Publications

Multi-Modal Protein Representation Learning with CLASP
Nicolas Bolouri
Joseph Szymborski
Amin Emad
2025-08-11
bioRxiv (prépublication)
doi.org
The Impact of a Pediatric Surgery Fundamentals Boot Camp on New Surgical Trainees' Perceived Knowledge and Confidence Levels.
Julia Ferreira
Simon Rahman
Fabio Botelho
Farhan Banji
W. A. Igrine
Gianluca Bertolizio
Sam Daniel
Thomas Engelhardt
Chantal Frigon
Lily H P Nguyen
Catherine Paquet
Dan Poenaru
Pramod Puligandla
Hussein Wissanji
Davinia Withington
Yasmine Yousef
Sherif Emil
2025-08-11
Journal of Pediatric Surgery (publié)
doi.org
Adversarial Attack Classification and Robustness Testing for Large Language Models for Code
Yang Liu
Armstrong Foundjem
Foutse Khomh
Heng Li
Large Language Models (LLMs) have become vital tools in software development tasks such as code generation, completion, and analysis. As the… (voir plus)ir integration into workflows deepens, ensuring robustness against vulnerabilities especially those triggered by diverse or adversarial inputs becomes increasingly important. Such vulnerabilities may lead to incorrect or insecure code generation when models encounter perturbed task descriptions, code, or comments. Prior research often overlooks the role of natural language in guiding code tasks. This study investigates how adversarial perturbations in natural language inputs including prompts, comments, and descriptions affect LLMs for Code (LLM4Code). It examines the effects of perturbations at the character, word, and sentence levels to identify the most impactful vulnerabilities. We analyzed multiple projects (e.g., ReCode, OpenAttack) and datasets (e.g., HumanEval, MBPP), establishing a taxonomy of adversarial attacks. The first dimension classifies the input type code, prompts, or comments while the second dimension focuses on granularity: character, word, or sentence-level changes. We adopted a mixed-methods approach, combining quantitative performance metrics with qualitative vulnerability analysis. LLM4Code models show varying robustness across perturbation types. Sentence-level attacks were least effective, suggesting models are resilient to broader contextual changes. In contrast, word-level perturbations posed serious challenges, exposing semantic vulnerabilities. Character-level effects varied, showing model sensitivity to subtle syntactic deviations.Our study offers a structured framework for testing LLM4Code robustness and emphasizes the critical role of natural language in adversarial evaluation. Improving model resilience to semantic-level disruptions is essential for secure and reliable code-generation systems.
2025-08-10
Empirical Software Engineering (publié)
doi.org
arxiv.org
Deep Reinforcement Learning-Based Intrusion Detection System: Defending Edge Gateways Against Mirai and Gafgyt
Saeid Jamshidi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
The rapid growth of the Internet of Things (IoT) has transformed industries, resulting in unprecedented opportunities alongside significant … (voir plus)cybersecurity challenges. Malware, for example, Mirai and Gafgyt, exploits IoT vulnerabilities, leading to large-scale attacks. Traditional Intrusion Detection Systems (IDS) struggle to detect these evolving threats due to their reliance on static rule-based or classic Machine Learning (ML) models, which lack adaptability to zero-day attacks and dynamic traffic patterns. This paper presents EdgeShield-DRL, a novel Deep Reinforcement Learning (DRL)-based IDS designed for IoT edge gateways. EdgeShield-DRL dynamically detects and mitigates evolving threats in real-time while ensuring efficient operation on resource-constrained edge devices. We evaluated EdgeShieldDRL on the N-BaIoT dataset, achieving a high detection accuracy of
2025-08-10
2025 12th International Conference on Future Internet of Things and Cloud (FiCloud) (publié)
doi.org
Predictive Gene Discovery with EPCY: A Density-Based Alternative to DE analysis
Éric Olivier Audemard
Jean-François Spinella
Vincent-Philippe Lavallee
Josée Hébert
Guy Sauvageau
Sébastien Lemieux
Identifying predictive genes from high-throughput data remains a key challenge in biomedical research. Most current approaches rely on stati… (voir plus)stical tests to select differentially expressed genes (DEGs), which may not align with the goal of predicting outcomes. We present EPCY, a method that ranks genes based on their predictive power using cross-validated classifiers and density estimation, without relying on null hypothesis testing. Applied to both bulk and single-cell RNA sequencing datasets, EPCY consistently outperforms benchmark DEG-based methods in selecting robust candidate genes. It also demonstrates greater stability across varying cohort sizes, enabling reproducible gene prioritization even in large, heterogeneous datasets. EPCY provides interpretable predictive scores, facilitating candidate selection aligned with downstream validation goals.
2025-08-10
bioRxiv (prépublication)
doi.org
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio
Tegan Maharaj
Luke Ong
Stuart Russell
Dawn Song
Max Tegmark
Lan Xue
Ya-Qin Zhang
Stephen Casper
Wan Sie Lee
Sören Mindermann
Vanessa Wilfred
Vidhisha Balachandran
Fazl Barez
Michael Belinsky
Imane Bello
Malo Bourgon
Mark Brakel
Simeon Campos
Duncan Cass-Beggs … (voir 68 de plus)
Jiahao Chen
Rumman Chowdhury
Kuan Chua Seah
Jeff Clune
Juntao Dai
Agnes Delaborde
Nouha Dziri
Francisco Eiras
Joshua Engels
Jinyu Fan
Adam Gleave
Noah Goodman
Fynn Heide
Johannes Heidecke
Dan Hendrycks
Cyrus Hodes
Bryan Low Kian Hsiang
Minlie Huang
Sami Jawhar
Wang Jingyu
Adam Tauman Kalai
Meindert Kamphuis
Mohan Kankanhalli
Subhash Kantamneni
Mathias Bonde Kirk
Thomas Kwa
Jeffrey Ladish
Kwok-Yan Lam
Wan Lee Sie
Taewhi Lee
Xiaojian Li
Jiajun Liu
Chaochao Lu
Yifan Mai
Richard Mallah
Julian Michael
Nick Moës
Simon Möller
Kihyuk Nam
Kwan Yee Ng
Mark Nitzberg
Besmira Nushi
Seán Ó hÉigeartaigh
Alejandro Ortega
Pierre Peigné
James Petrie
Benjamin Prud'homme
Reihaneh Rabbany
Nayat Sanchez-Pi
Sarah Schwettmann
Buck Shlegeris
Saad Siddiqui
Aradhana Sinha
Martín Soto
Cheston Tan
Dong Ting
William Tjhi
Robert Trager
Brian Tse
Anthony Tung K. H.
Vanessa Wilfred
John Willes
Denise Wong
Wei Xu
Rongwu Xu
Yi Zeng
HongJiang Zhang
Djordje Žikelić
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to en… (voir plus)sure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential – it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. This requires policymakers, industry, researchers and the broader public to collectively work toward securing positive outcomes from AI’s development. AI safety research is a key dimension. Given that the state of science today for building trustworthy AI does not fully cover all risks, accelerated investment in research is required to keep pace with commercially driven growth in system capabilities. Goals: The 2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety aims to support research in this important space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. The result, The Singapore Consensus on Global AI Safety Research Priorities, builds on the International AI Safety Report-A (IAISR) chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this document organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control). Through the Singapore Consensus, we hope to globally facilitate meaningful conversations between AI scientists and AI policymakers for maximally beneficial outcomes. Our goal is to enable more impactful R&D efforts to rapidly develop safety and evaluation mechanisms and foster a trusted ecosystem where AI is harnessed for the public good.
2025-08-10
SuperIntelligence - Robotics - Safety & Alignment (publié)
doi.org
arxiv.org
Untold stories: A qualitative investigation of patient and family experiences with congenital diaphragmatic hernia.
Alexandra Dimmer
Zanib Nafees
Sabrina Beauseigle
Franco A Carnevale
Elena Guadagno
Dan Poenaru
Pramod Puligandla
2025-08-10
Journal of Pediatric Surgery (publié)
doi.org
In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?
BEN BUCKNALL
Saad Siddiqui
LARA THURNHERR
CONOR MCGURK
BEN HARACK
Anka Reuel
PATRICIA PASKOV
CASEY MAHONEY
Sören Mindermann
Scott Singer
VINAY HIREMATH
Charbel-Raphael Segerie
OSCAR DELANEY
Alessandro Abate
Fazl Barez
Michael K. Cohen
Philip Torr
FERENC HUSZÁR
ANISOARA CALINESCU
GABRIEL DAVIS JONES … (voir 2 de plus)
Yoshua Bengio
Robert Trager
International cooperation is common in AI research, including between geopolitical rivals. While many experts advocate for greater internati… (voir plus)onal cooperation on AI safety to address shared global risks, some view cooperation on AI with suspicion, arguing that it can pose unacceptable risks to national security. However, the extent to which cooperation on AI safety poses such risks, as well as provides benefits, depends on the specific area of cooperation. In this paper, we consider technical factors that impact the risks of international cooperation on AI safety research, focusing on the degree to which such cooperation can advance dangerous capabilities, result in the sharing of sensitive information, or provide opportunities for harm. We begin by why nations historically cooperate on strategic technologies and analyse current US-China cooperation in AI as a case study. We further argue that existing frameworks for managing associated risks can be supplemented with consideration of key risks specific to cooperation on technical AI safety research. Through our analysis, we find that research into AI verification mechanisms and shared protocols may be suitable areas for such cooperation. Through this analysis we aim to help researchers and governments identify and mitigate the risks of international cooperation on AI safety research, so that the benefits of cooperation can be fully realised.
2025-08-10
SuperIntelligence - Robotics - Safety & Alignment (publié)
doi.org
arxiv.org
In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?
BEN BUCKNALL
Saad Siddiqui
Robert Trager
Yoshua Bengio
LARA THURNHERR
CONOR MCGURK
BEN HARACK
International cooperation is common in AI research, including between geopolitical rivals. While many experts advocate for greater internati… (voir plus)onal cooperation on AI safety to address shared global risks, some view cooperation on AI with suspicion, arguing that it can pose unacceptable risks to national security. However, the extent to which cooperation on AI safety poses such risks, as well as provides benefits, depends on the specific area of cooperation. In this paper, we consider technical factors that impact the risks of international cooperation on AI safety research, focusing on the degree to which such cooperation can advance dangerous capabilities, result in the sharing of sensitive information, or provide opportunities for harm. We begin by why nations historically cooperate on strategic technologies and analyse current US-China cooperation in AI as a case study. We further argue that existing frameworks for managing associated risks can be supplemented with consideration of key risks specific to cooperation on technical AI safety research. Through our analysis, we find that research into AI verification mechanisms and shared protocols may be suitable areas for such cooperation. Through this analysis we aim to help researchers and governments identify and mitigate the risks of international cooperation on AI safety research, so that the benefits of cooperation can be fully realised.
2025-08-10
SuperIntelligence - Robotics - Safety & Alignment (publié)
doi.org
An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects
Kaveh Shahedi
Nana Gyambrah
Heng Li
Maxime Lamothe
Foutse Khomh
Performance is a critical quality attribute in software development, yet the impact of method-level code changes on performance evolution re… (voir plus)mains poorly understood. While developers often make intuitive assumptions about which types of modifications are likely to cause performance regressions or improvements, these beliefs lack empirical validation at a fine-grained level. We conducted a large-scale empirical study analyzing performance evolution in 15 mature open-source Java projects hosted on GitHub. Our analysis encompassed 739 commits containing 1,499 method-level code changes, using Java Microbenchmark Harness (JMH) for precise performance measurement and rigorous statistical analysis to quantify both the significance and magnitude of performance variations. We employed bytecode instrumentation to capture method-specific execution metrics and systematically analyzed four key aspects: temporal performance patterns, code change type correlations, developer and complexity factors, and domain-size interactions. Our findings reveal that 32.7% of method-level changes result in measurable performance impacts, with regressions occurring 1.3 times more frequently than improvements. Contrary to conventional wisdom, we found no significant differences in performance impact distributions across code change categories, challenging risk-stratified development strategies. Algorithmic changes demonstrate the highest improvement potential but carry substantial regression risk. Senior developers produce more stable changes with fewer extreme variations, while code complexity correlates with increased regression likelihood. Domain-size interactions reveal significant patterns, with web server + small projects exhibiting the highest performance instability. Our study provides empirical evidence for integrating automated performance testing into continuous integration pipelines.
2025-08-08
ArXiv (prépublication)
doi.org
arxiv.org
Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
Taha Mustapha Nehdi
Nairouz Mrabah
Atif Belal
Marco Pedersoli
Eric Granger
2025-08-08
ArXiv (prépublication)
doi.org
arxiv.org
Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero
Ge Ya Luo
Nima Fathi
Justin Szeto
Douglas Arnold
Brennan Nichyporuk
Christopher Pal
Tal Arbel
2025-08-08
ArXiv (prépublication)
doi.org
arxiv.org