Publications

2019

• A Simple and Exact Algorithm to Solve Linear Problems with l1 -based Regularizers
  
  • Tendero Yohann
  • Ciril Igor
  • Darbon Jérôme
  , 2019.
• A Review of Sparse Recovery Algorithms
  
  • Crespo Marques Elaine
  • Maciel Nilson
  • Naviner Lirida
  • Cai Hao
  • Yang Jun
  IEEE Access, IEEE, 2019, 7, pp.1300-1322. Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, and data acquisition, the signals involved can be considered sparse or compressive in some domain. The compressive sensing theory could be a proper candidate to deal with these constraints. It can be used to recover sparse or compressive signals with fewer measurements than the traditional methods. Two problems must be addressed by compressive sensing theory: design of the measurement matrix and development of an efficient sparse recovery algorithm. These algorithms are usually classified into three categories: convex relaxation, non-convex optimization techniques, and greedy algorithms. This paper intends to supply a comprehensive study and a state-of-the-art review of these algorithms to researchers who wish to develop and use them. Moreover, a wide range of compressive sensing theory applications is summarized and some open research challenges are presented. (10.1109/ACCESS.2018.2886471)
  DOI : 10.1109/ACCESS.2018.2886471
• Codes, Cryptology and Information Security
  
  • Carlet Claude
  • Guilley Sylvain
  • Nitaj Abderrahmane
  • Souidi El Mamoun
  , 2019. (10.1007/978-3-030-16458-4)
  DOI : 10.1007/978-3-030-16458-4
• Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model
  
  • Pineau Edouard
  • Razakarivony Sebastien
  • Bonald Thomas
  , 2019. Finding understandable and meaningful feature representation of multivariate time series (MTS) is a difficult task, since information is entangled both in temporal and spatial dimensions. In particular, MTS can be seen as the observation of simultaneous causal interactions between dynamical variables. Standard way to model these interactions is the vector linear autoregression (VAR). The parameters of VAR models can be used as MTS feature representation. Yet, VAR cannot generalize on new samples, hence independent VAR models must be trained to represent different MTS. In this paper, we propose to use the inference capacity of neural networks to overpass this limit. We propose to associate a relational neural network to a VAR generative model to form an encoder-decoder of MTS. The model is denoted Seq2VAR for Sequence-to-VAR. We use recent advances in relational neural network to build our MTS encoder by explicitly modeling interactions between variables of MTS samples. We also propose to leverage reparametrization tricks for binomial sampling in neural networks in order to build a sparse version of Seq2VAR and find back the notion of Granger causality defined in sparse VAR models. We illustrate the interest of our approach through experiments on synthetic datasets.
• Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering
  
  • Charpentier Bertrand
  • Bonald Thomas
  , 2019. We introduce the tree sampling divergence (TSD), an information-theoretic metric for assessing the quality of the hierarchical clustering of a graph. Any hierarchical clustering of a graph can be represented as a tree whose nodes correspond to clusters of the graph. The TSD is the Kullback-Leibler divergence between two probability distributions over the nodes of this tree: those induced respectively by sampling at random edges and node pairs of the graph. A fundamental property of the proposed metric is that it is interpretable in terms of graph reconstruction. Specifically, it quantifies the ability to reconstruct the graph from the tree in terms of information loss. In particular, the TSD is maximum when perfect reconstruction is feasible, i.e., when the graph has a hierarchical structure and can be reconstructed exactly from the corresponding tree. Another key property of TSD is that it applies to any tree, not necessarily binary. In particular, the TSD applies to trees of height 2, corresponding to the case of usual clustering (not hierarchical) whose output is a partition of the set of nodes. The TSD can thus be viewed as a universal metric, applicable to any type of clustering. Moreover, the TSD can be used in practice to compress a binary tree while minimizing the information loss in terms of graph reconstruction, so as to get a compact representation of the hierarchical structure of a graph. We illustrate the behavior of TSD compared to existing metrics on experiments based on both synthetic and real datasets.
• Almost surely constrained convex optimization
  
  • Fercoq Olivier
  • Alacaoglu Ahmet
  • Necoara Ion
  • Cevher Volkan
  , 2019, 97, pp.1910-1919. We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to handle constraints without the need for matrix-valued projections. We show for our stochastic gradient algorithm $\mathcal{O}(\log(k)/\sqrt{k})$ convergence rate for general convex objectives and $\mathcal{O}(\log(k)/k)$ convergence rate for restricted strongly convex objectives. These rates are known to be optimal up to logarithmic factors, even without constraints. We demonstrate the performance of our algorithm with numerical experiments on basis pursuit, a hard margin support vector machines and a portfolio optimization and show that our algorithm achieves state-of-the-art practical performance.
• Addressing Failure and Aging Degradation in MRAM/MeRAM-on-FDSOI Integration
  
  • Cai Hao
  • Wang You
  • Naviner Lirida
  • Liu Xinning
  • Shan Weiwei
  • Yang Jun
  • Zhao Weisheng
  IEEE Transactions on Circuits and Systems I: Regular Papers, IEEE, 2019, 66 (1), pp.239-250. (10.1109/TCSI.2018.2854277)
  DOI : 10.1109/TCSI.2018.2854277
• Algorithmes gloutons avec la classe
  
  • Zayana Karim
  • Michalak Pierre
  • Beauseigneur Clément
  • Tanoh Hélène
  , 2019. Les algorithmes gloutons offrent une solution pratique, mais pas toujours optimale, à de nombreux problèmes arithmétiques. Nous en donnons ici deux exemples (fractions égyptiennes et algorithme du monnayeur) avant d'entrer dans des considérations plus théoriques.
• Towards Interpretability of Segmentation Networks by analyzing DeepDreams
  
  • Couteaux Vincent
  • Nempont O.
  • Pizaine Guillaume
  • Bloch Isabelle
  , 2019, LCNS 11797, pp.56-63. Interpretability of a neural network can be expressed as the identification of patterns or features to which the network can be either sensitive or indifferent. To this aim, a method inspired by DeepDream is proposed, where the activation of a neuron is maximized by performing gradient ascent on an input image. The method outputs curves that show the evolution of features during the maximization. A controlled experiment show how it enables assess the robustness to a given feature, or by contrast its sensitivity. The method is illustrated on the task of segmenting tumors in liver CT images.
• Optimal survey schemes for stochastic gradient descent with applications to M-estimation
  
  • Clémençon Stéphan
  • Bertail Patrice
  • Chautru Emilie
  • Papa Guillaume
  ESAIM: Probability and Statistics, EDP Sciences, 2019, 23, pp.310-337. Iterative stochastic approximation methods are widely used to solve M-estimation problems, in the context of predictive learning in particular. In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible. A natural and popular approach to gradient descent in this context consists in substituting the “full data” statistics with their counterparts based on subsamples picked at random of manageable size. It is the main purpose of this paper to investigate the impact of survey sampling with unequal inclusion probabilities on stochastic gradient descent-based M-estimation methods. Precisely, we prove that, in presence of some a priori information, one may significantly increase statistical accuracy in terms of limit variance, when choosing appropriate first order inclusion probabilities. These results are described by asymptotic theorems and are also supported by illustrative numerical experiments. (10.1051/ps/2018021)
  DOI : 10.1051/ps/2018021
• Progressive hologram transmission using a view-dependent scalable compression scheme
  
  • Rhammad Anas El
  • Gioia Patrick
  • Gilles Antonin
  • Cagnazzo Marco
  Annals of Telecommunications - annales des télécommunications, Springer, 2019. Over the last few years, holography has been emerging as an alternative to stereoscopic imaging since it provides users with the most realistic and comfortable three-dimensional (3D) experience. However, high quality holograms enabling a free-viewpoint visualization contain tremendous amount of data. Therefore, a user willing to access to a remote hologram repository would face high downloading time, even with high speed networks. To reduce transmission time, a joint viewpoint-quality scalable compression scheme is proposed. At the encoder side, the hologram is first decomposed into a sparse set of diffracted light rays using Matching Pursuit over a Gabor atoms dictionary. Then, the atoms corresponding to a given user's viewpoint are selected to form a sub-hologram. Finally, the pruned atoms are sorted and encoded according to their importance for the reconstructed view. The proposed approach allows a progressive decoding of the sub-hologram from the first received atom. Streaming simulations for a moving user reveal that our approach outperforms conventional scalable codecs such as scalable H.265 and enables a practical streaming with a better quality of experience.
• Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models
  
  • Brouard Celine
  • Bassé Antoine
  • d'Alché-Buc Florence
  • Rousu Juho
  Metabolites, MDPI, 2019, 9 (8), pp.160. In small molecule identification from tandem mass (MS/MS) spectra, input–output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum of the unknown molecule, and solving a pre-image problem to find the molecule with the most similar fingerprint. In this paper, we bring forward the following improvements to the IOKR framework: firstly, we formulate the IOKRreverse model that can be understood as mapping molecular structures into the MS/MS feature space and solving a pre-image problem to find the molecule whose predicted spectrum is the closest to the input MS/MS spectrum. Secondly, we introduce an approach to combine several IOKR and IOKRreverse models computed from different input and output kernels, called IOKRfusion. The method is based on minimizing structured Hinge loss of the combined model using a mini-batch stochastic subgradient optimization. Our experiments show a consistent improvement of top-k accuracy both in positive and negative ionization mode data (10.3390/metabo9080160)
  DOI : 10.3390/metabo9080160
• Gaps between prime numbers and tensor rank of multiplication in finite fields
  
  • Randriambololona Hugues
  Designs, Codes and Cryptography, Springer Verlag, 2019.
• Improving data collection in complex networks with failure-prone agents via local marking
  
  • Rodrigues Arles
  • Botina Nathaly
  • Gomez Jonatan
  • Diaconescu Ada
  Journal of Intelligent and Fuzzy Systems, IOS Press, 2019.
• Noninvasive vascular occlusion with HIFU for venous insufficiency treatment: preclinical feasibility experience in rabbits
  
  • Barnat N
  • Grisey A
  • Lecuelle B
  • Anquez J.
  • Gerold B
  • Yon S.
  • Aubry J.-F
  Physics in Medicine and Biology, IOP Publishing, 2019, 64 (2), pp.025003. Venous insufficiency is a common disease arising when veins of the lower limb become incompetent. A conventional surgical strategy consists in stripping the incompetent veins. However, this treatment option is invasive and carries complication risks. In the present study, we propose noninvasive high-intensity focused ultrasound (HIFU) to treat lower limbs venous insufficiency, in particular incompetent perforating veins (mean diameter between 2-6 mm). Sonication parameters were designed by numerical simulations using the k-Wave toolbox to ensure continuous coagulation of a vein with a diameter superior or equal to 2 mm. The selected ultrasound exposures were 4 seconds pulses in continuous wave mode. Two types of sonication were studied: (1) fixed pulses and (2) moving pulses at constant speed (0.75 mm.s-1) across the vein. The potential of these exposures to thermally occlude veins were investigated in vivo on rabbit saphenous veins. The impact of vein compression during ultrasonic exposure was also investigated. Fifteen rabbits were used in these trials. A total of 27 saphenous veins (mean diameter 2.0 ± 0.6 mm) were sonicated with a transducer operated at 3 MHz. After a mean 15 days follow-up, rabbits were euthanized and venous samples were extracted and sent for histologic assessment. Only samples with the vein within the HIFU lesion were considered for analysis. Simulated thermal damage distribution demonstrated that fixed pulses and moving pulses respectively placed every 1.5 and 0.5 mm along the vein and delivered at an acoustic power of 85 W and for 4 seconds were able to induce continuous thermal damages along the vein segments. Experimentally, both treatment parameters (1) and (2) have proven effective to occlude veins with a success rate of 82%. Occlusion was always observed when compression was applied. Our results demonstrate that HIFU can durably and non-invasively occlude veins of diameters comparable to human veins. (10.1088/1361-6560/aaf58d)
  DOI : 10.1088/1361-6560/aaf58d
• Introduction à l’optimisation continue et discrète
  
  • Charon Irène
  • Hudry Olivier
  , 2019, pp.500. Ce livre propose une introduction aux méthodes d’optimisation continue ou discrète en quatre parties : 1. optimisation linéaire (algorithme du simplexe, théorie de la dualité) 2. optimisation continue non linéaire (avec ou sans contraintes ; relaxation lagrangienne) 3. résolution de problèmes d’optimisation polynomiaux en théorie des graphes (arbres couvrants de poids minimum, plus courts et plus longs chemins, flot maximum et applications des flots) 4. résolution de problèmes difficiles en optimisation combinatoire (théorie de la NP-complétude, heuristiques et métaheuristiques, méthodes arborescentes par séparation et évaluation, programmation dynamique ; applications à des problèmes classiques). Chaque chapitre contient des exercices et leurs solutions. En outre, une cinquième partie propose des problèmes corrigés ; chacun de ces problèmes implique différents chapitres du livre, pour favoriser une meilleure compréhension des interactions entre ceux-ci. L’accent y est mis en particulier sur la modélisation des problèmes traités.
• Dynamic and nonlinear properties of epitaxial quantum dot lasers on silicon for isolator-free integration
  
  • Duan Jianan
  • Huang Heming
  • Dong Bozhang
  • Norman Justin C
  • Zhang Zeyu
  • Bowers John
  • Grillot Frederic
  Photonics research, Optical Society of America, 2019, 7 (11), pp.1222. (10.1364/PRJ.7.001222)
  DOI : 10.1364/PRJ.7.001222
• Crusts on the Eyelashes
  
  • Ouedraogo Muriel
  • Ventejou Sarah
  • Leducq Sophie
  • Desoubeaux Guillaume
  • Maruani Annabel
  J Pediatr, 2019. A 27-month-old boy was referred to pediatric dermatology for the evaluation of crusts on his eyelashes; they had been present for 2 months and were increasing in number. The crusts had appeared after a trip to Bulgaria, where his family had been sleeping in several different hotels and places. Physical examination showed crusts on both eyelashes and a few crusts on the front part of his scalp. He had slight conjunctivitis and palpebral pruri-tis. The child was in good health and the physical examination was otherwise normal. His weight was 13.5 kg. Findings of a dermoscopy examination revealed that the crusts actually consisted of agglomerates of nits on the prox-imal side of the eyelashes, and several parasites were visible (Figure). There were a few parasites on the front part of his scalp hair also. The hair on his father's chest had several nits. Parasitology examination revealed that the parasites were Phtirus pubis. The child and his parents were given oral ivermectin 400 mg/kg (day 1 and day 7) and topical ophthalmologic rifamycin ointment once a day for 7 days, along with manual removal of the nits and parasites. Complete remission was obtained after 3 weeks of treatment. After interrogation, we concluded that phthiriasis had been transmitted from the father's chest to the child, who used to sleep on the chest of his parents. Phthiriasis palpebrarum is very uncommon in children, especially infants. It can reach the eyelashes but also can extend to the hair and might be complicated by conjunctivitis by an irritative mechanism. 1,2 The clinical diagnosis is confirmed by parasitology examination. The treatment usually consists of mechanical (manual or with tweezers) removal of nits and parasites using petrolatum. In our case, ophthalmologic rifamycin ointment was used to treat the conjunctivitis and facilitated manual removal. We added ivermectin treatment with the dosage recommended for difficult-to-treat head lice, 3,4 which is greater than that for scabies. Phthiriasis in children must always raise the question of the contamination method, which in children is secondary to contact with an infested adult. The origins of this infection need to be carefully discerned, and the question of sexual abuse must be raised. 5 n (10.1016/j.jpeds.2019.02.002)
  DOI : 10.1016/j.jpeds.2019.02.002
• Automatic Knee Meniscus Tear Detection and Orientation Classification with Mask-RCNN
  
  • Couteaux Vincent
  • Si-Mohamed S.
  • Nempont O.
  • Lefevre T.
  • Popoff A.
  • Pizaine Guillaume
  • Villain N.
  • Bloch Isabelle
  • Cotten A.
  • Boussel L.
  Diagnostic and Interventional Imaging, Elsevier, 2019, 100, pp.235-242.
• Multilabel, multiscale topological transformation for cerebral MRI segmentation post-processing
  
  • Tor-Díez Carlos
  • Faisan Sylvain
  • Mazo Loïc
  • Bednarek Nathalie
  • Meunier Hélène
  • Bloch Isabelle
  • Passat Nicolas
  • Rousseau François
  , 2019, 11564, pp.471-482. Accurate segmentation of cerebral structures remains, after two decades of research, a complex task. In particular, obtaining satisfactory results in terms of topology, in addition to quantitative and geometrically correct properties is still an ongoing issue. In this paper, we investigate how recent advances in multilabel topology and homotopy-type preserving transformations can be involved in the development of multiscale topological modelling of brain structures, and topology-based post-processing of segmentation maps of brain MR images. In this context, a preliminary study and a proof-of-concept are presented. (10.1007/978-3-030-20867-7_36)
  DOI : 10.1007/978-3-030-20867-7_36