Publications

Towards sustainable energy use: Reinforcement learning for demand response in commercial buildings

Seyyedreza Madani

Pierre‐Olivier Pineau

Laurent Charlin

Ysaël Desage

Demand response (DR) is a crucial strategy for balancing electricity demand and reducing environmental impact, especially as power consumpti… (see more)on continues to rise. Small and medium-sized commercial buildings hold significant potential for DR implementation due to their widespread presence and substantial contribution to overall energy use. This study proposes a novel framework to optimize energy management in these buildings by considering three types of loads: non-controllable, controllable with discrete action spaces (HVAC), and controllable with continuous action spaces (lighting). The objective is to minimize costs, reduce CO 2 emissions, improve occupant comfort, and shave peak loads as a unified goal. State-of-the-art reinforcement learning (RL) algorithms are employed and compared against traditional heuristic methods using real-world data. Results show that RL-based approaches can significantly lower energy costs and environmental impacts while maintaining occupant comfort, even under varying outdoor temperature conditions. By incorporating risk assessments through Value at Risk (VaR) and Conditional Value at Risk (CVaR) metrics, this study offers a robust solution for sustainable energy management, providing insights for policymakers and industry practitioners aiming for a more resilient energy future.

2025-04-08

Energy and Buildings (published)

Advancing Sustainable Maritime Transport: A Machine Learning Approach to Predict and Mitigate Underwater Radiated Noise from Ships

Soukaina Boujdi

Ayoub Atanane

Pierre Cauchy

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (published)

Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalisable improvements in brain encoding and downstream tasks

Maelle Freteault

Loic Tetrel

Maximilien Le Clei

Lune P Bellec

Nicolas Farrugia

Artificial neural networks trained in the field of artificial intelligence (AI) have emerged as key tools to model brain processes, sparking… (see more) the idea of aligning network representations with brain dynamics to enhance performance on AI tasks. While this concept has gained support in the visual domain, we investigate here the feasibility of creating auditory artificial neural models directly aligned with individual brain activity. This objective raises major computational challenges, as models have to be trained directly with brain data, which is typically collected at a much smaller scale than data used to train AI models. We aimed to answer two key questions: (1) Can brain alignment of auditory models lead to improved brain encoding for novel, previously unseen stimuli? (2) Can brain alignment lead to generalisable representations of auditory signals that are useful for solving a variety of complex auditory tasks? To answer these questions, we relied on two massive datasets: a deep phenotyping dataset from the Courtois neuronal modelling project, where six subjects watched four seasons (36 hours) of the Friends TV series in functional magnetic resonance imaging and the HEAR benchmark, a large battery of downstream auditory tasks. We fine-tuned SoundNet, a small pretrained convolutional neural network with ∼2.5M parameters. Aligning SoundNet with brain data from three seasons of Friends led to substantial improvement in brain encoding in the fourth season, extending beyond auditory and visual cortices. We also observed consistent performance gains on the HEAR benchmark, particularly for tasks with limited training data, where brain-aligned models performed comparably to the best-performing models regardless of size. We finally compared individual and group models, finding that individual models often matched or outperformed group models in both brain encoding and downstream task performance, highlighting the data efficiency of fine-tuning with individual brain data. Our results demonstrate the feasibility of aligning artificial neural network representations with individual brain activity during auditory processing, and suggest that this alignment is particularly beneficial for tasks with limited training data. Future research is needed to establish whether larger models can achieve even better performance and whether the observed gains extend to other tasks, particularly in the context of few shot learning.

2025-04-07

Imaging Neuroscience (published)

A Comparative Analysis of AI Models for Short-Term Solar Irradiance Forecasting

Saad Benbrahim

Abdelaziz Berrado

Hanane Dagdougui

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (published)

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors

Connor Bottrell

Laurence Perreaul-Levasseur

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (see more) barriers to such analysis are the nontrivial noise properties of real astronomical images and the point-spread function, which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed Hubble Space Telescope data, recovers structures which have otherwise only become visible in next-generation James Webb Space Telescope imaging.

2025-04-07

Astronomical Journal (published)

Enhancing Hybrid Model for Photovoltaic Power Prediction: A Case Study of Morocco

Samira Abousaid

Abdelaziz Berrado

Hanane Dagdougui

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (published)

InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data

David R. Johnson

Smita Krishnaswamy

Michael Perlmutter

Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to… (see more) various powers, known as diffusion scales. Traditionally, the diffusion scales are chosen to be dyadic integers,

2025-04-07

ArXiv (preprint)

Predicting greenhouse gas Emissions in Shipping: A Case Study Of Canada

Abdelhak El Aissi

Abdelaziz Berrado

Stephane Carron

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (published)

TAPNext: Tracking Any Point (TAP) as Next Token Prediction

Artem Zholus

Carl Doersch

Yi Yang

Skanda Koppula

Viorica Patraucean

Xu Owen He

Ignacio Rocco

Mehdi S. M. Sajjadi

A. Chandar

Ross Goroshin

2025-04-07

ArXiv (preprint)

The role of AI for MRI-analysis in multiple sclerosis—A brief overview

Jean-Pierre R. Falet

Steven Nobile

Aliya Szpindel

Berardino Barile

Amar Kumar

Joshua D. Durso-Finley

Tal Arbel

Douglas Arnold

2025-04-07

Frontiers Artif. Intell. (published)

ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data

Dylan Mann-Krzisnik

Anjali Chawla

Gustavo Turecki

Corina Nagy

Yue Li

Integrating multimodal single-cell data such as scRNA-seq with scATAC-seq is essential for decoding gene regulatory networks, but remains di… (see more)fficult due to feature harmonization and limited paired multiome data. We introduce ECLARE, a framework that uses multi-teacher ensemble knowledge distillation with contrastive learning and optimal-transport alignment to integrate unpaired single-cell multi-omic datasets. Across benchmarks, ECLARE achieves competitive performance for multimodal integration and biological structure preservation. We further demonstrate utility in a major depressive disorder case study using unpaired snRNA-seq and snATAC-seq, identifying transcription factor–target gene programs that are differentially regulated with sex- and cell-type specificity. Finally, ECLARE learns continuous representations that capture longitudinal structure, highlighting altered neurodevelopmental programs associated with depression in female subjects. Altogether, ECLARE expands the practical reach of multimodal single-cell analysis by enabling diagonal integration of unpaired data with strong biological preservation, facilitating integrative regulatory studies across diverse cohorts and conditions.

2025-04-06

bioRxiv (preprint)