Publications

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
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 … (see 5 more)
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 … (see more)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 (published)
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 (published)
doi.org
Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Gyubin Lee
Truong Nhat Nguyen Bao
Jaesik Yoon
Dongwoo Lee
Minsu Kim
Yoshua Bengio
Sungjin Ahn
Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. Ho… (see more)wever, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands adaptively. We introduce the challenge of adaptive inference-time scaling-dynamically adjusting computational effort during inference-and propose Adaptive Bi-directional Cyclic Diffusion (ABCD), a flexible, search-based inference framework. ABCD refines outputs through bi-directional diffusion cycles while adaptively controlling exploration depth and termination. It comprises three components: Cyclic Diffusion Search, Automatic Exploration-Exploitation Balancing, and Adaptive Thinking Time. Experiments show that ABCD improves performance across diverse tasks while maintaining computational efficiency.
2025-05-19
ArXiv (preprint)
doi.org
openreview.net
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 … (see more)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 (preprint)
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) (published)
doi.org
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Ekin Dogus Cubuk
Rishabh Agarwal
Aaron Courville
Bellemare Marc-Emmanuel
Sergei Kalinin
Igor Mordatch
Pablo Samuel Castro
Kevin M Roccapriore
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (see more)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
2025-05-19
Advanced Materials Interfaces (published)
doi.org
arxiv.org
Multi‐center benchmarking of cervical spinal cord RF coils for 7 T MRI: A traveling spines study
Eva Alonso‐Ortiz
Daniel Papp
Robert L. Barry
Kyota Poëti
Alan C. Seifert
Kyle M. Gilbert
Nibardo Lopez‐Rios
Jan Paska
Falk Eippert
Nikolaus Weiskopf
Laura Beghini
Nadine N. Graedel
Robert Trampel
Martina F. Callaghan
Christoph S. Aigner
Patrick Freund
Maryam Seif
Aurélien Destruel
Virginie Callot
Johanna Vannesjo … (see 1 more)
Julien Cohen‐Adad
The depth within the body, small diameter, long length, and varying tissue surrounding the spinal cord impose specific considerations when d… (see more)esigning RF coils. The optimal coil configuration for 7 T cervical spinal cord MRI is unknown and currently there are very few coil options. The purpose of this work was (1) to establish a quality control protocol for evaluating 7 T cervical spinal cord coils, and (2) to use that protocol to evaluate the performance of four different coil designs. Three healthy volunteers and a custom anthropomorphic phantom (the traveling spines cohort) were scanned at seven 7 T imaging centers using a common protocol and each center's specific cervical spinal cord coil. Four different coil designs were tested (two in‐house, one Rapid Biomedical, and one MRI.TOOLS design). The Rapid Biomedical coil was found to have the highest B1+ efficiency, whereas one of the in‐house designs (NeuroPoly Lab) had the highest SNR and the largest spinal cord coverage. The MRI.TOOLS coil had the most uniform B1+ profile along the cervical spinal cord; however, it was limited in its ability to provide the requested flip angles (especially for larger individuals). The latter was also the case for the second in‐house coil (MSSM). The results of this study serve as a guide for the spinal cord MRI community in selecting the most suitable coil based on specific requirements and offer a standardized protocol for assessing future coils.
2025-05-19
Magnetic Resonance in Medicine (published)
doi.org
Generalizable Imitation Learning Through Pre-Trained Representations
Wei-Di Chang
Francois Hogan
David Meger
Scott Fujimoto
Gregory Dudek
In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (see more)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.
2025-05-18
2025 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org
PedMedQA: Comparing Large Language Model Accuracy in Pediatric and Adult Medicine
Nikhil Jaiswal
Yuanchao Ma
Bertrand Lebouché
Dan Poenaru
Esli Osmanlliu
Large language models (LLMs) have the potential to revolutionize healthcare, including aiding in clinical decision-making. However, recent w… (see more)ork suggests that LLM performance in pediatric cases may be weaker than adult cases. A key limitation in evaluating these differences is the lack of pediatric-specific benchmarks, making it difficult to systematically assess how well LLMs generalize to pediatric scenarios.
2025-05-18
Pediatrics Open Science (published)
doi.org