Publications

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses

3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurologica… (voir plus)l conditions, including neurodegenerative disorders and stroke. While deep learning models have shown promising results analyzing 3D MRI across a number of brain imaging tasks, most are highly tailored for specific tasks with limited labeled data, and are not able to generalize across tasks and/or populations. The development of self-supervised learning (SSL) has enabled the creation of large medical foundation models that leverage diverse, unlabeled datasets ranging from healthy to diseased data, showing significant success in 2D medical imaging applications. However, even the very few foundation models for 3D brain MRI that have been developed remain limited in resolution, scope, or accessibility. In this work, we present a general, high-resolution SimCLR-based SSL foundation model for 3D brain structural MRI, pre-trained on 18,759 patients (44,958 scans) from 11 publicly available datasets spanning diverse neurological diseases. We compare our model to Masked Autoencoders (MAE), as well as two supervised baselines, on four diverse downstream prediction tasks in both in-distribution and out-of-distribution settings. Our fine-tuned SimCLR model outperforms all other models across all tasks. Notably, our model still achieves superior performance when fine-tuned using only 20% of labeled training samples for predicting Alzheimer's disease. We use publicly available code and data, and release our trained model at https://github.com/emilykaczmarek/3D-Neuro-SimCLR, contributing a broadly applicable and accessible foundation model for clinical brain MRI analysis.

2025-09-11

ArXiv (prépublication)

doi.org

arxiv.org

SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets

Alzheimer's disease is a progressive, neurodegenerative disorder that causes memory loss and cognitive decline. While there has been extensi… (voir plus)ve research in applying deep learning models to Alzheimer's prediction tasks, these models remain limited by lack of available labeled data, poor generalization across datasets, and inflexibility to varying numbers of input scans and time intervals between scans. In this study, we adapt three state-of-the-art temporal self-supervised learning (SSL) approaches for 3D brain MRI analysis, and add novel extensions designed to handle variable-length inputs and learn robust spatial features. We aggregate four publicly available datasets comprising 3,161 patients for pre-training, and show the performance of our model across multiple Alzheimer's prediction tasks including diagnosis classification, conversion detection, and future conversion prediction. Importantly, our SSL model implemented with temporal order prediction and contrastive learning outperforms supervised learning on six out of seven downstream tasks. It demonstrates adaptability and generalizability across tasks and number of input images with varying time intervals, highlighting its capacity for robust performance across clinical applications. We release our code and model publicly at https://github.com/emilykaczmarek/SSL-AD.

2025-09-11

ArXiv (prépublication)

doi.org

arxiv.org

Fused Lasso Improves Accuracy of Co-occurrence Network Inference in Grouped Samples

Daniel Agyapong

Briana H. Beatty

Peter G. Kennedy

Jane C. Marks

Toby Dylan Hocking

Co-occurrence network inference algorithms have significantly advanced our understanding of microbiome communities. However, these algorithm… (voir plus)s typically analyze microbial associations within samples collected from a single environmental niche, often capturing only static snapshots rather than dynamic microbial processes. Previous studies have commonly grouped samples from different environmental niches together without fully considering how microbial communities adapt their associations when faced with varying ecological conditions. Our study addresses this limitation by explicitly investigating both spatial and temporal dynamics of microbial communities. We analyzed publicly available microbiome abundance data across multiple locations and time points, to evaluate algorithm performance in predicting microbial associations using our proposed Same-All Cross-validation (SAC) framework. SAC evaluates algorithms in two distinct scenarios: training and testing within the same environmental niche (Same), and training and testing on combined data from multiple environmental niches (All). To overcome the limitations of conventional algorithms, we propose fuser, an algorithm that, while not entirely new in machine learning, is novel for microbiome community network inference. It retains subsample-specific signals while simultaneously sharing relevant information across environments during training. Unlike standard approaches that infer a single generalized network from combined data, fuser generates distinct, environment-specific predictive networks. Our results demonstrate that fuser achieves comparable predictive performance to existing algorithms such as glmnet when evaluated within homogeneous environments (Same), and notably reduces test error compared to baseline algorithms in cross-environment (All) scenarios.

2025-09-10

ArXiv (prépublication)

doi.org

arxiv.org

Illusions of AI consciousness

Yoshua Bengio

Eric Elmoznino

The belief that AI is conscious is not without risk

2025-09-10

Science (publié)

doi.org

OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection

Akshatha Arodi

Ga'etan Marceau Caron

Jean-François Godbout

Reihaneh Rabbany

Deepfakes, synthetic media created using advanced AI techniques, pose a growing threat to information integrity, particularly in politically… (voir plus) sensitive contexts. This challenge is amplified by the increasing realism of modern generative models, which our human perception study confirms are often indistinguishable from real images. Yet, existing deepfake detection benchmarks rely on outdated generators or narrowly scoped datasets (e.g., single-face imagery), limiting their utility for real-world detection. To address these gaps, we present OpenFake, a large politically grounded dataset specifically crafted for benchmarking against modern generative models with high realism, and designed to remain extensible through an innovative crowdsourced adversarial platform that continually integrates new hard examples. OpenFake comprises nearly four million total images: three million real images paired with descriptive captions and almost one million synthetic counterparts from state-of-the-art proprietary and open-source models. Detectors trained on OpenFake achieve near-perfect in-distribution performance, strong generalization to unseen generators, and high accuracy on a curated in-the-wild social media test set, significantly outperforming models trained on existing datasets. Overall, we demonstrate that with high-quality and continually updated benchmarks, automatic deepfake detection is both feasible and effective in real-world settings.

2025-09-10

ArXiv (prépublication)

doi.org

arxiv.org

Visual objects refine head direction coding

Dominique Siegenthaler

Henry Denny

Sofia Skromne Carrasco

Johanna Luise Mayer

Daniel Levenstein

Adrien Peyrache

Stuart Trenholm

Émilie Macé

Animals use visual objects to guide navigation-related behaviors. However, visual object-preferring areas have yet to be described in the mo… (voir plus)use brain, limiting our understanding of how visual objects affect spatial navigation system processing. Using functional ultrasound imaging, we identified brain areas that were preferentially activated by images of objects compared with their scrambled versions. Whereas visual cortex did not show a preference, areas associated with spatial navigation were preferentially activated by visual objects. Electrophysiological recordings in postsubiculum, the cortical head direction (HD) system hub, confirmed a preference for visual objects in both HD cells and fast-spiking interneurons. In freely moving animals, visual objects increased firing rates of HD cells aligned with a visual object but decreased activity in HD cells coding for other directions.

2025-09-10

Science (publié)

doi.org

FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks

Moses Openja

Paolo Arcaini

Foutse Khomh

Fuyuki Ishikawa

2025-09-09

ACM Transactions on Software Engineering and Methodology (publié)

doi.org

arxiv.org

EZH2 Inhibition Induces an Integrated Stress Response Driving Glutamine-Dependent Vulnerability in TNBC

Lucas Porras

Marina Fukano

Ann-Sophie Gironne

Elise Quadri

Gabriel Alzial

Hugo Philippeau

Yousef Aleassa

Anie Monast

Faustine Gorse

Myriame Saint-Arnaud

Mariana De Sa Tavares Russo

Sylvie Mader

Daina Avizonis

Sébastien Lemieux

Morag Park

Geneviève Deblois

EZH2, the catalytic subunit of Polycomb Repressive Complex II, is highly expressed and associated with poor prognosis in triple-negative bre… (voir plus)ast cancer (TNBC). Despite inducing significant changes in chromatin profiles and gene expression, EZH2 inhibition in TNBC models has limited impact on growth, suggesting adaptive compensatory mechanisms. Here, we demonstrate that EZH2 inhibition induces accumulation of double-stranded RNA and misfolded proteins in TNBC, activating an integrated stress response (ISR) via the PKR/PERK-eIF2α pathway. We identify Activating Transcription Factor 4 (ATF4) as a key effector upon EZH2 inhibition, driving metabolic changes characterized by increased amino acid uptake and glutamine dependency. Targeting this ISR-ATF4-mediated metabolic response using glutaminase inhibitor in combination with EZH2 inhibition significantly impairs TNBC cell proliferation and tumor progression. These findings reveal a stress-driven metabolic adaptation that enables TNBC survival upon EZH2 blockade, highlighting inhibition of this pathway as a strategy to enhance the efficacy of EZH2 inhibitors in TNBC.

2025-09-08

bioRxiv (prépublication)

doi.org

RL Fine-Tuning Heals OOD Forgetting in SFT

Mohammad Hamdaqa

The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reas… (voir plus)oning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechanism behind the synergy of SFT and RL are still under-explored and inconclusive. In our study, we find the well-known claim "SFT memorizes, RL generalizes" is over-simplified, and discover that: (1) OOD performance peaks at the early stage of SFT and then declines (OOD forgetting), the best SFT checkpoint cannot be captured by training/test loss; (2) the subsequent RL stage does not generate fundamentally better OOD capability, instead it plays an \textbf{OOD restoration} role, recovering the lost reasoning ability during SFT; (3) The recovery ability has boundaries, \ie{} \textbf{if SFT trains for too short or too long, RL cannot recover the lost OOD ability;} (4) To uncover the underlying mechanisms behind the forgetting and restoration process, we employ SVD analysis on parameter matrices, manually edit them, and observe their impacts on model performance. Unlike the common belief that the shift of model capacity mainly results from the changes of singular values, we find that they are actually quite stable throughout fine-tuning. Instead, the OOD behavior strongly correlates with the \textbf{rotation of singular vectors}. Our findings re-identify the roles of SFT and RL in the two-stage fine-tuning and discover the rotation of singular vectors as the key mechanism. %reversing the rotations induced by SFT, which shows recovery from forgetting, whereas imposing the SFT parameter directions onto a RL-tuned model results in performance degradation. Code is available at https://github.com/xiaodanguoguo/RL_Heals_SFT

2025-09-07

arXiv (Cornell University) (prépublication)

doi.org

arxiv.org

An AI system to help scientists write expert-level empirical software

Eser Aygün

Anastasiya Belyaeva

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (voir 21 de plus)

Matthew Abraham

Erica Brand

Anna Bulanova

Jeffrey A. Cardille

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (voir plus)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-07

ArXiv (prépublication)

doi.org

arxiv.org

Discrete Audio Tokens: More Than a Survey!

Pooneh Mousavi

Gallil Maimon

Adel Moumen

Darius Petermann

Jiatong Shi

Haibin Wu

Haici Yang

Anastasia Kuznetsova

Artem Ploujnikov

Ricard Marxer

Bhuvana Ramabhadran

Benjamin Elizalde

Loren Lugosch

Jinyu Li

Yusuf Cem Sübakan

Phil Woodland

Minje Kim

Hung-yi Lee

Shinji Watanabe

Yossi Adi … (voir 1 de plus)

Mirco Ravanaelli

Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics whi… (voir plus)le enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks. They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.

2025-09-06

TMLR (accepté)

doi.org

openreview.net

Task Robustness via Re-Labelling Vision-Action Robot Data

Artur Kuramshin

Özgür Aslan

Cyrus Neary

Glen Berseth

2025-09-05

robot-learning.org/CoRL/2025/Workshop/Robot_Data (publié)

openreview.net