Zobrazeno 1 - 10
of 1 435
pro vyhledávání: '"P. Collas"'
Autor:
M. Udugama, L. Hii, A. Garvie, M. Cervini, B. Vinod, F.-L. Chan, P. P. Das, J. R. Mann, P. Collas, H. P. J. Voon, L. H. Wong
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Alternative Lengthening of Telomeres (ALT) is a telomere maintenance pathway utilised in 15% of cancers that have been associated with mutations in ATRX. Here the authors reveal a functional role of histone demethylases KDM4B in regulating ALT activa
Externí odkaz:
https://doaj.org/article/b39a9f1db7b5499c894032806f9966a3
Machine learning applications on signals such as computer vision or biomedical data often face significant challenges due to the variability that exists across hardware devices or session recordings. This variability poses a Domain Adaptation (DA) pr
Externí odkaz:
http://arxiv.org/abs/2407.14303
Autor:
Lalou, Yanis, Gnassounou, Théo, Collas, Antoine, de Mathelin, Antoine, Kachaiev, Oleksii, Odonnat, Ambroise, Gramfort, Alexandre, Moreau, Thomas, Flamary, Rémi
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift. While many methods have been proposed in the literature, fair and rea
Externí odkaz:
http://arxiv.org/abs/2407.11676
Autor:
Mellot, Apolline, Collas, Antoine, Chevallier, Sylvain, Gramfort, Alexandre, Engemann, Denis A.
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the data $X$ and in the biomedical variables of interest $y$, thus limiting
Externí odkaz:
http://arxiv.org/abs/2407.03878
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time, complicating analysis due to
Externí odkaz:
http://arxiv.org/abs/2403.15415
This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited labeled signals in the target dataset. Leveraging a domain-dependent mixing
Externí odkaz:
http://arxiv.org/abs/2402.03345
When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric. The geometry induced on the parameters by this metric is then referred to as the Fisher-R
Externí odkaz:
http://arxiv.org/abs/2310.01032
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data. A key requirement for DR is to incorporate global dependencies among original and embedded samples while preserving clusters in the embedding space. T
Externí odkaz:
http://arxiv.org/abs/2303.05119
Autor:
Mihaela C. Ion, Caitlin C. Bloomer, Tudor I. Bărăscu, Francisco J. Oficialdegui, Nathaniel F. Shoobs, Bronwyn W. Williams, Kevin Scheers, Miguel Clavero, Frédéric Grandjean, Marc Collas, Thomas Baudry, Zachary Loughman, Jeremy J. Wright, Timo J. Ruokonen, Christoph Chucholl, Simone Guareschi, Bram Koese, Zsombor M. Banyai, James Hodson, Margo Hurt, Katrin Kaldre, Boris Lipták, James W. Fetzner, Tommaso Cancellario, András Weiperth, Jạnis Birzaks, Teodora Trichkova, Milcho Todorov, Maksims Balalaikins, Bogna Griffin, Olga N. Petko, Ada Acevedo-Alonso, Guillermo D’Elía, Karolina Śliwińska, Anatoly Alekhnovich, Henry Choong, Josie South, Nick Whiterod, Katarina Zorić, Peter Haase, Ismael Soto, Daniel J. Brady, Phillip J. Haubrock, Pedro J. Torres, Denis Şadrin, Pavel Vlach, Cüneyt Kaya, Sang Woo Jung, Jin-Young Kim, Xavier H.C. Vermeersch, Maciej Bonk, Radu Guiaşu, Muzaffer M. Harlioğlu, Jane Devlin, Irmak Kurtul, Dagmara Błońska, Pieter Boets, Hossein Masigol, Paul R. Cabe, Japo Jussila, Trude Vrålstad, David V. Beresford, Scott M. Reid, Jiří Patoka, David A. Strand, Ali S. Tarkan, Frédérique Steen, Thomas Abeel, Matthew Harwood, Samuel Auer, Sandor Kelly, Ioannis A. Giantsis, Rafał Maciaszek, Maria V. Alvanou, Önder Aksu, David M. Hayes, Tadashi Kawai, Elena Tricarico, Adroit Chakandinakira, Zanethia C. Barnett, Ştefan G. Kudor, Andreea E. Beda, Lucian Vîlcea, Alexandru E. Mizeranschi, Marian Neagul, Anton Licz, Andra D. Cotoarbă, Adam Petrusek, Antonín Kouba, Christopher A. Taylor, Lucian Pârvulescu
Publikováno v:
PeerJ, Vol 12, p e18229 (2024)
Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non
Externí odkaz:
https://doaj.org/article/3f7985e4eb614ff08e77d8b3b373acc1
We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher-Rao Riemannian manifolds of widely used parametric families of probability distributions, such as normal, gamma, beta, Dirichlet distribu
Externí odkaz:
http://arxiv.org/abs/2211.11643