Publications

2017

• The notion of self-aware computing
  
  • Kounev Samuel
  • Lewis Peter
  • Bellman Kirstie
  • Bencomo Nelly
  • Camara Javier
  • Diaconescu Ada
  • Esterle Lukas
  • Geihs Kurt
  • Giese Holger
  • Gotz Sebastian
  • Inverardi Paola
  • Kephart Jeff
  • Zisman Andrea
  , 2017, pp.3-16.
• Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion
  
  • Urien Hélène
  • Buvat Irène
  • Rougon N. F.
  • Soussan Michael
  • Bloch Isabelle
  , 2017, LNCS 10225, pp.455-466. In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for \textsuperscript{18}$F$-FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this criterion is proposed, and was evaluated on five patients with brain metastases and tuberculosis, and quantitatively assessed using the true positive rates and positive predictive values. The experimental results show that the method detects all the lesions in the PET.
• Détection et segmentation de tumeurs cérébrales en imagerie hybride TEP-IRM
  
  • Urien Hélène
  • Buvat Irène
  • Rougon N. F.
  • Soussan Michael
  • Bloch Isabelle
  , 2017, pp.50.
• Bravo monsieur Le Monde !
  
  • Zayana Karim
  Bulletion de l'APMEP, 2017. Mesurer la circonférence terrestre sans tourner en rond : Ce texte reprend un exposé donné le 10 mars 2017 au lycée Jean Zay à Paris, dans le cadre du plan national de formation « Construction des croisements didactiques en mathématiques et physique-chimie au collège
• A Minimax Optimal Algorithm for Crowdsourcing
  
  • Bonald Thomas
  • Combes Richard
  , 2017. We consider the problem of accurately estimating the reliability of workers based on noisy labels they provide, which is a fundamental question in crowdsourcing. We propose a novel lower bound on the minimax estimation error which applies to any estimation procedure. We further propose Triangular Estimation (TE), an algorithm for estimating the reliability of workers. TE has low complexity, may be implemented in a streaming setting when labels are provided by workers in real time, and does not rely on an iterative procedure. We prove that TE is minimax optimal and matches our lower bound. We conclude by assessing the performance of TE and other state-of-the-art algorithms on both synthetic and real-world data.
• Quantitative analysis of normal and pathologic adrenal glands with 18F-FDOPA PET/CT
  
  • Moreau Aurélie
  • Giraudet Anne
  • Kryza David
  • Borson-Chazot Françoise
  • Bournaud Claire
  • Mognetti Thomas
  • Lifante Jean-Christophe
  • Combemale Patrick
  • Giammarile Francesco
  • Houzard Claire
  Nuclear Medicine Communications, Lippincott, Williams & Wilkins, 2017, 38 (9), pp.771-779. (10.1097/MNM.0000000000000708)
  DOI : 10.1097/MNM.0000000000000708
• Formal Semantics of Behavior Specifications in the Architecture Analysis and Design Language Standard
  
  • Besnard Loic
  • Gautier Thierry
  • Le Guernic Paul
  • Guy Clément
  • Talpin Jean-Pierre
  • Larson Brian
  • Borde Etienne
  , 2017. (10.1007/978-981-10-4436-6_3)
  DOI : 10.1007/978-981-10-4436-6_3
• Les Communications par Fibres Optiques : La Fin de l'Age de Cuivre
  
  • Gallion Philippe
  , 2017, pp.8.
• Dynamic mitigation of EDFA power excursions with machine learning
  
  • Huang Yishen
  • Gutterman Craig L.
  • Samadi Payman
  • Cho Patricia B.
  • Samoud Wiem
  • Ware Cédric
  • Lourdiane Mounia
  • Zussman Gil
  • Bergman Keren
  Optics Express, Optical Society of America - OSA Publishing, 2017, 25 (3), pp.2245 - 2258. Dynamic optical networking has promising potential to support the rapidly changing traffic demands in metro and long-haul networks. However, the improvement in dynamicity is hindered by wavelength-dependent power excursions in gain-controlled erbium doped fiber amplifiers (EDFA) when channels change rapidly. We introduce a general approach that leverages machine learning (ML) to characterize and mitigate the power excursions of EDFA systems with different equipment and scales. An ML engine is developed and experimentally validated to show accurate predictions of the power dynamics in cascaded EDFAs. Recommended channel provisioning based on the ML predictions achieves within 1% error of the lowest possible power excursion over 94% of the time. We also showcase significant mitigation of EDFA power excursions in super-channel provisioning when compared to the first-fit wavelength assignment algorithm (10.1364/OE.25.002245)
  DOI : 10.1364/OE.25.002245
• Décomposition de séries temporelles d'images SAR pour la détection de changements
  
  • Lobry Sylvain
  • Denis L.
  • Zhao Weiying
  • Tupin Florence
  Traitement du Signal, Lavoisier, 2017.
• Robust Downbeat Tracking Using an Ensemble of Convolutional Networks
  
  • Durand Simon
  • Bello Juan Pablo
  • David Bertrand
  • Richard Gael
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2017, 25 (1), pp.76-89. <div><p>In this paper, we present a novel state of the art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm and bass content to feed convolutional neural networks that are adapted to take advantage of the characteristics of each feature. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the assumed metrical continuity of a song. We then perform an evaluation of our system on a large base of 9 datasets, compare its performance to 4 other published algorithms and obtain a significant increase of 16.8 percent points compared to the second best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.</p></div> (10.1109/TASLP.2016.2623565)
  DOI : 10.1109/TASLP.2016.2623565
• Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison
  
  • Romain Blandine
  • Rouet Laurence
  • Ohayon Daniel
  • Lucidarme Olivier
  • d'Alché-Buc Florence
  • Letort Véronique
  Medical Image Analysis, Elsevier, 2017, 35, pp.360--374. Patients follow-up in oncology is generally performed through the acquisition of dynamic sequences of contrast-enhanced images. Estimating parameters of appropriate models of contrast intake diffusion through tissues should help characterizing the tumour physiology. However, several models have been developed and no consensus exists on their clinical use. In this paper, we propose a unified framework to analyse models of perfusion and estimate their parameters in order to obtain reliable and relevant parametric images. After defining the biological context and the general form of perfusion models, we propose a methodological framework for model assessment in the context of parameter estimation from dynamic imaging data: global sensitivity analysis, structural and practical identifiability analysis, parameter estimation and model comparison. Then, we apply our methodology to five of the most widely used compartment models (Tofts model, extended Tofts model, two-compartment model, tissue-homogeneity model and distributed-parameters model) and illustrate the results by analysing the behaviour of these models when applied to data acquired on five patients with abdominal tumours. (10.1016/j.media.2016.07.008)
  DOI : 10.1016/j.media.2016.07.008
• Déborder l'expérience pour laisser une trace. Vidéophénoménographie d'un rappeur
  
  • Kneubühler Marine
  , 2017, pp.147-242.
• On some bounds for symmetric tensor rank of multiplication in finite fields
  
  • Ballet Stéphane
  • Pieltant Julia
  • Rambaud Matthieu
  • Sijsling Jeroen
  , 2017, 686, pp.93 - 121. We establish new upper bounds about symmetric bilinear complexity in any extension of finite fields. Note that these bounds are not asymptotical but uniform. Moreover, we discuss the validity of certain published bounds. (10.1090/conm/686/13779)
  DOI : 10.1090/conm/686/13779
• Conception d'absorbants à base de métasurfaces et Application de la Transformation d'Espace pour le contrôle du rayonnement d’une antenne imprimée
  
  • Lepage A. C.
  • Begaud Xavier
  , 2017.
• LISP EID Block
  
  • Iannone Luigi
  • Lewis Darrel
  • Meyer Dave
  • Fuller Vince
  , 2017.
• On perturbed proximal gradient algorithms
  
  • Atchadé Yves
  • Fort Gersende
  • Moulines Éric
  Journal of Machine Learning Research, Microtome Publishing, 2017, 18 (10), pp.1-33.
• Demystifying the asymptotic behavior of global denoising
  
  • Houdard Antoine
  • Almansa Andrès
  • Delon Julie
  Journal of Mathematical Imaging and Vision, Springer Verlag, 2017. In this work, we revisit the global denoising framework recently introduced by Talebi and Milanfar. We analyze the asymptotic behavior of its mean-squared error restoration performance in the oracle case when the image size tends to infinity. We introduce precise conditions on both the image and the global filter to ensure and quantify this convergence. We also make a clear distinction between two different levels of oracle that are used in that framework. By reformulating global denoising with the classical formalism of diagonal estimation, we conclude that the second-level oracle can be avoided by using Donoho and Johnstone’s theorem, whereas the first-level oracle is mostly required in the sequel. We also discuss open issues concerning the most challenging aspect, namely the extension of these results to the case where neither oracle is required. (10.1007/s10851-017-0716-6)
  DOI : 10.1007/s10851-017-0716-6
• Planck intermediate results. LI. Features in the cosmic microwave background temperature power spectrum and shifts in cosmological parameters
  
  • Aghanim N.
  • Akrami Yashar
  • Ashdown M.
  • Aumont J.
  • Ballardini M.
  • Banday A.J.
  • Barreiro R.B.
  • Bartolo N.
  • Basak S.
  • Benabed K.
  • Bersanelli M.
  • Bielewicz P.
  • Bonaldi A.
  • Bonavera L.
  • Bond J.R.
  • Borrill J.
  • Bouchet F.R.
  • Burigana C.
  • Calabrese E.
  • Cardoso J.F.
  • Challinor A.
  • Chiang H.C.
  • Colombo L.P.L.
  • Combet C.
  • Crill B.P.
  • Curto A.
  • Cuttaia F.
  • de Bernardis P.
  • de Rosa A.
  • de Zotti G.
  • Delabrouille J.
  • Di Valentino E.
  • Dickinson C.
  • Diego J.M.
  • Dore O.
  • Ducout A.
  • Dupac X.
  • Dusini S.
  • Efstathiou G.
  • Elsner F.
  • Ensslin T.A.
  • Eriksen H.K.
  • Fantaye Y.
  • Finelli F.
  • Forastieri F.
  • Frailis M.
  • Franceschi E.
  • Frolov A.
  • Galeotta S.
  • Galli S.
  • Ganga K.
  • Genova-Santos R.T.
  • Gerbino M.
  • Gonzalez-Nuevo J.
  • Gorski K.M.
  • Gruppuso A.
  • Gudmundsson J.E.
  • Herranz D.
  • Hivon E.
  • Huang Z.
  • Jaffe A.H.
  • Jones W.C.
  • Keihanen E.
  • Keskitalo R.
  • Kiiveri K.
  • Kim J.
  • Kisner T.S.
  • Knox L.
  • Krachmalnicoff N.
  • Kunz M.
  • Kurki-Suonio H.
  • Lagache G.
  • Lamarre J.M.
  • Lasenby A.
  • Lattanzi M.
  • Lawrence C.R.
  • Le Jeune M.
  • Levrier F.
  • Lewis A.
  • Lilje P.B.
  • Lilley M.
  • Lindholm V.
  • Lopez-Caniego M.
  • Lubin P.M.
  • Ma Y.Z.
  • Macias-Perez J.F.
  • Maggio G.
  • Maino D.
  • Mandolesi N.
  • Mangilli A.
  • Maris M.
  • Martin P.G.
  • Martinez-Gonzalez E.
  • Matarrese S.
  • Mauri N.
  • Mcewen J.D.
  • Meinhold P.R.
  • Mennella A.
  • Migliaccio M.
  • Millea M.
  • Miville-Deschenes M.A.
  • Molinari D.
  • Moneti A.
  • Montier L.
  • Morgante G.
  • Moss A.
  • Narimani A.
  • Natoli P.
  • Oxborrow C.A.
  • Pagano L.
  • Paoletti D.
  • Patanchon G.
  • Patrizii L.
  • Pettorino V.
  • Piacentini F.
  • Polastri L.
  • Polenta G.
  • Puget J.L.
  • Rachen J.P.
  • Racine B.
  • Reinecke M.
  • Remazeilles M.
  • Renzi A.
  • Rossetti M.
  • Roudier G.
  • Rubino-Martin J.A.
  • Ruiz-Granados B.
  • Salvati L.
  • Sandri M.
  • Savelainen M.
  • Scott D.
  • Sirignano C.
  • Sirri G.
  • Stanco L.
  • Suur-Uski A.S.
  • Tauber J.A.
  • Tavagnacco D.
  • Tenti M.
  • Toffolatti L.
  • Tomasi M.
  • Tristram M.
  • Trombetti T.
  • Valiviita J.
  • van Tent F.
  • Vielva P.
  • Villa Francesca
  • Vittorio N.
  • Wandelt B.D.
  • Wehus I.K.
  • White M.
  • Zacchei A.
  • Zonca A.
  Astronomy & Astrophysics - A&A, EDP Sciences, 2017, 607, pp.A95. The six parameters of the standard ΛCDM model have best-fit values derived from the Planck temperature power spectrum that are shifted somewhat from the best-fit values derived from WMAP data. These shifts are driven by features in the Planck temperature power spectrum at angular scales that had never before been measured to cosmic-variance level precision. We have investigated these shifts to determine whether they are within the range of expectation and to understand their origin in the data. Taking our parameter set to be the optical depth of the reionized intergalactic medium τ, the baryon density ωb, the matter density ωm, the angular size of the sound horizon θ∗, the spectral index of the primordial power spectrum, ns, and Ase− 2τ (where As is the amplitude of the primordial power spectrum), we have examined the change in best-fit values between a WMAP-like large angular-scale data set (with multipole moment ℓ < 800 in the Planck temperature power spectrum) and an all angular-scale data set (ℓ < 2500Planck temperature power spectrum), each with a prior on τ of 0.07 ± 0.02. We find that the shifts, in units of the 1σ expected dispersion for each parameter, are { Δτ,ΔAse− 2τ,Δns,Δωm,Δωb,Δθ∗ } = { −1.7,−2.2,1.2,−2.0,1.1,0.9 }, with a χ2 value of 8.0. We find that this χ2 value is exceeded in 15% of our simulated data sets, and that a parameter deviates by more than 2.2σ in 9% of simulated data sets, meaning that the shifts are not unusually large. Comparing ℓ < 800 instead to ℓ> 800, or splitting at a different multipole, yields similar results. We examined the ℓ < 800 model residuals in the ℓ> 800 power spectrum data and find that the features there that drive these shifts are a set of oscillations across a broad range of angular scales. Although they partly appear similar to the effects of enhanced gravitational lensing, the shifts in ΛCDM parameters that arise in response to these features correspond to model spectrum changes that are predominantly due to non-lensing effects; the only exception is τ, which, at fixed Ase− 2τ, affects the ℓ> 800 temperature power spectrum solely through the associated change in As and the impact of that on the lensing potential power spectrum. We also ask, “what is it about the power spectrum at ℓ < 800 that leads to somewhat different best-fit parameters than come from the full ℓ range?” We find that if we discard the data at ℓ < 30, where there is a roughly 2σ downward fluctuation in power relative to the model that best fits the full ℓ range, the ℓ < 800 best-fit parameters shift significantly towards the ℓ < 2500 best-fit parameters. In contrast, including ℓ < 30, this previously noted “low-ℓ deficit” drives ns up and impacts parameters correlated with ns, such as ωm and H0. As expected, the ℓ < 30 data have a much greater impact on the ℓ < 800 best fit than on the ℓ < 2500 best fit. So although the shifts are not very significant, we find that they can be understood through the combined effects of an oscillatory-like set of high-ℓ residuals and the deficit in low-ℓ power, excursions consistent with sample variance that happen to map onto changes in cosmological parameters. Finally, we examine agreement between PlanckTT data and two other CMB data sets, namely the Planck lensing reconstruction and the TT power spectrum measured by the South Pole Telescope, again finding a lack of convincing evidence of any significant deviations in parameters, suggesting that current CMB data sets give an internally consistent picture of the ΛCDM model.Key words: cosmology: observations / cosmic background radiation / cosmological parameters / cosmology: theory (10.1051/0004-6361/201629504)
  DOI : 10.1051/0004-6361/201629504
• Nonequilibrium Green's functions theory for the alpha factor of quantum cascade lasers
  
  • Pereira Mauro
  • Winge David
  • Wacker Andreas
  • Jumpertz Louise
  • Michel Florian
  • Pawlus Robert
  • Elsassaer Wolfgang
  • Schires Kevin
  • Carras Mathieu
  • Grillot Frédéric
  , 2017.
• Étude des perturbations des systèmes de positionnement magnéto-inductifs en intérieur, » Journées nationales micorondes
  
  • Gharat Vighnesh
  • Colin Elizabeth
  • Baudoin Geneviève
  • Richard Damien Richard
  , 2017.
• Beat Gesture Prediction using Prosodic Features
  
  • Jain Varun
  • Clavel Chloé
  • Pelachaud Catherine I
  , 2017.
• Fuzzy Skeleton and Skeleton by Influence Zones: A Review
  
  • Bloch Isabelle
  , 2017, pp.71-87. <p> Skeleton is a widely addressed topic in binary image processing and object or shape representation. The difficulty raised by the discrete nature of images has led to different classes of methods. Among the most popular ones, distance based methods led to the important notion of centers of maximal balls (CMB), and mathematical morphology based methods led both to an efficient computation of CMB and to a completely different class of approaches, relying on homotopic thinning. Skeleton by influence zones (SKIZ) is another important related problem, often addressed using tools from mathematical morphology.</p> <p> </p> <p>When imprecision has to be explicitly modeled, then objects become fuzzy sets and all the previous approaches for skeleton and SKIZ have to be extended to deal with fuzzy sets and to cope with spatial imprecision. This chapter gives an overview of the main existing definitions of fuzzy skeleton and fuzzy SKIZ, including some derived from grey level image processing, and proposes a few novel definitions. </p>
• Method, device, and computer program for transmitting portions of encapsulated media content
  
  • Denoual Franck
  • Mazé Frédéric
  • Ruellan Hervé
  • Le Feuvre J.
  • Ouedraogo Nael
  , 2017.
• Autoreject: Automated artifact rejection for MEG and EEG data
  
  • Jas Mainak
  • Engemann Denis A
  • Bekhti Yousra
  • Raimondo Federico A
  • Gramfort Alexandre
  NeuroImage, Elsevier, 2017. We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience. (10.1016/j.neuroimage.2017.06.030)
  DOI : 10.1016/j.neuroimage.2017.06.030