Publications

Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids
Hadi Nekoei
Alexandre Blondin Mass'e
Rachid Hassani
A. Chandar
Vincent Mai
Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty, an essential challe… (voir plus)nge in real-world settings, particularly in the context of the energy transition. A representative example is remote microgrids that supply power to communities disconnected from the main grid. Enabling the energy transition in such systems requires coordinated control of renewable sources like wind turbines, alongside fuel generators and batteries, to meet demand while minimizing fuel consumption and battery degradation under exogenous and intermittent load and wind conditions. These systems must often conform to extensive regulations and complex operational constraints. To ensure that RL agents respect these constraints, it is crucial to provide interpretable guarantees. In this paper, we introduce Shielded Controller Units (SCUs), a systematic and interpretable approach that leverages prior knowledge of system dynamics to ensure constraint satisfaction. Our shield synthesis methodology, designed for real-world deployment, decomposes the environment into a hierarchical structure where each SCU explicitly manages a subset of constraints. We demonstrate the effectiveness of SCUs on a remote microgrid optimization task with strict operational requirements. The RL agent, equipped with SCUs, achieves a 24% reduction in fuel consumption without increasing battery degradation, outperforming other baselines while satisfying all constraints. We hope SCUs contribute to the safe application of RL to the many decision-making challenges linked to the energy transition.
2025-11-29
ArXiv (prépublication)
doi.org
arxiv.org
Freeze, Diffuse, Decode: Geometry-Aware Adaptation of Pretrained Transformer Embeddings for Antimicrobial Peptide Design
Pankhil Gawade
Adam Izdebski
Myriam Lizotte
Kevin R. Moon
Jake S. Rhodes
Guy Wolf
Ewa Szczurek
2025-11-27
ArXiv (prépublication)
doi.org
arxiv.org
Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Worrawat Engchuan
Jakub Kopal
Thomas Renne
Martineau Jean-Louis
Omar Shanta
Zohra Saci
Bhooma Thiruvahindrapuram
Jeffrey MacDonald
Josephine Mollon
Laura M Schultz
Emma E M Knowles
David Porteous
Gail Davies
Paul Redmond
Sarah Harris
Simon Cox
Gunter Schumann … (voir 9 de plus)
Zdenka Pausova
Celia Greenwood
Tomáš Paus
Stephen Scherer
Laura Almasy
Jonathan Sebat
David Glahn
Guillaume Dumas
Sébastien Jacquemont
Cognitive deficits are common across many neurodevelopmental and psychiatric conditions, including those studied in the current set of PGC-C… (voir plus)NV papers. How changes in regional gene expression across the cerebral cortex influence cognitive ability remains unknown. Population variation in gene dosage—which significantly impacts gene expression—represents a unique paradigm to address this question. We developed a cerebral-cortex gene-set burden analysis (CC-GSBA) to associate a trait with genomic deletions and duplications that disrupt genes with similar expression profiles across 180 cortical regions. We performed CC-GSBA across 180 cortical regions to test associations with cognitive ability in 260,000 individuals from general population cohorts. Most cortical gene sets were associated with a decrease in cognitive ability when deleted or duplicated, and this novel approach revealed opposing cortical patterns for the effect sizes of deletions and duplications. These cortical patterns of effect sizes followed the cortical gradient previously characterized at the molecular, cellular, and functional levels. We show that genes with preferential expression in sensorimotor regions demonstrated the largest effect on cognition when deleted. At the opposing end of the cortical gradient, genes with preferential expression in multimodal association regions affected cognition the most when duplicated. These two gene dosage cortical patterns could not be explained by particular cell types, developmental epochs, or genetic constraints, highlighting the fact that the macroscopic network organization of the cerebral cortex is key to understanding the effects of gene dosage on cognitive traits.
2025-11-27
Research Square (prépublication)
doi.org
Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning
Alexander Meulemans
Rajai Nasser
Maciej Wolczyk
Marissa A. Weis
Seijin Kobayashi
Blake Aaron Richards
Guillaume Lajoie
Angelika Steger
Marcus Hutter
James Manyika
Rif A. Saurous
João Sacramento
Blaise Agüera y Arcas
2025-11-26
ArXiv (prépublication)
doi.org
arxiv.org
Enhancing decision-making in glioblastoma surgery through an explainable human-Al collaboration: an international multicenter model development and external validation study
Julius M. Kernbach
Urte Schroeder
Karlijn Hakvoort
Jonas Ort
Hussam Hamou
Danilo Bzdok
Yasin Temel
Pieter Kubben
Charlotte Weyland
Martin Wiesmann
Victor Staartjes
Kevin Akeret
Moira Vieli
Carlo Serra
Luca Regli
Stefan Grau
Lasse Dührsen
Franz Ricklefs
Oliver Schnell
David Ryan Ormond … (voir 9 de plus)
Alexander Grote
Matthias Simon
Hagen Meredig
Marianne Schell
Martin Bendszus
Georg Neuloh
Hans Clusmann
Dieter-Henrik Heiland
Daniel Delev
Surgical resection improves survival in glioblastoma, yet predicting the extent of resection (EOR) remains highly challenging. We developed … (voir plus)and externally validated an explainable AI model to generate personalized EOR estimates in 811 glioblastoma patients undergoing microsurgical resection. EOR was categorized into gross-total (GTR), near-total (NTR), and subtotal resections (STR). An interpretable framework provided model explanations and sensitivity analyses to assess the model’s strengths and limitations. To demonstrate clinical impact, we compared the performance of the human expert (gold standard) with our AI model and a combined human-AI approach. External validation confirmed generalizability (AUC 0.78, CI 0.73-0.82). Class-specific AUCs were 0.75 (0.67-0.82) for GTR, 0.59 (0.50-0.69) for NTR, and 0.69 (0.53-0.85) for STR. Key predictors included KPS and NANO scores, age, tumor volume, and unfavorable anatomical locations. A combined human-AI collaboration outperformed human experts, with higher overall accuracies (0.53 to 0.94), F1 scores (0.30 to 0.92), and Cohen’s κ (0.41 to 0.84). Enhancing predictive performance through the clinician-AI collaboration, our explainable model supports preoperative planning and highlights the value of integrating machine intelligence into surgical decision-making.
2025-11-26
NPJ Precision Oncology (publié)
doi.org
From Speech to Sonography: Spectral Networks for Ultrasound Microstructure Classification
Ali K. Z. Tehrani
An Tang
Mirco Ravanelli
Guy Cloutier
Iman Rafati
Bich Ngoc Nguyen
Quoc-Huy Trinh
Ivan Rosado-Mendez
Hassan Rivaz
The frequency dependence of backscattered radiofrequency (RF) signals produced by ultrasound scanners carries rich information related to th… (voir plus)e tissue microstructure (i.e., scatterer size, attenuation). This information can be sue to classify tissues based on microstructural changes associated to disease onset and progression. Conventional convolutional neural networks (CNNs) can learn this information directly from radio-frequency (RF) data, but they often struggle to achieve adequate frequency selectivity. This increases model complexity and convergence time, and limits generalization. To overcome these challenges, SincNet, originally developed for speech processing, was adapted to classify RF data based on differences in frequency properties. Rather than learning every filter coefficient, SincNet only learns each filter's low frequency and bandwidth, dramatically reducing the number of parameters and improving frequency resolution. For model interpretability, a Gradient-Weighted Filter Contribution is introduced, which highlights the importance of spectral bands. The approach was validated on three datasets: simulated data with different scatterer sizes, experimental phantom data, and in vivo data of rats which were fed a methionine and choline- deficient diet to develop liver steatosis, inflammation, and fibrosis. The modified SincNet consistently achieved the best results in material/tissue classifications.
2025-11-26
IEEE transactions on bio-medical engineering (publié)
doi.org
Synthetic Validation of Pediatric Trust Instruments using Persona-Driven Large Language Models
Ella Boone
Katya Loban
Elena Guadagno
Dan Poenaru
Trust is foundational to patient-physician relationships and is associated with improved care-seeking and adherence in primary care. However… (voir plus), validated trust instruments for pediatric emergency and surgical contexts are lacking, and traditional instrument development is slow and resource-intensive. Large language models (LLMs) could streamline the validation process by serving as scalable, systematic expert panel surrogates. We developed four new trust assessment instruments: two for patient-families and two for physicians. Two-phase content validation was conducted using two parallel synthetic and human expert panels. Synthetic panels consisted of three persona-prompted LLMs (Claude Sonnet 4, GPT-5, Grok4). Human panels served as traditional comparators. Scale-Content Validity Index (S-CVI) and Fleiss’ kappa (k) acceptance thresholds were set at ≥0.80. Combined human–synthetic expert panels revealed substantial inter-rater reliability across all instruments. Fleiss’ kvalues for dimensional validation were: patient-family = 0.84 (95% CI [0.72, 0.96]), physician = 0.87 (95% CI [0.72, 1.00]);contextual validation: patient-family = 0.83 (95% CI [0.73, 0.93]), physician = 0.88 (95% CI [0.80, 0.96]). All instruments exceeded S-CVI ≥0.80 thresholds across both validation phases. Persona-prompted LLMs demonstrated comparable validity outcomes to human experts while accelerating validation timelines from months to weeks. Future research needs to evaluate this approach across psychometric testing phases. This synthetic instrument validation methodology offers a scalable blueprint for healthcare measurement development, enabling faster creation of validated tools to support evidence-based patient care.
2025-11-26
MedRxiv (prépublication)
doi.org
DENetwork unveils non-differentially expressed genes with functional relevance across conditions through information flow perturbation
Bowen Zhao
Ting-Yi Su
Jingtao Wang
Quazi S. Islam
Kailu Song
Steven K. Huang
Matthieu Allez
Gregory J. Fonseca
Carolyn J. Baglole
Jun Ding
Differential gene expression (DE) analysis of RNA-sequencing (RNA-seq) data is a standard approach for identifying phenotypic differences be… (voir plus)tween conditions. However, traditional DE methods such as DESeq2 focus on expression changes alone, often overlooking non-differentially expressed (non-DE) genes that may play key regulatory roles. This limits their ability to identify upstream drivers of transcriptomic variation. To address this gap, we introduce DENetwork, a network-based approach that prioritizes genes based on their influence on global information flow. Each gene is scored using an in silico knockout strategy that quantifies its impact across the inferred gene network, capturing both DE and non-DE genes with potential functional relevance. DENetwork deciphers intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Across simulated and disease-relevant RNA-seq datasets, DENetwork identifies non-DE regulators enriched in known pathways and phenotypic associations, providing mechanistic insights missed by standard DE analysis, with implications for target discovery and intervention.
2025-11-25
PubMed (inconnu)
doi.org
Development of a defacing algorithm to protect the privacy of head and neck cancer patients in publicly-accessible radiotherapy datasets
Kayla O'Sullivan‐Steben
Luc Galarneau
J. Kildea
2025-11-25
Medical Physics (publié)
doi.org
arxiv.org
scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization
Guo Jiang
Kailu Song
Gregory J. Fonseca
Darcy E. Wagner
Iain C. Clark
Hui Wang
Jun Ding
Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust c… (voir plus)ell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.
2025-11-25
Nature Communications (publié)
doi.org
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
Charles Y. Zhang
Yuanjia Yang
Aidan Sirbu
Elliott T.T. Abe
Emil Wärnberg
Eric J. Leonardis
Diego E. Aldarondo
Adam Lee
Aaditya Prasad
Jason Foat
Kaiwen Bian
Joshua Park
Rusham Bhatt
Hutton Saunders
Akira Nagamori
Ayesha R. Thanawalla
Kee Wui Huang
Fabian Plum
Hendrik K. Beck
Steven W. Flavell … (voir 6 de plus)
David Labonte
Blake A. Richards
Bingni W. Brunton
Eiman Azim
Bence P. Ölveczky
Talmo D. Pereira
The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex beha… (voir plus)vior, kinematic trajectories alone provide only indirect access to the underlying control processes. Here we present MIMIC-MJX, a framework for learning biologically-plausible neural control policies from kinematics. MIMIC-MJX models the generative process of motor control by training neural controllers that learn to actuate biomechanically-realistic body models in physics simulation to reproduce real kinematic trajectories. We demonstrate that our implementation is accurate, fast, data-efficient, and generalizable to diverse animal body models. Policies trained with MIMIC-MJX can be utilized to both analyze neural control strategies and simulate behavioral experiments, illustrating its potential as an integrative modeling framework for neuroscience.
2025-11-24
ArXiv (prépublication)
doi.org
arxiv.org
Neural Deprojection of Galaxy Stellar Mass Profiles
M. J. Yantovski-Barth
Hengyue Zhang
Nolan Smyth
Connor Stone
Martin Bureau
Yashar Hezaveh
Laurence Perreault-Levasseur
We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable… (voir plus) mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.
2025-11-24
arXiv (prépublication)
doi.org
arxiv.org