Publications

Assessing Computational Thinking Skills in K–12 Education: A Systematic Review
Yimei Zhang
Yajie Song
Maria Cutumisu
2026-04-24
Technology, Knowledge and Learning (published)
doi.org
Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers
Pierre‐Louis Benveniste
Laurent Létourneau‐Guillon
David Araújo
Lydia Chougar
Dumitru Fetco
Masaaki Hori
Kouhei Kamiya
Steven Messina
Charidimos Tsagkas
Bertrand Audoin
Rohit Bakshi
Élise Bannier
Daniel Blezek
Jean‐Christophe Brisset
Virginie Callot
Erik Charlson
Michelle Chen
Olga Ciccarelli
Sarah Demortière
Gilles Edan … (see 36 more)
M Filippi
Tobias Granberg
Cristina Granziera
Christopher C. Hemond
B. Mark Keegan
Anne Kerbrat
J Kirschke
Petr Kudlička
Pierre Labauge
Lisa Eunyoung Lee
Yaou Liu
Caterina Mainero
Julian McGinnis
Nilser Laines Medina
Mark Mühlau
Govind Nair
Kristin P. O’Grady
Jiwon Oh
Russell Ouellette
Alexandre Prat
Daniel S. Reich
Maria A. Rocca
Timothy M. Shepherd
Seth A. Smith
Leszek Stawiarz
Jason Talbott
Roger Tam
Shahamat Tauhid
Anthony Traboulsee
Constantina A. Treaba
Paola Valsasina
Zachary Vavasour
Marios Yiannakas
Hervé Lombaert
Julien Cohen-Adad
Shannon Kolind
The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barr… (see more)ier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher.Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast.
2026-04-23
Multiple Sclerosis Journal (published)
doi.org
Assessing the impact of dimensionality reduction on clustering performance -- a systematic study
Ousmane Assani Amate
Mohammadreza Bakhtyari
Émilie Roy
Vladimir Makarenkov
Dimensionality reduction is a critical preprocessing step for clustering high-dimensional data, yet comprehensive evaluation of its impact a… (see more)cross diverse methods and data types remains limited. In this study, we systematically assess the influence of five dimensionality reduction techniques - Principal Component Analysis (PCA), Kernel Principal Component Analysis (Kernel PCA), Variational Autoencoder (VAE), Isometric Mapping (Isomap), and Multidimensional Scaling (MDS) - on the performance of four popular clustering algorithms - k-means, Agglomerative Hierarchical Clustering (AHC), Gaussian Mixture Models (GMM), and Ordering Points to Identify the Clustering Structure (OPTICS). We evaluate clustering quality using the Adjusted Rand Index (ARI), comparing results without and with dimensionality reduction at different reduction levels recommended in the literature (i.e., k-1, where k is the number of clusters, and 25% and 50% of the original number of dimensions). Our findings underscore the importance of a careful selection of the dimensionality reduction technique and the dimensionality reduction level that should be tailored to intrinsic data geometry and clustering algorithms under consideration.
2026-04-22
arXiv (preprint)
doi.org
arxiv.org
Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL
Gabriele Malagoli
Patrick Hanel
Anna Danese
Guy Wolf
Maria Colomé-Tatché
High-throughput single-cell sequencing is widely used to study cell identity. We present SEAGALL (Single-cell Explainable Geometry-Aware Gra… (see more)ph Attention Learning pipeLine), a deep learning method to quantify the impact of molecular features on cellular phenotype, based on geometry-regularised autoencoders (GRAE) and explainable graph attention networks (X-GAT). The GRAE embeds the data into a latent space to build a reliable cell-cell graph. The GAT is trained to learn the annotations and XAI is used to explain the predictions, unravelling the features driving cell identity. SEAGALL extracts specific and stable signatures from multiple omics experiments, going beyond differential marker genes.
2026-04-22
Genome Biology (published)
doi.org
Papillae growth and molecular responses of juvenile sea cucumbers (Apostichopus japonicus) exposed to different light intensities
Weiyan Li
Ziyu Liu
Yajie Deng
Jiaqi Liu
Jinge Yu
Haoran Xiao
Fenglin Tian
Lingshu Han
Chong Zhao
Jun Ding
2026-04-21
Comparative Biochemistry and Physiology Part D: Genomics and Proteomics (published)
doi.org
SATCLIP-GNN: Cold-Start PM2.5 Forecasting with Satellite-Derived Location Priors
Siam Shibly Antar
Syem Shibly Ador
Steven H. H. Ding
Benjamin C. M. Fung
Spatiotemporal PM2.5 forecasting is commonly framed as diffusion on a station graph, yet many strong models rely on learned node embeddings … (see more)that are tightly coupled to the training station set and can generalize poorly when new monitoring sites are deployed. This paper studies a forecasting pipeline that replaces trainable station embeddings with frozen SatCLIP location representations derived from satellite imagery, and combines them with temporal modeling and graph message passing. We additionally investigate a physicsinspired wind-gated dynamic graph enforcing downwind transport. On a five-year U.S. dataset with 596 stations and a chronological split (train 2019-2021, val 2022, test 2023), a Graph WaveNet baseline provides slightly stronger 2023 performance under a PM2.5 only “proper baseline” setting (24 h history
2026-04-21
IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (published)
doi.org
Modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms
Colin Bredenberg
Fabrice Normandin
Blake Richards
Guillaume Lajoie
Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are coherent at a low level, but which have … (see more)surreal, dream-like qualities at a high level. While there are many hypotheses as to how classical psychedelics could induce these effects, there are no concrete mechanistic models that capture the variety of observed effects in humans, while remaining consistent with the known pharmacological effects of classical psychedelics on neural circuits. In this work, we propose the ‘oneirogen hypothesis,’ which posits that the perceptual effects of classical psychedelics are a result of their pharmacological actions inducing neural activity states that truly are more similar to dream-like states. We simulate classical psychedelics’ effects via manipulating neural network models trained on perceptual tasks with the Wake-Sleep algorithm. This established machine learning algorithm leverages two activity phases: a perceptual phase (wake) where sensory inputs are encoded, and a generative phase (dream) where the network internally generates activity consistent with stimulus-evoked responses. We simulate the action of psychedelics by partially shifting the model to the ‘Sleep’ state, which entails a greater influence of top-down connections, in line with the impact of psychedelics on apical dendrites. The effects resulting from this manipulation capture a number of experimentally observed phenomena, including the emergence of hallucinations, increases in stimulus-conditioned variability, and large increases in synaptic plasticity. We further provide a number of testable predictions which could be used to validate or invalidate our oneirogen hypothesis.
2026-04-20
eLife (published)
doi.org
Latent Fourier Transform
Mason Wang
Cheng-Zhi Anna Huang
We introduce the Latent Fourier Transform (LatentFT), a framework that provides novel frequency-domain controls for generative music models.… (see more) LatentFT combines a diffusion autoencoder with a latent-space Fourier transform to separate musical patterns by timescale. By masking latents in the frequency domain during training, our method yields representations that can be manipulated coherently at inference. This allows us to generate musical variations and blends from reference examples while preserving characteristics at desired timescales, which are specified as frequencies in the latent space. LatentFT parallels the role of the equalizer in music production: while traditional equalizers operates on audible frequencies to shape timbre, LatentFT operates on latent-space frequencies to shape musical structure. Experiments and listening tests show that LatentFT improves condition adherence and quality compared to baselines. We also present a technique for hearing frequencies in the latent space in isolation, and show different musical attributes reside in different regions of the latent spectrum. Our results show how frequency-domain control in latent space provides an intuitive, continuous frequency axis for conditioning and blending, advancing us toward more interpretable and interactive generative music models.
2026-04-19
arXiv (preprint)
doi.org
arxiv.org
Neuromodulation for gait disorders
Arjun Balachandar
Gianluca Sorrento
Eduardo M. Moraud
Marco Bonizzato
Alfonso Fasano
2026-04-19
Nature Reviews Bioengineering (published)
doi.org
Security risk assessment of android automotive OS software supply chain using firmware reverse engineering
Hanbo Yu
Faiyaz Khan
Steven H.H. Ding
Junjie Wu
Natalia Stakhanova
Benjamin C.M. Fung
As Android Automotive OS (AAOS) becomes the in-vehicle platform of choice for infotainment and domain-controller functions in modern passeng… (see more)er cars, its software supply chain has emerged as a critical security frontier. AAOS spans both infotainment and vehicle-control domains within the automotive electronics architecture by supporting media streaming, over-the-air updates, navigation, and sensor fusion. Its open-source foundations and reliance on third-party libraries introduce risks, from outdated components to malicious modules, that can undermine vehicle functionality and passenger safety. In recognition of these threats, ISO/SAE 21434 and UNECE WP.29 R155 mandate structured security assessments for vehicular systems to prevent software-chain vulnerabilities from compromising safety. In this study, we apply a shift-right security analysis via firmware reverse engineering to AAOS images from four leading OEMs. We unpack each firmware image, extract software bills of materials (SBOMs), map Common Vulnerabilities and Exposures (CVE) to components, and characterize system-level attack surfaces across infotainment and control subsystems. Proof-of-concept exploits were developed for high-risk vulnerabilities. One critical CVE was successfully triggered, while others were mitigated by missing dependencies or built-in protections. Our work delivers a reproducible firmware-analysis workflow for automotive supply-chain risk assessment, a comparative survey of third-party and proprietary component management, and the evidence of inconsistent security postures in AAOS-based vehicular electronics. These vulnerabilities underscore the need for harmonized SBOM practices and targeted hardening in next-generation in-vehicle systems.
2026-04-19
Computers & Security (published)
doi.org
The implicated scientist: on the role of AI researchers in the development of weapons systems
Alexandra Volokhova
Alex Hernández-García
Artificial intelligence (AI) technologies are increasingly used in modern weapons systems. Notably, these systems have recently been involve… (see more)d in mass killings and destruction at scale. Furthermore, there is currently a strong interest and competition among powerful players to accelerate the proliferation of weapons with automated or AI-based components, a phenomenon known as AI arms race. This competition poses a risk of causing even more deaths and devastation in the future, as well as increased power and wealth inequality. In this work, we aim to shed light on the role of AI researchers as implicated subjects in the harms caused by weapons enabled by AI technologies. We investigate and discuss the specifics of this implication and explore ways to transfigure this position of implication into one of differentiated, long-distance solidarity with the victims of technologically fortified injustices.
2026-04-19
arXiv (preprint)
doi.org
arxiv.org
Beyond Distribution Sharpening: The Importance of Task Rewards
Sarthak Mittal
Leo Gagnon
Guillaume Lajoie
Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into t… (see more)heir training pipelines, enabling systems to evolve from pure reasoning models into sophisticated agents. However, debate persists regarding whether RL genuinely instills new skills within a base model or merely sharpens its existing distribution to elicit latent capabilities. To address this dichotomy, we present an explicit comparison between distribution sharpening and task-reward-based learning, utilizing RL as a tool to implement both paradigms. Our analysis reveals the inherent limitations of distribution sharpening, demonstrating from first principles how and why the optima can be unfavorable and the approach fundamentally unstable. Furthermore, our experiments using Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct and Qwen3-4B-Instruct-2507 on math datasets confirm that sharpening yields limited gains, whereas incorporating task-based reward signal can greatly help achieve robust performance improvements and stable learning.
2026-04-16
arXiv (preprint)
doi.org
arxiv.org