Zobrazeno 1 - 10
of 2 338
pro vyhledávání: '"Nahon, P."'
Autor:
Nahon, Mickaël, Oudet, Édouard
We introduce a method to compute efficiently and with arbitrary precision a basis of harmonic functions with prescribed singularities on a general compact surface of genus two and more. This basis is obtained as a composition of theta functions and t
Externí odkaz:
http://arxiv.org/abs/2410.06763
Minimizing negative impacts of Artificial Intelligent (AI) systems on human societies without human supervision requires them to be able to align with human values. However, most current work only addresses this issue from a technical point of view,
Externí odkaz:
http://arxiv.org/abs/2408.04655
Publikováno v:
Journal of Experimental Social Psychology, 114, Article 104632 (2024)
An analysis drawing on Signal Detection Theory suggests that people may fall for misinformation because they are unable to discern true from false information (truth insensitivity) or because they tend to accept information with a particular slant re
Externí odkaz:
http://arxiv.org/abs/2406.01621
Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic biases that can lead to unfair models, emerges. Several debiasing approaches have been proposed in the realm of deep learning, employing more or less sophisticated approache
Externí odkaz:
http://arxiv.org/abs/2403.14200
In this paper, we explore prior research and introduce a new methodology for classifying mental state levels based on EEG signals utilizing machine learning (ML). Our method proposes an optimized training method by introducing a validation set and a
Externí odkaz:
http://arxiv.org/abs/2312.09379
We prove that the degree-one vortex solution is the unique minimizer for the Ginzburg--Landau functional for gradient fields (that is, the Aviles--Giga model) in the unit ball $B^N$ in dimension $N \geq 4$ and with respect to its boundary value. A si
Externí odkaz:
http://arxiv.org/abs/2310.11384
Despite significant research efforts, deep neural networks are still vulnerable to biases: this raises concerns about their fairness and limits their generalization. In this paper, we propose a bias-agnostic approach to mitigate the impact of bias in
Externí odkaz:
http://arxiv.org/abs/2305.03691
Let $\Omega\subset\mathbb{R}^n$ be an open set with same volume as the unit ball $B$ and let $\lambda_k(\Omega)$ be the $k$-th eigenvalue of the Laplace operator of $\Omega$ with Dirichlet boundary conditions in $\partial\Omega$. In this work, we ans
Externí odkaz:
http://arxiv.org/abs/2304.10916
In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings
Externí odkaz:
http://arxiv.org/abs/2303.11371
Autor:
Sarah Proteau, Imène Krossa, Chrystel Husser, Maxime Guéguinou, Federica Sella, Karine Bille, Marie Irondelle, Mélanie Dalmasso, Thibault Barouillet, Yann Cheli, Céline Pisibon, Nicole Arrighi, Sacha Nahon-Estève, Arnaud Martel, Lauris Gastaud, Sandra Lassalle, Olivier Mignen, Patrick Brest, Nathalie M Mazure, Frédéric Bost, Stéphanie Baillif, Solange Landreville, Simon Turcotte, Dan Hasson, Saul Carcamo, Christophe Vandier, Emily Bernstein, Laurent Yvan-Charvet, Mitchell P Levesque, Robert Ballotti, Corine Bertolotto, Thomas Strub
Publikováno v:
EMBO Molecular Medicine, Vol 16, Iss 9, Pp 2262-2267 (2024)
Externí odkaz:
https://doaj.org/article/4674eb9ca9f241b4a3996ff03dc79709
