Publications

Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG

Adrien E. E. Dubois

Elisabeth Audet-Duchesne

Inga Sophia Knoth

Charles-Olivier Martin

Khadije Jizi

Petra Tamer

Nadine Younis

Sébastien Jacquemont

Guillaume Dumas

Sarah Lippé

Research has shown that many copy number variations (CNVs) increase the risk of neurodevelopmental disorders (e.g., autism, ADHD, schizophre… (see more)nia). However, little is known about the effects of CNVs on brain development and function. Resting-state electroencephalography (EEG) is a suitable method to study the disturbances of neuronal functioning in CNVs. We aimed to determine whether there are resting-state EEG signatures that are characteristic of children with pathogenic CNVs. EEG resting-state brain activity of 109 CNV carriers (66 deletion carriers, 43 duplication carriers) aged 3 to 17 years was recorded for 4 minutes. To better account for developmental variations, EEG indices (power spectral density and functional connectivity) were corrected with a normative model estimated from 256 Healthy Brain Network controls. Results showed a decreased exponent of the aperiodic activity and a reduced alpha peak frequency in CNV carriers. Additionally, the study showed altered periodic components and connectivity in several frequency bands. Deletion and duplication carriers exhibited a similar overall pattern of deviations in spectral and connectivity measures, although the significance and effect sizes relative to the control group varied across frequency bands. Deletion and duplication carriers can be differentiated by their periodic power in the gamma band and connectivity in the low alpha band, with duplication carriers showing more disrupted alterations than deletion carriers. The distinctive alterations in spectral patterns were found to be most prominent during adolescence. The results suggest that CNV carriers show electrophysiological alterations compared to neurotypical controls, regardless of the gene dosage effect and their affected genomic region. At the same time, while duplications and deletions share common electrophysiological alterations, each exhibits distinct brain alteration signatures that reflect gene dosage-specific effects.

2025-04-09

Translational Psychiatry (published)

Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things

Saeid Jamshidi

Amin Nikanjam

Kawser Wazed Nafi

Foutse Khomh

As the Internet of Things (IoT) continues to expand, ensuring the security of connected devices has become increasingly critical. Traditiona… (see more)l Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks. This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments. We provide a thorough overview of various IDS deployment strategies and categorize the types of intrusions common in IoT systems. A range of ML methods -- including Support Vector Machines, Naive Bayes, K-Nearest Neighbors, Decision Trees, and Random Forests -- are examined alongside advanced DL models such as LSTM, CNN, Autoencoders, RNNs, and Deep Belief Networks. Each technique is evaluated based on its accuracy, efficiency, and suitability for real-world IoT applications. We also address major challenges such as high false positive rates, data imbalance, encrypted traffic analysis, and the resource constraints of IoT devices. In addition, we highlight the emerging role of Generative AI and Large Language Models (LLMs) in improving threat detection, automating responses, and generating intelligent security policies. Finally, we discuss ethical and privacy concerns, underscoring the need for responsible and transparent implementation. This paper aims to provide a comprehensive framework for developing adaptive, intelligent, and secure IDS solutions tailored for the evolving landscape of IoT.

2025-04-08

ArXiv (preprint)

Lugha-Llama: Adapting Large Language Models for African Languages

Happy Buzaaba

Alexander Wettig

David Ifeoluwa Adelani

Christiane Fellbaum

2025-04-08

ArXiv (preprint)

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)