Publications

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
Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Masoumeh Sharafi
Soufiane Belharbi
Houssem Ben Salem
Ali Etemad
Alessandro Lameiras Koerich
Marco Pedersoli
Simon Bacon
Eric Granger
Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interac… (see more)tion and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve their performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain data, thereby avoiding data privacy, storage, and transmission constraints. This paper addresses a challenging scenario where source data is unavailable for adaptation, and only unlabeled target data consisting solely of neutral expressions is available. SFDA methods are not typically designed to adapt using target data from only a single class. Further, using models to generate facial images with non-neutral expressions can be unstable and computationally intensive. In this paper, personalized feature translation (PFT) is proposed for SFDA. Unlike current image translation methods for SFDA, our lightweight method operates in the latent space. We first pre-train the translator on the source domain data to transform the subject-specific style features from one source subject into another. Expression information is preserved by optimizing a combination of expression consistency and style-aware objectives. Then, the translator is adapted on neutral target data, without using source data or image synthesis. By translating in the latent space, PFT avoids the complexity and noise of face expression generation, producing discriminative embeddings optimized for classification. Using PFT eliminates the need for image synthesis, reduces computational overhead (using a lightweight translator), and only adapts part of the model, making the method efficient compared to image-based translation.
2025-08-07
ArXiv (preprint)
doi.org
arxiv.org
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
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 … (see 3 more)
Yuemei Li
Corina Nagy
Gustavo Turecki
2025-08-04
Nature Genetics (published)
doi.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
CellForge: Agentic Design of Virtual Cell Models
Xiangru Tang
Zhuoyun Yu
Jiapeng Chen
Yan Cui
Yanjun Shao
Weixu Wang
Fang Wu
Yuchen Zhuang
Wenqi Shi
Zhi Huang
Arman Cohan
Xihong Lin
Fabian Theis
Smita Krishnaswamy
Mark B. Gerstein
Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantiti… (see more)es such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.
2025-08-03
ArXiv (preprint)
doi.org
arxiv.org
Infrared Object Detection with Ultra Small ConvNets: Is ImageNet Pretraining Still Useful?
Srikanth Muralidharan
Heitor Rapela Medeiros
Masih Aminbeidokhti
Eric Granger
Marco Pedersoli
Many real-world applications require recognition models that are robust to different operational conditions and modalities, but at the same … (see more)time run on small embedded devices, with limited hardware. While for normal size models, pre-training is known to be very beneficial in accuracy and robustness, for small models, that can be employed for embedded and edge devices, its effect is not clear. In this work, we investigate the effect of ImageNet pretraining on increasingly small backbone architectures (ultra-small models, with
2025-08-03
ArXiv (preprint)
doi.org
arxiv.org
The impact of gender on pediatric surgical access and outcomes in Africa
Sacha Williams
Olivia Serhan
Jenny Wang
Christian Guindi,
Elena Guadagno
Maeve Trudeau
Emmanuel Ameh
Kokila Lakhoo
Dan Poenaru
Girls, whose care is often affected by barriers steeped in gender inequity, may be at higher risk of poor surgical outcomes. This study expl… (see more)ored the impact of gender on pediatric surgical care in Africa. Differences in access to care and clinical outcomes for boys and girls were examined for pediatric surgical conditions that do not differ by physiological sex. A systematic review of African pediatric surgical studies ensued, followed by a random effects meta-analysis, and risk of bias assessment. Of the 12281 records retrieved, 54 were selected for review. Most studies were retrospective (57.4%), single-site (94.4%), from Egypt, Nigeria, Ghana, or Ethiopia (55.6%), focussed on gastrointestinal conditions (63.0%), published in 2010 or sooner (85.1%), had study durations of 5 years or less (68.5%), and cohorts of less than 200 children (57.4%). Sixty percent reported the outcome of mortality. Meta-analysis odds ratios revealed surgery was performed 3.6 times more often on boys (95% CI: 2.6, 4.9); and mortality was 1.6 times greater for girls (95% CI: 1.3, 2.0). African girls appear to face gender inequities in pediatric surgical care. Findings will be further explored in a mixed-methods study. I Gender disparities in global surgical care have been documented in the African adult population. However gender specific differentials in surgical access and outcomes have yet to be documented for African pediatric populations. This study provides first-time evidence of gender inequity in pediatric surgical care in Africa.
2025-08-03
Pediatric Surgery International (published)
doi.org