Publications

Additional file 1 of Beta power as a neural correlate of sensory features in autistic individuals
J. Chaudet
Julien Pichot
Amandine Pedoux
Mathis Fleury
Anna Maruani
Valérie Vantalon
Elise Humeau
Thomas Bourgeron
Josselin Houenou
Guillaume Dumas
Edouard Duchesnay
Richard Delorme
Anton Iftimovici
Aline Lefebvre
Supplementary Material 1.
2026-03-17
Figshare (inconnu)
doi.org
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
Liang Shi
Jiarui Lu
Junqi Liu
Chence Shi
Zhi Yang
Jian Tang
Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While … (voir plus)Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing to model temporal relationship or built only for monomeric proteins. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture long-range temporal dependencies, with a diffusion-based module to decode trajectory frames in an autoregressive manner. ATMOS is trained across crystal structures from PDB and conformation trajectory from large-scale MD simulation datasets including mdCATH and MISATO. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems. By enabling efficient inference of atomic trajectory of motions, this work establishes a promising foundation for modeling biomolecular dynamics.
2026-03-17
arXiv (prépublication)
doi.org
arxiv.org
Beta power as a neural correlate of sensory features in autistic individuals
Julie Chaudet
Julien Pichot
Amandine Pedoux
Mathis Fleury
Anna Maruani
Valérie Vantalon
Elise Humeau
Thomas Bourgeron
Josselin Houenou
Guillaume Dumas
Edouard Duchesnay
Richard Delorme
Anton Iftimovici
Aline Lefebvre
2026-03-17
Journal of Neurodevelopmental Disorders (publié)
doi.org
CETP alternative splicing variation impacts human traits
Isabel Gamache
Marc‐André Legault
Jean‐Christophe Grenier
Éric Rhéaume
J.C. Tardif
Marie‐Pierre Dubé
Julie Hussin
Abstract The cholesteryl ester transfer protein (CETP) is an important protein in reverse cholesterol transport and has been identified as a… (voir plus) significant factor associated with cardiovascular disease (CVD), making it a widely studied pharmaceutical target. Three protein-coding isoforms of CETP exist, distinguished by the alternative splicing of one exon each. The isoform primarily responsible for cholesterol-related functions in the plasma is well studied, but specific functions of each isoform remain poorly understood. In this study, we demonstrate the significance of considering CETP’s isoforms in analyses of human traits. Using bulk RNA-seq data from multiple tissues, we characterized the expression patterns and genetic regulation determinants of CETP transcripts. Leveraging publicly available GWAS summary statistics, we conducted multivariable Mendelian Randomisation (MVMR) to estimate the impact of variation in isoform proportions on phenotypes, highlighting the importance of CETP’s isoforms in pituitary and thyroid glands. Furthermore, we uncovered tissue-specific associations between CETP’s isoforms and CVD-associated phenotypes. Additionally, we observed that the epistatic interaction previously reported between CETP and ADCY9 , a gene implicated in modulating a CETP modulator’s response, may be mediated through the regulation of alternative splicing of exon 9. Our results underscore the importance of a comprehensive understanding of CETP’s isoforms, which can significantly impact both fundamental and clinical research efforts.
2026-03-17
bioRxiv (prépublication)
doi.org
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
Philippe Formont
Maxime Darrin
Ismail Ben Ayed
Pablo Piantanida
Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex problem-solving tasks.… (voir plus) Motivated by these advances, several works have explored the application of reasoning LLMs to drug discovery and molecular design. However, most existing approaches either focus on evaluation or rely on training setups that require ground-truth labels, such as molecule pairs with known property modifications. Such supervision is unavailable in \textit{de novo} molecular generation, where the objective is to generate novel molecules that optimize a desirability score without prior knowledge of high-scoring candidates. To bridge this gap, we introduce MolRGen, a large-scale benchmark and dataset for training and evaluating reasoning-based LLMs on \textit{de novo} molecular generation. Our contributions are threefold. First, we propose a setting to evaluate and train models for \textit{de novo} molecular generation and property prediction. Second, we introduce a novel diversity-aware top-
2026-03-17
arXiv (prépublication)
doi.org
arxiv.org
Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costs
Abhishek Gupta
Aditya Mahajan
Markov decision processes (MDPs) is viewed as an optimization of an objective function over certain linear operators over general function s… (voir plus)paces. Using the well-established perturbation theory of linear operators, this viewpoint allows one to identify derivatives of the objective function as a function of the linear operators. This leads to generalization of many well-known results in reinforcement learning to cases with generate state and action spaces. Prior results of this type were only established in the finite-state finite-action MDP settings and in settings with certain linear function approximations. The framework also leads to new low-complexity PPO-type reinforcement learning algorithms for general state and action space MDPs.
2026-03-17
arXiv (prépublication)
doi.org
arxiv.org
CoB Doped Ni-MOF/NF Composite Catalyst for Efficient Hydrogen Evolution Reaction in Alkaline Media
Haibo Liu
Hongming Zhang
Jiasheng Wang
Bo Li
Hanqing Zhao
Yicong Zhu
Yuchen Zhang
Junteng Lv
Zhiwu Qiao
Jinxiang Yang
The advancement of efficient and stable non-precious metal electrocatalysts is crucial for promoting the development of alkaline water elect… (voir plus)rolysis, a key clean energy technology for hydrogen production. This study presents a rational design of a self-supported CoB@Ni-MOF/NF catalyst for scalable hydrogen production, constructed by building a hierarchical Ni-MOF/NF conductive scaffold, incorporating amorphous CoB active phases, and establishing a synergistic Ni-Co-B interface. The optimized electrode exhibits exceptional hydrogen evolution reaction performance in alkaline media, achieving an ultralow overpotential of 33.2 mV at 10 mA cm-2-performance that rivals some noble-metal-doped systems—along with stable operation exceeding 28 hours. Comprehensive characterization confirms that the superior activity originates from abundant accessible active sites and optimized reaction energetics enabled by the composite architecture, offering a generalizable design strategy that integrates MOFs, conductive substrates, and transition metal borides for advanced energy conversion materials.
2026-03-16
Journal of the Electrochemical Society (publié)
doi.org
Contextual Preference Distribution Learning
Benjamin Hudson
Laurent Charlin
Emma Frejinger
Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we … (voir plus)propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse formulations. We focus on human choice settings that can be formulated as (integer) linear programs. In such settings, existing inverse optimization and choice modelling methods infer preferences from observed choices but typically produce point estimates or fail to capture contextual shifts, making them unsuitable for risk-averse decision-making. Using a bounded-variance score function gradient estimator, we train a predictive model mapping contextual features to a rich class of parameterizable distributions. This approach yields a maximum likelihood estimate. The model generates scenarios for unseen contexts in the subsequent optimization phase. In a synthetic ridesharing environment, our approach reduces average post-decision surprise by up to 114
2026-03-16
arXiv (prépublication)
doi.org
arxiv.org
SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion
Elham Daneshmand
Shafeef Omar
Glen Berseth
Majid Khadiv
Hsiu-Chin Lin
Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tunin… (voir plus)g these policies directly on hardware is problematic, as it poses risks of mechanical failure and suffers from high sample inefficiency. In this paper, we address the challenge of safely and efficiently fine-tuning reinforcement learning (RL) policies for dynamic locomotion tasks. Specifically, we focus on fine-tuning policies learned in simulation directly on hardware, while explicitly enforcing safety constraints. In doing so, we introduce SLowRL, a framework that combines Low-Rank Adaptation (LoRA) with training-time safety enforcement via a recovery policy. We evaluate our method both in simulation and on a real Unitree Go2 quadruped robot for jump and trot tasks. Experimental results show that our method achieves a
2026-03-16
arXiv (prépublication)
doi.org
arxiv.org
Use of Conventional Artificial Intelligence Methods in the Identification of Frailty: A Scoping Review
Kunal Ashok Dalsania
Alixe Ménard
Shruthi Sundararaman
Arya Rahgozar
Solange Rito Lima
Xintong Lu
Aya Al‐Ali
Krishnpriya Singh
Ramtin Hakimjavadi
Hui Yan
Claire Sethuram
Howard Bergman
Jim LaPlante
Daniel I. McIsaac
Samira Abbasgholizadeh Rahimi
Nadia Sourial
Manpreet Thandi
Sabrina Wong
Clare Liddy
Karen Bandeen‐Roche … (voir 1 de plus)
Sathya Karunananthan
OSF Registries [https://doi.org/10.17605/OSF.IO/T54G8].
2026-03-16
Journal of the American Geriatrics Society (publié)
doi.org
CUBE: A Standard for Unifying Agent Benchmarks
Alexandre Lacoste
Nicolas Gontier
Oleh Shliazhko
Aman Jaiswal
Kusha Sareen
Shailesh Nanisetty
Joan Cabezas
Manuel Del Verme
Omar G. Younis
Simone Baratta
Matteo Avalle
Imene Kerboua
Xing Han Lu
Elron Bandel
Michal Shmueli-Scheuer
Asaf Yehudai
Leshem Choshen
Jonathan Lebensold
Sean Hughes
Massimo Caccia … (voir 6 de plus)
Alexandre Drouin
Siva Reddy
Tao Yu
Yu Su
Graham Neubig
Dawn Song
The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires s… (voir plus)ubstantial custom integration, creating an "integration tax" that limits comprehensive evaluation. We propose CUBE (Common Unified Benchmark Environments), a universal protocol standard built on MCP and Gym that allows benchmarks to be wrapped once and used everywhere. By separating task, benchmark, package, and registry concerns into distinct API layers, CUBE enables any compliant platform to access any compliant benchmark for evaluation, RL training, or data generation without custom integration. We call on the community to contribute to the development of this standard before platform-specific implementations deepen fragmentation as benchmark production accelerates through 2026.
2026-03-15
arXiv (prépublication)
doi.org
arxiv.org
Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Evolving Assistance for Everyday Life
Bahar Irfan
Nikhil Churamani
Michelle Zhao
Rajat Kumar Jenamani
Ali Ayub
Silvia Rossi
Today's high-capacity generalist robot policies provide a strong foundation for broad task-level competence, yet achieving effective and equ… (voir plus)itable support for people in everyday settings remains a significant challenge. Real-world environments are dynamic and unstructured, and human needs evolve over time, requiring robots that can adapt accordingly. The ultimate evaluator of any robotic system is the person it assists, and personalization is essential to ensuring equitable and meaningful support across diverse users and contexts. Developing robots that can continually learn from interaction, adapt their behaviors over time, and flexibly assume roles as learners and collaborators is a critical step toward realizing effective integration of robots into daily life. With this year's theme of "Evolving Assistance for Everyday Life", and in alignment with the conference theme "HRI Empowering Society", the sixth edition of the "Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)" workshop aims to bring together insights across diverse disciplines, focusing on how robots can progressively adapt their support to suit diverse individuals, each with unique and changing needs, across real-world contexts. Through this lens, the workshop aims to discuss current and future directions in how assistive systems can flexibly respond, continually improve over time, and deliver more inclusive and empowering support in everyday life.
2026-03-15
International Conference on Human-Robot Interaction (publié)
doi.org