Publications

Task Robustness via Re-Labelling Vision-Action Robot Data
Artur Kuramshin
Özgür Aslan
Cyrus Neary
Glen Berseth
2025-09-05
robot-learning.org/CoRL/2025/Workshop/Robot_Data (publié)
openreview.net
openreview.net
Massive Extremely High-velocity Outflow in the Quasar J164653.72+243942.2
Paola Rodríguez Hidalgo
Hyunseop Choi (최현섭)
Patrick B. Hall
Karen M. Leighly
Liliana Flores
Mikel M. Charles
Cora DeFrancesco
Julie Hlavacek-Larrondo
Laurence Perreault-Levasseur
We present the analysis of one of the most extreme quasar outflows found to date in our survey of extremely high velocity outflows (EHVO). J… (voir plus)164653.72+243942.2 (z ~ 3.04) shows variable CIV1548,1551 absorption at speeds larger than 0.1c, accompanied by SiIV, NV and Lya, and disappearing absorption at lower speeds. We perform absorption measurements using the Apparent Optical Depth method and SimBAL. We find the absorption to be very broad (Δv ~35,100 km/s in the first epoch and ~13,000 km/s in the second one) and fast (vmax ~ -50,200 km/s and -49,000 km/s, respectively). We measure large column densities (
2025-09-03
The Astrophysical Journal (publié)
doi.org
arxiv.org
MISTRAL: a model for AGN winds from radiatively efficient accretion in cosmological simulations
Marion Farcy
Michaela Hirschmann
Rachel S. Somerville
Ena Choi
Sophie Koudmani
Thorsten Naab
Rainer Weinberger
Jake S. Bennett
Aklant K. Bhowmick
Hyunseop Choi
Lars Hernquist
Julie Hlavacek-Larrondo
Bryan A. Terrazas
Francesco Valentino
ABSTRACT Feedback from active galactic nuclei (AGNs) is crucial for regulating galaxy evolution. Motivated by observations of broad absorpti… (voir plus)on line winds from rapidly accreting supermassive black holes (SMBHs), we introduce the mistral AGN feedback model, implemented in the arepo code. mistral comes in two versions: continuous radial (mistral-continuous) and stochastic bipolar momentum deposition (mistral-stochastic). Using the framework of the IllustrisTNG simulations, we explore the effect of mistral on BH and galaxy properties, through an idealized Milky Way-mass galaxy and cosmological zoom simulations run down to
2025-09-03
Monthly Notices of the Royal Astronomical Society (publié)
doi.org
arxiv.org
Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients
Gwen Legate
Irina Rish
Eugene Belilovsky
Federated learning enables collaborative model training across numerous edge devices without requiring participants to share data; however, … (voir plus)memory and communication constraints on these edge devices may preclude their participation in training. We consider a setting in which a subset of edge devices are below a critical memory or communication threshold required to conduct model updates. Under typical federated optimization algorithms, these devices are excluded from training which renders their data inaccessible and increases system induced bias. We are inspired by MeZO, a zeroth-order method used for memory-efficient fine-tuning. The increased variance inherent to zeroth-order gradient approximations has relegated previous zeroth-order optimizers exclusively to the domain of fine tuning; a limitation we seek to correct. We devise a federated, memory-efficient zeroth-order optimizer, ZOWarmUp that permits zeroth-order training from a random initialization. ZOWarmUp leverages differing client capabilities and careful variance reduction techniques to facilitate participation of under-represented, low-resource clients in model training. Like other federated zeroth-order methods, ZOWarmUp eliminates the need for edge devices to transmit their full gradients to the server and instead relies on only a small set of random seeds, rendering the up-link communication cost negligible. We present experiments using various datasets and model architectures to show that ZOWarmUp is a robust algorithm that can can be applied under a wide variety of circumstances. For systems with a high proportion of edge devices that would otherwise be excluded from training, this algorithm provides access to a greater volume and diversity of data, thus improving training outcomes.
2025-09-02
ArXiv (prépublication)
doi.org
arxiv.org
Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Rishav
Somjit Nath
Vincent Michalski
S Ebrahimi Kahou
2025-09-01
Transactions on Machine Learning Research (accepté)
doi.org
openreview.net
Learning Laplacian Eigenvectors: a Pre-training Method for Graph Neural Networks
Howard Dai
Nyambura Njenga
Catherine Ma
Siddharth Viswanath
Ryan Pellico
Ian Adelstein
Smita Krishnaswamy
2025-09-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Early Deforestation Detection in the Tropics using L-band SAR and Optical multi-sensor data and Bayesian Statistics
Africa I. Flores-Anderson
Jeffrey A. Cardille
Josef Kellndorfer
Franz J. Meyer
Pontus Olofsson
2025-08-31
International Journal of Applied Earth Observation and Geoinformation (publié)
doi.org
Metabolic Control and Frequency of Clinical Monitoring Among Canadian Children With Phenylalanine Hydroxylase Deficiency: A Retrospective Cohort Study
Nataliya Yuskiv
Ammar Saad
Beth K. Potter
Sylvia Stockler‐Ipsiroglu
John J. Mitchell
Steven Hawken
Kylie Tingley
Michael Pugliese
Monica Lamoureux
Andrea J. Chow
Jonathan B. Kronick
Kumanan Wilson
Annette Feigenbaum
Sharan Goobie
Michal Inbar-Feigenberg
Julian Little
Saadet Mercimek‐Andrews
Amy Pender
Chitra Prasad
Andreas Schulze … (voir 9 de plus)
Yannis Trakadis
Gloria Ho
Hilary Vallance
Valerie Austin
Anthony Vandersteen
Andrea C. Yu
Cheryl Rockman‐Greenberg
Aizeddin Mhanni
Pranesh Chakraborty
2025-08-31
JIMD Reports (publié)
doi.org
Relative Trajectory Balance is equivalent to Trust-PCL
Tristan Deleu
Padideh Nouri
Yoshua Bengio
Doina Precup
2025-08-31
ArXiv (prépublication)
doi.org
arxiv.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 (publié)
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 (publié)
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 … (voir 23 de plus)
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 … (voir plus)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 (prépublication)
doi.org
arxiv.org