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Publications

Cette page présente les publications du Centre Borelli déposées sur le serveur HAL.

Affichage des publications 831 à 840 sur 1523 au total

Machine Learning

Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning

Adaptive learning is an area of educational technology that consists in delivering personalized learning experiences to address the unique needs of each learner. An important subfield of adaptive learning is learning path personalization: it aims at…

Jean Vassoyan, Jill-Jênn Vie, Pirmin Lemberger

EDM 2023 - 16th International Conference on Educational Data Mining -

Computation

Experimental Improvements of Global Optimization Algorithms for Lipschitz Functions

In this paper, we define an experimental context in which we tested the performances of LIPO and AdaLIPO, two global optimization algorithms for Lipschitz functions, introduced in [10]. We provide experimental proofs of the efficiency of those…

Perceval Beja-Battais, Gaëtan Serré, Sophia Chirrane

Image Processing On Line -

Life Sciences

A new score combining compound muscle action potential (CMAP) amplitudes and motor score is predictive of motor outcome after AVXS-101 (Onasemnogene Abeparvovec) SMA therapy

Spinal muscular atrophy 1 (SMA1) is a severe early genetic disease with degeneration of motor neurons. Motor development is still suboptimal after gene replacement therapy in symptomatic patients. In this study, compound muscle action potential …

Rémi Pierre Marie Barrois, Christine Barnerias, Elodie Deladrière, V. Leloup-Germa, Brian Tervil, Frédérique Audic, Christophe Boulay, Claude Cances, Pascal Cintas, Jean Baptiste Davion, Caroline Espil-Taris, Véronique Manel, Yann Pereon, Julie Piarroux, Susana Quijano Roy, Carole Vuillerot, Ulrike Walther-Louvier, Isabelle Desguerre, Cyril Gitiaux

Neuromuscular Disorders -

Biomechanics

Systematic review of candidate prognostic factors for falling in older adults identified from motion analysis of challenging walking tasks

The objective of this systematic review is to identify motion analysis parameters measured during challenging walking tasks which can predict fall risk in the older population. Numerous studies have attempted to predict fall risk from the motion…

Rosemary Dubbeldam, Yu Yuan Lee, Juliana Pennone, Luis Mochizuki, Charlotte Le Mouel

European Review of Aging and Physical Activity -

Humanities and Social Sciences

Telepsychology in Europe since COVID-19: How to Foster Social Telepresence?

All over the world, measures were taken to prevent the spread of COVID-19. Social distancing not only had a strong influence on mental health, but also on the organization of care systems. It changed existing practices, as we had to rapidly move…

Lise Haddouk, Carine Milcent, Benoît Schneider, Tom van Daele, Nele de Witte

Journal of Clinical Medicine -

Human health and pathology

Dual-task versus single-task gait rehabilitation after stroke: the protocol of the cognitive-motor synergy multicenter, randomized, controlled superiority trial (SYNCOMOT)

Background Gait disorders and cognitive impairments are prime causes of disability and institutionalization after stroke. We hypothesized that relative to single-task gait rehabilitation (ST GR), cognitive-motor dual-task (DT) GR initiated at the…

Sophie Tasseel-Ponche, Martine Roussel, Monica Toba, Thibaud Sader, Vincent Barbier, Arnaud Delafontaine, Jonathan Meynier, Carl Picard, Jean-Marc Constans, Alexis Schnitzler, Olivier Godefroy, Alain Pierre Yelnik

Trials -

Mathematics

Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network

Owing to the complexity of obtaining the landslide inventory data, it is a challenge to establish a landslide spatial prediction model with limited labeled samples. This paper proposed a novel strategy, namely transfer learning with attributes (TLAs…

Mansheng Lin, Shuai Teng, Gongfa Chen, David Bassir

Land -

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) -