Zobrazeno 1 - 10
of 58 032
pro vyhledávání: '"Arnaud, P."'
Autor:
Eteve, Arnaud, Xue, Cong
Let $G$ be a generically reductive group over a smooth projective curve $X$ over a finite field. For any finite set $I$, we show that nearby cycles commute with proper direct image from stacks of shtukas to $X^I$. This generalizes some results of Sal
Externí odkaz:
http://arxiv.org/abs/2409.16474
We study the convergence of a Zakharov system driven by a time white noise, colored in space, to a multiplicative stochastic nonlinear Schr{\"o}dinger equation, as the ion-sound speed tends to infinity. In the absence of noise, the conservation of en
Externí odkaz:
http://arxiv.org/abs/2409.14777
Autor:
Skaf, Nour, Jensen-Clem, Rebecca, Hunter, Aaron, Guyon, Olivier, Deo, Vincent, Hinz, Phil, Cetre, Sylvain, Chambouleyron, Vincent, Fowler, J., Sengupa, Aditya, Salama, Maissa, Males, Jared, McEwen, Eden, Douglas, Ewan S., Van Gorkom, Kyle, Por, Emiel, Lucas, Miles, Ferreira, Florian, Sevin, Arnaud, Bowens-Rubin, Rachel, Cranney, Jesse, Calvin, Ben
Real-time control (RTC) is pivotal for any Adaptive Optics (AO) system, including high-contrast imaging of exoplanets and circumstellar environments. It is the brain of the AO system, and what wavefront sensing and control (WFS\&C) techniques need to
Externí odkaz:
http://arxiv.org/abs/2409.13126
Autor:
Debussche, Arnaud, Mémin, Etienne
This work investigates variational frameworks for modeling stochastic dynamics in incompressible fluids, focusing on large-scale fluid behavior alongside small-scale stochastic processes. The authors aim to develop a coupled system of equations that
Externí odkaz:
http://arxiv.org/abs/2409.12654
Autor:
Yu, Ju-Chi, Borgne, Julie Le, Krishnan, Anjali, Gloaguen, Arnaud, Yang, Cheng-Ta, Rabin, Laura A, Abdi, Hervé, Guillemot, Vincent
Correspondence analysis, multiple correspondence analysis and their discriminant counterparts (i.e., discriminant simple correspondence analysis and discriminant multiple correspondence analysis) are methods of choice for analyzing multivariate categ
Externí odkaz:
http://arxiv.org/abs/2409.11789
This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques.
Externí odkaz:
http://arxiv.org/abs/2409.10203
Context. IRAS 16293E is a rare case of a prestellar core being subjected to the effects of at least one outflow.Aims. We want to disentangle the actual structure of the core from the outflow impact and evaluate the evolutionary stage of the core. Met
Externí odkaz:
http://arxiv.org/abs/2409.10093
Genome rearrangements are events in which large blocks of DNA exchange pieces during evolution. The analysis of such events is a tool for understanding evolutionary genomics, based on finding the minimum number of rearrangements to transform one geno
Externí odkaz:
http://arxiv.org/abs/2409.09734
Mass transport problems arise in many areas of machine learning whereby one wants to compute a map transporting one distribution to another. Generative modeling techniques like Generative Adversarial Networks (GANs) and Denoising Diffusion Models (DD
Externí odkaz:
http://arxiv.org/abs/2409.09347
Autor:
Jansen, Yvonne, Bucchieri, Federica, Dragicevic, Pierre, Hachet, Martin, Koval, Morgane, Petiot, Léana, Prouzeau, Arnaud, Schmalstieg, Dieter, Yao, Lijie, Isenberg, Petra
Publikováno v:
VIS4Good - Visualization for Social Good workshop held as part of IEEE VIS 2022, Oct 2022, Oklahoma City, United States
We present the results of a brainstorming exercise focused on how situated visualizations could be used to better understand the state of the environment and our personal behavioral impact on it. Specifically, we conducted a day long workshop in the
Externí odkaz:
http://arxiv.org/abs/2409.07006