Publications

Relative Trajectory Balance is equivalent to Trust-PCL
Tristan Deleu
Padideh Nouri
Yoshua Bengio
Doina Precup
2025-08-31
ArXiv (preprint)
doi.org
arxiv.org
A systematic review of hyperscanning in clinical encounters
Lena Adel
Lisane Moses
Elisabeth Irvine
Kyle T Greenway
Guillaume Dumas
Michael Lifshitz
2025-08-31
Neuroscience & Biobehavioral Reviews (published)
doi.org
On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
Antoine Lesage-Landry
2025-08-31
Mathematics and Computers in Simulation (published)
doi.org
Using machine learning to predict the consumption of a Mediterranean diet with untargeted metabolomics data from controlled feeding studies.
Mélina Côté
Didier Brassard
Pier-Luc Plante
Francis Brière
J. Corbeil
P. Couture
Simone Lemieux
B. Lamarche
2025-08-31
Nutrition, Metabolism and Cardiovascular Diseases (published)
doi.org
Does Language Matter for Early Detection of Parkinson's Disease from Speech?
Peter Plantinga
Briac Cordelle
Dominique Louër
Mirco Ravanaelli
Denise Klein
Using speech samples as a biomarker is a promising avenue for detecting and monitoring the progression of Parkinson's disease (PD), but ther… (see more)e is considerable disagreement in the literature about how best to collect and analyze such data. Early research in detecting PD from speech used a sustained vowel phonation (SVP) task, while some recent research has explored recordings of more cognitively demanding tasks. To assess the role of language in PD detection, we tested pretrained models with varying data types and pretraining objectives and found that (1) text-only models match the performance of vocal-feature models, (2) multilingual Whisper outperforms self-supervised models whereas monolingual Whisper does worse, and (3) AudioSet pretraining improves performance on SVP but not spontaneous speech. These findings together highlight the critical role of language for the early detection of Parkinson's disease.
2025-08-30
International Workshop on Machine Learning for Signal Processing (published)
doi.org
A Multimodal and Multi-centric Head and Neck Cancer Dataset for Tumor Segmentation and Outcome Prediction
Numan Saeed
Salma Hassan
Shahad Hardan
Ahmed Aly
Darya Taratynova
Umair Nawaz
Ufaq Khan
Muhammad Ridzuan
Vincent Andrearczyk
Adrien Depeursinge
Yutong Xie
Thomas Eugene
Raphaël Metz
Mélanie Dore
Gregory Delpon
Vijay Ram Kumar Papineni
Kareem Wahid
Cem Dede
Alaa Mohamed Shawky Ali
Carlos Sjogreen … (see 23 more)
Mohamed Naser
Clifton D. Fuller
Valentin Oreiller
Mario Jreige
John O. Prior
Catherine Cheze Le Rest
Olena Tankyevych
Pierre Decazes
Su Ruan
Stephanie Tanadini-Lang
Martin Vallières
Hesham Elhalawani
Ronan Abgral
Romain Floch
Kevin Kerleguer
Ulrike Schick
Maelle Mauguen
David Bourhis
Jean-Christophe Leclere
Amandine Sambourg
Arman Rahmim
Mathieu Hatt
Mohammad Yaqub
We describe a publicly available multimodal dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies for head … (see more)and neck cancer research. The dataset includes 1123 FDG-PET/CT studies from patients with histologically confirmed head and neck cancer, acquired from 10 international medical centers. All examinations consisted of co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity across institutions. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following standardized guidelines and quality control measures. We provide anonymized NifTi files of all studies, along with expert-annotated segmentation masks, radiotherapy dose distribution for a subset of patients, and comprehensive clinical metadata. This metadata includes TNM staging, HPV status, demographics (age and gender), long-term follow-up outcomes, survival times, censoring indicators, and treatment information. We demonstrate how this dataset can be used for three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, providing benchmark results using state-of-the-art deep learning models, including UNet, SegResNet, and multimodal prognostic frameworks.
2025-08-29
ArXiv (preprint)
doi.org
arxiv.org
Scalable Option Learning in High-Throughput Environments
Mikael Henaff
Scott Fujimoto
Michael Matthews
Michael G. Rabbat
Hierarchical reinforcement learning (RL) has the potential to enable effective decision-making over long timescales. Existing approaches, wh… (see more)ile promising, have yet to realize the benefits of large-scale training. In this work, we identify and solve several key challenges in scaling hierarchical RL to high-throughput environments. We propose Scalable Option Learning (SOL), a highly scalable hierarchical RL algorithm which achieves a 25x higher throughput compared to existing hierarchical methods. We train our hierarchical agents using 20 billion frames of experience on the complex game of NetHack, significantly surpassing flat agents and demonstrating positive scaling trends. We also validate our algorithm on MiniHack and Mujoco environments, showcasing its general applicability. Our code is open sourced at github.com/facebookresearch/sol.
2025-08-29
ArXiv (preprint)
doi.org
arxiv.org
A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction
Camille Rochefort-Boulanger
Matthew Scicluna
Raphaël Poujol
Jean-Christophe Grenier
Pierre Luc Carrier
Sébastien Lemieux
Julie G Hussin
1 Accurately capturing genetic ancestry is critical for ensuring reproducibility and fairness in genomic st… (see more)udies and downstream health research. This study aims to address the prediction of ancestry from genetic data using deep learning, with a focus on generalizability across datasets with diverse populations and on explainability to improve model transparency. We adapt the Diet Network, a deep learning architecture proven effective in handling high-dimensional data, to learn population ancestry from single nucleotide polymorphisms (SNPs) data using the populational Thousand Genomes Project dataset. Our results highlight the model’s ability to generalize to diverse populations in the CARTaGENE and Montreal Heart Institute biobanks and that predictions remain robust to high levels of missing SNPs. We show that, despite the lack of North African populations in the training dataset, the model learns latent representations that reflect meaningful population structure for North African individuals in the biobanks. To improve model transparency, we apply Saliency Maps, DeepLift, GradientShap and Integrated Gradients attribution techniques and evaluate their performance in identifying SNPs leveraged by the model. Using DeepLift, we show that model’s predictions are driven by population-specific signals consistent with those identified by traditional population genetics metrics. This work presents a generalizable and interpretable deep learning framework for genetic ancestry inference in large-scale biobanks with genetic data. By enabling more widespread genomic ancestry characterization in these cohorts, this study contributes practical tools for integrating genetic data into downstream biomedical applications, supporting more inclusive and equitable healthcare solutions.
2025-08-29
bioRxiv (preprint)
doi.org
Assessing the exposure of buildings to long-term sea level rise across the Global South
M. Willard-Stepan
N. Gomez
Jeffrey A. Cardille
E. D. Galbraith
E. M. Bennett
2025-08-28
npj Urban Sustainability (published)
doi.org
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach (Extended Version)
Théo Laurentin
Patrick Coirault
Emmanuel Moulay
Antoine Lesage-Landry
Jerome Le Ny
2025-08-28
ArXiv (preprint)
arxiv.org
arxiv.org
Aperiodic and Periodic EEG Component Lifespan Trajectories: Monotonic Decrease versus Growth-then-Decline
Min Li
Ying Wang
Yaqi Chen
Adrien E. E. Dubois
Gangyong Jia
Q. M. Jonathan Wu
Maria L. Bringas-Vega
Guillaume Dumas
Pedro A. Valdés‐Sosa
1.1 Unraveling the lifespan trajectories of human brain development is critical for understanding brain health and … (see more)disease. Recent research demonstrates that electroencephalography signals are composed of periodic and aperiodic components reflecting distinct physiological substrates. This dissociation raises the possibility that they follow different developmental tendencies. Here, we delineate the lifespan trajectories of aperiodic and periodic neural oscillations using a large international cohort (N=1,563, ages 5–95, resting state, eyes closed). We reveal two fundamental developmental patterns: a Monotonic decrease in aperiodic activity and a Growth-and-Decline pattern for periodic activity. Both components have inflections around age 20 and transition to a stable senescent phase around age 40. Spatially, anterior regions mainly exhibit aperiodic activity, while periodic activity concentrate on posterior regions and these patterns remain stable throughout life. Crucially, multimodal analysis shows these trajectories map onto distinct biological substrates. The periodic component’s Growth and Decline trajectory aligns with GABAergic function and myelination. In contrast, the monotonically decreasing trajectory of aperiodic activity mirrors fundamental biomarkers of biological aging, such as DNA methylation and telomere length. Transforming age to a logarithmic scale simplifies these nonlinear trajectories into a linear decreasing and a piecewise concave linear model for aperiodic and periodic components. This form provides a robust and parsimonious framework for quantifying maturation and identifying neurological deviations. We delineate distinct lifespan trajectories of aperiodic and periodic neural activity in a large-scale international cohort (N=1,563, ages 5–95). Aperiodic activity undergoes a Monotonic Decrease with age. In contrast, periodic activity follows a Growth-then-Decline trajectory, peaking in early adulthood. Both trajectories feature a critical transition around age 20 and stabilize into a protracted senescent phase from approximately 40 onward. These neural trajectories map onto distinct biological substrates: periodic activity tracks integrative functions (myelination, GABAergic, and aperiodic decline mirrors fundamental aging processes (DNA methylation). A stable pattern observed throughout the lifespan is the spatial segregation of neural activity, where aperiodic signals are dominant in anterior regions and periodic signals are concentrated in posterior ones. Logarithmically transforming age linearized the developmental trajectories, yielding a monotonic decline for the aperiodic component and a concave piecewise for the periodic one. This process establishes robust linear norms for the personalized assessment of brain dysfunction.
2025-08-26
bioRxiv (preprint)
doi.org
R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression.
Xiaoqiang Wang 0007
Suyuchen Wang
Yun Zhu
Bang Liu
2025-08-26
DBLP.org (unknown)
openreview.net
openreview.net