Publications

Genetic connectivity of Uroteuthis sibogae (Cephalopoda: Loliginidae) in the Sulu Sea with notes on morphology and statolith microchemistry
Jessica M. Legaspi
Lorenzo C. Halasan
Kris Angeli S. Sanchez-Delos Reyes
Tomoyo Okumura
Hsiu‐Chin Lin
2026-04-25
Regional Studies in Marine Science (published)
doi.org
MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing
Rahma Nouaji
Stella Bitchebe
Ricardo Macedo
Oana Balmau
Machine learning (ML) frameworks, such as PyTorch and TensorFlow, rely on data loaders to preprocess data before feeding it to accelerators.… (see more) When preprocessing is inefficiently pipelined, GPUs can remain idle over long periods of time, leading to substantial training delays. For example, PyTorch's default data loaders can cause up to 76% GPU idleness. A key bottleneck is the variability in preprocessing time across samples within the same dataset. Existing data loaders are oblivious to this variability, training all samples uniformly. In this case, a single slow sample can stall the entire batch, causing head-of-line blocking.
2026-04-25
European Conference on Computer Systems (published)
doi.org
arxiv.org
PostLearn: Towards A Learned Index For PostgreSQL
Abrar Fuad
Shubham Vashisth
Oana Balmau
Bettina Kemme
2026-04-25
Challenges and Opportunities of Efficient and Performant Storage Systems @ European Conference on Computer Systems (published)
doi.org
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
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