Publications

Learning to Extract Context for Context-Aware LLM Inference
Minseon Kim
Lucas Caccia
Zhengyan Shi
Matheus Pereira
Marc-Alexandre Côté
Xingdi Yuan
Alessandro Sordoni
2025-12-11
ArXiv (prépublication)
doi.org
arxiv.org
AI Models of Human Brain and Behaviour During Naturalistic Videogame Play
Lune Bellec
Yann Harel
François Paugam
André Cyr
Basile Pinsard
Julie Boyle
Slides of the presentation given at the Montreal Artificial Intelligence and Neuroscience (MAIN) conference, December 2025, by Lune Bellec. … (voir plus)A video recording of the presentation is available on youtube.
2025-12-11
Zenodo (inconnu)
doi.org
Transformer Embeddings for Fast Microlensing Inference
Nolan Smyth
Laurence Perreault-Levasseur
Yashar Hezaveh
2025-12-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Critical Role of EEG Signals in Assessment of Sex-Specific Insights in Neurological Diagnostics via Machine Learning Approach
Mohammad-Javad Darvishi-Bayazi
Mohammad Sajjad Ghaemi
Irina Rish
Jocelyn Faubert
Abstract

Early detection and diagnosis of pathology are essential for efficient treatment and therapeutic … (voir plus)interventions. The emergence of Artificial Intelligence (AI) and deep machine learning techniques have demonstrated the promising capability of brain imaging data to predict various pathological diseases. However, plenty of diseases have imbalanced distribution across different sexes. Furthermore, the impact of sex-specific patterns and biomarkers in predicting diseases has remained unexplored as a fundamental subject matter to inform the treatment paradigms. This paper underscored the generalization and transferability of sex-related patterns in functional data, specifically Electroencephalogram (EEG) signals through Artificial Deep Neural Networks. We conducted training on a broad spectrum of EEG recordings involving participants ranging from 221 to 12,000, including healthy and pathological subjects. Our evaluation leveraged datasets from various sources and participant groups, featuring distribution shifts. While the artificial models demonstrated accurate sex detection on datasets without fine-tuning, their performance declined with significant distribution shifts. Furthermore, we explored the relationship between sex and pathology by visualizing salient features for target detection in distinct subgroups. Our findings revealed unprecedented insights into the negligible role of sex-specific patterns in pathology detection despite the presence of prominent and consistent patterns within sex groups. These results are essential for developing more robust and unbiased AI models for disease prediction and informing the treatment paradigms.

2025-12-09
Scientific Reports (publié)
doi.org
FALCON: Few-step Accurate Likelihoods for Continuous Flows
Danyal REHMAN
Tara Akhound-Sadegh
Artem Gazizov
Yoshua Bengio
Alexander Tong
2025-12-09
ArXiv (prépublication)
doi.org
arxiv.org
On Mobile Ad Hoc Networks for Coverage of Partially Observable Worlds
Edwin Meriaux
Shuo Wen
Louis-Roy Langevin
Doina Precup
Antonio Lor'ia
Gregory Dudek
2025-12-09
ArXiv (prépublication)
doi.org
arxiv.org
SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation
Yasmin Jolasun
Kailu Song
Yumin Zheng
Jingtao Wang
Gregory Fonseca
David H. Eidelman
Jun Ding
Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cellular heterogeneity but remains costly, restricting its use… (voir plus) to small cohorts that often lack comprehensive clinical data, reducing translational relevance. In contrast, bulk RNA sequencing is scalable and cost-effective but obscures critical single-cell insights. We introduce SIDISH, a neural network framework that integrates the granularity of scRNA-seq with the scalability of bulk RNA-seq. Using a variational autoencoder, deep Cox regression, and transfer learning, SIDISH identifies high-risk cell populations while enabling robust clinical predictions from large-cohort data. Its in silico perturbation module identifies therapeutic targets by simulating interventions that reduce high-risk cells associated with adverse outcomes. SIDISH also generalizes to spatial transcriptomics, identifying high-risk cells and mapping them within their native tissue microenvironment. Applied across diverse diseases, SIDISH establishes the link between cellular dynamics and clinical phenotypes, facilitating biomarker discovery and precision medicine. By unifying single-cell insights with large-scale clinical data, SIDISH advances computational tools for disease risk assessment and therapeutic prioritization, offering an integrative and scalable approach to precision medicine. SIDISH integrates single-cell and bulk RNA sequencing data using deep learning to identify high-risk cell populations and prognostic biomarkers, enabling in silico perturbations that could guide precision therapeutics and advance personalized medicine.
2025-12-09
Nature Communications (publié)
doi.org
Conformal Selection for Efficient and Accurate Compound Screening in Drug Discovery
Tian Bai
Peng Tang
Yuting Xu
Vladimir Svetnik
Bingjia Yang
Abbas Khalili
Xiang Yu
Archer Y. Yang
In drug discovery, the reliability of compound screening based on manual assessments is compromised by potential bias, while existing method… (voir plus)s lack robust risk control measures. To address these challenges, we introduced conformal selection as an enhanced approach to optimize the compound screening process with balanced risks and benefits. Leveraging conformal inference, our approach constructs p-values for each candidate molecule to quantify statistical evidence for selection. The final selection of molecules is determined by comparing these p-values against thresholds derived from multiple testing principles. Our approach offers rigorous control over the false discovery rate, ensuring validity independent of dataset size and requiring minimal assumptions. By avoiding the estimation of prediction errors required in previous approaches, our method achieves higher accuracy (power), thereby improving the ability to identify promising candidates. Furthermore, our method demonstrates superior computational efficiency. We validate these advantages through numerical simulations on real-world datasets.
2025-12-08
Journal of Chemical Information and Modeling (inconnu)
doi.org
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach
Théo Laurentin
Patrick Coirault
Emmanuel Moulay
Antoine Lesage-Landry
Jerome Le Ny
2025-12-08
2025 IEEE 64th Conference on Decision and Control (CDC) (publié)
doi.org
arxiv.org
Generalized certainty equivalence based policies in partially observable systems
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
In this paper, we present a generalization of the certainty equivalence principle of stochastic control. One interpretation of the classical… (voir plus) certainty equivalence principle for linear systems with output feedback and quadratic costs is as follows: the optimal action at each time is obtained by evaluating the optimal state-feedback policy of the stochastic linear system at the minimum mean square error (MMSE) estimate of the state. Motivated by this interpretation, we consider certainty equivalent policies for general (non-linear) partially observed stochastic systems and allow for any state estimate rather than restricting to MMSE estimates. In such settings, the certainty equivalent policy is not optimal. For models with Lipschitz cost and dynamics, we derive upper bounds on the sub-optimality of certainty equivalent policies in terms of expected error of the proposed estimator. We present several examples to illustrate the results.
2025-12-08
IEEE Conference on Decision and Control (publié)
doi.org
Low-Dimensional solutions for optimal control of network-coupled subsystems over a directed network
Mohamed-Amine Azzouz
Shuang Gao
Aditya Mahajan
In this paper, we investigate optimal control of network-coupled subsystems, where the coupling between the dynamics of the subsystems is re… (voir plus)presented by the adjacency or Laplacian matrix of a directed graph. Under the assumption that the coupling matrix is normal and the cost coupling is compatible with the dynamics coupling, we use the spectral decomposition of the coupling matrix to decompose the overall system into at most n systems with noise coupled dynamics and decoupled cost, where n is the size of the network. Furthermore, the optimal control input at each subsystem can be computed by solving n1 decoupled Riccati equations where n1 (n1 ≤ n) denotes the number of distinct eigenvalues of the coupling matrix, where complex conjugate pairs are not double-counted. A salient feature of the result is that the solution complexity depends on the number of distinct eigenvalues of the coupling matrix rather than the size of the network. Therefore, the proposed solution framework provides a scalable method for synthesizing and implementing optimal control laws for large-scale network-coupled subsystems.
2025-12-08
IEEE Conference on Decision and Control (publié)
doi.org
Reference radiation selection is confirmed as a significant source of relative biological effectiveness variation for neutrons.
Laura C Paterson
Stephen Pecoskie
Farrah Norton
Norma Ybarra
J. Kildea
Richard B Richardson
2025-12-08
International Journal of Radiation Biology (publié)
doi.org