Publications

Life Sciences

Site-Specific Phosphorylation of RTK KIT Kinase Insert Domain: Interactome Landscape Perspectives

The kinase insert domain (KID) of RTK KIT is a key recruitment region for downstream signalling proteins (DSPs). KID, as a multisite phosphorylation region, provides alternative recognition sites for DSPs and activates them by binding a…

Julie Ledoux, Luba Tchertanov

Kinases and Phosphatases - 2023

Document and Text Processing

Etude approfondie des représentations de données textuelles dans l'apprentissage non supervisé

Dense text representations are gaining great interest in several supervised tasks but much less is known about how suitable they are when dealing with an unlabeled dataset. In this paper, we investigate the use of such representations in…

Mira Ait-Saada, Mohamed Nadif

23ème Conférence francophone sur l'Extraction et la Gestion des Connaissances (EGC'2023) - 2023

Computer Science

Biclustering Basé sur le Transport Optimal

Les graphes bipartis peuvent être utilisés pour modéliser une grande variété d’informations dyadiques telles que les paires utilisateur-score, document-terme et gène-conditions expérimentales. Le biclustering est une extension du clustering au…

Chakib Fettal, Lazhar Labiod, Mohamed Nadif

23ème Conférence francophone sur l'Extraction et la Gestion des Connaissances (EGC'2023) - 2023

Computer Science

Subspace Co-clustering avec Convolution Bilatérale sur Graphe

Le subspace clustering vise à partitionner un ensemble d’observations de haute dimension. Si cette approche a donné de bons résultats dans le domaine du partitionnement d’images, elle s’est avérée inefficace pour le partitionnement de données…

Chakib Fettal, Lazhar Labiod, Mohamed Nadif

Conférence Extraction et Gestion des Connaissances (EGC 2023) - 2023

Law

IA et données de santé : regards croisés sur les droits, devoirs et responsabilités des personnes publiques

Thomas Guyet, Pierre Saurel, Hélène Muscat, Christine Paillard, Tristan Allard, Nolwenn Le Meur, Danièle Bourcier, Emmanuel Oger, André Happe

Enjeux scientifiques et sociaux de l’IA - 2023

Analysis of PDEs

Refined Rellich boundary inequalities for the derivatives of a harmonic function

The classical Rellich inequalities imply that the L 2-norms of the normal and tangential derivatives of a harmonic function are equivalent. In this note, we prove several refined inequalities, which make sense even if the domain is not Lipschitz…

Siddhant Agrawal, Thomas Alazard

Proceedings of the American Mathematical Society - 2023

Numerical Analysis

Monotone discretization of anisotropic differential operators using Voronoi's first reduction

We consider monotone discretization schemes, using adaptive finite differences on Cartesian grids, of partial differential operators depending on a strongly anisotropic symmetric positive definite matrix. For concreteness, we focus on a linear…

Joseph Frédéric Bonnans, Guillaume Bonnet, Jean-Marie Mirebeau

Constructive Approximation - 2023

Signal and Image processing

Video joint denoising and demosaicing with recurrent CNNs

Denoising and demosaicing are two critical components of the image/video processing pipeline. While historically these two tasks have mainly been considered separately, current neural network approaches allow to obtain state of-the-art results by…

Valéry Dewil, Adrien Courtois, Mariano Rodríguez, Thibaud Ehret, Nicola Brandonisio, Denis Bujoreanu, Gabriele Facciolo, Pablo Arias

Winter Conference on Applications of Computer Vision (WACV) - 2023

Engineering Sciences

Multicriteria optimization of 3D printed wing using PLA reinforced with carbon fiber

The purpose of this work is the optimal conception of a wing built using fused deposition modeling (FDM), which is one of the main additive manufacturing (AM) process. PLA reinforced with carbon fiber was selected for the filament as we have defined…

David Bassir, Hao Yue, Khaoula Abouzaid, Patrice Salzenstein

AIP Conference Proceedings - 2023

Machine Learning

On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent

Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the solution. Many kinds of priors have been explored in the literature,…

Rémi Laumont, Valentin de Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra

Journal of Mathematical Imaging and Vision - 2023