Publications

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
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
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 (publié)
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 (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
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… (voir plus)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 (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