Publications

Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression
Anjali Chawla
Doruk Cakmakci
Laura M. Fiori
Wenmin Zang
Malosree Maitra
Jennie Yang
Dariusz Żurawek
Gabriella Frosi
Reza Rahimian
Haruka Mitsuhashi
Maria Antonietta Davoli
MA Davoli
Ryan Denniston
Gary Gang Chen
Volodymyr Yerko
Deborah Mash
Kiran Girdhar
Schahram Akbarian
Naguib Mechawar
Matthew Suderman … (voir 3 de plus)
Yuemei Li
Corina Nagy
Gustavo Turecki
2025-07-31
Nature Genetics (publié)
doi.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
2025-07-31
arXiv (publié)
doi.org
arxiv.org
Towards an Interpretable Machine Learning Model for Predicting Antimicrobial Resistance
Mohamed Mediouni
Vladimir Makarenkov
Abdoulaye Banire Diallo
2025-07-31
Journal of Global Antimicrobial Resistance (publié)
doi.org
Zero-Shot Anomaly Detection with Dual-Branch Prompt Learning
Zihan Wang
S Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (voir plus)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-07-31
arXiv (publié)
doi.org
arxiv.org
Advancing science- and evidence-based AI policy.
Rishi Bommasani
Sanjeev Arora
Jennifer Chayes
Yejin Choi
Mariano-Florentino Cuéllar
Li Fei-Fei
Daniel E. Ho
Dan Jurafsky
Sanmi Koyejo
Hima Lakkaraju
Arvind Narayanan
Alondra Nelson
Emma Pierson
Joelle Pineau
Scott Singer
Gael Varoquaux
Suresh Venkatasubramanian
Ion Stoica
Percy Liang
Dawn Song
2025-07-30
Science (publié)
doi.org
arxiv.org
Computing Approximate Nash Equilibria for Integer Programming Games
Aloïs Duguet
Margarida Carvalho
Gabriele Dragotto
Sandra-ulrich Ngueveu
2025-07-30
Optimization Letters (publié)
doi.org
arxiv.org
Evaluating and Improving LitLLMs with Deep Research
Gaurav Sahu
Shubham Agarwal
Abhay Puri
Issam Hadj Laradji
Krishnamurthy Dj Dvijotham
Jason Stanley
Laurent Charlin
Christopher Pal
Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially du… (voir plus)e to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews based on an abstract. We decompose the task into two components: (1) Retrieving related works given a query abstract and (2) Writing a literature review based on the retrieved results. We analyze how effective LLMs are for both components. For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper and then retrieves potentially relevant papers by querying an external knowledge base. Additionally, we study a prompting-based re-ranking mechanism with attribution and show that re-ranking doubles the normalized recall compared to naive search methods while providing insights into the LLM's decision-making process. In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review. To evaluate different LLM-based literature review methods, we create test sets from arXiv papers using a protocol designed for rolling use with newly released LLMs to avoid test set contamination in zero-shot evaluations. We release this evaluation protocol to promote additional research and development in this regard. Our empirical results suggest that LLMs show promising potential for writing literature reviews when the task is decomposed into smaller components of retrieval and planning. Particularly, our ``Deep Research" retrieval variant improves coverage by over 5x compared to standard keyword search, addressing a key bottleneck in the pipeline. Further, we demonstrate that our planning-based approach achieves higher-quality reviews by minimizing hallucinated references in the generated review by 18-26\% compared to existing simpler LLM-based generation methods.
2025-07-30
colmweb.org/COLM/2025/Workshop/LM4Sci (publié)
openreview.net
openreview.net
Towards a General GNN Framework for Combinatorial Optimization
Frederik Wenkel
Semih Cantürk
Stefan Horoi
Michael Perlmutter
Guy Wolf
2025-07-29
Proceedings of the Third Learning on Graphs Conference (publié)
doi.org
proceedings.mlr.press
Latent brain subtypes of chronotype reveal unique behavioral and health profiles: an across-cohort validation
Le Zhou
Karin Saltoun
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Julie Carrier
Kai-Florian Storch
Robin Dunbar
Danilo Bzdok
Chronotype is shaped by the complex interplay of endogenous and exogenous factors. This trait ties into various behaviors in the wider socie… (voir plus)ty and is linked to the prevalence of psychiatric and metabolic conditions. Despite its multifaceted nature, prior research has treated chronotype as a monolithic trait across the population, risking overlooking substantial heterogeneity in neural and behavioral fingerprints of both early risers and night owls. To test for such hidden subgroups, we developed a supervised pattern-learning framework for trait subtyping, integrating three complementary brain-imaging modalities with deep behavior, diagnosis, and drug prescription profiling from 27,030 UK Biobank participants. We identified and characterized five distinct biologically valid chronotype subtypes: (1) typical eveningness, (2) depression-associated eveningness, (3) typical morningness, (4) morningness with greater expression in females, and (5) eveningness with greater expression in males. Each uncovered subtype showed unique patterns across brain, behavioral and health profiles. We finally externally validated these subtypes in 10,550 US children from the ABCD Study® cohort, which revealed reversed age distributions and replicated sex-associated brain-behavioral patterns, underscoring the fact that potential divergences between chronotype traits observed throughout adulthood may begin to emerge early in life. These findings highlight underappreciated sources of population variation that echo the rhythm of people’s inner clock.
2025-07-28
Research Square (prépublication)
doi.org
QComp: A QSAR-Based Imputation Framework for Drug Discovery.
Bingjia Yang
Yunsie Chung
Archer Y. Yang
Bo Yuan
Tianchi Chen
Xiang Yu
In drug discovery, in vitro and in vivo experiments generate biochemical activity data that are crucial for evaluating the efficacy and toxi… (voir plus)city of compounds. These data sets are massive, sparse, and ever-evolving. Quantitative structure-activity relationship (QSAR) models, which predict biochemical activities from compound structures, face challenges in integrating the evolving experimental data agilely as studies progress. We developed QSAR-Complete (QComp), an imputation framework, to address these challenges. While QSAR models are updated at a slow pace through extensive retraining on enlarging data sets, QComp leverages existing QSAR models to immediately exploit new experimental data and improves the imputation of missing data. We demonstrate that the improvement is robust and substantial for imputing in vivo assays with only in vitro experimental data. Additionally, QComp assists in finding the optimal sequence of experiments by quantifying the reduction in statistical uncertainty for specific end points, aiding in rational decision-making throughout the drug discovery process.
2025-07-27
Journal of Chemical Information and Modeling (publié)
doi.org
Semantic change in adults is not primarily a generational phenomenon
Gaurav Kamath
Michelle Yang
Siva Reddy
Morgan Sonderegger
Dallas Card
A central question in the study of language change is whether or not such change is generational. If a language changes over time generation… (voir plus)-by-generation, the process looks as follows: New generations of speakers introduce innovations, while older speakers conserve their usage patterns, and the language changes as new generations replace older ones. At the opposite extreme, language change could be a zeitgeist phenomenon, in which changes are universally adopted by speakers simultaneously, regardless of age or generational cohort. This paper asks this question in the context of word meaning change. We analyze meaning change in over 100 words across more than 7.9 million U.S. congressional speeches, to observe whether, when a word sense rises or falls in prominence, adult speakers from different generations uniformly adopt it, or those from older generations conserve their prior usage. Using language model-based word sense induction methods, we identify different senses of each word, and then model the prevalence of each of these word senses as a function of time and speaker age. We find that most words show a small but statistically significant effect of speaker age; across almost 140 y of Congress, older speakers typically take longer than younger speakers to follow changes in word usage, but nevertheless do so within a few years. Our findings indicate that despite minor age-based differences, word meaning change among mature speakers is likely not a generational process, but rather a zeitgeist process, in which older adult speakers can readily adopt new word usage patterns.
2025-07-27
Proceedings of the National Academy of Sciences (publié)
doi.org
A systematic review of risk stratification for pediatric appendicitis
Mahshid Mortazavi
Alexandra Dimmer
Elena Guadagno
Dan Poenaru
Sherif Emil
2025-07-26
Pediatric surgery international (Print) (publié)
doi.org