Publications

Latent brain subtypes of chronotype reveal unique behavioral and health profiles across population cohorts
Le Zhou
Karin Saltoun
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Julie Carrier
Kai-Florian Storch
Robin I. M. Dunbar
Danilo Bzdok
Chronotype is shaped by the complex interplay of endogenous and exogenous factors. This time-enduring trait ties into societal behaviors an… (see more)d is linked to psychiatric and metabolic conditions. Despite its multifaceted nature, prior research has treated chronotype as a monolithic trait across the population, risking overlooking substantial heterogeneity in neural and behavioral fingerprints. To uncover hidden subgroups, we develop a supervised pattern-learning framework integrating three complementary brain-imaging modalities with deep behavioral and health profiling from 27,030 UK Biobank participants. We identify five distinct, biologically valid chronotype subtypes. Each demonstrates unique patterns across brain, behavioral and health profiles. External validation in 10,550 US children from the ABCD Study cohort reveals reversed age distributions and replicates sex-associated brain-behavioral patterns, suggesting that potential divergences between chronotype traits observed throughout adulthood may begin to emerge early in life. These findings highlight underappreciated sources of population variation that echo the rhythm of people’s inner clock.
2025-12-21
Nature Communications (published)
doi.org
Latent brain subtypes of chronotype reveal unique behavioral and health profiles across population cohorts
Le Zhou
Karin Saltoun
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Julie Carrier
Kai-Florian Storch
Robin I. M. Dunbar
Danilo Bzdok
Chronotype is shaped by the complex interplay of endogenous and exogenous factors. This time-enduring trait ties into societal behaviors an… (see more)d is linked to psychiatric and metabolic conditions. Despite its multifaceted nature, prior research has treated chronotype as a monolithic trait across the population, risking overlooking substantial heterogeneity in neural and behavioral fingerprints. To uncover hidden subgroups, we develop a supervised pattern-learning framework integrating three complementary brain-imaging modalities with deep behavioral and health profiling from 27,030 UK Biobank participants. We identify five distinct, biologically valid chronotype subtypes. Each demonstrates unique patterns across brain, behavioral and health profiles. External validation in 10,550 US children from the ABCD Study cohort reveals reversed age distributions and replicates sex-associated brain-behavioral patterns, suggesting that potential divergences between chronotype traits observed throughout adulthood may begin to emerge early in life. These findings highlight underappreciated sources of population variation that echo the rhythm of people’s inner clock.
2025-12-21
Nature Communications (published)
doi.org
Perspective on patient and non-academic partner engagement for the responsible integration of large language models in health chatbots
Nikhil Jaiswal
Yuanchao Ma
Bertrand Lebouché
Dan Poenaru
Marie-Pascale Pomey
Sofiane Achiche
David Lessard
Kim Engler
Zully Montiel
Hector Acevedo
Rodrigo Rosa Gameiro
Leo Anthony Celi
Esli Osmanlliu
Uses of large language models (LLMs) in health chatbots are expanding into high-stakes clinical contexts, heightening the need for tools tha… (see more)t are evidence-based, accountable, accurate, and patient-centred. This conceptual, practice-informed Perspective reflects on engaging patients and non-academic partners for the responsible integration of LLMs, grounded in the co-construction of MARVIN (for people living with HIV) and in an emerging collaboration with MIT Critical Data. Organised by the Software Development Life Cycle, we describe: conception/needs assessment with patient partners to identify use cases, acceptable trade-offs, and privacy expectations; development that prioritises grounding via vetted sources, structured human feedback, and data-validation committees including patient partners; testing and evaluation using patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) chosen in collaboration with patients to capture usability, acceptability, trust, and perceived safety, alongside task performance and harmful-output monitoring; and implementation via diverse governance boards, knowledge-mobilisation materials to set expectations, and risk-management pathways for potentially unsafe outputs. Based on our experience with MARVIN, we recommend early and continuous engagement of patients and non-academic partners, fair compensation, shared decision-making power, transparent decision logging, and inclusive, adaptable governance that can evolve with changing models and standards. These lessons highlight how patient partnership can directly shape chatbot design and oversight, helping teams align LLM-enabled tools with patient-centred goals while building accountable, safe, and equitable systems. Health chatbots powered by large language models (LLMs) can make medical information more accessible, but most are developed without meaningful input from the people who will use them. This risks unsafe answers, hidden bias, and tools that mainly work for privileged groups. Our team built a chatbot called MARVIN to support people living with HIV, and we are now adapting it for cancer care and children’s health. Patients, caregivers, and community partners shaped what MARVIN should do, chose which sources it should trust, and tested early versions. Their feedback led to concrete improvements including clearer language, more relevant features, and safeguards against misinformation. We are also partnering with MIT Critical Data, which brings patients, members of the public, clinicians, engineers, and policymakers together at events to find and fix bias in medical AI. We have learned that technical fixes alone are not enough: trust, fairness, and accountability require active involvement of diverse users at every stage. Based on these lessons, we recommend: (1) including patients and non-academic partners from the start so their insights can shape core design decisions; (2) compensating them fairly so participation is sustainable; (3) giving them real decision-making power so their input is not tokenistic; and (4) being transparent about the limits of AI so expectations are realistic. In our experience, responsible health AI depends on the lived expertise of the people it serves.
2025-12-21
Research Involvement and Engagement (published)
doi.org
The Historical Literature of Nicolae Filimon and the Reconciliation of Realism with the Tradition of the Popular Novel
A.R. Olteanu
2025-12-21
Philobiblon. Transylvanian Journal of Multidisciplinary Research in the Humanities (published)
doi.org
Coord2Region: A Python Package for Mapping 3D Brain Coordinates to Atlas Labels, Literature, and AI Summaries
Hamza Abdelhedi
Yorguin-Jose Mantilla-Ramos
Sina Esmaeili
Annalisa Pascarella
Vanessa Hadid
Karim Jerbi CoCo Lab
We present Coord2Region, an open-source Python package that streamlines coordinate-based neuroimaging workflows by automatically mapping 3D … (see more)brain coordinates (e.g., MNI or Talairach) to anatomical regions across multiple atlases. The package links mapped coordinates to meta-analytic resources via the Neuroimaging Meta-Analysis Research Environment (NiMARE) , providing direct integration with Neurosynth and NeuroQuery. This directly connects coordinates and regions to the broader neuroimaging literature. In addition to atlas-based labeling and literature retrieval, Coord2Region offers an optional large language model (LLM) functionality that generates text summaries of linked studies and illustrative images of queried regions. These AI-assisted features are intended to support interpretation and exploration, while remaining clearly complementary to peer-reviewed literature and established neuroimaging tools. Coord2Region provides a unified pipeline with a robust command-line interface, flexible dataset management, and provider-agnostic LLM utilities, and it supports both single-coordinate and high-throughput batch queries with nearest-region fallback for volume and surface atlases. Furthermore, Coord2Region includes a web interface for interactive configuration (via JSON Schema forms) and cloud execution (via Hugging Face), enabling users to build YAML configurations and run analyses in-browser without local installation. Together, these capabilities lower friction, reduce manual errors, and improve reproducibility in coordinate-centric neuroimaging workflows, promoting more robust and transparent research practices.
2025-12-19
ArXiv (preprint)
arxiv.org
arxiv.org
E-RGB-D: Real-Time Event-Based Perception with Structured Light
Seyed Ehsan Marjani Bajestani
Giovanni Beltrame
Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed… (see more) and efficiency in vision sensing. Despite their high dynamic range, temporal resolution, low power consumption, and computational simplicity, traditional monochrome ECs face limitations in detecting static or slowly moving objects and lack color information essential for certain applications. To address these challenges, we present a novel approach that integrates a Digital Light Processing (DLP) projector, forming Active Structured Light (ASL) for RGB-D sensing. By combining the benefits of ECs and projection-based techniques, our method enables the detection of color and the depth of each pixel separately. Dynamic projection adjustments optimize bandwidth, ensuring selective color data acquisition and yielding colorful point clouds without sacrificing spatial resolution. This integration, facilitated by a commercial TI LightCrafter 4500 projector and a monocular monochrome EC, not only enables frameless RGB-D sensing applications but also achieves remarkable performance milestones. With our approach, we achieved a color detection speed equivalent to 1400 fps and 4 kHz of pixel depth detection, significantly advancing the realm of computer vision across diverse fields from robotics to 3D reconstruction methods. Our code is publicly available: https://github.com/MISTLab/event_based_rgbd_ros
2025-12-19
arXiv (preprint)
doi.org
arxiv.org
Responsible AI measures dataset for ethics evaluation of AI systems
Shalaleh Rismani
Leah Davis
Bonam Mingole
Negar Rostamzadeh
Renee Shelby
AJung Moon
Meaningful governance of any system requires the system to be assessed and monitored effectively. In the domain of Artificial Intelligence (… (see more)AI), global efforts have established a set of ethical principles, including fairness, transparency, and privacy upon which AI governance expectations are being built. The computing research community has proposed numerous means of measuring an AI system's normative qualities along these principles. Current reporting of these measures is principle-specific, limited in scope, or otherwise dispersed across publication platforms, hindering the domain's ability to critique its practices. To address this, we introduce the Responsible AI Measures Dataset, consolidating 12,067 data points across 791 evaluation measures covering 11 ethical principles. It is extracted from a corpus of computing literature (n = 257) published between 2011 and 2023. The dataset includes detailed descriptions of each measure, AI system characteristics, and publication metadata. An accompanying, interactive visualization tool supports usability and interpretation of the dataset. The Responsible AI Measures Dataset enables practitioners to explore existing assessment approaches and critically analyze how the computing domain measures normative concepts.
2025-12-19
Scientific Data (published)
doi.org
Revisiting the Learning Objectives of Vision-Language Reward Models
Simon Roy
Samuel Barbeau
Giovanni Beltrame
Christian Desrosiers
Nicolas Thome
Learning generalizable reward functions is a core challenge in embodied intelligence. Recent work leverages contrastive vision language mode… (see more)ls (VLMs) to obtain dense, domain-agnostic rewards without human supervision. These methods adapt VLMs into reward models through increasingly complex learning objectives, yet meaningful comparison remains difficult due to differences in training data, architectures, and evaluation settings. In this work, we isolate the impact of the learning objective by evaluating recent VLM-based reward models under a unified framework with identical backbones, finetuning data, and evaluation environments. Using Meta-World tasks, we assess modeling accuracy by measuring consistency with ground truth reward and correlation with expert progress. Remarkably, we show that a simple triplet loss outperforms state-of-the-art methods, suggesting that much of the improvements in recent approaches could be attributed to differences in data and architectures.
2025-12-19
ArXiv (preprint)
doi.org
arxiv.org
Observations of AGN-driven feedback: dynamics and ionization of the filaments in M87
Camille Poitras
Marie-Lou Gendron-Marsolais
Valeria Olivares
Yuan Li
Adrien Picquenot
Aurora Simionescu
Matteo Fossati
Alessandro Boselli
Laura Hermosa Muñoz
Sara Cazzoli
Julie Hlavacek-Larrondo
Annabelle Richard-Laferrière
We present a comprehensive kinematic and ionization analysis of the warm ionized filaments (…
2025-12-18
arXiv (preprint)
doi.org
arxiv.org
A Message from AI Research Leaders: Join Us in Supporting OpenReview
Joelle Pineau
Doina Precup
Kyunghyun Cho
Andrew Y. Ng
Yoshua Bengio
Ruslan Salakhutdinov
Fernando Pereira
2025-12-17
OpenReview (unknown)
openreview.net
openreview.net
Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
Mehil B. Shah
Mohammad Masudur Rahman
Foutse Khomh
Despite their wide adoption in various domains (e.g., healthcare, finance, software engineering), Deep Learning (DL)-based applications suff… (see more)er from many bugs, failures, and vulnerabilities. Reproducing these bugs is essential for their resolution, but it is extremely challenging due to the inherent nondeterminism of DL models and their tight coupling with hardware and software environments. According to recent studies, only about 3% of DL bugs can be reliably reproduced using manual approaches. To address these challenges, we present RepGen, a novel, automated, and intelligent approach for reproducing deep learning bugs. RepGen constructs a learning-enhanced context from a project, develops a comprehensive plan for bug reproduction, employs an iterative generate-validate-refine mechanism, and thus generates such code using an LLM that reproduces the bug at hand. We evaluate RepGen on 106 real-world deep learning bugs and achieve a reproduction rate of 80.19%, a 19.81% improvement over the state-of-the-art measure. A developer study involving 27 participants shows that RepGen improves the success rate of DL bug reproduction by 23.35%, reduces the time to reproduce by 56.8%, and lowers participants'cognitive load.
2025-12-16
ArXiv (preprint)
doi.org
arxiv.org
Effect of Document Packing on the Latent Multi-Hop Reasoning Capabilities of Large Language Models
Gabriele Prato
Shagun Sodhani
Alessandro Sordoni
A. Chandar
The standard practice for training large language models involves packing multiple documents together to optimize computational efficiency. … (see more)However, the impact of this process on the models' capabilities remains largely unexplored. To address this gap, we investigate how different document-packing strategies influence the latent multi-hop reasoning abilities of LLMs. Our findings indicate that packing can improve model performance compared to training on individual documents, at the expense of more compute. To further understand the underlying mechanisms, we conduct an ablation study, identifying key factors that explain the advantages of packing. Ultimately, our research deepens the understanding of LLM training dynamics and provides practical insights for optimizing model development.
2025-12-15
ArXiv (preprint)
doi.org
arxiv.org