirected, directed or oriented graph, and show that they have the same complexity, up to polynomials, as the problem U-SAT of the uniqueness of an assignment satisfying C. As a consequence, these Hamiltonian problems are NP-hard and belong to the class DP, like U-SAT. (10.37394/23206.2022.21.51)
  DOI : 10.37394/23206.2022.21.51
• Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments
  
  • Du Yicheng
  • Nugraha Aditya Arie
  • Sekiguchi Kouhei
  • Bando Yoshiaki
  • Fontaine Mathieu
  • Yoshii Kazuyoshi
  , 2022. This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication with in real multiparty conversational environments. A major approach that has actively been studied in simulated environments is to sequentially perform speech enhancement and automatic speech recognition (ASR) based on deep neural networks (DNNs) trained in a supervised manner. In our task, however, such a pretrained system fails to work due to the mismatch between the training and test conditions and the head movements of the user. To enhance only the utterances of a target speaker, we use beamforming based on a DNN-based speech mask estimator that can adaptively extract the speech components corresponding to a head-relative particular direction. We propose a semi-supervised adaptation method that jointly updates the mask estimator and the ASR model at run-time using clean speech signals with ground-truth transcriptions and noisy speech signals with highly-confident estimated transcriptions. Comparative experiments using the state-of-theart distant speech recognition system show that the proposed method significantly improves the ASR performance.
• Spectral dispersion of the linewidth enhancement factor and four wave mixing conversion efficiency of an InAs/GaAs multimode quantum dot laser
  
  • Ding Shihao
  • Dong Bozhang
  • Huang Heming
  • Bowers John
  • Grillot Frédéric
  Applied Physics Letters, American Institute of Physics, 2022, 120 (8), pp.081105. The spectral dependence of the linewidth enhancement factor (a H-factor) of a multimode InAs/GaAs quantum dot laser is analyzed. Amplified spontaneous and high-frequency modulation methods are used to experimentally retrieve the a H-factor of each longitudinal mode below and above the threshold. A dispersion of the a H-factor is unlocked across the entire optical spectrum, which is further illustrated in the context of four wave mixing experiments. The results show that the induced conversion efficiency is increased at lasing wavelengths where the linewidth enhancement is lower. These results highlight the importance of carefully monitoring the linewidth enhancement factor in quantum dot lasers especially for frequency combs and mode-locking applications in future optical communication systems. (10.1063/5.0077221)
  DOI : 10.1063/5.0077221
• Safety, Security and Performance Assessment of Security Countermeasures with SysML-Sec
  
  • Sultan Bastien
  • Apvrille Ludovic
  • Jaillon Philippe
  , 2022. Deploying security countermeasures on Cyber-Physical Systems (CPS) can induce side-effects that can exceed their benefits. When CPS are safety-critical systems, performing efficiency and impact assessments of security countermeasures early in the design flow is essential. The paper introduces the W-Sec method, based on SysML-Sec. The W-Sec method consists in two interwoven formal modeling and verification cycles aiming at providing countermeasures with objective and quantitative efficiency and impact assessments in terms of safety, security and performance. The paper evaluates the W-Sec method with an autonomous rover swarm case-study, and finally discusses the method's strengths and weaknesses highlighted by the case-study results. (10.5220/0010832300003119)
  DOI : 10.5220/0010832300003119
• Empirical Risk Minimization under Random Censorship
  
  • Ausset Guillaume
  • Clémençon Stéphan
  • Portier François
  Journal of Machine Learning Research, Microtome Publishing, 2022, 23 (1), pp.168–226. We consider the classic supervised learning problem where a continuous non-negative random label Y (e.g. a random duration) is to be predicted based upon observing a random vector X valued in R d with d ≥ 1 by means of a regression rule with minimum least square error. In various applications, ranging from industrial quality control to public health through credit risk analysis for instance, training observations can be right censored, meaning that, rather than on independent copies of (X, Y), statistical learning relies on a collection of n ≥ 1 independent realizations of the triplet (X, min{Y, C}, δ), where C is a nonnegative random variable with unknown distribution, modelling censoring and δ = I{Y ≤ C} indicates whether the duration is right censored or not. As ignoring censoring in the risk computation may clearly lead to a severe underestimation of the target duration and jeopardize prediction, we consider a plug-in estimate of the true risk based on a Kaplan-Meier estimator of the conditional survival function of the censoring C given X, referred to as Beran risk, in order to perform empirical risk minimization. It is established, under mild conditions, that the learning rate of minimizers of this biased/weighted empirical risk functional is of order O P (log(n)/n) when ignoring model bias issues inherent to plug-in estimation, as can be attained in absence of censoring. Beyond theoretical results, numerical experiments are presented in order to illustrate the relevance of the approach developed. (10.5555/3586589.3586594)
  DOI : 10.5555/3586589.3586594
• A new family of polyphase sequences with low correlation
  
  • Gu Zhi
  • Zhou Zhengchun
  • Mesnager Sihem
  • Parampalli Udaya
  Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences, Springer, 2022, 14 (1), pp.135-144. (10.1007/s12095-021-00522-x)
  DOI : 10.1007/s12095-021-00522-x
• Preimages of p −Linearized Polynomials over ${\mathbb {F}}_{p}$
  
  • Kim Kwang Ho
  • Mesnager Sihem
  • Choe Jong Hyok
  • Lee Dok Nam
  Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences, Springer, 2022, 14 (1), pp.75-86. (10.1007/s12095-021-00514-x)
  DOI : 10.1007/s12095-021-00514-x
• Unipolar quantum technology enabling high-speed free-space communication in the long-wave infrared regime
  
  • Didier P.
  • Dely H.
  • Spitz O.
  • Awwad Elie
  • Bonazzi Thomas
  • Rodriguez E.
  • Sirtori C.
  • Grillot Frédéric
  , 2022. <p>A combination of unipolar quantum laser, modulator and detector allows us to demonstrate a free-space communication at 40 Gbits/s and 9 µm far-infrared wavelength. The distance between the emitter and the receiver is 31 meters.</p> (10.1364/CLEO_AT.2022.JTh6A.5)
  DOI : 10.1364/CLEO_AT.2022.JTh6A.5
• Linear convergence of dual coordinate descent on non-polyhedral convex problems
  
  • Necoara Ion
  • Fercoq Olivier
  Mathematics of Operations Research, INFORMS, 2022. This paper deals with constrained convex problems, where the objective function is smooth strongly convex and the feasible set is given as the intersection of a large number of closed convex (possibly non-polyhedral) sets. In order to deal efficiently with the complicated constraints we consider a dual formulation of this problem. We prove that the corresponding dual function satisfies a quadratic growth property on any sublevel set, provided that the objective function is smooth and strongly convex and the sets verify the Slater's condition. To the best of our knowledge, this work is the first deriving a quadratic growth condition for the dual under these general assumptions. Existing works derive similar quadratic growth conditions under more conservative assumptions, e.g., the sets need to be either polyhedral or compact. Then, for finding the minimum of the dual problem, due to its special composite structure, we propose random (accelerated) coordinate descent algorithms. However, with the existing theory one can prove that such methods converge only sublinearly. Based on our new quadratic growth property derived for the dual, we now show that such methods have faster convergence, that is the dual random (accelerated) coordinate descent algorithms converge linearly. Besides providing a general dual framework for the analysis of randomized coordinate descent schemes, our results resolve an open problem in the literature related to the convergence of Dykstra algorithm on the best feasibility problem for a collection of convex sets. That is, we establish linear convergence rate for the randomized Dykstra algorithm when the convex sets satisfy the Slater's condition and derive also a new accelerated variant for the Dykstra algorithm. (10.1287/moor.2021.1222)
  DOI : 10.1287/moor.2021.1222
• Autoregressive Moving Average Jointly-Diagonalizable Spatial Covariance Analysis for Joint Source Separation and Dereverberation
  
  • Sekiguchi Kouhei
  • Bando Yoshiaki
  • Nugraha Aditya Arie
  • Fontaine Mathieu
  • Yoshii Kazuyoshi
  • Kawahara Tatsuya
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2022, 30, pp.2368 - 2382. This article describes a computationally-efficient statistical approach to joint (semi-)blind source separation and dereverberation for multichannel noisy reverberant mixture signals. A standard approach to source separation is to formulate a generative model of a multichannel mixture spectrogram that consists of source and spatial models representing the time-frequency power spectral densities (PSDs) and spatial covariance matrices (SCMs) of source images, respectively, and find the maximum-likelihood estimates of these parameters. A state-of-the-art blind source separation method in this thread of research is fast multichannel nonnegative matrix factorization (FastMNMF) based on the lowrank PSDs and jointly-diagonalizable full-rank SCMs. To perform mutually-dependent separation and dereverberation jointly, in this paper we integrate both moving average (MA) and autoregressive (AR) models that represent the early reflections and late reverberations of sources, respectively, into the FastMNMF formalism. Using a pretrained deep generative model of speech PSDs as a source model, we realize semi-blind joint speech separation and dereverberation. We derive an iterative optimization algorithm based on iterative projection or iterative source steering for jointly and efficiently updating the AR parameters and the SCMs. Our experimental results showed the superiority of the proposed ARMA extension over its AR-or MA-ablated version in a speech separation and/or dereverberation task. (10.1109/taslp.2022.3190734)
  DOI : 10.1109/taslp.2022.3190734
• Le pillage de la communauté des logiciels libres
  
  • O'Neil Mathieu
  • Muselli Laure
  • Pailler Fred
  • Zacchiroli Stefano
  Le Monde Diplomatique, Le Monde, 2022, pp.20-21.
• A Style-Based GAN Encoder for High Fidelity Reconstruction of Images and Videos
  
  • Yao Xu
  • Newson Alasdair
  • Gousseau Yann
  • Hellier Pierre
  , 2022, pp.581-597.
• Mid-infrared free-space cryptosystem
  
  • Spitz Olivier
  • Herdt Andreas
  • Didier Pierre
  • Elsäßer Wolfgang
  • Grillot Frédéric
  Nonlinear Theory and Its Applications, IEICE, The Institute of Electronics, Information and Communication Engineers, 2022, 13, pp.44 - 52. Communication privacy is one of the key requirements for always expanding networks. Furthermore, fibre systems are becoming saturated and many remote areas do not have access to broadband connection because current systems are too difficult or expensive to deploy. In this work, we experimentally demonstrate a mid-infrared free-space cryptosystem that is based on chaos synchronization between two quantum cascade lasers. Optimal amplitude conditions to ensure both privacy and acceptable deciphering are described, paving the way towards a wide adoption of quantum cascade lasers for future communication systems. (10.1587/nolta.13.44)
  DOI : 10.1587/nolta.13.44
• Pulling, Pressing, and Sensing with In-Flat: Transparent Touch Overlay for Smartphones
  
  • Zhang Zhuoming
  • Alvina Jessalyn
  • Détienne Françoise
  • Lecolinet Eric
  , 2022, pp.1-9. (10.1145/3531073.3531111)
  DOI : 10.1145/3531073.3531111
• Revisiting Distributed Acoustic Sensing: A Telecom Approach Inspired from Optical Transmission
  
  • Dorize Christian
  • Guerrier Sterenn
  • Awwad Elie
  • Renaudier Jérémie
  , 2022. <jats:p>we review digital techniques used in transmission through telecom fibres and show they can be tailored to Distributed Acoustic Sensing (DAS), with key advantages in terms of noise floor and scalability to targeted applications.</jats:p> (10.1364/OFS.2022.Th4.7)
  DOI : 10.1364/OFS.2022.Th4.7
• Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
  
  • Pethick Thomas
  • Latafat Puya
  • Patrinos Panagiotis
  • Fercoq Olivier
  • Cevher Volkan
  , 2022. This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems. It is well-known that finding a local solution for general minimax problems is computationally intractable. This observation has recently motivated the study of structures sufficient for convergence of first order methods in the more general setting of variational inequalities when the so-called weak Minty variational inequality (MVI) holds. This problem class captures non-trivial structures as we demonstrate with examples, for which a large family of existing algorithms provably converge to limit cycles. Our results require a less restrictive parameter range in the weak MVI compared to what is previously known, thus extending the applicability of our scheme. The proposed algorithm is applicable to constrained and regularized problems, and involves an adaptive stepsize allowing for potentially larger stepsizes. Our scheme also converges globally even in settings where the underlying operator exhibits limit cycles. Moreover, a variant with stochastic oracles is proposed-making it directly relevant for training of generative adversarial networks. For the stochastic algorithm only one of the stepsizes is required to be diminishing while the other may remain constant, making it interesting even in the monotone setting.
• GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
  
  • Saltori Cristiano
  • Krivosheev Evgeny
  • Lathuilière Stéphane
  • Sebe Nicu
  • Galasso Fabio
  • Fiameni Giuseppe
  • Ricci Elisa
  • Poiesi Fabio
  , 2022. 3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when handling dynamic scenes. This can significantly hinder the navigation capabilities of self-driving vehicles. This paper advances the state of the art in this research field. Our first contribution consists in analysing a new unexplored scenario in point cloud segmentation, namely Source-Free Online Unsupervised Domain Adaptation (SF-OUDA). We experimentally show that state-of-the-art methods have a rather limited ability to adapt pre-trained deep network models to unseen domains in an online manner. Our second contribution is an approach that relies on adaptive self-training and geometric-feature propagation to adapt a pre-trained source model online without requiring either source data or target labels. Our third contribution is to study SF-OUDA in a challenging setup where source data is synthetic and target data is point clouds captured in the real world. We use the recent SynLiDAR dataset as a synthetic source and introduce two new synthetic (source) datasets, which can stimulate future synthetic-to-real autonomous driving research. Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds. Code and synthetic datasets are available at https://github.com/saltoricristiano/gipso-sfouda.
• Reflection sensitivity of InAs/GaAs epitaxial quantum dot lasers under direct modulation
  
  • Ding Shihao
  • Dong Bozhang
  • Huang Heming
  • Bowers John E
  • Grillot Frédéric
  Electronics Letters, IET, 2022, 58 (9), pp.363-365. This paper reports on the reflection sensitivity under direct modulation operation of a 1.3 μm InAs/GaAs quantum dot laser that is epitaxially grown on silicon. The quantum dot laser exhibits a high tolerance to back reflections with low error transmission at 6 Gbps. This study paves the way for developing directly modulated isolator-free photonic integrated circuits based on quantum dot lasers. (10.1049/ell2.12440)
  DOI : 10.1049/ell2.12440