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pro vyhledávání: '"Favier P"'
In previous works, we analysed the internal shear layers excited by a viscous forcing (longitudinal libration) in a spherical shell geometry (He et al., J. Fluid Mech. 939, A3, 2022; 974, A3, 2023). We now consider the stronger inviscid forcing corre
Externí odkaz:
http://arxiv.org/abs/2411.12504
Publikováno v:
Journal of Fluid Mechanics, 2024, 994, pp.A8
The yield stress and shear thinning properties of mucus are identified as critical for ciliary coordination and mucus transport in human airways. We use here numerical simulations to explore the hydrodynamic coupling of cilia and mucus with these two
Externí odkaz:
http://arxiv.org/abs/2410.11337
Autor:
Chammouma, Marwan, Jouanlanne, Manon, Egelé, Antoine, Favier, Damien, Farago, Jean, Hourlier-Fargette, Aurélie
Spontaneous mechanical self-assembly of monodisperse bubbles generally leads to disordered foams at low density: producing crystalline structures such as Kelvin foams has proven to be challenging experimentally, despite them being a minimum of energy
Externí odkaz:
http://arxiv.org/abs/2409.20114
We present a substantial extension of our Human-Aware Task Planning framework, tailored for scenarios with intermittent shared execution experiences and significant belief divergence between humans and robots, particularly due to the uncontrollable n
Externí odkaz:
http://arxiv.org/abs/2409.18545
Autor:
Favier, Marco, Calders, Toon
The pipeline of a fair ML practitioner is generally divided into three phases: 1) Selecting a fairness measure. 2) Choosing a model that minimizes this measure. 3) Maximizing the model's performance on the data. In the context of group fairness, this
Externí odkaz:
http://arxiv.org/abs/2408.00330
Autor:
Favier, Marco, Calders, Toon
Fair cake-cutting is a mathematical subfield that studies the problem of fairly dividing a resource among a number of participants. The so-called ``cake,'' as an object, represents any resource that can be distributed among players. This concept is c
Externí odkaz:
http://arxiv.org/abs/2406.16606
We study uniqueness of best approximation in Orlicz spaces L$\Phi$, for different types of convex functions $\Phi$ and for some finite dimensional approximation classes of functions, where Tchebycheff spaces, and more general approximation ones, are
Externí odkaz:
http://arxiv.org/abs/2406.10021
Publikováno v:
Machine Learning 112.12 (2023): 5081-5104
It is widely accepted that biased data leads to biased and thus potentially unfair models. Therefore, several measures for bias in data and model predictions have been proposed, as well as bias mitigation techniques whose aim is to learn models that
Externí odkaz:
http://arxiv.org/abs/2403.14282
In this paper, we consider the best multivalued polynomial approximation operator for functions in an Orlicz Space $L^{\varphi}(\Omega)$. We obtain its characterization involving $\psi^-$ and $\psi^+$, which are the left and right derivatives functio
Externí odkaz:
http://arxiv.org/abs/2402.17048
Autor:
Favier, Benjamin, Dizès, Stéphane Le
We consider inertial waves propagating in a fluid contained in a non-axisymmetric three-dimensional rotating cavity. We focus on the particular case of a fluid enclosed inside a truncated cone or frustum, which is the volume that lies between two hor
Externí odkaz:
http://arxiv.org/abs/2401.05071