Publications

Sparse Decomposition of Graph Neural Networks

Yaochen Hu

Mai Zeng

Ge Zhang

Pavel Rumiantsev

Liheng Ma

Yingxue Zhang

Mark J. Coates

2025-03-16

TMLR (accepted)

Sample Compression for Continual Learning

Jacob Comeau

Mathieu Bazinet

Pascal Germain

Yusuf Cem Sübakan

2025-03-12

ArXiv (preprint)

Carriers of
<i>LRRK2</i>
pathogenic variants show a milder, anatomically distinct brain signature of Parkinson’s disease

Jakub Kopal

Andrew Vo

Tanya Simuni

Lana M. Chahine

Danilo Bzdok

Alain Dagher

LRRK2 gene variants are a major genetic risk factor for both familial and sporadic Parkinson’s disease (PD), opening an … (see more)unattended window on the disease’s mechanisms and potential therapies. Investigating the influence of pathogenic variants in LRRK2 gene on brain structure is a crucial step toward enabling early diagnosis and personalized treatment. Yet, despite its significance, the ways in which LRRK2 genotype affects brain structure remain largely unexplored. Work in this domain is plagued by small sample sizes and differences in cohort composition, which can obscure genuine distinctions among clinical subgroups. In this study, we overcome such important limitations by combining explicit modeling of population background variation and pattern matching. Specifically, we leveraged a large cohort of 641 participants (including 364 with a PD diagnosis) to examine MRI-detectable cortical atrophy patterns associated with the LRRK2 pathogenic variants in people with PD and non-manifesting individuals. LRRK2 PD patients exhibited milder cortical thinning compared to sporadic PD, with notable preservation in temporal and occipital regions, suggesting a distinct pattern of neurodegeneration. Non-manifesting LRRK2 carriers showed no significant cortical atrophy, indicating no structural signs of subclinical PD. We further analyzed the relationship between aggregated alpha-synuclein in cerebrospinal fluid and atrophy. We found that those with evidence of aggregated alpha-synuclein experienced pronounced neurodegeneration and increased cortical thinning, possibly defining another aggressive PD subtype. Our findings highlight avenues for distinguishing PD subtypes, which could lead to more targeted treatment approaches and a more complete understanding of Parkinson’s disease progression.

2025-03-09

medRxiv (published)

Relative biological effectiveness of 31 meV thermal neutrons in peripheral blood lymphocytes

Laura C Paterson

Fawaz Ali

Mohsen Naseri

David Perez Loureiro

Amy Festarini

Marilyne Stuart

Chad Boyer

Ronald Rogge

Christie Costello

Norma Ybarra

J. Kildea

Richard B Richardson

2025-03-09

Radiation Protection Dosimetry (published)

Understanding the impact of IoT security patterns on CPU usage and energy consumption: a dynamic approach for selecting patterns with deep reinforcement learning

Saeid Jamshidi

Amin Nikanjam

Kawser Wazed Nafi

Foutse Khomh

2025-03-09

International Journal of Information Security (published)

Vincent Taschereau-Dumouchel

What makes a theory of consciousness unscientific?

Derek H. Mark G. Tristan A. Yoshua James W. Jacob Dean D Arnold Baxter Bekinschtein Bengio Bisley Browning

Derek H. Arnold

Mark G. Baxter

Tristan A. Bekinschtein

Yoshua Bengio

James W. Bisley

Jacob Browning

Dean Buonomano

David Carmel

Marisa Carrasco

Peter Carruthers

Olivia Carter

Dorita H. F. Chang

Ian Charest

Mouslim Cherkaoui

Axel Cleeremans

Michael A. Cohen

Philip R. Corlett

Kalina Christoff

Sam Cumming … (see 84 more)

Cody A. Cushing

Beatrice de Gelder

Felipe De Brigard

Daniel C. Dennett

Nadine Dijkstra

Adrien Doerig

Paul E. Dux

Stephen M. Fleming

Keith Frankish

Chris D. Frith

Sarah Garfinkel

Melvyn A. Goodale

Jacqueline Gottlieb

Jake R. Hanson

Ran R. Hassin

Michael H. Herzog

Cecilia Heyes

Po-Jang Hsieh

Shao-Min Hung

Robert Kentridge

Tomas Knapen

Nikos Konstantinou

Konrad Kording

Timo L. Kvamme

Sze Chai Kwok

Renzo C. Lanfranco

Hakwan Lau

Joseph LeDoux

Alan L. F. Lee

Camilo Libedinsky

Matthew D. Lieberman

Ying-Tung Lin

Ka-Yuet Liu

Maro G. Machizawa

Julio Martinez-Trujillo

Janet Metcalfe

Matthias Michel

Kenneth D. Miller

Partha P. Mitra

Dean Mobbs

Robert M. Mok

Jorge Morales

Myrto Mylopoulos

Brian Odegaard

Charles C.-F. Or

Adrian M. Owen

David Pereplyotchik

Franco Pestilli

Megan A. K. Peters

Ian Phillips

Rosanne L. Rademaker

Dobromir Rahnev

Geraint Rees

Dario L. Ringach

Adina Roskies

Daniela Schiller

Aaron Schurger

D. Samuel Schwarzkopf

Ryan B. Scott

Aaron R. Seitz

Joshua Shepherd

Juha Silvanto

Heleen A. Slagter

Barry C. Smith

Guillermo Solovey

David Soto

Hugo Spiers

Timo Stein

Frank Tong

Peter U. Tse

Jonas Vibell

Sebastian Watzl

Taylor Webb

Josh Weisberg

Thalia Wheatley

Michael H. Herzog

Martijn E. Wokke

Hakwan Lau

Michał Klincewicz

Tony Cheng

Michael Schmitz

Miguel Ángel Sebastián

Joel S. Snyder

2025-03-09

Nature Neuroscience (unknown)

NNetscape Navigator: Complex Demonstrations for Web Agents Without a Demonstrator

Shikhar Murty

Dzmitry Bahdanau

Hao Zhu

Christopher D Manning

We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely through synthetic demonstrations. These demonstrations… (see more) are collected by first interacting with a browser to generate trajectory rollouts, which are then retroactively labeled into instructions using a language model. Most work on training browser agents has relied on expensive human supervision, and the limited previous work on such interaction-first synthetic data techniques has failed to provide effective search through the exponential space of exploration. In contrast, NNetnav exploits the hierarchical structure of language instructions to make this search more tractable: complex instructions are typically decomposable into simpler subtasks, allowing NNetnav to automatically prune interaction episodes when an intermediate trajectory cannot be annotated with a meaningful sub-task. We use NNetnav demonstrations from a language model for supervised fine-tuning of a smaller language model policy, and find improvements of 6 points on WebArena and over 20 points on MiniWoB++, two popular environments for web-agents. Notably, on WebArena, we observe that language model policies can be further enhanced when fine-tuned with NNetnav demonstrations derived from the same language model. Finally, we collect and release a dataset of over 6k NNetnav demonstrations on WebArena, spanning a diverse and complex set of instructions.

2025-03-06

ICLR.cc/2025/Workshop/SSI-FM (poster)

Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings

Billy Joe Franks

Moshe Eliasof

Semih Cantürk

Guy Wolf

Carola-Bibiane Schönlieb

Sophie Fellenz

Marius Kloft

Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced thei… (see more)r performance across various graph learning tasks. However, the general applicability of these encodings and their potential to serve as foundational representations for graphs remain uncertain. This paper investigates the fine-tuning efficiency, scalability with sample size, and generalization capability of learnable PSEs across diverse graph datasets. Specifically, we evaluate their potential as universal pre-trained models that can be easily adapted to new tasks with minimal fine-tuning and limited data. Furthermore, we assess the expressivity of the learned representations, particularly, when used to augment downstream GNNs. We demonstrate through extensive benchmarking and empirical analysis that PSEs generally enhance downstream models. However, some datasets may require specific PSE-augmentations to achieve optimal performance. Nevertheless, our findings highlight their significant potential to become integral components of future graph foundation models. We provide new insights into the strengths and limitations of PSEs, contributing to the broader discourse on foundation models in graph learning.

2025-03-06

TMLR (accepted)

Cross-validation for training and testing co-occurrence network inference algorithms

Daniel Agyapong

Jeffrey Ryan Propster

Jane Marks

Toby Dylan Hocking

2025-03-05

BMC Bioinformatics (published)

DASFormer: self-supervised pretraining for earthquake monitoring

Qianggang Ding

Zhichao Shen

Weiqiang Zhu

Bang Liu

Earthquake monitoring is a fundamental task to unravel the underlying physics of earthquakes and mitigate associated hazards for public safe… (see more)ty. Distributed acoustic sensing, or DAS, which transforms pre-existing telecommunication cables into ultra-dense seismic networks, offers a cost-effective and scalable solution for next-generation earthquake monitoring. However, current approaches for earthquake monitoring like PhaseNet and PhaseNet-2 primarily rely on supervised learning, while manually labeled DAS data is quite limited and it is difficult to obtain more annotated datasets. In this paper, we present DASFormer, a novel self-supervised pretraining technique on DAS data with a coarse-to-fine framework that models spatial-temporal signal correlation. We treat earthquake monitoring as an anomaly detection task and demonstrate DASFormer can be directly utilized as a seismic phase detector. Experimental results demonstrate that DASFormer is effective in terms of several evaluation metrics and outperforms state-of-the-art time-series forecasting, anomaly detection, and foundation models on the unsupervised seismic detection task. We also demonstrate the potential of fine-tuning DASFormer to downstream tasks through case studies.

2025-03-05

ICLR.cc/2025/Workshop/FM-Wild (published)

EMA-Net: Efficient Multitask Affinity Learning for Dense Scene Predictions

Dimitrios Sinodinos

Narges Armanfard

2025-03-05

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (published)