Publications

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 … (see more)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) (published)
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… (see more)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 (preprint)
doi.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 (published)
doi.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… (see more)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 (published)
doi.org
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
Hager Radi Abdelwahed
Mélisande Teng
Robin Zbinden
Laura Pollock
Hugo Larochelle
Devis Tuia
David Rolnick
Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological re… (see more)search and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.
2025-08-07
ArXiv (preprint)
doi.org
arxiv.org
Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps
Matt Schmittle
Rohan Baijal
Nathan Hatch
Rosario Scalise
Mateo Guaman Castro
Sidharth Talia
Khimya Khetarpal
Byron Boots
Siddhartha Srinivasa
2025-08-07
robot-learning.org/CoRL/2025/Conference (poster)
doi.org
proceedings.mlr.press
Bias-inducing geometries: an exactly solvable data model with fairness implications
Stefano Sarao Mannelli
Federica Gerace
Negar Rostamzadeh
Luca Saglietti
2025-08-06
Physical Review E (published)
doi.org
arxiv.org
Revealing dynamic temporal trajectories and underlying regulatory networks with
<i>Cflows</i>
Alexander Tong
Manik Kuchroo
Shabarni Gupta
Aarthi Venkat
Chen Liu
Beatriz P. San Juan
Laura Rangel
Brandon Zhu
John G. Lock
Christine L. Chaffer
Smita Krishnaswamy
While single-cell technologies provide snapshots of tumor states, building continuous trajectories and uncovering causative gene regulatory … (see more)networks remains a significant challenge. We present Cflows , an AI framework that combines neural ODE networks with Granger causality to infer continuous cell state transitions and gene regulatory interactions from static scRNA-seq data. In a new 5-time point dataset capturing tumorsphere development over 30 days, Cflows reconstructs two types of trajectories leading to tumorsphere formation or apoptosis. Trajectory-based cell-of-origin analysis delineated a novel cancer stem cell profile characterized by CD44 hi EPCAM + CAV1 + , and uncovered a cell cycle–dependent enrichment of tumorsphere-initiating potential in G2/M or S-phase cells. Cflows uncovers ESRRA as a crucial causal driver of the tumor-forming gene regulatory network. Indeed, ESRRA inhibition significantly reduces tumor growth and metastasis in vivo. Cflows offers a powerful framework for uncovering cellular transitions and dynamic regulatory networks from static single-cell data.
2025-08-06
bioRxiv (preprint)
doi.org
Whither symbols in the era of advanced neural networks?
Thomas L. Griffiths
Brenden M. Lake
R. Thomas McCoy
Ellie Pavlick
Taylor Webb
Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, pr… (see more)oduce novelty, and learn quickly. We argue that modern neural networks -- and the artificial intelligence systems built upon them -- exhibit similar abilities. This undermines the argument that the cognitive processes and representations used by human minds are symbolic, although the fact that these neural networks are typically trained on data generated by symbolic systems illustrates that such systems play an important role in characterizing the abstract problems that human minds have to solve. This argument leads us to offer a new agenda for research on the symbolic basis of human thought.
2025-08-06
ArXiv (preprint)
doi.org
arxiv.org
Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History
Tommaso Tosato
Saskia Helbling
Yorguin-Jose Mantilla-Ramos
Mahmood Hegazy
Alberto Tosato
D. Lemay
Irina Rish
Guillaume Dumas
2025-08-05
ArXiv (preprint)
doi.org
arxiv.org
Manifold Filter-Combine Networks
Joyce Chew
Edward De Brouwer
Smita Krishnaswamy
Deanna Needell
Michael Perlmutter
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). Our filter-combine fra… (see more)mework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggests many interesting families of MNNs which can be interpreted as manifold analogues of various popular GNNs. We propose a method for implementing MFCNs on high-dimensional point clouds that relies on approximating an underlying manifold by a sparse graph. We then prove that our method is consistent in the sense that it converges to a continuum limit as the number of data points tends to infinity, and we numerically demonstrate its effectiveness on real-world and synthetic data sets.
2025-08-04
Sampling Theory, Signal Processing, and Data Analysis (published)
doi.org
arxiv.org
Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens
Usman Anwar
Johannes Von Oswald
Louis Kirsch
David M. Krueger
Spencer Frei
In this work, we make two contributions towards understanding of in-context learning of linear models by transformers. First, we investigate… (see more) the adversarial robustness of in-context learning in transformers to hijacking attacks — a type of adversarial attacks in which the adversary’s goal is to manipulate the prompt to force the transformer to generate a specific output. We show that both linear transformers and transformers with GPT-2 architectures are vulnerable to such hijacking attacks. However, adversarial robustness to such attacks can be significantly improved through adversarial training --- done either at the pretraining or finetuning stage --- and can generalize to stronger attack models. Our second main contribution is a comparative analysis of adversarial vulnerabilities across transformer models and other algorithms for learning linear models. This reveals two novel findings. First, adversarial attacks transfer poorly between larger transformer models trained from different seeds despite achieving similar in-distribution performance. This suggests that transformers of the same architecture trained according to the same recipe may implement different in-context learning algorithms for the same task. Second, we observe that attacks do not transfer well between classical learning algorithms for linear models (single-step gradient descent and ordinary least squares) and transformers. This suggests that there could be qualitative differences between the in-context learning algorithms that transformers implement and these traditional algorithms.
2025-08-04
TMLR (accepted)
openreview.net
openreview.net