Publications

Numerical Analysis

ZERO: a Local JPEG Grid Origin Detector Based on the Number of DCT Zeros and its Applications in Image Forensics

This work describes a method for detecting JPEG compression as well as its grid origin. The JPEG algorithm performs a quantization of the DCT coefficients of non-overlapping 8×8 blocks of images, setting many of those coefficients to zero. The…

Tina Nikoukhah, Jérémy Anger, Miguel Colom, Jean-Michel Morel, Rafael Grompone von Gioi

Image Processing On Line - 2021

Life Sciences

Cholinesterases in Tripartite Neuromuscular Synapse

The neuromuscular junction (NMJ) is a tripartite synapse in which not only presynaptic and post-synaptic cells participate in synaptic transmission, but also terminal Schwann cells (TSC). Acetylcholine (ACh) is the neurotransmitter that mediates the…

Konstantin Petrov, Svetlana Proskurina, Eric Krejci

Frontiers in Molecular Neuroscience - 2021

Mathematical Physics

Thermo-hydro-dynamic consistency and stiffness in general compressible multiphase flows

This thesis project was carried out at the Commissariat à l'Énergie Atomique (CEA), Direction des Applications Militaire (DAM). It is a continuation of the thesis of T.Vazquez-Gonzalez(2016) and is contemporary with Paulin(2021). The aim is to…

Eric Heulhard de Montigny

Bioengineering

Learning spatial filters from EEG signals with Graph Signal Processing methods

In this paper, we propose to learn a spatial filter directly from Electroencephalography (EEG) signals using graph signal processing tools. We combine a graph learning algorithm with a high-pass graph filter to remove spatially large signals from…

Pierre Humbert, Laurent Oudre, Clément Dubost

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - 2021

Artificial Intelligence

Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation

In this paper, we consider the problem of learning a graph structure from multivariate signals, known as graph signals. Such signals are multivariate observations carrying measurements corresponding to the nodes of an unknown graph, which we desire…

Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis

Journal of Machine Learning Research - 2021

Signal and Image processing

ADAPTIVE SUBSAMPLING OF MULTIDOMAIN SIGNALS WITH PRODUCT GRAPHS

In this paper, we propose an adaptive subsampling method for multidomain signals based on the constrained learning of a product graph. Given an input multidomain signal, we search for a product graph on which the signal is bandlimited, i.e. have…

Théo Gnassounou, Pierre Humbert, Laurent Oudre

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 - 2021

Machine Learning

Implicit consensus clustering from multiple graphs

Dealing with relational learning generally relies on tools modeling relational data. An undirected graph can represent these data with vertices depicting entities and edges describing the relationships between the entities. These relationships can…

Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif

Data Mining and Knowledge Discovery - 2021

Signal and Image processing

Tensor Convolutional Dictionary Learning with CP Low-Rank activations

In this paper, we propose an extension of the standard CDL problem with tensor representation, where each activation is constrained to be "low-rank" through a Canonical Polyadic decomposition. We show that this important additional constraint…

Pierre Humbert, Laurent Oudre, Nicolas Vayatis, Julien Audiffren

IEEE Transactions on Signal Processing - 2021

Bioengineering

Adaptive Change-Point Detection for Studying Human Locomotion

This paper presents an innovative method to analyze inertial signals recorded in a semi-controlled environment. It uses an adaptive and supervised change point detection procedure to decompose the signals into homogeneous segments, allowing a…

Sylvain Jung, Laurent Oudre, Charles Truong, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - 2021

Signal and Image processing

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections

The Sliced-Wasserstein distance (SW) is being increasingly used in machine learning applications as an alternative to the Wasserstein distance and offers significant computational and statistical benefits. Since it is defined as an expectation over…

Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Şimşekli

35th Conference on Neural Information Processing Systems (NeurIPS 2021) - 2021