Publications

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field
Arthur Dehgan
Hamza Abdelhedi
Vanessa Hadid
Irina Rish
Karim Jerbi
Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (see more)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.
2025-05-26
Journal of Neural Engineering (published)
doi.org
arxiv.org
A Python Toolbox for Representational Similarity Analysis
Jasper JF van den Bosch
Tal Golan
Benjamin Peters
JohnMark Taylor
Mahdiyar Shahbazi
Baihan Lin
Ian Charest
Jörn Diedrichsen
Nikolaus Kriegeskorte
Marieke Mur
Heiko H. Schütt
2025-05-26
bioRxiv (preprint)
doi.org
Quantifying antimicrobial resistance in food-producing animals in North America
Mohamed Mediouni
Abdoulaye Banire Diallo
Vladimir Makarenkov
The global misuse of antimicrobial medication has further exacerbated the problem of antimicrobial resistance (AMR), enriching the pool of g… (see more)enetic mechanisms previously adopted by bacteria to evade antimicrobial drugs. AMR can be either intrinsic or acquired. It can be acquired either by selective genetic modification or by horizontal gene transfer that allows microorganisms to incorporate novel genes from other organisms or environments into their genomes. To avoid an eventual antimicrobial mistreatment, the use of antimicrobials in farm animal has been recently reconsidered in many countries. We present a systematic review of the literature discussing the cases of AMR and the related restrictions applied in North American countries (including Canada, Mexico, and the USA). The Google Scholar, PubMed, Embase, Web of Science, and Cochrane databases were searched to find plausible information on antimicrobial use and resistance in food-producing animals, covering the time period from 2015 to 2024. A total of 580 articles addressing the issue of antibiotic resistance in food-producing animals in North America met our inclusion criteria. Different AMR rates, depending on the bacterium being observed, the antibiotic class being used, and the farm animal being considered, have been identified. We determined that the highest average AMR rates have been observed for pigs (60.63% on average), the medium for cattle (48.94% on average), and the lowest for poultry (28.43% on average). We also found that Cephalosporines, Penicillins, and Tetracyclines are the antibiotic classes with the highest average AMR rates (65.86%, 61.32%, and 58.82%, respectively), whereas the use of Sulfonamides and Quinolones leads to the lowest average AMR (21.59% and 28.07%, respectively). Moreover, our analysis of antibiotic-resistant bacteria shows that Streptococcus suis (S. suis) and S. auerus provide the highest average AMR rates (71.81% and 69.48%, respectively), whereas Campylobacter spp. provides the lowest one (29.75%). The highest average AMR percentage, 57.46%, was observed in Mexico, followed by Canada at 45.22%, and the USA at 42.25%, which is most probably due to the presence of various AMR control strategies, such as stewardship programs and AMR surveillance bodies, existing in Canada and the USA. Our review highlights the need for better strategies and regulations to control the spread of AMR in North America.
2025-05-26
Frontiers in Microbiology (published)
doi.org
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi
Yann Batiste Pequignot
Jean-François Lalonde
Christian Gagné
Adversarial perturbations can deceive neural networks by adding small, imperceptible noise to the input. Recent object trackers with transfo… (see more)rmer backbones have shown strong performance on tracking datasets, but their adversarial robustness has not been thoroughly evaluated. While transformer trackers are resilient to black-box attacks, existing white-box adversarial attacks are not universally applicable against these new transformer trackers due to differences in backbone architecture. In this work, we introduce TrackPGD, a novel white-box attack that utilizes predicted object binary masks to target robust transformer trackers. Built upon the powerful segmentation attack SegPGD, our proposed TrackPGD effectively influences the decisions of transformer-based trackers. Our method addresses two primary challenges in adapting a segmentation attack for trackers: limited class numbers and extreme pixel class imbalance. TrackPGD uses the same number of iterations as other attack methods for tracker networks and produces competitive adversarial examples that mislead transformer and non-transformer trackers such as MixFormerM, OSTrackSTS, TransT-SEG, and RTS on datasets including VOT2022STS, DAVIS2016, UAV123, and GOT-10k.
2025-05-26
Proceedings of the Conference on Robots and Vision (published)
doi.org
openreview.net
Adaptive Inertia Estimation of a Power System Area Using Variable Forgetting Factor Recursive Least-Squares
Anoosh Dini
Keyhan Sheshyekani
Hanane Dagdougui
The increasing use of converter-interfaced generators (CIGs) in modern power grids has affected system inertia and posed challenges to grid … (see more)stability. In this regard, accurate and real-time monitoring of inertia is crucial for maintaining system stability, especially in low-inertia grids where even small disturbances can lead to rapid frequency deviations. This paper proposes a novel approach for inertia estimation using a variable forgetting factor recursive least squares (VFF-RLS) algorithm, which dynamically adapts to time-varying conditions in power systems. By using ambient measurements provided by the widearea measurement system (WAMS), the proposed approach can capture inertia variations of areas in power systems. The method is validated through simulations on the IEEE 39-bus system, demonstrating higher accuracy compared to existing approaches under both time-constant and time-varying inertia conditions.
2025-05-25
Canadian Conference on Electrical and Computer Engineering (published)
doi.org
ALAS: Measuring Latent Speech-Text Alignment For Spoken Language Understanding In Multimodal LLMs
Pooneh Mousavi
Yingzhi Wang
Mirco Ravanaelli
Yusuf Cem Sübakan
2025-05-25
ArXiv (preprint)
doi.org
arxiv.org
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
Masoumeh Sharafi
Emma Ollivier
Muhammad Osama Zeeshan
Soufiane Belharbi
Marco Pedersoli
Alessandro Lameiras Koerich
Simon Bacon
Eric Granger
2025-05-25
2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG) (published)
doi.org
arxiv.org
Improving Multilingual Math Reasoning for African Languages
Odunayo Ogundepo
Akintunde Oladipo
Kelechi Ogueji
Esther Adenuga
David Ifeoluwa Adelani
Jimmy Lin
Researchers working on low-resource languages face persistent challenges due to limited data availability and restricted access to computati… (see more)onal resources. Although most large language models (LLMs) are predominantly trained in high-resource languages, adapting them to low-resource contexts, particularly African languages, requires specialized techniques. Several strategies have emerged for adapting models to low-resource languages in todays LLM landscape, defined by multi-stage pre-training and post-training paradigms. However, the most effective approaches remain uncertain. This work systematically investigates which adaptation strategies yield the best performance when extending existing LLMs to African languages. We conduct extensive experiments and ablation studies to evaluate different combinations of data types (translated versus synthetically generated), training stages (pre-training versus post-training), and other model adaptation configurations. Our experiments focuses on mathematical reasoning tasks, using the Llama 3.1 model family as our base model.
2025-05-25
ArXiv (preprint)
doi.org
arxiv.org
SCAR: Shapley Credit Assignment for More Efficient RLHF
Meng Cao
Shuyuan Zhang
Xiao-Wen Chang
Doina Precup
2025-05-25
ArXiv (preprint)
doi.org
arxiv.org
BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Behavioural Change
Manuela Gonz'alez-Gonz'alez
Soufiane Belharbi
Muhammad Osama Zeeshan
Masoumeh Sharafi
Muhammad Haseeb Aslam
Marco Pedersoli
Alessandro Lameiras Koerich
Simon Bacon
Eric Granger
Recognizing complex emotions linked to ambivalence and hesitancy (A/H) can play a critical role in the personalization and effectiveness of … (see more)digital behaviour change interventions. These subtle and conflicting emotions are manifested by a discord between multiple modalities, such as facial and vocal expressions, and body language. Although experts can be trained to identify A/H, integrating them into digital interventions is costly and less effective. Automatic learning systems provide a cost-effective alternative that can adapt to individual users, and operate seamlessly within real-time, and resource-limited environments. However, there are currently no datasets available for the design of ML models to recognize A/H. This paper introduces a first Behavioural Ambivalence/Hesitancy (BAH) dataset collected for subject-based multimodal recognition of A/H in videos. It contains videos from 224 participants captured across 9 provinces in Canada, with different age, and ethnicity. Through our web platform, we recruited participants to answer 7 questions, some of which were designed to elicit A/H while recording themselves via webcam with microphone. BAH amounts to 1,118 videos for a total duration of 8.26 hours with 1.5 hours of A/H. Our behavioural team annotated timestamp segments to indicate where A/H occurs, and provide frame- and video-level annotations with the A/H cues. Video transcripts and their timestamps are also included, along with cropped and aligned faces in each frame, and a variety of participants meta-data. We include results baselines for BAH at frame- and video-level recognition in multi-modal setups, in addition to zero-shot prediction, and for personalization using unsupervised domain adaptation. The limited performance of baseline models highlights the challenges of recognizing A/H in real-world videos. The data, code, and pretrained weights are available.
2025-05-24
ArXiv (preprint)
doi.org
arxiv.org
Response letter to “Confounding by indication and exposure misclassification may undermine corticosteroid effect estimates in ICU patients with alcohol-related hepatitis”
Guillaume Dumas
Maxime Gasperment
Hafid AIT-OUFELLA
2025-05-23
Annals of Intensive Care (published)
doi.org
Introduction to the special issue on Computational Terminology
Ayla Rigouts Terryn
Patrick Drouin
2025-05-22
Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication (published)
doi.org