Publications

Topological mapping for traversability-aware long-range navigation in off-road terrain
Jean-François Tremblay
Julie Alhosh
Louis Petit
Faraz Lotfi
Lara Landauro
David Meger
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 m2 forest sites unseen during training, in difficult conditions for navigation.
2025-05-18
2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting
He Zhu
Jun Bai
Na Li
Xiaoxiao Li
Dianbo Liu
David L. Buckeridge
Yue Li
Federated learning (FL) enables collaborative analysis of decentralized medical data while preserving patient privacy. However, the covariat… (voir plus)e shift from demographic and clinical differences can reduce model generalizability. We propose FedWeight, a novel FL framework that mitigates covariate shift by reweighting patient data from the source sites using density estimators, allowing the trained model to better align with the distribution of the target site. To support unsupervised applications, we introduce FedWeight ETM, a federated embedded topic model. We evaluated FedWeight in cross-site FL on the eICU dataset and cross-dataset FL between eICU and MIMIC III. FedWeight consistently outperforms standard FL baselines in predicting ICU mortality, ventilator use, sepsis diagnosis, and length of stay. SHAP-based interpretation and ETM-based topic modeling reveal improved identification of clinically relevant characteristics and disease topics associated with ICU readmission.
2025-05-16
NPJ Digital Medicine (publié)
doi.org
A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition
Marc-André Legault
Jason Hartford
Benoit J. Arsenault
Archer Y. Yang
Joelle Pineau
2025-05-14
American Journal of Human Genetics (publié)
doi.org
Persistent signs of poisoning after massive drug ingestion: move the ultrasound probe to the stomach.
N. Lautrou-cabasson
H. Pirollet
C. Lombois
Guillaume Dumas
2025-05-14
Intensive Care Medicine (publié)
doi.org
The CASTOR mission
Patrick Côté
T. Woods
John Hutchings
J. Rhodes
R. Sánchez-Janssen
Alan D. Scott
J. Pazder
Melissa Amenouche
Michael Balogh
Simon Blouin
Alain Cournoyer
M. Drout
Nick Kuzmin
Katherine J. Mack
Laura Ferrarese
Wesley C. Fraser
S. Gallagher
Frederic J. Grandmont
Daryl Haggard
P. Harrison … (voir 160 de plus)
V. Hénault-Brunet
J. Kavelaars
V. Khatu
J. Roediger
J. Rowe
Marcin Sawicki
Jesper Skottfelt
Matt Taylor
L. van Waerbeke
Laurie Amen
Dhananjhay Bansal
Martin Bergeron
Toby Brown
Greg Burley
Hum Chand
Isaac Cheng
Ryan Cloutier
N. Dickson
Oleg Djazovski
Ivana Damjanov
James Doherty
K. Finner
Macarena García Del Valle Espinosa
Jennifer Glover
A. I. Gómez de Castro
Or Graur
Tim Hardy
Michelle Kao
D A Leahy
Deborah Lokhorst
A. I. Malz
Allison Man
Madeline A. Marshall
Sean McGee
Ryan McKenzie
Kai Michaud
Surhud S. More
David Morris
Patrick W. Morris
T. Moutard
Wasi Naqvi
Matthew Nicholl
G. Noirot
M. S. Oey
C. Opitom
Samir Salim
Bryan R. Scott
Charles Shapiro
Daniel Stern
Ashwin Subramaniam
David Thilke
I. Wevers
Dmitri Vorobiev
L. Y. Aaron Yung
Frédéric Zamkotsian
S. Aigrain
A. Alavi
Martin Barstow
Peter Bartosik
H. Bluhm
J. Bovy
Peter Cameron
R. Carlberg
J. Christiansen
Yuyang Chen
P. Crowther
Kristen Dage
Aaron Dotter
Patrick Dufour
Jean Dupuis
B. Dryer
A. Duara
Gwendolyn M. Eadie
Marielle R. Eduardo
V. Estrada-Carpenter
Sébastien Fabbro
A. Faisst
N. M. Ford
M. Fraser
Boris T. Gaensicke
Shashkiran Ganesh
Poshak Gandhi
Melissa L. Graham
R. Hamel
Martin Hellmich
John J. Hennessy
Kaitlyn Hessel
J. Heyl
Catherine Heymans
Yashar Hezaveh
Renée Hložek
Michael Hoenk
Andrew Holland
Eric Huff
Ian Hutchinson
I. Iwata
April D. Jewell
Doug Johnstone
Maia Jones
Todd J. Jones
D. Lang
J. Lapington
Justin Larivière
C. Lawlor-Forsyth
Denis Laurin
Charles Lee
Ronan Legin
Ting S. Li
S. Lim
B. Ludwig
Matt Kozun
V. M
Robert Mann
Alan McConnachie
Evan McDonough
S. Metchev
David R. Miller
Takashi Moriya
Cameron Morgan
Julio F. Navarro
Y. Nazé
Shouleh Nikzad
Vivek Oad
N. N.-Q. Ouellette
E. Pass
Will J. Percival
Laurence Perreault-Levasseur
Joe Postma
Nayyer Raza
G. T. Richards
Harvey Richer
Carmelle Robert
Erik Rosolowsky
J. Ruan
Sarah Rugheimer
S. Safi-Harb
Kanak Saha
Vicky Scowcroft
F. Sestito
Himanshu Sharma
James Sikora
G. Sivakoff
T. S. Sivarani
Patrick Smith
Warren Soh
R. Sorba
S. Subramanian
Hossen Teimoorinia
H. Teplitz
Shaylin Thadani
Shavon Thadani
Aaron Tohuvavohu
K. Venn
Nicholas Vieira
Jeremy J. Webb
P. Wiegert
Ryan Wierckx
Yanqin Wu
J. Yeung
S. K. Yi
2025-05-13
Journal of Astronomical Telescopes Instruments and Systems (publié)
doi.org
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Hiroki Naganuma
Ryuichiro Hataya
Kotaro Yoshida
Ioannis Mitliagkas
2025-05-12
TMLR (accepté)
openreview.net
openreview.net
Cosmological constraints using simulation-based inference of galaxy clustering with marked power spectra
Elena Massara
ChangHoon Hahn
Michael Eickenberg
Shirley Ho
Jiamin Hou
Pablo Lemos
Chirag Modi
Azadeh Moradinezhad Dizgah
Liam Parker
Bruno Régaldo-Saint Blancard
International audience
2025-05-11
Physical Review D (publié)
doi.org
arxiv.org
Influence of scanning plane on Human Spinal Cord functional Magnetic Resonance echo planar imaging
Marta Moraschi
Silvia Tommasin
Laura Maugeri
Mauro DiNuzzo
Julien Cohen-Adad
Marco Masullo
Fabio Mangini
Lorenzo Giovannelli
Daniele Mascali
Tommaso Gili
Valerio Pisani
Ugo Nocentini
Federico Giove
Michela Fratini
BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) is based on the Blood Oxygenation Level Dependent contrast and has been exploited f… (voir plus)or the indirect study of the neuronal activity within both the brain and the spinal cord. However, the interpretation of spinal cord fMRI (scfMRI) is still controversial and its diffusion is rather limited because of technical limitations. Overcoming these limitations would have a beneficial effect for the assessment and follow-up of spinal injuries and neurodegenerative diseases. PURPOSE: This study was aimed at systematically verify whether sagittal scanning in scfMRI using EPI readout is a viable alternative to the more common axial scanning, and at optimizing a pipeline for EPI-based scfMRI data analysis, based on Spinal Cord Toolbox (SCT). METHODS: Forty-five healthy subjects underwent MRI acquisition in a Philips Achieva 3T MRI scanner. T2*-weighted fMRI data were acquired using a GE-EPI sequence along sagittal and axial planes during an isometric motor task. Differences on benchmarks were assessed via paired two-sample t-test at p=0.05. RESULTS: We investigated the impact of the acquisition strategy by means of various metrics such as Temporal Signal to Noise Ratio (tSNR), Dice Coefficient to assess geometric distortions, Reproducibility and Sensitivity. tSNR was higher in axial than in sagittal scans, as well as reproducibility within the whole cord mask (t=7.4, p0.01) and within the GM mask (t=4.2, p0.01). The other benchmarks, associated with distortion and functional response, showed no differenc
2025-05-11
PLOS One (publié)
doi.org
arxiv.org
Learning Penalty for Optimal Partitioning via Automatic Feature Extraction
Tung L. Nguyen
Toby Dylan Hocking
Changepoint detection identifies significant shifts in data sequences, making it important in areas like finance, genetics, and healthcare. … (voir plus)The Optimal Partitioning algorithms efficiently detect these changes, using a penalty parameter to limit the changepoints number. Determining the appropriate value for this penalty can be challenging. Traditionally, this process involved manually extracting statistical features, such as sequence length or variance to make the prediction. This study proposes a novel approach that uses recurrent neural networks to learn this penalty directly from raw sequences by automatically extracting features. Experiments conducted on 20 benchmark genomic datasets show that this novel method surpasses traditional methods in partitioning accuracy in most cases.
2025-05-11
ArXiv (prépublication)
doi.org
arxiv.org
OpenLex3D: A New Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
Christina Kassab
Sacha Morin
Martin Büchner
Matias Mattamala
Kumaraditya Gupta
Abhinav Valada
Liam Paull
Maurice Fallon
2025-05-11
IEEE.org/ICRA/2025/Workshop/Safe-VLM (spotlight)
openreview.net
openreview.net
Source-Free Domain Adaptation for YOLO Object Detection
Simon Varailhon
Masih Aminbeidokhti
Marco Pedersoli
Éric Granger
Source-free domain adaptation (SFDA) is a challenging problem in object detection, where a pre-trained source model is adapted to a new targ… (voir plus)et domain without using any source domain data for privacy and efficiency reasons. Most state-of-the-art SFDA methods for object detection have been proposed for Faster-RCNN, a detector that is known to have high computational complexity. This paper focuses on domain adaptation techniques for real-world vision systems, particularly for the YOLO family of single-shot detectors known for their fast baselines and practical applications. Our proposed SFDA method - Source-Free YOLO (SF-YOLO) - relies on a teacher-student framework in which the student receives images with a learned, target domain-specific augmentation, allowing the model to be trained with only unlabeled target data and without requiring feature alignment. A challenge with self-training using a mean-teacher architecture in the absence of labels is the rapid decline of accuracy due to noisy or drifting pseudo-labels. To address this issue, a teacher-to-student communication mechanism is introduced to help stabilize the training and reduce the reliance on annotated target data for model selection. Despite its simplicity, our approach is competitive with state-of-the-art detectors on several challenging benchmark datasets, even sometimes outperforming methods that use source data for adaptation.
2025-05-11
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion
Ge Ya Luo
Zhi Hao Luo
Anthony Gosselin
Alexia Jolicoeur-Martineau
Christopher Pal
2025-05-09
TMLR (accepté)
openreview.net
openreview.net