Publications

Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways
Saeid Jamshidi
Kawser Wazed Nafi
Amin Nikanjam
Samira Keivanpour
Omar Abdul-Wahab
Martine Bellaiche
The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity challenges, particularly in mitigating Distributed Denial-… (see more)of-Service (DDoS) attacks at the network edge. Traditional Intrusion Detection Systems (IDSs) face significant limitations, including poor adaptability to evolving and zero-day attacks, reliance on static signatures and labeled datasets, and inefficiency on resource-constrained edge gateways. Moreover, most existing DRL-based IDS studies overlook sustainability factors such as energy efficiency and carbon impact. To address these challenges, this paper proposes two novel Deep Reinforcement Learning (DRL)-based IDS: DeepEdgeIDS, an unsupervised Autoencoder-DRL hybrid, and AutoDRL-IDS, a supervised LSTM-DRL model. Both DRL-based IDS are validated through theoretical analysis and experimental evaluation on edge gateways. Results demonstrate that AutoDRL-IDS achieves 94% detection accuracy using labeled data, while DeepEdgeIDS attains 98% accuracy and adaptability without labels. Distinctly, this study introduces a carbon-aware, multi-objective reward function optimized for sustainable and real-time IDS operations in dynamic IoT networks.
Comparing Virtual Reality Trauma Training Across Diverse Clinical Backgrounds: A Mixed-Methods Study in Canada And India.
Boaz Laor
Samia Benabess
S. Kundu
Ayla Gerk
F. Botelho
Jean-Robert Kwizera
Arjunaditya Kundu
Tom Dolby
Elena Guadagno
Dhruva Ghosh
Vishal Micheal
Rohit Theodore
Thejus Varghese
Lightweight Autoencoder-Isolation Forest Anomaly Detection for Green IoT Edge Gateways
Saeid Jamshidi
Fatemeh Erfan
Omar Abdul-Wahab
Martine Bellaiche
Majority of the Bests: Improving Best-of-N via Bootstrapping
Amin Rakhsha
Amir Khasahmadi
Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N… (see more)) candidate is a popular approach to achieve higher accuracy in tasks with discrete final answers. Best-of-N (BoN) selects the output with the highest reward, and with perfect rewards, it often achieves near-perfect accuracy. With imperfect rewards from reward models, however, BoN fails to reliably find the correct answer and its performance degrades drastically. We consider the distribution of BoN's outputs and highlight that, although the correct answer does not usually have a probability close to one under imperfect rewards, it is often the most likely outcome. This suggests that the mode of this distribution can be more reliably correct than a sample from it. Based on this idea, we propose Majority-of-the-Bests (MoB), a novel selection mechanism that estimates the output distribution of BoN via bootstrapping and selects its mode. Experimental results across five benchmarks, three different base LLMs, and two reward models demonstrate consistent improvements over BoN in 25 out of 30 setups. We also provide theoretical results for the consistency of the bootstrapping. MoB serves as a simple, yet strong alternative to BoN and self-consistency, and more broadly, motivates further research in more nuanced selection mechanisms.
Meditation induces shifts in neural oscillations, brain complexity, and critical dynamics: novel insights from MEG
Annalisa Pascarella
David Meunier
Jordan O'Byrne
Tarek Lajnef
Antonino Raffone
Roberto Guidotti
Vittorio Pizzella
Laura Marzetti
While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We exam… (see more)ined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel–Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.
Biotuner: A python toolbox integrating music theory and signal processing for harmonic analysis of physiological and natural time series
Antoine Bellemare-Pepin
The Biotuner Toolbox is an open-source Python toolbox for biosignals that integrates concepts from neuroscience, music theory, and signal pr… (see more)ocessing. It introduces a harmonic perspective on physiological oscillations by applying musical constructs such as consonance, rhythm, and scale construction. The core biotuner_object processes neural, cardiac, and auditory time series, providing a unified interface for extracting spectral peaks, computing harmonicity metrics, and supporting downstream analyses. Companion modules extend harmonic analyses across temporal (time-resolved harmonicity), spatial (harmonic connectivity), and spectral (harmonic spectrum) dimensions. Biotuner identifies harmonic structure across different biosignals, revealing significant variations in harmonicity between physiological states. Specifically, the toolbox extracts spectral peaks from complex signals using multiple algorithms, ensuring robust peak detection under varying signal-to-noise ratios. Moreover, we show how harmonicity metrics change across distinct sleep stages and capture variations in the slopes of the aperiodic (1/f) component of the power spectrum. Biotuner provides an extensible framework that unifies music-theoretic constructs with biosignal processing, enabling hypothesis-driven analyses for researchers and, in parallel, creative exploration of complex natural patterns for artists.
Leveraging a Fully Differentiable Integrated Assessment Model for RL and Inference
Koen Ponse
Kai-Hendrik Cohrs
Phillip Wozny
Andrew Robert Williams
Erman Acar
Aske Plaat
Thomas M. Moerland
Pierre Gentine
Gustau Camps-Valls
Determinants of pleiotropy and monotonic gene dosage responses across human traits
Sayeh Kazem
Kuldeep Kumar
Josephine Mollon
Thomas Renne
Laura M. Schultz
Emma E.M. Knowles
Worrawat Engchuan
Omar Shanta
Bhooma Thiruvahindrapuram
Jeffrey R. MacDonald
Celia M. T. Greenwood
Stephen W. Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Sébastien Jacquemont
While pleiotropic effects of gene dosage are of particular relevance for comorbidities observed in the developmental pediatric and psychiatr… (see more)ic clinic, the biological processes underlying such pleiotropy remain unknown. We developed a new functional burden analysis (FunBurd) to investigate all CNVs, genome-wide, beyond well-studied recurrent CNVs. In ~500,000 UK-Biobank participants, we tested the association between 43 traits and CNVs disrupting 172 tissue or cell-type gene-sets. CNVs affected all traits. Pleiotropy was correlated with genetic constraint and was higher in the brain compared to non-brain functions, even after normalizing for genetic constraint. The levels of pleiotropy, measured by burden correlation, were similar in deletions and loss-of-function SNVs and higher compared to common variants and duplications. Gene sets under high genetic constraint showed less monotonic gene dosage responses across traits. Even in the absence of a monotonic response, we observed a negative correlation between deletion and duplication effect sizes across most traits. Overall, functional gene sets are preferentially associated with a given trait when either deleted or duplicated, but rarely both.
Risk factors for catastrophic healthcare expenditure and high economic burden for children with anorectal malformations in Southwestern Uganda
Felix Oyania
Caroline Q. Stephens
Sarah Ullrich
Amy M. Shui
Meera Kotagal
Godfrey Zari Rukundo
Joseph Ngonzi
Ava Yap
Francis Bajunirwe
Doruk Ozgediz
Towards an informational account of interpersonal coordination
Edoardo Chidichimo
Andrea Luppi
Pedro A. Mediano
Victoria Leong
Andres Canales-Johnson
Richard A.I. Bethlehem

Human sociality is grounded in the dynamic coordination of individuals as they interact with one another. Indeed, interpersonal coordinat… (see more)ion on various levels—neural, behavioural, physiological, affective, linguistic—are hallmarks of successful social communication and cooperation. However, describing these complex, interdependent dynamics has been limited by current methodological approaches, owing to a restrictive repertoire of tools and the absence of a unified, standardised methodological framework. Here, we identify information theory, the mathematical theory of communication, as a particularly well-suited conceptual framework to address this shortfall, given its appropriate sensitivity to complex dynamics, including potential nonlinearity and higher-order interactions, and its data-driven, model-agnostic foundations. With deep roots in computational, cognitive, and systems neuroscience, the formal introduction of information-theoretic quantities and methods into the study of interpersonal coordination is perhaps overdue. This Perspective advances the case for a unified information-theoretic framework for the field while paving the path for a new generation of empirically testable, theoretically grounded research questions.

Excitatory-Inhibitory Dynamics in Adaptive Decision-Making
From Technical Excellence to Practical Adoption: Lessons Learned Building an ML-Enhanced Trace Analysis Tool
Kaveh Shahedi
Matthew Khouzam
Heng Li
Maxime Lamothe