Publications

Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal
Christina Butsko
Gabriel Tseng
Kristof Van Tricht
Giorgia Milli
David Rolnick
Ruben Cartuyvels
Inbal Becker Reshef
Zoltan Szantoi
Hannah Kerner
The increasing availability of geospatial foundation models has the potential to transform remote sensing applications such as land cover cl… (voir plus)assification, environmental monitoring, and change detection. Despite promising benchmark results, the deployment of these models in operational settings is challenging and rare. Standardized evaluation tasks often fail to capture real-world complexities relevant for end-user adoption such as data heterogeneity, resource constraints, and application-specific requirements. This paper presents a structured approach to integrate geospatial foundation models into operational mapping systems. Our protocol has three key steps: defining application requirements, adapting the model to domain-specific data and conducting rigorous empirical testing. Using the Presto model in a case study for crop mapping, we demonstrate that fine-tuning a pre-trained model significantly improves performance over conventional supervised methods. Our results highlight the model’s strong spatial and temporal generalization capabilities. Our protocol provides a replicable blueprint for practitioners and lays the groundwork for future research to operationalize foundation models in diverse remote sensing applications. Application of the protocol to the WorldCereal global crop-mapping system showcases the framework’s scalability.
2025-12-01
Proceedings of The TerraBytes {ICML} Workshop: Towards global datasets and models for Earth Observation (publié)
doi.org
proceedings.mlr.press
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
Charles Y. Zhang
Yuanjia Yang
Aidan Sirbu
Elliott T.T. Abe
Emil Wärnberg
Eric J. Leonardis
Diego E. Aldarondo
Adam Lee
Aaditya Prasad
Jason Foat
Kaiwen Bian
Joshua Park
Rusham Bhatt
Hutton Saunders
Akira Nagamori
Ayesha R. Thanawalla
Kee Wui Huang
Fabian Plum
Hendrik K. Beck
Steven W. Flavell … (voir 6 de plus)
David Labonte
Blake A. Richards
Bingni W. Brunton
Eiman Azim
Bence P. Ölveczky
Talmo D. Pereira
The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex beha… (voir plus)vior, kinematic trajectories alone provide only indirect access to the underlying control processes. Here we present MIMIC-MJX, a framework for learning biologically-plausible neural control policies from kinematics. MIMIC-MJX models the generative process of motor control by training neural controllers that learn to actuate biomechanically-realistic body models in physics simulation to reproduce real kinematic trajectories. We demonstrate that our implementation is accurate, fast, data-efficient, and generalizable to diverse animal body models. Policies trained with MIMIC-MJX can be utilized to both analyze neural control strategies and simulate behavioral experiments, illustrating its potential as an integrative modeling framework for neuroscience.
2025-12-01
ArXiv (publié)
doi.org
arxiv.org
The Cloud-Based Geospatial Benchmark: Challenges and LLM Evaluation
Jeffrey A. Cardille
Renee Johnston
Simon Ilyushchenko
Johan Kartiwa
Zahra Shamsi
Matthew Abraham
Khashayar Azad
Kainath Ahmed
Emma Bergeron Quick
Nuala Caughie
Noah Jencz
Karen Dyson
Andrea Puzzi Nicolau
Maria Fernanda Lopez-Ornelas
David Saah
Michael Brenner
Subhashini Venugopalan
Sameera S Ponda
2025-12-01
Proceedings of The TerraBytes {ICML} Workshop: Towards global datasets and models for Earth Observation (publié)
proceedings.mlr.press
proceedings.mlr.press
Training neural networks from scratch in a videogame leads to brittle brain encoding
François Paugam
Basile Pinsard
Marie St-Laurent
Guillaume Lajoie
Lune Bellec
Recent brain-encoding studies using videogame tasks suggest that the training objective of an artificial neural network plays a central role… (voir plus) in how well the network’s representations align with brain activity. This study investigates the alignment of artificial neural network activations with brain activity elicited by a video game task using models trained from scratch in controlled settings. We specifically compared three model training objectives: reinforcement learning, imitation learning, and a vision task, while accounting for other potential factors which may impact performance such as training data and model architecture. We tested models on brain encoding, i.e. their ability to predict functional magnetic resonance imaging (fMRI) signals acquired while human subjects played different levels of the video game Super Mario Bros. When tested on new playthroughs from the game levels seen at training, the reinforcement learning objective had a small but significant advantage in brain encoding, followed by the imitation learning and vision models. We hypothesized that brain-aligned representations would emerge only in task-competent models, and that the specific brain regions well encoded by a model would depend on the nature of the task it was trained on. While brain encoding did improve during model training, even an untrained model with matching architecture approached the performance of the best models. Contrary to our hypotheses, no model layers or specific training objectives aligned preferentially with specific brain areas. Large performance gaps also persisted in fully trained models across game levels, both those seen during training and entirely novel ones. Overall, even though reinforcement learning presented a small advantage to train brain encoding models for videogame data, all tested brain encoding models exhibited brittle performance with limited generalization both within- and out-of-distribution. Overall, our results suggest that training small artificial models from scratch is not sufficiently reliable, and that incorporating pretrained models such as foundation vision–action models may ultimately be necessary to support robust inferences about brain representations.
2025-12-01
bioRxiv (prépublication)
doi.org
Accelerated Inorganic Materials Design with Generative Al Agents
Izumi Takahara
Teruyasu Mizoguchi
Bang Liu
Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative computati… (voir plus)onal methods often struggle to efficiently explore desired targets with sufficient interpretability. Here, we present MatAgent, a generative approach for inorganic materials discovery that harnesses the powerful reasoning capabilities of large language models (LLMs). By combining a diffusion-based generative model for crystal structure estimation with a predictive model for property evaluation, MatAgent uses iterative, feedback-driven guidance to steer material exploration precisely toward user-defined targets. Integrated with external cognitive tools-including short-term memory, long-term memory, the periodic table, and a comprehensive materials knowledge base-MatAgent emulates human expert reasoning to vastly expand the accessible compositional space. Our results demonstrate that MatAgent robustly directs exploration toward desired properties while consistently achieving high compositional validity, uniqueness, and material novelty. This framework thus provides a highly interpretable, practical, and versatile AI-driven solution to accelerate the discovery and design of next-generation inorganic materials.
2025-11-30
Cell Reports Physical Science (publié)
doi.org
arxiv.org
Biomechanical finite element simulation of the pelvic organs under dynamic loading and validation against experimental data from magnetic resonance imaging.
Camille Lafond
Louise Hohnadel
Thomas Brunel
Nicolas Pirró
Bellemare Marc-Emmanuel
Dominique Chamoret
Sébastien Roth
2025-11-30
Medical Engineering & Physics (publié)
doi.org
Building a library of acute traumatic spinal cord injury images across Canada: a retrospective cohort study protocol
Naama Rotem-Kohavi
Suzanne Humphreys
Vanessa K Noonan
Christiana L Cheng
Mathieu Guay-Paquet
Maxime Bouthillier
Jan Valosek
Enamundram Naga Karthik
Emma Lichtenstein
Nick Guenther
Kalum Ost
Naj Attabib
Michael Hardisty
Jetan Badhiwala
Jeremie Larouche
Markian Pahuta
Sean Christie
Michael G Fehlings
Daryl Fourney
Brian K Kwon … (voir 6 de plus)
Jean Marc Mac-Thiong
Jérôme Paquet
Philippe Phan
Christopher Witiw
Julien Cohen-Adad
David W Cadotte
MRI is increasingly recognised as a valuable tool for assessing prognosis and predicting outcomes following traumatic spinal cord injury (SC… (voir plus)I). Several potential MRI biomarkers have been identified, but efforts are still needed to improve the accuracy and feasibility of these biomarkers in clinical practice. This study aims to build a national Canadian SCI imaging repository for storing and analysing imaging data for SCI, with the goal of improving SCI MRI biomarkers to predict outcomes and inform clinical management. As a substudy of the Rick Hansen SCI Registry (RHSCIR), this retrospective multisite study includes individuals who sustained a traumatic cervical SCI between 2015 and 2021, were previously enrolled in RHSCIR, and had MRI scans acquired within 72 hours of injury and before any surgical intervention. Individuals with a penetrating trauma and/or with any prior spine surgery are excluded. The study principal investigator and research associates, experienced with data curation and with the standardised format and specifications of the Brain Imaging Data Structure standard, guide the site’s curator on the steps to perform image deidentification and curation to create standardised datasets across all sites. These datasets are transferred to a Digital Research Alliance of Canada (‘the Alliance’) server designated for this project and concatenated to form the national Canadian SCI imaging repository (Neurogitea). We are using a semiautomated processing pipeline to quantify lesion morphology, together with additional imaging measures that are manually extracted from the images (for instance, the relative maximal spinal cord compression and the maximum canal compromise). Through linkage to RHSCIR clinical and epidemiological data already available on eligible participants, regression analysis is planned to predict neurological outcomes at discharge, including the American Spinal Injury Association Impairment Scale grade, upper and lower extremity motor and sensory scores. This protocol has been submitted by the participating sites to obtain ethics and institutional approvals prior to the study initiation at each site. All 12 sites across Canada have now obtained ethics and institutional approvals. Study results will be disseminated at local, national and international conferences and by journal publications.
2025-11-30
BMJ Open (publié)
doi.org
Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics
Victor Livernoche
Andreea Musulan
Zachary Yang
Jean-François Godbout
Reihaneh Rabbany
Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes actually appear and circ… (voir plus)ulate across social platforms during major events in democratic countries. In this study, we present one of the first in-depth analyses of how these realistic synthetic media shape the political landscape online, focusing specifically on the 2025 Canadian federal election. By analyzing 187,778 posts from X, Bluesky, and Reddit with a high-accuracy detection framework trained on a diverse set of modern generative models, we find that 5.86% of election-related images were deepfakes. Right-leaning accounts shared them more frequently, with 8.66% of their posted images flagged compared to 4.42% for left-leaning users, often with defamatory or conspiratorial intent. Yet, most detected deepfakes were benign or non-political, and harmful ones drew little attention, accounting for only 0.12% of all views on X. Overall, deepfakes were present in the election conversation, but their reach was modest, and realistic fabricated images, although less common, drew higher engagement, highlighting growing concerns about their potential misuse.
2025-11-30
arXiv (publié)
doi.org
arxiv.org
Effect of Document Packing on the Latent Multi-Hop Reasoning Capabilities of Large Language Models
Gabriele Prato
Shagun Sodhani
Alessandro Sordoni
A. Chandar
The standard practice for training large language models involves packing multiple documents together to optimize computational efficiency. … (voir plus)However, the impact of this process on the models' capabilities remains largely unexplored. To address this gap, we investigate how different document-packing strategies influence the latent multi-hop reasoning abilities of LLMs. Our findings indicate that packing can improve model performance compared to training on individual documents, at the expense of more compute. To further understand the underlying mechanisms, we conduct an ablation study, identifying key factors that explain the advantages of packing. Ultimately, our research deepens the understanding of LLM training dynamics and provides practical insights for optimizing model development.
2025-11-30
arXiv (publié)
doi.org
arxiv.org
FALCON: Few-step Accurate Likelihoods for Continuous Flows
Danyal REHMAN
Tara Akhound-Sadegh
Artem Gazizov
Yoshua Bengio
Alexander Tong
2025-11-30
arXiv (publié)
doi.org
arxiv.org
Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction
Vincent Pauline
Tobias Höppe
Kirill Neklyudov
Alexander Tong
Stefan Bauer
Andrea Dittadi
2025-11-30
arXiv (publié)
doi.org
arxiv.org
Genetic and Causal Insights Into White Matter Hyperintensities Across the Brain‐Body Axis
Manpreet Singh
Kimia Shafighi
Flavie E. Detcheverry
Gabrielle Dagasso
Fanta Dabo
Ikrame Housni
Sridar Narayanan
Nils D. Forkert
Sarah A Gagliano Taliun
Danilo Bzdok
AmanPreet Badhwar
White matter hyperintensities (WMHs), visible as bright regions on T2‐weighted FLAIR MRI, are frequent with age and elevated in Alzheimer'… (voir plus)s disease (AD). Representing axonal damage, demyelination, and edema, WMHs are driven by vascular mechanisms, including endothelial dysfunction and impaired cerebrovascular autoregulation. WMHs also exhibit strong heritability (55–73%), with overlapping genetic pathways shared with AD. Emerging evidence suggests systemic factors across the brain‐body axis influence WMHs, yet these contributions and their genetic overlap with AD remain underexplored. Our study investigated genetic underpinnings specific to WMHs and those shared with AD by assessing partitioned heritability of WMHs and AD across the brain‐body axis with SNP level tissue‐ and cell‐specific annotations; identifying genes associated with WMHs and AD through integration of gene expression data, establishing causal links between SNP‐level findings and imaging‐derived phenotypes (IDPs), particularly structural variations in regional brain volumes. Partitioned heritability was assessed using stratified‐linkage disequilibrium score regression (sLDSC) on GWAS summary statistics ( N  = 3 WMH studies; N  = 6 AD studies) using human A1) tissue level annotations ( N  = 10) and A2) continuous cell‐specific annotations ( N  = 64). MAGMA and FUSION analyses highlighted genes associated with WMH and AD for further bioinformatics analysis (using human protein atlas (HPA) and STRING database). MACAW (Vigneshwaran et al, 2024) modeled causal relationships between WMH‐associated SNPs (from FUMA analysis) and IDPs ( N  = 172), leveraging directed acyclic graphs to evaluate genetic effects while controlling for confounders (Figure 2). Tissue‐specific analysis revealed significant enrichment of WMH‐associated SNPs in the CNS, liver, cardiovascular system, and kidneys, while AD‐associated SNPs were enriched in the CNS, connective bone, liver, and immune tissues. (Figure 1). Cell‐specific analysis identified vascular endothelial cells as enriched across WMH‐enriched tissues. MAGMA analysis, combined with HPA analysis, corroborated sLDSC tissue‐level findings. MAGMA and FUSION analyses highlighted genes associated with WMHs ( N  = 39 and 69) and AD ( N  = 291 and 193). MACAW linked WMH‐associated SNP to 172 IDPs, consistently impacting WM hypointensities and regional brain volumes (e.g., left inferior temporal volume). Our findings highlight systemic multi‐tissue contributions (CNS, liver, cardiovascular system, and kidneys) to WMHs, driven by vascular endothelial dysfunction and shared AD genetics, with SNPs across the body also affecting brain imaging derived phenotypes.
2025-11-30
Alzheimer's & Dementia (publié)
doi.org