Publications

The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities
Saeid Jamshidi
Negar Shahabi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Carol Fung
2025-11-01
Internet of Things (publié)
doi.org
High IL1R1 expression predicts poor survival and benefit from stem cell transplant in intermediate-risk acute myeloid leukemia from the Leucegene cohort
Guillaume Richard-Carpentier
Francois Béliveau
Sandrine Lacoste
Banafsheh Khakipoor
Véronique Lisi
Michael Vladovsky
Miriam Marquis
Jean-Francois Spinella
Patrick Gendron
Sébastien Lemieux
Vincent-Philippe Lavallee
Guy Sauvageau
Josée Hébert
2025-10-23
Biomarker Research (publié)
doi.org
The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem
Julie Kienzle
Serge Bisaillon
T. Crainic
Emma Frejinger
Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses t… (voir plus)actical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a scheduled service network design with resource management (SSND-RM) formulation, expressed as an integer linear program. While such formulations are challenging to solve at scale, we demonstrate that our problem can be tackled with a general-purpose solver when provided with high-quality warm-start solutions. To this end, we design a construction heuristic inspired by a relax-and-fix procedure. We evaluate the methodology on realistic, large-scale instances from our industrial partner, the Canadian National Railway Company: a North American Class I railroad. The computational experiments show that the proposed approach efficiently solves practically relevant instances, and that solutions to the SSND-RM formulation yield substantially more accurate capacity estimations compared to those obtained from simpler baseline models. Managerial insights from our study highlight that ignoring railcar fleet management or container loading constraints can lead to a severe underestimation of required capacity, which may result in costly operational inefficiencies. Furthermore, our results show that the use of multi-platform railcars improves overall capacity utilization and benefits the network, even if they can locally lead to less efficient loading as measured by terminal-level slot utilization performance indicators.
2025-10-23
ArXiv (prépublication)
arxiv.org
arxiv.org
BugPilot: Complex Bug Generation for Efficient Learning of SWE Skills
Atharv Sonwane
Isadora White
Hyunji Lee
Matheus Pereira
Lucas Caccia
Minseon Kim
Zhengyan Shi
Chinmay Singh
Alessandro Sordoni
Marc-Alexandre Cot'e
Xingdi Yuan
2025-10-22
ArXiv (prépublication)
arxiv.org
arxiv.org
Controllable Generation of Drug-like Molecules with Multi-modal Variational Flow
Fang Sun
Zhihao Zhan
Hongyu Guo
Ming Zhang
Jian Tang
Yizhou Sun
Designing drug molecules that bind effectively to target proteins while maintaining desired pharmacological properties remains a fundamental… (voir plus) challenge in drug discovery. Current approaches struggle to simultaneously control molecular topology and 3D geometry, often requiring expensive retraining for new design objectives. We propose a multi-modal variational flow framework that addresses these limitations by integrating a 2D topology encoder with a 3D geometry generator. Our architecture encodes molecular graphs into a learned latent distribution via junction tree representations, then employs normalizing flows to autoregressively generate atoms in 3D space conditioned on the protein binding site. This design enables zero-shot controllability: by manipulating the latent prior distribution, we can generate molecules with specific substructures or optimized properties without model retraining. Experiments on the CrossDocked benchmark show that our model achieves 31.1% high-affinity rate, substantially outperforming existing methods, while maintaining superior drug-likeness and structural diversity. Our framework opens new possibilities for on-demand molecular design, allowing medicinal chemists to rapidly explore chemical space with precise control over both structural motifs and physicochemical properties.
2025-10-22
logconference.io/LOG/2025/Conference (poster)
openreview.net
openreview.net
A Derandomization Framework for Structure Discovery: Applications in Neural Networks and Beyond
Nikos Tsikouras
Yorgos Pantis
Ioannis Mitliagkas
Christos Tzamos
2025-10-22
ArXiv (prépublication)
arxiv.org
arxiv.org
Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon
Shuang Ni
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization…
2025-10-22
logconference.io/LOG/2025/Conference (poster)
doi.org
openreview.net
The Intricate Dance of Prompt Complexity, Quality, Diversity, and Consistency in T2I Models
Xiaofeng Zhang
Aaron Courville
Michal Drozdzal
Adriana Romero Soriano
2025-10-22
ArXiv (prépublication)
arxiv.org
arxiv.org
The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
Eirini Angeloudi
Marc Huertas-Company
Jesús Falcón-Barroso
Laurence Perreault-Levasseur
Alexandre Adam
Alina Boecker
2025-10-21
Astronomy & Astrophysics (publié)
doi.org
arxiv.org
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani
Reza Nasirigerdeh
Georgios Kaissis
Daniel Rueckert
Gintare Karolina Dziugaite
Eleni Triantafillou
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after… (voir plus) it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this work, we study localized unlearning, where the unlearning algorithm operates on a (small) identified subset of parameters. Drawing inspiration from the memorization literature, we propose an improved localization strategy that yields strong results when paired with existing unlearning algorithms. We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them. Our extensive experiments on different datasets, forget sets and metrics reveal that DEL sets a new state-of-the-art for unlearning metrics, against both localized and full-parameter methods, while modifying a small subset of parameters, and outperforms the state-of-the-art localized unlearning in terms of test accuracy too.
2025-10-20
TMLR (accepté)
doi.org
openreview.net
High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares
Shurong Lin
Eric Kolaczyk
Adam Smith
Elliot Paquette
2025-10-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Andreas Filipp
Tri Nguyen
Laurence Perreault-Levasseur
J. Rose
Chris Lovell
Nicolas Payot
Francisco Villaescusa-navarro
Yashar Hezaveh
2025-10-16
ArXiv (prépublication)
arxiv.org
arxiv.org