S³ seminar: High dimensional sampling with the Unadjusted Langevin Algorithm

Seminar on November 23, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Alain Durmus (LTCI, Telecom ParisTech)

Recently, the problem of designing MCMC sampler adapted to high-dimensional distributions and with sensible theoretical guarantees has received a lot of interest. The applications are numerous, including large-scale inference in machine learning,  Bayesian nonparametrics, Bayesian inverse problem, aggregation of experts among others. When the density is L-smooth (the log-density is continuously differentiable and its derivative is Lipshitz), we will advocate the use of a “rejection-free” algorithm, based on the discretization of the  Euler diffusion with either constant or decreasing stepsizes. We will present several new results allowing convergence to stationarity under different conditions for the log-density (from the  weakest, bounded oscillations on a compact set and super-exponential in the tails to the log concave).
When the density is strongly log-concave, the convergence of an appropriately weighted empirical measure is also investigated and bounds for the mean square error and exponential deviation inequality for Lipschitz functions will be reported.
Finally, based on optimzation techniques we will propose new methods to sample from high dimensional distributions. In particular, we will be interested  in densities which are not continuously differentiable. Some Monte Carlo experiments will be presented to support our findings.

Gaussian Channels: I-MMSE at Every SNR

Seminar on October 20, 2016, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Prof. Shlomo Shamai, The Andrew and Erna Viterbi Faculty of Electrical Engineering at the Technion-Israel Institute of Technology

Multi-user information theory presents many open problems, even in the simple Gaussian regime. One such prominent problem is the two-user Gaussian interference channel which has been a long standing open problem for over 30 years. We distinguish between two families of multi-user scalar Gaussian settings; a single transmitter (one dimension) and two transmitters (two dimensions), not restricting the number and nature of the receivers. Our first goal is to fully depict the behavior of asymptotically optimal, capacity
achieving, codes in one dimensional settings for every SNR. Such an understanding provides important insight to capacity achieving schemes and also gives an exact measure of the disturbance such codes have on unintended receivers.

We first discuss the Gaussian point-to-point channel and enhance some known results. We then consider the Gaussian wiretap channel and the Gaussian Broadcast channel (with and without secrecy demands) and reveal MMSE properties that confirm "rules of thumb" used in the achievability proofs of the capacity region of these channels and provide insights to the design of such codes.
We also include some recent observations that give a graphical interpretation to rate and equivocation in this one dimensional setting.
Our second goal is to employ these observations to the analysis of the two dimensional setting. Specifically, we analyze the two-user Gaussian interference channel, where simultaneous transmissions from two users interfere with each other. We employ our understanding of asymptotically point-to-point optimal code sequences to the analysis of this channel. Our results also resolve the "Costa Conjecture"
(a.k.a the "missing corner points" conjecture), as has been recently proved by Polyanskiy-Wu, applying Wasserstein Continuity of Entopy.

The talk is based on joint studies with R. Bustin, H. V. Poor and R. F. Schaefer.

S³: Material-by-Design for Synthesis, Modeling, and Simulation of Innovative Systems and Devices

Seminar on September 30, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Giacomo Oliveri (ELEDIA, University of Trento)

Several new devices and architectures have been proposed in the last decade to exploit the unique features of innovative artificially-engineered materials (such as metamaterials, nanomaterials, biomaterials) with important applications in science and engineering. In such a framework, a new set of techniques belonging to the Material-by-Design (MbD) framework [1]-[5] have been recently introduced to synthesize innovative devices comprising task-oriented artificial materials. MbD is an instance of the System-by-Design paradigm [6][7] defined in short as “How to deal with complexity”. More specifically, MbD considers the problem of designing artificial-material enhanced-devices from a completely new perspective, that is "The application-oriented synthesis of advanced systems comprising artificial materials whose constituent properties are driven by the device functional requirements". The aim of this seminar will be to review the fundamentals, features, and potentialities of the MbD paradigm, as well as to illustrate selected state-of-the-art applications of this design framework in sensing and communications scenarios.

Bio: Giacomo Oliveri received the B.S. and M.S. degrees in Telecommunications Engineering and the PhD degree in Space Sciences and Engineering from the University of Genoa, Italy, in 2003, 2005, and 2009 respectively. He is currently an Tenure Track Associate Professor at the Department of Information Engineering and Computer Science (University of Trento), Professor at CentraleSupélec, member of the Laboratoire des signaux et systèmes (L2S)@CentraleSupélec, and member of the ELEDIA Research Center. He has been a visiting researcher at L2S, Gif-sur-Yvette, France, in 2012, 2013, and 2015, and he has been an Invited Associate Professor at the University of Paris Sud, France, in 2014. In 2016, he has been awarded the "Jean d'Alembert" Scholarship by the IDEX Université Paris-Saclay. He is author/co-author of over 250 peer-reviewed papers on international journals and conferences, which have been cited above 2200 times, and his H-Index is 26 (source: Scopus). His research work is mainly focused on electromagnetic direct and inverse problems, system-by-design and metamaterials, compressive sensing techniques and applications to electromagnetics, and antenna array synthesis. Dr. Oliveri serves as an Associate Editor of the International Journal of Antennas and Propagation, of the Microwave Processing journal, and of the International Journal of Distributed Sensor Networks. He is the Chair of the IEEE AP/ED/MTT North Italy Chapter.

The Appointment Scheduling Problem: The Doctor, Her Patients and The Waiting Room

Seminar on July 08, 2016, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Prof. Stijn De Vuyst, Ghent University (UGent), Belgium.

We consider the appointment scheduling problem in the case of one doctor who sequentially provides service to the patients in the waiting room, in particular with respect to the incurred waiting times for both the patients and the doctor. This problem is different from a classical single-service queueing system in at least two ways: (1) the arrivals happen at pre-determined instants instead of randomly and (2) equilibrium solutions are of no use here since we need to know the waiting time of each individual patient. Given the length of the session and the consultation time distribution of each of K scheduled patient, we obtain the moments of the patient's waiting time and of the doctor's idle times. We also discuss the complicating factors such as the impact of unpunctuality, i.e. what happens if patients do not arrive exactly as appointed as usually the case in practice. A mild degree of unpunctuality can be handled by our model, but problems arise as soon as patients can overtake each other. Finally, we use the our results to construct suitable heuristics for finding optimal optimal appointment schedules.

Biography: Stijn De Vuyst is currently assistant professor at the Faculty of Engineering and Architecture of Ghent University (UGent), Belgium, in the Department of Industrial Systems Engineering and Product Design. His expertise is in operations research, in particular stochastic modelling, simulation, queueing theory and scheduling with application to the design, planning and performance evaluation of production systems as well as telecommunication systems. He obtained a master degree in Electrical Engineering and a PhD degree in Engineering Sciences at Ghent University. Prior to 2012, he was a post-doctoral researcher affiliated with the department of Telecommunication and Information Processing and for 6 months with the Informatics department at Université Libre de Bruxelles. From 2012 to 2015 he presided the faculty's educational board for the Master program Industrial Engineering and Operations Research. He currently teaches various courses on stochastic simulation, quality engineering and industrial statistics.

Almost Lossless Variable-Length Source Coding on Countably Infinite Alphabets

Seminar on July 08, 2016, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Prof. Jorge F. Silva, University of Chile, Santiago.

Motivated from the fact that universal source coding on countably infinite alphabets is not feasible, in this talk a notion of almost lossless source coding will be introduced.  This idea —analog to the  weak variable-length source coding proposed by Han 2000— aims at relaxing the lossless block-wise assumption to allow a distortion that vanishes asymptotically as the block-length goes to infinity.  In this almost lossless coding setting, new source coding results will be presented that on one hand show that Shannon entropy characterizes the minimum achievable rate (known statistics), while on the other,  that almost lossless universal source coding becomes feasible for the family of finite entropy stationary and memoryless sources with countably infinite alphabets.

Biography: Jorge F. Silva is Associate Professor at the Electrical Engineering Department and director of the Information and Decision Systems (IDS) Group at the University of Chile, Santiago, Chile. He received the Master of Science (2005) and Ph.D. (2008) in Electrical Engineering from the University of Southern California (USC). He is IEEE member of the Signal Processing and Information Theory Societies and he is associate editor of the  IEEE Transactions on Signal Processing.  Dr. Silva is recipient of the Viterbi Doctoral Fellowship  2007–2008 and Simon Ramo Scholarship 2007–2008 at USC.   Dr. Silva general research interests include: detection and estimation, information theory and statistics, universal source coding, sparse and compressible models and  compressed sensing.

S³: Condition monitoring using vibration signals

Seminar on May 24, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Asoke K. Nandi

Condition monitoring of machines is an essential part of smooth, efficient, safe, and productive operation of machines. In this presentation, focus will be on rotating machines and in the use of vibration signals. Classification of vibration signals to different states of machines has been achieved through the developments and applications of signal processing and machine learning. This presentation will cover research efforts and some case studies carried out over many years.

Bio: Professor Asoke K. Nandi received the degree of Ph.D. in Physics from the University of Cambridge, Cambridge (UK). He held academic positions in several universities, including Oxford (UK), Imperial College London (UK), Strathclyde (UK), and Liverpool (UK) as well as Finland Distinguished Professorship in Jyvaskyla (Finland). In 2013 he moved to Brunel University London (UK), to become the Chair and Head of Electronic and Computer Engineering. Professor Nandi is a Distinguished Visiting Professor at Tongji University (China) and an Adjunct Professor at University of Calgary (Canada).
In 1983 Professor Nandi contributed to the discovery of the three fundamental particles known as W+, W− and Z0 (by the UA1 team at CERN), providing the evidence for the unification of the electromagnetic and weak forces, which was recognized by the Nobel Committee for Physics in 1984. His current research interests lie in the areas of signal processing and machine learning, with applications to communications, gene expression data, functional magnetic resonance data, and biomedical data. He has made many fundamental theoretical and algorithmic contributions to many aspects of signal processing and machine learning. He has much expertise in “Big Data”, dealing with heterogeneous data, and extracting information from multiple datasets obtained in different laboratories and different times. He has authored over 500 technical publications, including 200 journal papers as well as four books, entitled Automatic Modulation Classification: Principles, Algorithms and Applications (Wiley, 2015), Integrative Cluster Analysis in Bioinformatics (Wiley, 2015), Automatic Modulation Recognition of Communications Signals (Springer, 1996), and Blind Estimation Using Higher-Order Statistics (Springer, 1999),. Recently he published in Blood, BMC Bioinformatics, IEEE TWC, NeuroImage, PLOS ONE, Royal Society Interface, and Signal Processing. The h-index of his publications is 63 (Google Scholar).

Professor Nandi is a Fellow of the Royal Academy of Engineering and also a Fellow of seven other institutions including the IEEE and the IET. Among the many awards he received are the Institute of Electrical and Electronics Engineers (USA) Heinrich Hertz Award in 2012, the Glory of Bengal Award for his outstanding achievements in scientific research in 2010, the Water Arbitration Prize of the Institution of Mechanical Engineers (UK) in 1999, and the Mountbatten Premium, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers (UK) in 1998.

S³: Time Frequency Array Signal Processing: Multi-Dimensional processing for non-stationary signals

Seminar on May 20, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Adel Belouchrani

Conventional time-frequency analysis methods are being extended to data arrays, and there is a potential for a great synergistic development of new advanced tools by exploiting the joint properties of time-frequency methods and array signal processing methods. Conventional array signal processing assumes stationary signals and mainly employs the covariance matrix of the data array. This assumption is motivated by the crucial need in practice for estimating sample statistics by resorting to temporal averaging under the additional hypothesis of ergodic signals. When the frequency content of the measured signals is time varying (i.e., nonstationary signals), this class of approaches can still be applied. However, the achievable performances in this case are reduced with respect to those that would be achieved in a stationary environment. Instead of considering the nonstationarity as a shortcoming, Time Frequency Array Processing   takes advantage of the nonstationarity by considering it as a source of information in the design of efficient algorithms in such environments. This talk deals with this  relationship between time-frequency methods and array signal processing methods. Recent results on the performance analysis of the Time Frequency MUSIC algorithm will be also presented.

Bio: Adel Belouchrani was born in Algiers, Algeria, on May 5, 1967. He received the State Engineering degree in 1991 from Ecole Nationale Polytechnique (ENP), Algiers, Algeria, the M.S. degree in signal processing from the Institut National Polytechnique de Grenoble (INPG), France, in 1992, and the Ph.D. degree in signal and image processing from Télécom Paris (ENST), France, in 1995. He was a Visiting Scholar at the Electrical Engineering and Computer Sciences Department, University of California, Berkeley, from 1995 to 1996. He was with the Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, as a Research Associate from 1996 to 1997. From 1998 to 2005, he has been with the Electrical Engineering Department of ENP as Associate Professor. He is currently and since 2006 Full Professor at ENP. His research interests are in statistical signal processing, (blind) array signal processing, time-frequency analysis and time-frequency array signal processing with applications in biomedical and telecommunications. Professor Adel Belouchrani is an IEEE Senior Member and has published over 180 technical publications including 48  journal papers, 4 book chapters and 4 patents that have been cited over 5400 times according Google Scholar  and  over 2000 time according to ISI Web Of Science. He has supervised over 19 PhD students. Professor Adel Belouchrani is currently Associated Editor of the IEEE Transactions on Signal Processing and Editorial board member of the Digital signal processing Journal (Ed. Elsevier).  He has been recently nominated  as a founding member of the Algerian Academy of Science  and Technology.

Improved Millimeter-Wave Radar Concealed-Threat Person Scanning

Seminar on April 08, 2016, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Carey M. Rappapor

Metal-detecting airport security scanners for airline passengers are being replaced by millimeter-wave imagers. These new systems are much better at revealing concealed manmade objects, but they can be improved. At our Advanced Imaging Technology Lab at Northeastern University in Boston, we are developing a custom-designed elliptical toroid reflector antenna which allows multiple overlapping beams for focused wide-angle illumination to speed data acquisition and accurately image strongly inclined body surfaces.  We have developed the concept of the Blade Beam Reflector both as a single transmitting antenna and a multi-beam Toroidal Reflector, with multiple feeds. Each feed generates a different incident beam with different viewing angles, while still maintaining the blade beam configuration of narrow slit illumination in the vertical direction.  Having multiple transmitters provides horizontal resolution and imaging of full 120 deg. of body.  Furthermore, the reflector can simultaneously be used for receiving the scattered field, with high gain, overlapping, high vertical resolution beams for each transmitting or receiving array element. The multistatic transmitting and receiving array configuration sensing avoids dihedral artifacts from body crevices and reduces non-specular drop-outs, and will leads to a faster, higher resolution, and less expensive security system.

Bio — Carey M. Rappaport received five degrees from the Massachusetts Institute of Technology:  the SB in Mathematics, the SB, SM, and EE in Electrical Engineering in June 1982, and the PhD in Electrical Engineering in June 1987.  He is married to Ann W. Morgenthaler, and has two children, Sarah and Brian. Prof. Rappaport joined the faculty at Northeastern University in Boston, MA in 1987.  He has been Professor of Electrical and Computer Engineering since July 2000. In 2011, he was appointed College of Engineering Distinguished Professor.  He was Principal Investigator of an ARO-sponsored Multidisciplinary University Research Initiative on Humanitarian Demining, Co-Principal Investigator of the NSF-sponsored Engineering Research Center for Subsurface Sensing and Imaging Systems (CenSSIS), and Co-Principal Investigator and Deputy Director of the DHS-sponsored Awareness and Localization of Explosive Related Threats (ALERT) Center of Excellence. Prof. Rappaport has authored over 400 technical journal and conference papers in the areas of microwave antenna design, electromagnetic wave propagation and scattering computation, and bioelectromagnetics, and has received two reflector antenna patents, two biomedical device patents and three subsurface sensing device patents.  He was awarded the IEEE Antenna and Propagation Society's H.A. Wheeler Award for best applications paper, as a student in 1986.  He is a member of Sigma Xi and Eta Kappa Nu professional honorary societies.

S³: Topological Pattern Selection in Recurrent Networks

Seminar on April 01, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Alireza Bahraini

One of the differences between memory function of hypocampus and  neural networks situated at neocortex is that in the  latter memory operation still reflect the topography informing synaptic connections. This means that the activity of a unit relates also to its position in the tissue.
We introduce two approaches for incorporating the information of the geometry  of the underlying neural network into its dynamics. This phenomenon is carried out based on two probability rules for selecting storing patterns. First  a Gibbs type distribution inspired by the architecture of the network is applied. We are then led to a second method to introduce topological effects on the dynamics of the network. In both approaches a significant enhancement on the capacity of the network is observed after considerable rigorous computations.

Some References:
1- Bahraini,A, .  Abbassian,A.  Topological Pattern Selection in Recurrent Networks, Journal of Neural Networks, 31, 2012, 22-32.
2- Roudi, Y, Treves, A,. An associative Network with Spatially Organized Connectivity,2004, Journal of Statistical Mechanics.
3- Gallan, R., F., On how network architecture determines the dominant patterns of spontaneous neural activity, PLoS One , 2008.

Bio: I obtained my DEA in 2001 and my PhD in 2004 at University Paris 7 under the supervision of Professor Daniel Bennequin. The title of my thesis was Super-symmetry and Complex Geometry. I joined the department of mathematical sciences of Sharif university of technology as an assistant
professor in 2004 and in 2012 I became an associate professor at the same department.

Approche bayésienne de l'estimation des composantes périodiques des signaux en chronobiologie.

Mircea DUMITRU
Thesis defended on March 25, 2016, 10:00 AM at CentraleSupelec (Gif-sur-Yvette) Amphi Ampère

La toxicité et l'efficacité de plus de 30 agents anticancéreux présente de très fortes variations
en fonction du temps de dosage. Par conséquent, les biologistes qui étudient le rythme circadien ont
besoin une méthode très précise pour estimer le vecteur de composantes périodiques (CP) de signaux
chronobiologiques En outre, dans les développements récents, non seulement la période dominante
ou le vecteur de CP présentent un intérêt crucial, mais aussi leur stabilités ou variabilités. Dans les
expériences effectuées en traitement du cancer, les signaux enregistrés correspondant à différentes
phases de traitement sont courts, de sept jours pour le segment de synchronisation jusqu'à deux ou
trois jours pour le segment après traitement. Lorsque on étudie la stabilité de la période dominante nous
devons considérer des signaux très court par rapport à la connaissance a priori de la période dominante,
placée dans le domaine circadien. Les approches classiques basées sur la transformée de Fourier (TF)
sont inefficaces (i.e. manque de précision) compte tenu de la particularité des données (i.e. la courte
longueur). Une autre particularité des signaux qui est prise en considération dans ces expériences,
est le niveau de bruit. Ces signaux étant très bruités, il est difficile de déterminer les composantes
périodiques associées aux phénomènes biologiques et de les distingue de celle qui sont associées au
bruit. Dans cette thèse, nous proposons une nouvelle méthode pour l'estimation du vecteur de CP des
signaux biomédicaux, en utilisant les informations biologiques a priori et en considérant un modèle
qui représente le bruit.

Les signaux enregistrés dans le cadre d'expériences développées pour le traitement du cancer ont
un nombre limité de périodes. Cette information a priori peut être traduit comme la parcimonie du
vecteur de CP. La méthode proposée considère l'estimation de vecteur de CP comme un problème in-
verse en utilisant l'inférence bayésienne générale afin de déduire toutes les inconnues de notre modèle,
à savoir le vecteur de CP mais aussi les hyperparamètres (i.e. les variances associées). L'information
a priori de parcimonie est modélisée en utilisant une loi a priori renforcent la parcimonie. Dans cette
thèse, nous proposons une distribution de Student, considérée comme la distribution marginale d'une
loi bivariée - la distribution Normale - Inverse Gamma. En fait, lorsque l'égalité entre les paramètres de
forme et d'échelle, de la distribution Inverse Gamma n'est pas imposée, la marginale de la distribution
Normale-Inverse Gamma est une généralisation de la distribution de Student. Nous construisons un mo-
dèle hiérarchique où nous attribuons aussi une loi a priori pour les hyperparamètres. L'expression de
la loi conjointe a posteriori du vecteur de CP et des hyperparamètres est obtenue par la règle de Bayes
et les inconnues sont estimées soit par Maximum A Posteriori (MAP) soit par l'espérance a posteriori
(EAP). Pour le calcul de EAP, l'expression de la loi a posteriori est approchée par une loi séparables en
utilisant l'approximation bayésienne variationnelle (ABV), via la divergence de Kullback-Leibler (KL).
Deux possibilités sont envisagées : une approximation avec des lois partiellement séparables ou entiè-
rement séparable. Ces algorithmes sont présentés en détail et sont comparées avec ceux correspondant
au modèle gaussien. Nous examinons la convergence des algorithmes et donnons des résultats de si-
mulation afin de comparer leurs performances. Enfin, nous montrons des résultats de simulation sur
des données synthétiques et réelles dans une application de traitement du cancer. Les données réelles
utilisées dans cette thèse representent des modèles de repos-activité et d'expression des gènes de KI /
KI Per2 : :luc souris luc, âgées de 10 semaines, seules dans leur cages des RT-BIO.

Mots-clefs: Estimation de composantes périodiques, Problèmes inverses, Approches bayesiennes, Mo-
del hierarchique, Renforcement de parcimonie, Student-t generalisé, chronobiologie, chronothérapie,
Gènes de l'horloge, Rythme circadien, Traitement du cancer.

Composition du jury

M. Ali MOHAMMAD-DJAFARI, Directeur de recherche CNRS, L2S, Gif-sur-Yvette, Directeur de thèse
M. Francis LÉVI, Professeur des Universités, University of Warwick, Angleterre, Co-directeur de thèse
M. Jean-François GIOVANELLI, Professeur des Universités, IMS, Bordeaux, Rapporteur
M. Ercan Engin KURUOGLU, Chercheur sénior CNRS, ISTI, Italie, Rapporteur
M. Alexandre RENAUX, Maître de conférences, Paris-Sud, Orsay, Examinateur
M. Michel KIEFFER, Professeur des Universités, Paris-Sud, Orsay, Examinateur

S³: Solving large-scale inverse problems using forward-backward based methods

Seminar on March 11, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Audrey Repett: post-doctoral researcher at the Heriot-Watt university, in Scotland

Recent developments in imaging and data analysis techniques came along with an increasing need for fast convex optimization methods for solving large scale problems.  A simple optimization strategy to minimize the sum of a Lipschitz differentiable function and a non smooth function is the forward-backward algorithm. In this presentation, several approaches to accelerate convergence speed and to reduce complexity of this algorithm will be proposed. More precisely, in a first part, preconditioning methods adapted to non convex minimization problems will be presented, and in a second part, stochastic optimization techniques will be described in the context of convex optimization. The different proposed methods will be used to solve several inverse problems in signal and image processing.

Bio: Audrey Repetti is a post-doctoral researcher at the Heriot-Watt university, in Scotland. She received her M.Sc. degree from the Université Pierre et Marie Curie (Paris VI) in applied mathematics, and her Ph.D. degree from the Université Paris-Est Marne-la-Vallée in signal and image processing. Her research interests include convex and non convex optimization, and signal and image processing.

S³: Data-driven, Interactive Scientific Articles in a Collaborative Environment with Authorea

Seminar on March 04, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Nathan Jenkins

Most tools that scientists use for the preparation of scholarly manuscripts, such as Microsoft Word and LaTeX, function offline and do not account for the born-digital nature of research objects. Also, most authoring tools in use today are not designed for collaboration and as scientific collaborations grow in size, research transparency and the attribution of scholarly credit are at stake. In this talk, I will show how Authorea allows scientists to collaboratively write rich data-driven manuscripts on the web–articles that would natively offer readers a dynamic, interactive experience with an article’s full text, images, data, and code–paving the road to increased data sharing, data reuse, research reproducibility, and Open Science.

Bio: Nathan Jenkins is co-founder and CTO of Authorea.  A condensed matter physicist, Nathan completed his Ph.D. at the University of Geneva where he studied electronic properties of high temperature superconductors at the atomic scale. He was then awarded a Swiss National Science Foundation scholarship to study as a postdoc at NYU where examined the dynamics of protein folding via atomic force microscopy.  Hailing from California, Nathan resides between Geneva, Switzerland and New York City.

S³: Robust Factor Analysis of Time Series with Long-Memory and Outliers: Application to Air Pollution data

Seminar on February 19, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Valderio Anselmo Reisen

This paper considers the factor modeling for high-dimensional time series with short and long-memory properties and in the presence of additive outliers. For this, the factor model studied by Lam and Yao (2012) is extended to consider the presence of additive outliers. The estimators of the number of factors are obtained by an eigenanalysis of a non-negative definite matrix, i.e., the covariance matrix or the robust covariance matrix. The proposed methodology is analyzed in terms of the convergence rate of the number factors by means of Monte Carlo simulations. As an example of application, the robust factor analysis is utilized to identify pollution behavior for the pollutant PM10 in the Greater Vitoria region ( ES, Brazil) aiming to reduce the dimensionality of the data and for forecasting investigation.

Bio: Valderio Anselmo Reisen is full Professor of Statistics at the Federal University of Espirito Santo (UFES), Vitoria, Brazil. His main interests are time series analysis, forecasting, econometric modeling, bootstrap, robustness in time series, unit root processes, counting processes,  environmental and economic data analysis, periodically correlated processes, and multivariate time series.

scube.l2s.centralesupelec.fr

S³: Robust spectral estimators for long-memory processes: Time and frequency domain approaches.

Seminar on January 29, 2016, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Valderio Anselmo Reisen

This paper discusses the outlier effects on the estimation of a spectral estimator for long memory process under additive outliers and proposes robust spectral estimators. Some asymptotic properties of the proposed robust methods are derived and Monte Carlo simulations investigate their empirical properties.  Pollution series, such as, PM (Particulate matter), SO2 (Sulfur dioxide), are the applied examples investigated here to show the usefulness of the proposed  robust methods in real applications.  These pollutants present, in general, observations with high levels of pollutant concentrations which may produce sample densities with heavy tails  and these high levels of concentrations can be identified as outliers which can destroy the statistical properties of sample functions such as the standard mean,  covariance and the periodogram.

Bio: Valderio Anselmo Reisen is full Professor of Statistics at the Federal University of Espirito Santo (UFES), Vitoria, Brazil. His main interests are time series analysis, forecasting, econometric modeling, bootstrap, robustness in time series, unit root processes, counting processes,  environmental and economic data analysis, periodically correlated processes, and multivariate time series.

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S³ seminar: A Two-Round Interactive Receiver Cooperation Scheme for Multicast Channels

Seminar on January 08, 2016, 4:30 PM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Victor Exposito

We consider the problem of transmitting a common message from a transmitter to two receivers over a broadcast channel, which is also called multicast channel in this case. The two receivers are allowed to cooperate with each other in full-duplex over non-orthogonal channels. We investigate the information-theoretic upper and lower bounds on the achievable rate of such channels. In particular, we propose a two-round cooperation scheme in which the receivers interactively perform compress-forward (CF) and then decode-forward (DF) to improve the achievable rate. Numerical results comparing the proposed scheme to existing schemes and the cutset upper bound are provided. We show that the proposed scheme outperforms the non-interactive DF and CF schemes as well as the noisy network coding. The gain over the DF scheme becomes larger when the channel becomes symmetric, while the gain over the CF scheme becomes larger when the channel becomes asymmetric.
 
Bio: Victor Exposito received the Engineering and M.Sc. degree (valedictorian) in communication systems and networks from the Institut National des Sciences Appliquées de Rennes (INSA-Rennes), Rennes, France, in 2014. He is currently working at Mitsubishi Electric R&D Centre Europe (MERCE-France), Rennes, France and Ecole Supérieure d’Electricité (CentraleSupélec), Gif-sur-Yvette, France, toward the Ph.D. degree. His current research interests mainly lie in the area of network information theory.
 

Structured data analysis with Regularized Generalized Canonical Correlation Analysis

A. Tenenhaus
Habilitation à Diriger des Recherches (HDR) onJanuary 05, 2016, 9:30 AM at

The challenges related to the use of massive amounts of data (e.g omics data, imaging-genetic data, etc)
include identifying the relevant variables, reducing dimensionality, summarizing information in a comprehensible way
and displaying it for interpretation purposes. Often, these data are intrinsically structured in blocks of variables, in groups
of individuals or in tensor. Classical statistical tools cannot be applied without altering their structure leading to the risk of
information loss. The need to analyze the data by taking into account their natural structure appears to be essential but
requires the development of new statistical techniques that constitutes the core of my research for many years.
In particular, I am interested in multiblock, multigroup and multiway structures. In that context a general framework
for structured data analysis based on Regularized Generalized Canonical Correlation Analysis (RGCCA) is defined.


Membres du Jury :

* Hervé Abdi, Professeur, Université of Texas, Rapporteur
* Florence d’Alché-Buc, Professeur, Telecom ParisTech, Rapporteur
* Jean-Philippe Vert, Directeur de Recherche, Mines ParisTech, Rapporteur
* Mohamed Hanafi, Ingénieur-Chercheur, ONIRIS, Examinateur
* Jean-Michel Poggi, Professeur, Paris V, Examinateur
* Gilbert Saporta, Professeur, CNAM, Examinateur

S³: Gegenbauer polynomials and positive definiteness

Seminar on November 27, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Christian Berg, University of Copenhagen, Denmark

Abstract file

Bio: Professor Christian Berg graduated from Næstved Gymnasium 1963 and studied mathematics at the University of Copenhagen. He became cand.scient. in 1968, lic.scient. (ph.d.) in 1971, and dr. phil. in 1976. Christian Berg received the gold medal of the University of Copenhagen in 1969 for a paper about Potential Theory.
He became assistant professor at University of Copenhagen in 1971, associated professor in 1972 and professor since 1978. Christian Berg had several research visits abroad, in USA, France, Spain, Sweden and Poland.
He became member of The Royal Danish Academy of Sciences and Letters 1982, vice-president 1999-2005. Member of The Danish Natural Sciences Research Council 1985-1992. President of the Danish Mathematical Society 1994-98. Member of the editorial board of Journal of Theoretical Probability (1988-1999) and Expositiones Mathematicae since 1993. Member of the advisory board of Arab Journal of Mathematical Sciences since 1995.
At the Department of Mathematics of the University of Copenhagen, he was Member of the Study Board 1972-74, member of the Board 1977-1984, 1993-1995, chairman 1996-97, and Director of the Institute for Mathematical Sciences 1997-2002.
Christian Berg  has so far published app. 110 scientific papers in international journals, mainly about potential theory, harmonic analysis and moment problems.

S³:Bayesian Fusion of Multiple Images - Beyond Pansharpening

Seminar on November 13, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Jean-Yves Tourneret, University of Toulouse, FR

This presentation will discuss new methods for fusing high spectral resolution images (such as hyperspectral images) and high spatial resolution images (such as panchromatic images) in order to provide images with improved spectral and spatial resolutions. These methods are based on Bayesian estimators exploiting prior information about the target image to be recovered, constructed by interpolation or by using dictionary learning techniques. Different implementations based on MCMC methods, optimization strategies or on the resolution of Sylvester equations will be explored

Bio: Jean-Yves TOURNERET (SM08) received the ingenieur degree in electrical engineering from the Ecole Nationale Supérieure d'Electronique, d'Electrotechnique, d'Informatique, d'Hydraulique et des Télécommunications (ENSEEIHT) de Toulouse in 1989 and the Ph.D. degree from the National Polytechnic Institute from Toulouse in 1992. He is currently a professor in the university of Toulouse (ENSEEIHT) and a member of the IRIT laboratory (UMR 5505 of the CNRS). His research activities are centered around statistical signal and image processing with a particular interest to Bayesian and Markov chain Monte Carlo (MCMC) methods. He has been involved in the organization of several conferences including the European conference on signal processing EUSIPCO'02 (program chair), the international conference ICASSP'06 (plenaries), the statistical signal processing workshop SSP'12 (international liaisons), the International Workshop on Computational Advances in Multi-Sensor Adaptive Processing CAMSAP 2013 (local arrangements), the statistical signal processing workshop SSP'2014 (special sessions), the workshop on machine learning for signal processing MLSP'2014 (special sessions). He has been the general chair of the CIMI workshop on optimization and statistics in image processing hold in Toulouse in 2013 (with F. Malgouyres and D. Kouamé) and of the International Workshop on Computational Advances in Multi-Sensor Adaptive Processing CAMSAP 2015 (with P. Djuric). He has been a member of different technical committees including the Signal Processing Theory and Methods (SPTM) committee of the IEEE Signal Processing Society (2001-2007, 2010-present). He has been serving as an associate editor for the IEEE Transactions on Signal Processing (2008-2011, 2015-present) and for the EURASIP journal on Signal Processing (2013-present).

Access information are available on the website http://www.lss.supelec.fr/scube/

S³: Algorithmes d’Estimation et de Détection en contexte Hétérogène Rang Faible

Seminar on November 06, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
A. Breloy, Ecole Normale Supérieure de Cachan, FR

Covariance Matrix (CM) estimation is an ubiquitous problem in statistical signal processing. In terms of application purposes, the accuracy of the CM estimate directly impacts the performance of the considered adaptive process. In the context of modern data-sets, two major problems are currently at stake:

- Samples are often drawn from heterogeneous (non gaussian) distributions.
- Only a low sample support is available.

To respond to these problems, one has to develop new estimation tools that are based on an appropriate modeling of the data.

Bio: Arnaud Breloy graduated from Ecole Centrale Marseille and recived a Master's degree of Signal and Image Processing from university of Aix-Marseille in 2012-13. Formerly Ph.D student at the SATIE and SONDRA laboratories, he is currently lecturer at University Institute of Technology of Ville d’Avray. His research interests focuses on statistical signal processing, array and radar signal processing, robust estimation methods and low rank methods.

Panneaux complexes anisotropes et imagerie électromagnétique rapide.

Giacomo RODEGHIERO
Thesis defended on September 29, 2015, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Amphi Ampère

Le Contrôle Non Destructif (CND) de matériaux composites multicouches pour des problèmes de qualité, viabilité,  sécurité et disponibilité des systèmes qui impliquent des pièces fabriquées dans les industries aéronautiques et de l'automobile est devenu une tâche essentielle aujourd'hui. L'objectif visé par cette thèse est l'imagerie électromagnétique de structures complexes multicouches anisotropes, de plus en plus utilisées dans des applications, et encore source de sérieux défis à l'étape de leur modélisation et encore plus à l'étape souvent en enfance de leur imagerie. En utilisant une vaste gamme de fréquences, qui va des courants de Foucault jusqu'aux micro-ondes, il y a un fort besoin de rendre disponibles des procédures de modélisation et d'imagerie qui sont robustes, rapides, précises et utiles à la décision des utilisateurs finaux sur des défauts potentiels, tant donc en basse fréquence (BF) (matériaux conducteurs, type fibre de carbone) qu'en haute fréquence (HF) (matériaux diélectriques, type fibre de verre). De plus, il est important d'obtenir des résultats en des temps brefs. Cependant, cela nécessite la connaissance d'une réponse précise à des sources externes aux multicouches, en considérant les couches des composites comme non endommagées ou endommagées : on parle donc de solution du problème direct, avec le cas particulier de sources élémentaires conduisant aux dyades de Green (DGF).

La modélisation et la simulation numérique du problème direct sont gérés principalement via une solution au premier ordre de la formulation intégrale de contraste de source impliquant le tenseur de dépolarisation des défauts, quand ceux-ci sont assez petits vis-à-vis de l'épaisseur de peau locale (cas BF) ou de la longueur d'onde locale (cas HF). La précision des DGF doit nécessairement être assurée alors, même si les sources se situent loin de l'origine, ce qui donne un spectre de dyades qui oscille très rapidement. La technique d'interpolation-intégration dite de Padua-Domínguez est ainsi introduite dans le but d'évaluer de façon efficace des intégrales fortement oscillantes.

Néanmoins, les matériaux composites peuvent souffrir de divers défauts, lors du processus de fabrication ou pendant leurs utilisations. Vides d'air, cavités remplies de liquide, fissures, etc., peuvent affecter le fonctionnement correct des structures composites. Il est donc indispensable de pouvoir détecter la présence des défauts. Ici, l'insistance est sur la méthode bien connue d'imagerie dite MUltiple SIgnal Classification (MUSIC), qui est basée sur la décomposition en valeurs singulières (SVD) des DGF ; celle-ci est développée afin de localiser les positions de multiples petits défauts volumiques en interaction faible enfouis dans des milieux anisotropes uniaxiaux. Le principal inconvénient de la méthode MUSIC est cependant sa sensibilité par rapport au bruit. Par conséquent, des méthodes MUSIC avec une résolution améliorée et la Recursively Applied and Projected (RAP) MUSIC sont introduites afin de surmonter un tel inconvénient de l'algorithme standard et de fournir des résultats de qualité avec une meilleure résolution. De nombreuses simulations numériques illustrent ces investigations.

Composition du jury :

H. Haddar, Directeur de recherche INRIA, DEFI-CMAP, Palaiseau, rapporteur,
A. Tamburrino, Professeur, Università degli Studi di Cassino e del Lazio Meridionale, Cassino, rapporteur,
M. Bonnet, Directeur de recherche CNRS, POems, Unité de Mathématiques Appliquées, Palaiseau, examinateur,
J.-P. Groby, Chargé de recherche CNRS, Laboratoire d'Acoustique de l'Université du Maine, Le Mans, examinateur,
C. Reboud, Ingénieur-chercheur, CEA LIST, Département Imagerie Simulation pour le Contrôle, Saclay, examinateur,
D. Lesselier, Directeur de recherche CNRS, L2S, Gif-sur-Yvette, Directeur de thèse.

 

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