IEEE-IISc Student Branch & Signal Processing Society Chapter
Technical talk

Talk 1: 4th Feb

Decomposition of dynamical networks with tensor decomposition

Dr. Pierre Borgnat
Director of research, LPENSL, CNRS, ENS Lyon, France
Directeur de l'IXXI (Complex Systems Institute Rhône-Alpes)

Golden Jubilee Seminar Hall
ECE Dept., IISc
04/02/2020, 2.30-3.30 pm

All are Welcome
Coffee/tea 3.30pm

Talk 2: 6th Feb

Deep Learning for multivariate fractal texture synthesis:
Does it work? How do we know that it works?

Dr. Patrice Abry (IEEE Fellow)
Director of research, CNRS, ENS Lyon, France

Golden Jubilee Seminar Hall
ECE Dept., IISc
06/02/2020, 2.30-3.30 pm

All are Welcome
Coffee/tea 3.30pm

Host: Dr. Sundeep Prabhakar Chepuri, ECE dept., IISc.

Talk 1 details: 4th Feb

Abstract: Dynamical networks are an instance of data that ate studied in many domains, e.g. transportation, social and economical studies, communication networks or biological networks such as brain activity. The range of dynamical features that exist is really wild and many methods have been proposed to extract a reduced number of components, jointly in time and across the (evolving) graph topology. The purpose of the talk is to present some of the works we did in this direction in the context of neuroscience studies, using a novel tensor decomposition approach followed by clustering to extract components representative of various activities in time. The application context is the study of Functional connectivity (FC) of EEG, which is a graph-like data structure commonly used by neuroscientists to study the dynamic behavior of brain activity. We will show in examples how the proposed approach allows us to decompose data of EEG brain activity of patients suffering from epilepsy, allowing us to infer network components corresponding to the different stages of an epileptic seizure. Joint work with G. Frusque, P. Gonçalves, J. Jung, R. Cazabet, R. Hamon

About the Speaker:

Pierre Borgnat born in France in 1974. He received the degree from École Normale Supérieure de Lyon, Lyon, France in 1994 and the Ph.D. degree in physics and signal processing in 2002. He is currently a CNRS Senior Scientist at the Laboratory of Physics, ENS de Lyon, Lyon, France. He was a Professeur Agrégé in Physical Sciences in 1997. During 2003–2004, he worked with the ISR, IST (Lisbon, Portugal). Since 2004, he has been a CNRS Chargé de Recherche and since 2016, he has been the Directeur de Recherche. Since 2014, he has been the Director of IXXI (Complex System Institute of Rhône-Alpes). He works on several applications of these signal processing methods: Internet traffic modeling and measurements, fluid mechanics, analysis of social data, and transportation studies. His research interests include statistical signal processing, mainly graph signal processing, complex networks, nonstationary signals, or scaling phenomena.

Talk 2 Details: 6th Feb

Abstract: Deep Convolutional Generative Adversarial Networks (DCGAN) have been widely used to synthesize images. Their use remains however concentrated on geometrical images (such as faces) and they have been much less used for the synthesis of textured images. Our aim is to investigate the potential of DCGAN to generate multivariate textures. To that end, we make use of a large set of synthetic multivariate multifractal textures, which consists of a collection of scale-free (or fractal) textures with non-trivial cross-dependencies (cross-selfsimilarity and cross-multifractality) to train a DCGAN.  We make use of wavelet transforms and wavelet-leaders to compare the quality of the DCGAN synthesized textures against those of the original textures. We discuss reproductiblity and convergence issues. Joint work with : V. Mauduit, S. Roux, E. Quemener, ENS Lyon, France

About the speaker:
Dr. Patrice Abry (IEEE Fellow) was born in Bourg-en-Bresse, France in 1966. He received the degree of Professeur-Agr ́eg ́e de Sciences Physiques, in 1989 at Ecole Normale Sup ́erieure de Cachan and completed a PhD in Physics and Signal Processing, at Université Claude-Bernard University in Lyon in 1994. He is a CNRS Senior Scientist, at the Physics dept. of Ecole Normale Superieure de Lyon, where he is in charge of the Signal, systems and Physics research team. Patrice Abry received the AFCET-MESR-CNRS prize for best PhD in Signal Processing for the years 93-94 and has been elected IEEE Fellow in 2011. He is the author of a book in French dedicated to wavelet, scale invariance and hydrodynamic turbulence and is also the coeditor of a book entitled “Scaling, Fractals and Wavelets”. He has been elected IEEE fellow in 2011 and he serves as an elected memberof the IEEE SPS Signal Processing Theory and Methods Technical Committee. His current research interests include wavelet-based analysis and modeling of statistical scale-free dynamics (self-similarity, stable processes, multi-fractal, 1/f processes, long-range dependence, local regularity of processes, infinitely divisible cascades, departures from exact scale invariance). Beyond theoretical developments and contributions in multifractal analysis and stochastic process design. Patrice Abry shows a strong interest into real-world applications, such as hydrodynamic turbulence, computer network teletraffic, heart rate variability, neurosciences and art investigations.

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