Publications

Linear Weight Interpolation Leads to Transient Performance Gains

2024-09-27

TMLR (accepté)

openreview.net

Refining SARS-CoV-2 intra-host variation by leveraging large-scale sequencing data

Fatima Mostefai

Jean-Christophe Grenier

Raphaël Poujol

Julie Hussin

Understanding viral genome evolution during host infection is crucial for grasping viral diversity and evolution. Analyzing intra-host singl… (voir plus)e nucleotide variants (iSNVs) offers insights into new lineage emergence, which is important for predicting and mitigating future viral threats. Despite next-generation sequencing’s potential, challenges persist, notably sequencing artifacts leading to false iSNVs. We developed a workflow to enhance iSNV detection in large NGS libraries, using over 130 000 SARS-CoV-2 libraries to distinguish mutations from errors. Our approach integrates bioinformatics protocols, stringent quality control, and dimensionality reduction to tackle batch effects and improve mutation detection reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how related groups of data points are represented within a two-dimensional space, enhancing clustering structure explanation based on genetic similarities. This workflow advances accurate intra-host mutation detection, facilitating a deeper understanding of viral diversity and evolution.

2024-09-27

NAR Genomics and Bioinformatics (publié)

doi.org

Harnessing population diversity: in search of tools of the trade

Danilo Bzdok

Guy Wolf

Jakub Kopal

Abstract Big neuroscience datasets are not big small datasets when it comes to quantitative data analysis. Neuroscience has now witnessed th… (voir plus)e advent of many population cohort studies that deep-profile participants, yielding hundreds of measures, capturing dimensions of each individual’s position in the broader society. Indeed, there is a rebalancing from small, strictly selected, and thus homogenized cohorts toward always larger, more representative, and thus diverse cohorts. This shift in cohort composition is prompting the revision of incumbent modeling practices. Major sources of population stratification increasingly overshadow the subtle effects that neuroscientists are typically studying. In our opinion, as we sample individuals from always wider diversity backgrounds, we will require a new stack of quantitative tools to realize diversity-aware modeling. We here take inventory of candidate analytical frameworks. Better incorporating driving factors behind population structure will allow refining our understanding of how brain–behavior relationships depend on human subgroups.

2024-09-26

GigaScience (publié)

doi.org

Harnessing population diversity: in search of tools of the trade

Danilo Bzdok

Guy Wolf

Jakub Kopal

Big neuroscience datasets are not big small datasets when it comes to quantitative data analysis. Neuroscience has now witnessed the advent … (voir plus)of many population cohort studies that deep-profile participants, yielding hundreds of measures, capturing dimensions of each individual’s position in the broader society. Indeed, there is a rebalancing from small, strictly selected, and thus homogenized cohorts toward always larger, more representative, and thus diverse cohorts. This shift in cohort composition is prompting the revision of incumbent modeling practices. Major sources of population stratification increasingly overshadow the subtle effects that neuroscientists are typically studying. In our opinion, as we sample individuals from always wider diversity backgrounds, we will require a new stack of quantitative tools to realize diversity-aware modeling. We here take inventory of candidate analytical frameworks. Better incorporating driving factors behind population structure will allow refining our understanding of how brain–behavior relationships depend on human subgroups.

2024-09-26

GigaScience (publié)

doi.org

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

Adel Guitouni

Nabil Belacel

Loubna Benabbou

Belaid Moa

Munire Erman

Halim Abdul

2024-09-26

Scientific Reports (publié)

doi.org

Replication of a GWAS signal near
<i>HLA-DQA2</i>
with acute myeloid leukemia using a disease-only cohort and external population-based controls

Rose Laflamme

Véronique Lisi

Josée Hébert

Guy Sauvageau

Sébastien Lemieux

Vincent-Philippe Lavallee

Guillaume Lettre

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. Its risk factors include rare and highly penetrant somatic… (voir plus) mutations. Genome-wide association studies (GWAS) have also identified four common inherited variants associated with AML risk, but these findings have not yet been confirmed in many independent datasets. Here, we performed a replication study with 567 AML cases from the Leucegene cohort and 1,865 controls from the population-based cohort CARTaGENE (CaG). Because genotypes were generated using different technologies in the two datasets (e.g. low- vs. high-coverage whole-genome sequencing), we applied stringent quality-control filters to minimize type I errors. We showed using data reduction methods (e.g. principal component analysis [PCA] and uniform manifold approximation and projection [UMAP]) that our approach successfully integrated the Leucegene and CaG genetic data. We replicated the association between cytogenetically normal (CN)-AML and rs3916765, a variant located near HLA-DQA2 (odds ratio [95% confidence interval] = 1.88 [1.21-2.93], P- value=0.005). The effect size of this association was stronger when we restricted the analyses to AML patients with NPM1 mutations (odds ratios >2.35). We found HLA- DOB to be the most significantly upregulated gene in Leucegene participants with the CN-AML protective A-allele at rs3916765. We further found that several HLA class II genes are also differentially expressed albeit at lower statistical significance. Our results confirm that a common genetic variant at the HLA locus associates with AML risk, providing new opportunities to improve disease prognosis and treatment.

2024-09-26

medRxiv (prépublication)

doi.org

CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark

David LE MEUR

David Orlando Romero Mogrovejo

Chenyang Lyu

Haryo Akbarianto Wibowo

Teresa Lynn

Injy Hamed

Aditya Nanda Kishore Khandavally

Aishik Mandal

Alina Dragonetti

Artem Abzaliev

Atnafu Lambebo Tonja

Bontu Fufa Balcha

Chenxi Whitehouse

Christian Salamea-Palacios

Dan John Velasco

David Ifeoluwa Adelani

D. Meur

Emilio Villa Cueva

Fajri Koto

Fauzan Farooqui … (voir 57 de plus)

Frederico Belcavello

Ganzorig Batnasan

Gisela Vallejo

Gráinne Caulfield

Guido Ivetta

Haiyue Song

Henok Biadglign Ademtew

Hernán Maina

Holy Lovenia

Israel Abebe Azime

Jan Christian Blaise Cruz

Jay Gala

Jiahui Geng

Jesus-German Ortiz-Barajas

Jinheon Baek

Jocelyn Dunstan

Laura Alonso Alemany

Teresa Clifford

Kumaranage Ravindu Yasas Nagasinghe

Luciana Benotti

Luis Fernando D'Haro

Marcelo Viridiano

Marcos Estecha-Garitagoitia

Maria Camila Buitrago Cabrera

Mario Rodríguez-Cantelar

Mélanie Jouitteau

Mihail Minkov Mihaylov

Mohamed Fazli Mohamed Imam

Muhammad Farid Adilazuarda

Munkhjargal Gochoo

Munkh-Erdene Otgonbold

Naome Etori

Olivier NIYOMUGISHA

Paula Mónica Silva

Pranjal A Chitale

Raj Dabre

Rendi Chevi

Ruochen Zhang

Ryandito Diandaru

Samuel Cahyawijaya

Santiago Góngora

Soyeong Jeong

Sukannya Purkayastha

Tatsuki Kuribayashi

Thanmay Jayakumar

Tiago Timponi Torrent

Toqeer Ehsan

Vladimir Araujo

Yova Kementchedjhieva

Zara Burzo

Zheng Wei Lim

Zheng Xin Yong

Oana Ignat

Joan Nwatu

Rada Mihalcea

Thamar Solorio

Alham Fikri Aji

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (présentation orale)

doi.org

openreview.net

Learning Action and Reasoning-Centric Image Editing from Videos and Simulation

Varun Jampani

Christopher Pal

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)

openreview.net

Reconstructing Spatio-Temporal Trajectories of Visual Object Memories in the Human Brain

Julia Lifanov

Benjamin J. Griffiths

Juan Linde-Domingo

Catarina S. Ferreira

Martin Wilson

Stephen D. Mayhew

Ian Charest

Maria Wimber

2024-09-25

eNeuro (publié)

doi.org

RedPajama: an Open Dataset for Training Large Language Models

Maurice Weber

Daniel Y Fu

Quentin Gregory Anthony

Yonatan Oren

Shane Adams

Anton Alexandrov

Xiaozhong Lyu

Huu Nguyen

Xiaozhe Yao

Virginia Adams

Ben Athiwaratkun

Rahul Chalamala

Kezhen Chen

Max Ryabinin

Tri Dao

Percy Liang

Christopher Re

Irina Rish

Ce Zhang

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)

doi.org

openreview.net

Using Unity to Help Solve Reinforcement Learning

Connor Brennan

Andrew Robert Williams

Omar G. Younis

Vedant Vyas

Daria Yasafova

Irina Rish

Leveraging the depth and flexibility of XLand as well as the rapid prototyping features of the Unity engine, we present the United Unity Uni… (voir plus)verse — an open-source toolkit designed to accelerate the creation of innovative reinforcement learning environments. This toolkit includes a robust implementation of XLand 2.0 complemented by a user-friendly interface which allows users to modify the details of procedurally generated terrains and task rules with ease. Additionally, we provide a curated selection of terrains and rule sets, accompanied by implementations of reinforcement learning baselines to facilitate quick experimentation with novel architectural designs for adaptive agents. Furthermore, we illustrate how the United Unity Universe serves as a high-level language that enables researchers to develop diverse and endlessly variable 3D environments within a unified framework. This functionality establishes the United Unity Universe (U3) as an essential tool for advancing the field of reinforcement learning, especially in the development of adaptive and generalizable learning systems.

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

openreview.net

Amortizing intractable inference in diffusion models for vision, language, and control

Nikolay Malkin

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-09-24

Neural Information Processing Systems (poster)

doi.org

openreview.net

Formation d'entrepreneurs scientifiques

Boussole des politiques en IA

Demandes de supervision

Publications

Formation d'entrepreneurs scientifiques

Boussole des politiques en IA

Demandes de supervision

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Publications