Publications

The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities

Saeid Jamshidi

Negar Shahabi

Amin Nikanjam

Kawser Wazed Nafi

Foutse Khomh

Carol Fung

2025-11-01

Internet of Things (publié)

doi.org

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

Shin (Alexandre) Koseki

2025-10-01

Habitat International (publié)

doi.org

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

Shin (Alexandre) Koseki

2025-10-01

Habitat International (publié)

doi.org

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

Shin (Alexandre) Koseki

2025-10-01

Habitat International (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.

2025-09-03

ArXiv (prépublication)

arxiv.org

Behaviour Discovery and Attribution for Explainable Reinforcement Learning

Rishav

Somjit Nath

Vincent Michalski

Samira Ebrahimi Kahou

2025-09-02

TMLR (accepté)

openreview.net

Learning Laplacian Eigenvectors: a Pre-training Method for Graph Neural Networks

Howard Dai

Nyambura Njenga

Benjamin Whitsett

Catherine Ma

Darwin Deng

Sara de 'Angel

Alexandre Van Tassel

Siddharth Viswanath

Ryan Pellico

Ian Adelstein

Smita Krishnaswamy

2025-09-02

ArXiv (prépublication)

arxiv.org

Relative Trajectory Balance is equivalent to Trust-PCL

2025-09-01

ArXiv (prépublication)

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

Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach (Extended Version)

Th'eo Laurentin

Patrick Coirault

Emmanuel Moulay

Antoine Lesage-Landry

J'erome Le Ny

2025-08-29

ArXiv (prépublication)

arxiv.org

Rootlets-based registration to the PAM50 spinal cord template

Sandrine Bédard

Jan Valosek

Valeria Oliva

Kenneth A. Weber

Julien Cohen-Adad

Abstract Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxel-wise group analyses. Traditiona… (voir plus)l template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n = 267, 44 sites) and a multi-subject dataset with various neck positions (n = 10, 3 sessions). We further validated the method on task-based functional MRI (n = 23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared with the traditional disc-based method on n = 226 individuals, and on n = 176 individuals for morphological analyses. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based registration increased Z scores and activation cluster size compared with disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.

2025-08-26

Imaging Neuroscience (publié)

doi.org

arxiv.org

Communication Efficient LLM Pre-training with SparseLoCo

Amir M. Sarfi

Benjamin Therien

Joel Lidin

Eugene Belilovsky

2025-08-21

ArXiv (prépublication)

arxiv.org