Publications

On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
Antoine Lesage-Landry
2025-09-01
Mathematics and Computers in Simulation (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
2025-09-01
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
Jacques Corbeil
P. Couture
Simone Lemieux
B. Lamarche
2025-09-01
Nutrition, Metabolism and Cardiovascular Diseases (publié)
doi.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.
2025-09-01
arXiv (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
Mathieu Hatt
Thomas Eugene
Raphael Metz
M'elanie Dore
G. Delpon
V. Papineni
K. Wahid
Cem Dede
A. M. Ali
Carlos Sjogreen … (voir 19 de plus)
Mohamed A. Naser
Clifton D Fuller
Valentin Oreiller
Mario Jreige
J. Prior
Catherine Cheze Le Rest
Olena Tankyevych
P. Decazes
Su Ruan
Stephanie Tanadini-Lang
Martin Vallières
Hesham M. Elhalawani
R. Abgral
R. Floch
K. Kerleguer
Ulrike Schick
M. Mauguen
Arman Rahmim
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.
2025-08-30
ArXiv (prépublication)
arxiv.org
arxiv.org
A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis 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
Rapha¨el Metz
M´elanie Dore
G. Delpon
Vijay Ram Papineni
K. Wahid
Cem Dede
Alaa Mohamed Shawky Ali
Carlos Sjogreen … (voir 23 de plus)
Mohamed A. Naser
Clifton D Fuller
Valentin Oreiller
Mario Jreige
John O. Prior
Catherine Cheze Le Rest
Olena Tankyevych
P. Decazes
Su Ruan
Stephanie Tanadini-Lang
Martin Vallières
Hesham M. Elhalawani
R. Abgral
R. Floch
K. Kerleguer
Ulrike Schick
M. Mauguen
D. Bourhis
J. Leclère
Amandine Sambourg
Arman Rahmim
Mathieu Hatt
Mohammad Yaqub
We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography… (voir plus)/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks.
2025-08-30
ArXiv (prépublication)
doi.org
arxiv.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
Mathieu Hatt
Thomas Eugene
Raphael Metz
M'elanie Dore
G. Delpon
V. Papineni
K. Wahid
Cem Dede
A. M. Ali
Carlos Sjogreen … (voir 19 de plus)
Mohamed A. Naser
Clifton D Fuller
Valentin Oreiller
Mario Jreige
J. Prior
Catherine Cheze Le Rest
Olena Tankyevych
P. Decazes
Su Ruan
Stephanie Tanadini-Lang
Martin Vallières
Hesham M. Elhalawani
R. Abgral
R. Floch
K. Kerleguer
Ulrike Schick
M. Mauguen
Arman Rahmim
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.
2025-08-30
ArXiv (prépublication)
arxiv.org
arxiv.org
A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis 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
Rapha¨el Metz
M´elanie Dore
G. Delpon
Vijay Ram Papineni
K. Wahid
Cem Dede
Alaa Mohamed Shawky Ali
Carlos Sjogreen … (voir 23 de plus)
Mohamed A. Naser
Clifton D Fuller
Valentin Oreiller
Mario Jreige
John O. Prior
Catherine Cheze Le Rest
Olena Tankyevych
P. Decazes
Su Ruan
Stephanie Tanadini-Lang
Martin Vallières
Hesham M. Elhalawani
R. Abgral
R. Floch
K. Kerleguer
Ulrike Schick
M. Mauguen
D. Bourhis
J. Leclère
Amandine Sambourg
Arman Rahmim
Mathieu Hatt
Mohammad Yaqub
We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography… (voir plus)/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks.
2025-08-30
ArXiv (prépublication)
doi.org
arxiv.org
A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis 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
Rapha¨el Metz
M´elanie Dore
G. Delpon
Vijay Ram Papineni
K. Wahid
Cem Dede
Alaa Mohamed Shawky Ali
Carlos Sjogreen … (voir 23 de plus)
Mohamed A. Naser
Clifton D Fuller
Valentin Oreiller
Mario Jreige
John O. Prior
Catherine Cheze Le Rest
Olena Tankyevych
P. Decazes
Su Ruan
Stephanie Tanadini-Lang
Martin Vallières
Hesham M. Elhalawani
R. Abgral
R. Floch
K. Kerleguer
Ulrike Schick
M. Mauguen
D. Bourhis
J. Leclère
Amandine Sambourg
Arman Rahmim
Mathieu Hatt
Mohammad Yaqub
We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography… (voir plus)/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks.
2025-08-30
ArXiv (prépublication)
arxiv.org
arxiv.org
Scalable Option Learning in High-Throughput Environments
Mikael Henaff
Scott Fujimoto
Michael Matthews
Michael Rabbat
Hierarchical reinforcement learning (RL) has the potential to enable effective decision-making over long timescales. Existing approaches, wh… (voir plus)ile promising, have yet to realize the benefits of large-scale training. In this work, we identify and solve several key challenges in scaling online hierarchical RL to high-throughput environments. We propose Scalable Option Learning (SOL), a highly scalable hierarchical RL algorithm which achieves a ~35x higher throughput compared to existing hierarchical methods. To demonstrate SOL's performance and scalability, we train hierarchical agents using 30 billion frames of experience on the complex game of NetHack, significantly surpassing flat agents and demonstrating positive scaling trends. We also validate SOL on MiniHack and Mujoco environments, showcasing its general applicability. Our code is open sourced at: github.com/facebookresearch/sol.
2025-08-30
ArXiv (prépublication)
arxiv.org
arxiv.org
Scalable Option Learning in High-Throughput Environments
Mikael Henaff
Scott Fujimoto
Michael Matthews
Michael Rabbat
Hierarchical reinforcement learning (RL) has the potential to enable effective decision-making over long timescales. Existing approaches, wh… (voir plus)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.
2025-08-30
ArXiv (prépublication)
doi.org
arxiv.org
Assessing the exposure of buildings to long-term sea level rise across the Global South
M. Willard-Stepan
N. Gomez
Jeff Cardille
E. D. Galbraith
E. M. Bennett
2025-08-29
npj Urban Sustainability (publié)
doi.org