Publications

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
Éric Granger
2025-05-25
2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG) (publié)
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… (voir plus)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 (prépublication)
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 (prépublication)
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
Éric Granger
Recognizing complex emotions linked to ambivalence and hesitancy (A/H) can play a critical role in the personalization and effectiveness of … (voir plus)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 (prépublication)
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 (publié)
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 (publié)
doi.org
Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance
Johannes C. Ayena
Amina Bouayed
Myriam Ben Arous
Youssef Ouakrim
Karim Loulou
Darine Ameyed
Isabelle Savard
Leila El Kamel
Neila Mezghani
Background: Wearable devices offer innovative solutions for chronic pain (CP) management by enabling real-time monitoring and personalized p… (voir plus)ain control. Although they are increasingly used to monitor pain-related parameters, their potential for predicting CP progression remains underutilized. Current studies focus mainly on correlations between data and pain levels, but rarely use this information for accurate prediction. Objective: This study aims to review recent advancements in wearable technology for CP management, emphasizing the integration of multimodal data, sensor quality, compliance with data security standards, and the effectiveness of predictive models in identifying CP episodes. Methods: A systematic search across six major databases identified studies evaluating wearable devices designed to collect pain-related parameters and predict CP. Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed. Results: Wearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. Random Forest and multilevel models have demonstrated consistent performance, while advanced models like Convolutional Neural Network-Long Short-Term Memory have faced challenges with data quality and computational demands. Despite compliance with regulations like General Data Protection Regulation and ISO standards, data security and privacy concerns persist. Additionally, the integration of multimodal data, including physiological, psychological, and demographic factors, remains underexplored, presenting an opportunity to improve prediction accuracy. Conclusions: Future research should prioritize developing robust predictive models, standardizing data protocols, and addressing security and privacy concerns to maximize wearable devices' potential in CP management. Enhancing real-time capabilities and fostering interdisciplinary collaborations will improve clinical applicability, enabling personalized and preventive pain management.
2025-05-21
Frontiers in Digital Health (publié)
doi.org
Structure-Aligned Protein Language Model
Can Chen
David Heurtel-Depeiges
Robert M. Vernon
Christopher J. Langmead
Yoshua Bengio
Quentin Fournier
2025-05-21
ArXiv (prépublication)
doi.org
arxiv.org
Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer
Anca Apavaloaei
Qingchuan Zhao
Leslie Hesnard
Maxime Cahuzac
Chantal Durette
Jean-David Larouche
Marie-Pierre Hardy
Krystel Vincent
Sylvie Brochu
Jean-Philippe Laverdure
Joël Lanoix
Mathieu Courcelles
Patrick Gendron
Mathieu Lajoie
Maria Virginia Ruiz Cuevas
Eralda Kina
Julie Perrault
Juliette Humeau
Gregory Ehx
Sébastien Lemieux … (voir 5 de plus)
Ian R. Watson
Daniel E. Speiser
Michal Bassani-Sternberg
Pierre Thibault
Claude Perreault
Melanoma and non-small cell lung cancer (NSCLC) display exceptionally high mutational burdens. Hence, immune targeting in these cancers has … (voir plus)primarily focused on tumor antigens (TAs) predicted to derive from nonsynonymous mutations. Using comprehensive proteogenomic analyses, we identified 589 TAs in cutaneous melanoma (n = 505) and NSCLC (n = 90). Of these, only 1% were derived from mutated sequences, which was explained by a low RNA expression of most nonsynonymous mutations and their localization outside genomic regions proficient for major histocompatibility complex (MHC) class I-associated peptide generation. By contrast, 99% of TAs originated from unmutated genomic sequences specific to cancer (aberrantly expressed tumor-specific antigens (aeTSAs), n = 220), overexpressed in cancer (tumor-associated antigens (TAAs), n = 165) or specific to the cell lineage of origin (lineage-specific antigens (LSAs), n = 198). Expression of aeTSAs was epigenetically regulated, and most were encoded by noncanonical genomic sequences. aeTSAs were shared among tumor samples, were immunogenic and could contribute to the response to immune checkpoint blockade observed in previous studies, supporting their immune targeting across cancers.
2025-05-21
Nature Cancer (publié)
doi.org
ImmunoStruct: a multimodal neural network framework for immunogenicity prediction from peptide-MHC sequence, structure, and biochemical properties
Kevin Bijan Givechian
João Felipe Rocha
Edward Yang
Chen Liu
Kerrie Greene
Rex Ying
Etienne Caron
Akiko Iwasaki
Smita Krishnaswamy
2025-05-20
Research Square (publié)
doi.org
Cosmic Ray Muon Polarization to Facilitate Atmospheric Neutrino Physics
Mingchen Sun
Shihan Zhao
Rui-Xuan Gao
He-Sheng Liu
Aiyu Bai
Jian Tang
Atmospheric neutrinos (ATNs) offer a paradigm for understanding neutrino properties, while it is critical to quantify uncertainties in flux … (voir plus)modeling. Since ATNs are produced simultaneously with cosmic ray muons, precision measurements of cosmic ray muons, including arrival direction, energy spectra, and spin polarization, will help reduce ATN production uncertainties and facilitate atmospheric neutrino physics. This letter proposes using an array strategy to measure the spin polarization of cosmic ray muons, thereby strengthening the emergent synergies between cosmic ray and atmospheric neutrino physics. Constraints on long-standing atmospheric neutrino flux uncertainties at the percentage level in a few-GeV energy range are achievable within one year using a
2025-05-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Determinants of surgical approach to pediatric appendicitis in Brazil.
Ayla Gerk
Paulo Henrique Moreira Melo
Mohsen Amoei
Shreenik Kundu
Luiza Telles
Justina O. Seyi-Olajide
Dunya Moghul
Gabriel Schnitman
Cristina Camargo
David P. Mooney
Joaquim Bustorff-Silva
Dan Poenaru
2025-05-19
Pediatric surgery international (Print) (publié)
doi.org