Výsledky vyhledávání - "Vincent Barra"

Akademický článek

DeepMPTB: a vaginal microbiome-based deep neural network as artificial intelligence strategy for efficient preterm birth prediction

Autor: Oshma Chakoory, Vincent Barra, Emmanuelle Rochette, Loïc Blanchon, Vincent Sapin, Etienne Merlin, Maguelonne Pons, Denis Gallot, Sophie Comtet-Marre, Pierre Peyret

Publikováno v: Biomarker Research, Vol 12, Iss 1, Pp 1-5 (2024)

Abstract In recent decades, preterm birth (PTB) has become a significant research focus in the healthcare field, as it is a leading cause of neonatal mortality worldwide. Using five independent study cohorts including 1290 vaginal samples from 561 pr

Externí odkaz: https://doaj.org/article/d7036c412bcd4df49d89e46037612c75

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Akademický článek

External factors driving surface temperature changes above geothermal systems: answers from deep learning

Autor: Michail Giannoulis, Sophie Pailot-Bonnétat, Vincent Barra, Andrew Harris

Publikováno v: Frontiers in Earth Science, Vol 12 (2024)

Introduction: The surface expression of enhanced geothermal heat fluxes above an active hydrothermal system causes a surface thermal anomaly (ΔT). The thermal anomaly is expressed by the difference between the temperature within the heated zone (Th)

Externí odkaz: https://doaj.org/article/cfdcc291bd2b40c18973459bb378d723

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Akademický článek

An Experimental Study on EUV‐To‐Magnetogram Image Translation Using Conditional Generative Adversarial Networks

Autor: Markus Dannehl, Véronique Delouille, Vincent Barra

Publikováno v: Earth and Space Science, Vol 11, Iss 4, Pp n/a-n/a (2024)

Abstract Deep generative models have recently become popular in heliophysics for their capacity to fill in gaps in solar observational data sets, thereby helping mitigating the data scarcity issue faced in space weather forecasting. A particular type

Externí odkaz: https://doaj.org/article/d779664785144f288d3f0af3239c474e

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Akademický článek

GAN-AE: an anomaly detection algorithm for New Physics search in LHC data

Autor: Louis Vaslin, Vincent Barra, Julien Donini

Publikováno v: European Physical Journal C: Particles and Fields, Vol 83, Iss 11, Pp 1-8 (2023)

Abstract In recent years, interest has grown in alternative strategies for the search for New Physics beyond the Standard Model. One envisaged solution lies in the development of anomaly detection algorithms based on unsupervised machine learning tec

Externí odkaz: https://doaj.org/article/ec575b6f34944a99b38a681447954518

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Akademický článek

Top-Down System for Multi-Person 3D Absolute Pose Estimation from Monocular Videos

Autor: Amal El Kaid, Denis Brazey, Vincent Barra, Karim Baïna

Publikováno v: Sensors, Vol 22, Iss 11, p 4109 (2022)

Two-dimensional (2D) multi-person pose estimation and three-dimensional (3D) root-relative pose estimation from a monocular RGB camera have made significant progress recently. Yet, real-world applications require depth estimations and the ability to

Externí odkaz: https://doaj.org/article/7f5470321fa449cbb02bedacfec01d48

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pyBumpHunter: A model independent bump hunting tool in Python for High Energy Physics analyses

Autor: Louis Vaslin, Samuel Calvet, Vincent Barra, Julien Donini

Publikováno v: SciPost Physics
SciPost Physics, 2023
HAL

The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e90bd5ea95a3af32001b49eb2181be0
https://cnrs.hal.science/hal-04158308

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