Publications

1D Pre‐Acquisition Navigator Correcting Respiratory‐Induced Field Fluctuations in Multi‐Echo Gradient‐Echo Imaging of the Thoracic Spinal Cord
Alicia E. Cronin
Alexandre D’Astous
Nathan Williams
Antoine Guénette
Aimee Salakhov
Seth Stubblefield
Colin D. Mcknight
Lipika Narisetti
Subramaniam Sriram
Seth A. Smith
Ryan K. Robison
Guillaume Gilbert
Julien Cohen‐Adad
Kristin P. O’Grady
PURPOSE: In the spinal cord (SC), multi-echo gradient echo (ME-GRE) increases gray (GM) and white matter (WM) contrast and improves sensitiv… (voir plus)ity to lesions in people with multiple sclerosis (pwMS). However, SC ME-GRE is susceptible to breathing-induced field fluctuations, causing ghosting artifacts and signal loss. Recent work introduced a 1D phase navigator following the last echo to measure field variations; however, susceptibility to phase wrapping increases at longer echo times. We propose a 1D phase navigator preceding the first echo, reducing phase accumulation and eliminating the need for respiratory monitoring. METHODS: ME-GRE data covering the lower (T9-T12 vertebrae) and upper (T4-T8 vertebrae) thoracic SC were acquired in 20 healthy volunteers and 3 pwMS at 3T. Standard and navigator-corrected images were acquired in the same acquisition. To evaluate image quality, WM and GM signal-to-noise ratio (SNR), WM/GM contrast-to-noise ratio (CNR), and background ghosting signals were measured and compared between the two reconstructions. Both were blindly assessed for artifacts, structural delineation, and diagnostic confidence in pwMS. RESULTS: Navigator correction significantly increased GM and WM SNR and CNR, reduced posterior ghosting across both thoracic regions, and significantly reduced artifacts while increasing structural delineation. Preliminary evaluation in three pwMS showed consistent improvements in artifact mitigation, structural delineation, and lesion conspicuity with navigator correction, providing proof-of-concept for potential clinical application. CONCLUSION: A 1D navigator prior to the first echo reduces ghosting and improves thoracic SC image quality without respiratory monitoring. This approach could improve the diagnostic value and enhance the reliability of thoracic SC ME-GRE.
2026-07-23
Magnetic Resonance in Medicine (publié)
doi.org
A unified 12-lead ECG-language model for interpretation and clinical-endpoint prediction
Rohan Banerjee
Juliette Beaulieu
Nicolas Dostie
B Mondesert
Ram Ahuja
Alexis Nolin-Lapalme
Gilbert Jabbour
Shreya Shree Srikanth
Guillaume Marquis‐Gravel
Olivier Tastet
A. Sowa
Julia Cadrin‐Tourigny
Jacques Delfrate
Robert Avram
Automated electrocardiogram (ECG) interpretation has advanced, yet most systems remain narrow classifiers that emit fixed labels rather than… (voir plus) the narratives or endpoint-specific answers clinicians need. Generative approaches could instead produce rich narratives, but are constrained by the gap between continuous biosignals and discrete language tokens. Here we present DeepECG-Tok, which reframes ECG interpretation as a unified instruction-following problem. A residual vector-quantization tokenizer (QINCo) maps 12-lead waveforms to language-model-compatible tokens. Its frozen embeddings achieved a macro-averaged area under the receiver operating characteristic curve (AUROC) of 0.96 for 77-condition classification, outperforming supervised and self-supervised baselines. Aligned with a large language model, a single instruction-tuned model performed ECG interpretation, structured reporting and clinical endpoint prediction, including left ventricular ejection fraction, structural heart disease and atrial fibrillation risk, using 7.27 million question–answer pairs. Frozen-tokenizer diagnostic classification transferred without retraining to two external cohorts, retaining macro-averaged AUROCs of 0.88–0.90, and clinical-endpoint prediction transferred to two further cohorts (external LVEF ≤40% AUROC 0.74–0.76). Evaluated end to end using an ontology-grounded large language model as a judge, which achieved a mean agreement (Cohen’s κ) of 0.82 against two cardiologists, the unified instruction-tuned model scored 0.71 internally and 0.50–0.53 in the same external cohorts. In a blinded reader study, board-certified cardiologists and residents rated its free-text reports comparably to reference clinician reports, with a paired win–tie–loss distribution of 32:35:33 and a forced-choice preference of 0.53 among decided cases, with no significant difference between the model and reference reports in either comparison. These findings establish discrete ECG tokenization as a foundation for general-purpose models that generate clinically useful interpretations and answer diverse questions directly from cardiac waveforms.
2026-07-23
medRxiv (accepté)
doi.org
Controllable and Content-Based Recommendations
Fırat Öncel
Jihoon Jeong
Emiliano Penaloza
Mirco Ravanelli
Laurent Charlin
Cem Subakan
Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Co… (voir plus)ntrollable and Content-Based Recommendations (CCBR) framework, which builds its recommendations from textual user profile representations. CCBR plugs into collaborative filtering models and introduces controllability via text bottlenecks. We show that CCBR enables text-based and multimodal interventions, allowing users to steer the model towards the directions they prefer. Different from existing controllable recommendation systems, CCBR infers the text summaries directly from item contents (images, audio or video). Across image-, audio-, and video-based datasets, we demonstrate that the proposed framework obtains competitive model performance with standard (latent-representation) models while providing controllable model summaries via text. The model also outperforms TEARS, a recent baseline for controllable recommendation systems. Through systematic interventions, we demonstrate the efficacy of the user steering mechanism.
2026-07-22
arXiv (prépublication)
doi.org
arxiv.org
Cortical microstructural integrity predicts an exploitation bias in older adulthood
Patrick Hewan
Alfie Wearn
Jeremy Hogeveen
Kayla Williams
R Nathan Spreng
Gary R. Turner
Sylvia Villeneuve
Judes Poirier
John C S Breitner
Sylvain Baillet
Andrée-Ann Baril
Bellec Pierre
Véronique Bohbot
Danilo Bzdok
Mallar Chakravarty
D Louis Collins
Mahsa Dadar
Simon Ducharme
Alan Evans
Claudine Gauthier … (voir 80 de plus)
Maiya R Geddes
Rick Hoge
Yasser Ituria‐Medina
Gerhard Multhaup
Lisa-Marie Münter
Natasha Rajah
Pedro Rosa-Neto
Taylor Schmitz
Soucy Jp
Nathan Spreng
Christine Tardif
Etienne Vachon-Presseau
Mohammadali Javanray
Meishan Ai
Philippe Amouyel
Nicholas Ashton
Gabriel Aumont‐Rodrigue
Julie Bailly
Guilia Baracchini
Kaj Blennow
Christian Bocti
Lianne Boisvert
Sophie Boutin
Ann Brinkmalm Westman
A P Dagher
Xing Dai
Samir Das
Marina Dauar‐Tedeschi
Louis De Beaumont
Christine Déry
Maxime Descoteaux
Elena Drobotea
M Elie
Alfonso Fajardo Valdez
Vladimir Fonov
David Morgan
Jonathan Gallago
Greco Cr
Louise Hudon
Gabriel Jean
Anne Labonté
Robert Laforce
Marc Lalancette
Jean-Charles Lambert
Jeannie‐Marie Leoutsakos
Danaé Lussier Dumouchel
B Misic
Béry Mohammediyan
Holly NewboldFox
Eugenia Nita Capota
Alix Noly‐Gandon
Adrian Eduardo Noriega de la Colina
Pierre Orban
Valentin Ourry
Cynthia Picard
Alexa Pichet Binette
A. L. Poirier
Nathalie Prenevost
Ting Qiu
Marc James Quesnel
Charles Ramassamy
Jean‐Michel Raoult
Jordana Remz
Safa Sanami
Frederic St‐Onge
Cherie Strikwerda‐Brown
Elisabeth Sylvain
Andràs Tikàsz
Christina Tremblay
Stefanie Tremblay
Jennifer Tremblay‐Mercier
Stéphanie Tullo
Irem Ulku
Paolo Vitali
Yara Yakoub
Robert Zatorre
Henrik Zetterberg
Pierre Bellec
Jean-Paul Soucy
Claudia Greco
OBJECTIVES: Prefrontal regions are implicated in explore-exploit decision-making during foraging. Older adults often show an exploitation bi… (voir plus)as, and this age period is also marked by deteriorating prefrontal myelination. To investigate whether these phenomena are linked, we examined whether lower magnetization transfer saturation (MTsat), a myelin-sensitive quantitative MRI (qMRI) measure, in these regions predicts greater exploitation bias during foraging, and whether cortical microstructure is a better predictor of bias than macrostructure (i.e., cortical thickness). METHODS: Cognitively healthy older adults with familial risk of Alzheimer's disease (AD) (N=118, 60-88 years) completed a foraging task indexing explore-exploit decision-making. qMRI was used to derive MTsat values for the frontopolar cortex (FPC), medial orbitofrontal cortex (OFC), rostral middle frontal gyrus (rMFG), dorsal anterior cingulate cortex (dACC), as well as the locus coeruleus (LC), a core subcortical region strongly implicated in explore-exploit decision-making. Secondary analyses examined associations between available AD risk markers and foraging. RESULTS: Lower MTsat in the FPC, OFC, rMFG, and LC was associated with an exploitation bias, with LC and FPC emerging as the strongest predictors. No relationship was observed for the dACC. MTsat remained a significant predictor of foraging after controlling for cortical thickness. Observed associations were largely unrelated to AD risk markers. DISCUSSION: Individual differences in cortical microstructural integrity within a well-defined explore-exploit circuit are associated with an exploitative decision-making bias in older adults. These findings highlight the value of qMRI microstructural integrity markers, beyond standard macrostructural assays, in characterizing the neural correlates of exploitation biases in later life.
2026-07-22
Journals of Gerontology Series B: Psychological Sciences and Social Sciences (publié)
doi.org
FedAgentKE: Federated Semantic Knowledge Evolution for Heterogeneous Agents
Weihao Li
Jun Bai
Ziyang Song
Large language model (LLM)-based agents increasingly rely on reasoning, tool use, and iterative execution, yet existing agent frameworks sti… (voir plus)ll operate largely in isolation. While recent memory-based agent systems improve individual agents through local retrieval and workflow reuse, local experiences remain fragmented across isolated agent frameworks, limiting cross-framework knowledge transfer and collaborative reasoning evolution. We propose FedAgentKE, a lightweight framework for Federated Semantic Knowledge Evolution across heterogeneous agents. FedAgentKE enables distributed agent frameworks to collaboratively evolve transferable reasoning abstractions through iterative semantic knowledge distillation, aggregation, and adaptation without sharing raw reasoning trajectories. Experiments demonstrate consistent improvements under both cross-framework and cross-task settings, highlighting the potential of federated semantic knowledge evolution for future collaborative agent ecosystems.
2026-07-22
arXiv (prépublication)
doi.org
arxiv.org
High-resolution dissection of concept acquisition in different families of protein language models
Shawn Whitfield
Tom Marty
Robert M. Vernon
Christopher J. Langmead
Dhanya Sridhar
Quentin Fournier
Protein language models have been increasingly successful on tasks ranging from fitness prediction to functional design, yet what biological… (voir plus) knowledge they acquire and where it is encoded within their internal representations remain underexplored. Through a high-resolution layer-by-layer interpretability analysis of 8 models from the ESM2 and AMPLIFY families on 22 concepts from human proteome annotations, we found that these models encode concepts of increasing levels of complexity along their depth: basic physicochemical properties and linear motifs are best captured by early-layer embeddings, secondary structure from subsequent layers, and domain-level semantics from middle layers. Principal component projections of these embeddings showed that they separate biologically meaningful protein groupings, and molecular-biology-inspired interventions demonstrated that pLM embeddings can discriminate phosphomimic-active from inactive mutants. Perhaps surprisingly, we observed that pretraining data and compute had a greater impact on the linear emergence of biological concepts than scaling up parameters. By revealing where biological knowledge is captured in pLMs and which choices shape its emergence, our work offers insights to develop more robust, biologically grounded protein language models.
2026-07-22
bioRxiv (prépublication)
doi.org
MSpecTmol: A Multi-Modal Spectroscopic Learning Framework for Automated Molecular Structure Elucidation
Wenjie Du
Xiaohan Qin
Ye Wei
Jun Xia
Yang Wang
Spectroscopic techniques are indispensable for the elucidation of molecular structures, particularly for novel molecules with unknown config… (voir plus)urations. However, a fundamental limitation of any single spectroscopic modality is that it provides an inherently circumscribed and fragmented view, capturing only specific facets of the complete molecular structure, which is often insufficient for unequivocal and robust characterization. Consequently, the integration of data from multiple spectroscopic sources is imperative to overcome these intrinsic limitations and achieve a comprehensive and accurate structural characterization. In this work, we introduce \textbf{MSpecTmol}, a novel \textbf{M}ulti-modal \textbf{Spec}trum information fusion learning framework for automated \textbf{Mol}ecular structure elucidation. By extending information bottleneck theory, our framework provides a principled and adaptive approach to fusing spectra. It designates a primary modality to extract core molecular features while leveraging auxiliary inputs to enrich the representation. To validate the end-to-end effectiveness of our framework, we design a two-fold evaluation: molecular substructure classification to probe its discriminative power in identifying substructures, and extends this knowledge to reconstruct plausible 3D structures. Our results not only demonstrate state-of-the-art performance in molecular substructure classification but also achieve near-experimental accuracy (\textasciitilde 0.68\AA) in molecular conformation reconstruction. These findings underscore the model’s capacity to learn interpretable features aligned with chemical intuition, thereby paving the way for future advances in automated and reliable spectroscopic analysis. Our code can be found at \href{https://anonymous.4open.science/r/MspecTmol-6B4D}{https://anonymous.4open.science.}
2026-07-21
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net
Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering
Zhuohan Xie
Yuyang Dai
Rania Elbadry
Vanshikaa Jani
Georgi Georgiev
Dimitar Dimitrov
Fan Zhang
Xueqing Peng
Lingfei Qian
Jimin Huang
Jiahui Geng
Yankai Chen
Ye Yuan
Haolun Wu
Yuxia Wang
Ivan Koychev
Veselin Stoyanov
Mingzi Song
Yu Chen
Xue Liu … (voir 1 de plus)
Preslav Nakov
FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task t… (voir plus)ests whether systems can select the correct answer to finance questions involving domain terminology, numerical interpretation, and conceptual financial reasoning across languages and scripts. The final-test set contains 800 questions, with 200 questions per language; gold answers were withheld during submission, and each language was ranked independently by accuracy. The final leaderboards contain 13 English, 11 Chinese, 11 Arabic, and 10 Hindi ranked submissions. Top accuracies range from 92.0% in Hindi to 97.5% in English and Arabic, with the same leading teams appearing near the top across all four languages. The documented systems used retrieval augmentation, direct answer-option scoring, language-specific prompting, selective self-consistency, confidence checks, and LLM-based review stages.
2026-07-21
arXiv (prépublication)
doi.org
arxiv.org
Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering
Zhuohan Xie
Xueqing Peng
Georgi Georgiev
Dimitar Dimitrov
Yuyang Dai
Rania Elbadry
Vanshikaa Jani
Lingfei Qian
Fan Zhang
Jimin Huang
Jiahui Geng
Yankai Chen
Ye Yuan
Haolun Wu
Yuxia Wang
Ivan Koychev
Veselin Stoyanov
Mingzi Song
Yu Chen
Xue Liu … (voir 1 de plus)
Preslav Nakov
FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence. Each final-test item pairs an English … (voir plus)question with financial statements and news in English, Chinese, Japanese, Spanish, and Greek. Participating systems submit one concise answer per item in JSONL format. The final-test set contains 256 items, split evenly between easy and expert tiers; each tier contains four question templates instantiated over 32 company-report groups. Gold answers were withheld during submission, and systems were ranked by macro-averaged item-level ROUGE-1 F1 against organizer-held reference answers. The final leaderboard includes 12 ranked submissions. The strongest systems are closely clustered, with the top four separated by less than one percentage point in ROUGE-1 F1. The submitted system papers document retrieval-augmented generation, cross-lingual evidence handling, structured prompting, answer compression, and validation strategies.
2026-07-21
arXiv (prépublication)
doi.org
arxiv.org
SpatialJEPA: JEPA-inspired graph-context distillation for spatially aware multiomics integration
Dylan Mann-Krzisnik
Yue Li
Abstract Computational frameworks for integrating spatial genomics modalities extend cell-based representation learning across molecular lay… (voir plus)ers, but many paired RNA–ATAC datasets are dissociated and lack spatial coordinates. We introduce SpatialJEPA , a JEPA-inspired teacher–student framework for transferring spatial context from spatial multiomics data to non-spatial multiome data. In contrast to patch- or feature-masking objectives, SpatialJEPA masks spatial context by replacing the teacher’s spatial neighborhood graph with a self-only identity graph during student training, making the spatial sample appear dissociated to the student. The student learns to match teacher embeddings from this graph-context-restricted view and can therefore be applied to dissociated RNA–ATAC data at inference time. In mouse brain multiomics, the resulting representation supports source–target alignment, recovers spatially organized transcriptomic and chromatin-accessibility programs, and shows concordance with ligand–receptor pathway structure compared with non-spatial references.
2026-07-21
bioRxiv (accepté)
doi.org
Addressing Sparse-Rewards in RL with Scalable Hierarchical Novel Eigen Options
Priyesh Vijayan
Élodie Coté-Gauthier
Mathieu Reymond
Sarath Chandar
Doina Precup
Isabeau Prémont-Schwarz
Temporally extended exploration via graph Laplacian-based options is a promising approach to sparse-reward reinforcement learning (RL), but … (voir plus)existing methods either do not explicitly target novelty or fail to scale to pixel-based domains under function approximation. Novel Exploration via Orthogonality (NEO) addresses the first issue by constructing options that navigate from highly visited regions toward less visited ones, yet prior results were limited to settings where exact eigenvectors can be computed. We present a scalable extension of NEO to pixel-based domains, built on three contributions. First, we use a novelty-weighted continuous Laplacian graph-drawing objective, which enables RL with continuous observations. Second, we embed the resulting eigen-potential options within a hierarchical reinforcement learning framework, enabling coherent temporally extended behavior. Third, we observe that learned eigen-potential rewards are directional but locally unreliable under online approximation; we therefore augment each option reward with a novelty bonus, a novel design idea that proves essential for stabilizing option learning while preserving novelty-directed exploration. Together, these contributions yield stronger and more persistent exploration, enabling longer option rollouts and better access to hard-to-reach novel states. Empirically, our method significantly outperforms both the prior scalable Laplacian-option baseline and a direct extension of NEO on sparse-reward benchmarks under a fixed budget. On Montezuma's Revenge, our best variant achieves approximately 1.8x higher return than both baselines. On Venture, both baselines yield returns near zero, whereas our method achieves a return of 1135. Across seven hard ProcGen games, our method achieves approximately 3.5x and 5.6x higher aggregate normalized return than the two baselines, respectively.
2026-07-20
RLBRew @ Reinforcement Learning Conference (publié)
openreview.net
openreview.net
AIMSDistill: Distilling knowledge from specialised AI teachers for cross-jurisdictional compliance analysis of modern slavery statements
Adriana Eufrosina Bora
Duoyi Zhang
Md. Abul Bashar
Richi Nayak
Kerrie Mengersen
Modern slavery in global supply chains remains a critical concern, prompting governments in countries such as the UK, Australia, and Canada … (voir plus)to introduce corporate transparency legislation, including Modern Slavery Acts. However, the growing volume of compliance reports has made manual analysis increasingly impractical. This challenge is compounded by the fact that many government agencies and NGOs responsible for assessing these statements operate in resource-constrained environments, limiting their ability to deploy large-scale language models. To address both issues, we present a novel AI framework for cross-jurisdictional compliance analysis. Our approach integrates four specialised teacher models, each trained using contrastive learning, prompt engineering, pseudo-labelling, and context-enhanced learning, which are distilled into a single lightweight, computationally efficient student model. This design enhances generalisability across legal frameworks while remaining affordable to deploy in low-resource settings. Experimental evaluations demonstrate that our method achieves higher accuracy and efficiency than existing approaches, offering a scalable, accessible solution for regulators and other stakeholders.
2026-07-20
Expert Systems with Applications (publié)
doi.org