Publications

Swarm robotics localization: comparing methods from infrared to foundation models
Ali Imran
Vivek Shankar Vardharajan
Rafael Gomes Braga
Giovanni Beltrame
David St-Onge
2026-02-01
Swarm Intelligence (published)
doi.org
Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery
Xuemin Yu
Ankur Garg
S Ebrahimi Kahou
Hassan Sajjad
Deep Learning models encode rich semantic information in their hidden representations. However, it remains challenging to understand which p… (see more)arts of this information models actually rely on when making predictions. A promising line of post-hoc concept-based explanation methods relies on clustering token representations. However, commonly used approaches such as hierarchical clustering are computationally infeasible for large-scale datasets, and K-Means often yields shallow or frequency-dominated clusters. We propose the vector quantized latent concept (VQLC) method, a framework built upon the vector quantized-variational autoencoder (VQ-VAE) architecture that learns a discrete codebook mapping continuous representations to concept vectors. We perform thorough evaluations and show that VQLC improves scalability while maintaining comparable quality of human-understandable explanations.
2026-02-01
ArXiv (preprint)
arxiv.org
arxiv.org
Assessing Language Bias in Pediatric Surgical Systematic Reviews: A Meta-epidemiological Study.
Dunya Moghul
Elena Guadagno
Shreenik Kundu
Dan Poenaru
Robert Baird
2026-01-31
Journal of Pediatric Surgery (published)
doi.org
ASSESSMENT OF PREGNANT WOMEN'S INTENTION TO USE A MOBILE APPLICATION-BASED DECISION AID FOR PRENATAL SCREENING FOR TRISOMIES 21, 18 AND 13: A MIXED-METHODS CROSS-SECTIONAL STUDY
Candide Ahouehome
Alexandre Bureau
S. A. Rahimi
S. Gadio
Yan Julien
O. Assan
S. Guay-Bélanger
François Rousseau
Jean-Claude Forest
Sylvie Langlois
Vardit Ravitsky
Patrick Archambault
F. Légaré
2026-01-31
Patient Education and Counseling (published)
doi.org
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
Cassandre Notton
Vasudev Sharma
Vincent Quoc-Huy Trinh
Lina Chen
Minqi Xu
Sonal Varma
Mahdi S. Hosseini
Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyo… (see more)ne over 50 years of age in Canada is eligible for CRC screening. About 20\% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As such, these polyp biopsies make up the bulk of a pathologist's workload. Developing an efficient computational model to help screen these polyp biopsies can improve the pathologist's workflow and help guide their attention to critical areas on the slide. DL models face significant challenges in computational pathology (CPath) because of the gigapixel image size of whole-slide images and the scarcity of detailed annotated datasets. It is, therefore, crucial to leverage self-supervised learning (SSL) methods to alleviate the burden and cost of data annotation. However, current research lacks methods to apply SSL frameworks to analyze pathology data effectively. This paper aims to propose an optimized Barlow Twins framework for colorectal polyps screening. We adapt its hyperparameters, augmentation strategy and encoder to the specificity of the pathology data to enhance performance. Additionally, we investigate the best Field of View (FoV) for colorectal polyps screening and propose a new benchmark dataset for CRC screening, made of four types of colorectal polyps and normal tissue, by performing downstream tasking on MHIST and NCT-CRC-7K datasets. Furthermore, we show that the SSL representations are more meaningful and qualitative than the supervised ones and that Barlow Twins benefits from the Swin Transformer when applied to pathology data. Codes are avaialble from https://github.com/AtlasAnalyticsLab/PathBT.
2026-01-31
Medical Image Analysis (published)
doi.org
arxiv.org
Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment
Adrien Aumon
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
2026-01-31
ArXiv (preprint)
arxiv.org
arxiv.org
Telomere-to-telomere assembly detects genomic diversity in Canadian strains of
<i>Borrelia burgdorferi</i>
Atia B. Amin
Ana Victoria Ibarra Meneses
Simon Gagnon
Georgi Merhi
Martin Olivier
Momar Ndao
Mathieu Blanchette
Christopher Fernandez-Prada
David Langlais
2026-01-31
Cell Reports (published)
doi.org
VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
Stanley Wu
Mohamad H. Danesh
Simon Li
Hanna Yurchyk
Amin Abyaneh
Anas El Houssaini
David Meger
Hsiu-Chin Lin
Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many ex… (see more)isting approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when generalizing to novel terrains. To overcome these challenges, we introduce VOCALoco, a modular skill-selection framework that dynamically adapts locomotion strategies based on perceptual input. Given a set of pre-trained locomotion policies, VOCALoco evaluates their viability and energy-consumption by predicting both the safety of execution and the anticipated cost of transport over a fixed planning horizon. This joint assessment enables the selection of policies that are both safe and energy-efficient, given the observed local terrain. We evaluate our approach on staircase locomotion tasks, demonstrating its performance in both simulated and real-world scenarios using a quadrupedal robot. Empirical results show that VOCALoco achieves improved robustness and safety during stair ascent and descent compared to a conventional end-to-end DRL policy
2026-01-31
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
Parallel Stochastic Gradient-Based Planning for World Models
Michael Psenka
Michael G. Rabbat
Aditi Krishnapriyan
Yann Lecun
Amir Bar
World models simulate environment dynamics from raw sensory inputs like video. However, using them for planning can be challenging due to th… (see more)e vast and unstructured search space. We propose a robust and highly parallelizable planner that leverages the differentiability of the learned world model for efficient optimization, solving long-horizon control tasks from visual input. Our method treats states as optimization variables ("virtual states") with soft dynamics constraints, enabling parallel computation and easier optimization. To facilitate exploration and avoid local optima, we introduce stochasticity into the states. To mitigate sensitive gradients through high-dimensional vision-based world models, we modify the gradient structure to descend towards valid plans while only requiring action-input gradients. Our planner, which we call GRASP (Gradient RelAxed Stochastic Planner), can be viewed as a stochastic version of a non-condensed or collocation-based optimal controller. We provide theoretical justification and experiments on video-based world models, where our resulting planner outperforms existing planning algorithms like the cross-entropy method (CEM) and vanilla gradient-based optimization (GD) on long-horizon experiments, both in success rate and time to convergence.
2026-01-30
ArXiv (preprint)
arxiv.org
arxiv.org
SkeleShare: Algorithmic Skeletons and Equality Saturation for Hardware Resource Sharing
Jonathan Van der Cruysse
Tzung-Han Juang
Shakiba Bolbolian Khah
Christophe Dubach
Compiling functional programs into efficient Field Programmable Gate Array (FPGA) designs is difficult. Hardware resources must be explicitl… (see more)y allocated and shared to maximize resource efficiency. This requires careful orchestration of several transformations to expose and exploit sharing opportunities.This paper introduces SkeleShare, a novel approach that automates the problem of resource allocation and sharing. It leverages equality saturation and algorithmic skeletons to expose sharing opportunities across abstraction levels. A solver-based extractor then selects a design that consolidates computations, meeting resource constraints while maintaining performance.This approach is evaluated on neural networks and image processing targeting a real FPGA. The paper shows how SkeleShare is used to express the various algorithmic patterns and transformation rules inherent in neural network operators. The experimental evaluation demonstrates that SkeleShare’s fully automated resource allocation and sharing matches and exceeds the performance of prior work, which involves expert manual extraction of sharing opportunities.
2026-01-30
IEEE/ACM Symposium on Code Generation and Optimization (published)
doi.org
Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization
Luca Della Libera
Yusuf Cem Sübakan
Mirco Ravanaelli
Neural audio codecs are at the core of modern conversational speech technologies, converting continuous speech into sequences of discrete to… (see more)kens that can be processed by LLMs. However, existing codecs typically operate at fixed frame rates, allocating tokens uniformly in time and producing unnecessarily long sequences. In this work, we introduce DyCAST, a Dynamic Character-Aligned Speech Tokenizer that enables variable-frame-rate tokenization through soft character-level alignment and explicit duration modeling. DyCAST learns to associate tokens with character-level linguistic units during training and supports alignment-free inference with direct control over token durations at decoding time. To improve speech resynthesis quality at low frame rates, we further introduce a retrieval-augmented decoding mechanism that enhances reconstruction fidelity without increasing bitrate. Experiments show that DyCAST achieves competitive speech resynthesis quality and downstream performance while using significantly fewer tokens than fixed-frame-rate codecs. Code and checkpoints will be released publicly at https://github.com/lucadellalib/dycast.
2026-01-29
ArXiv (preprint)
arxiv.org
arxiv.org
Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models
Chen Liu
Xingzhi Sun
Xi Xiao
Alexandre Van Tassel
Ke Xu
Kristof Reimann
Danqi Liao
Mark B. Gerstein
Tianyang Wang
Xiao Wang
Smita Krishnaswamy
Large language models (LLMs) achieve remarkable performance through ever-increasing parameter counts, but scaling incurs steep computational… (see more) costs. To better understand LLM scaling, we study representational differences between LLMs and their smaller counterparts, with the goal of replicating the representational qualities of larger models in the smaller models. We observe a geometric phenomenon which we term
2026-01-29
ArXiv (preprint)
arxiv.org
arxiv.org