Publications

Map-based supervisory control strategies of a single-pipe CO2 thermal network connected to local heat pumps
Sepehr Gholamrezaie
Massimo Cimmino
Parham Eslami-Nejad
2026-07-27
Energy and Buildings (publié)
doi.org
Mapping T cell phenotype with machine learning to optimize cancer immunotherapy 2307563
Hassan Jamaleddine
Grégoire Altan‐Bonnet
Paul François
Mahmood Mazarei
Madison Wahlsten
Timothy West
Abstract Introduction T cells play a central role in adaptive immune responses, enabling vertebrates to fight infections and eliminate cance… (voir plus)r cells. Cancer immunotherapies, and in particular adoptive T-cell therapies, thus aim to harness their tumor-destroying capabilities to treat, or even cure, cancer patients. Despite recent advances in the development of these therapies, a major limitation remains our inability to predict, a priori, which tumor-infiltrating T cells will be best equipped to carry out anti-tumor immunity. Indeed, this multifactorial problem requires a model that integrates information on T cell receptor (TCR) specificity, tumor antigen abundance, and T cell activation history within the complex tumor microenvironment, yet such considerations are largely absent from current T cell selection strategies. Methods To address this gap, we are using a combination approach of high-throughput robotic multiplexing with computational and machine learning techniques to identify signatures of T cell phenotype that best predict response to tumor antigens. Specifically, we aim to study the roles of antigen presentation, TCR/antigen affinity, and inflammatory milieu on shaping T cell phenotype at the single-cell level, and training machine learning models to re-derive the activation history of T cells both in vivo and ex vivo. Results Preliminary results from in vitro co-culture data of T cells with cognate antigen-bearing splenocytes suggests that T cell antigen strength can indeed be back-calculated from single cell phenotype as measured by spectral flow cytometry. Conclusion With a validated model of tumor antigenicity in T cells, this research project aims to better inform T cell selection and optimal preparation strategies for adoptive T-cell therapies. Funding Source n/a Topic Categories Computational and Systems Immunology (COMP)
2026-07-27
Journal of Immunology (publié)
doi.org
Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellation
Cesare Spinoso-Di Piano
Verna Dankers
Marius Mosbach
Jackie Chi Kit Cheung
Human language is driven by unspoken beliefs and belief updates, making these critical to model for successful communication between large l… (voir plus)anguage models (LLMs) and their users. In this paper, we evaluate the ability of LLMs to recognize unspoken beliefs made through implicatures and to understand their updates through implicature cancellation: the pragmatic phenomenon whereby an utterance's implied meaning is weakened or negated. We create the first expert-annotated implicature cancellation dataset, ImplicatureX, crowdsourced for human judgements of implicatures and their corresponding cancellations. We find that LLM belief update understanding lags behind that of humans, especially in more naturally-occurring scenarios. Additional control experiments suggest that successes in LLM belief updates may stem in part from a reliance on prior beliefs, and that failures in belief updates may depend on their type and on their form. Overall, our study suggests that current LLMs have not yet reached human-level understanding of unspoken beliefs and belief updates. Code and data are available at https://github.com/cesare-spinoso/ImplicatureX.
2026-07-26
arXiv (prépublication)
doi.org
arxiv.org
Online Policy Evaluation for MDPs with Dynamic UBSR Measures
Weikai Wang
Erick Delage
Developing efficient function-approximation methods for policy evaluation is a fundamental challenge in risk-aware reinforcement learning. E… (voir plus)xisting approaches either focus on restrictive classes of risk measures or rely on access to a simulator, limiting their applicability in fully online settings. In this work, we propose computationally efficient online learning algorithms for policy evaluation in Markov decision processes (MDPs) with dynamic utility-based shortfall risk (UBSR) measures under linear function approximation. Specifically, we introduce the UBSR-TD algorithm, establish conditions under which it converges almost surely, and develop several variants designed to accelerate convergence. Our formulation shows that existing policy evaluation algorithms for risk-neutral MDPs can be readily adapted to dynamic UBSR settings by incorporating a loss function into the temporal-difference error. Numerical experiments support our theoretical findings, and an application to a perishable inventory management problem with shelf-life uncertainty demonstrates the practical effectiveness of the proposed methods.
2026-07-24
arXiv (prépublication)
doi.org
arxiv.org
Modern Convolutional Design Improves Uterine MRI Segmentation, while nnU-Net Remains Most Robust Across Heterogeneous Datasets
Daniel A. Di Giovanni
Akiyo Takada
Evan McNabb
Jérémy Dana
Hajime Yokota
Takahiro Tsuboyama
Rita Zakarian
Martin Vallières
James Man Git Tsui
Caroline Reinhold
Abstract Purpose To evaluate how segmentation architecture and dataset-adaptive configuration influence uterine MRI segmentation across hete… (voir plus)rogeneous benign and malignant tasks. Methods U-Net, Swin-UNETR, and MedNeXt were compared with nnU-Net as a self-configuring reference across T2-weighted MRI datasets: public multiclass UMD anatomy/fibroid segmentation (n=300), institutional endometrial cancer tumor segmentation (n=206), and institutional uterine mass lesion segmentation (n=234). A relabeled external UMD-style cohort (n=12) assessed domain shift. Models used fixed partitions, fold ensembling, Dice, HD95, ASSD, volume error, and paired bootstrap comparisons with Holm correction. Results MedNeXt was the strongest manually controlled architecture. nnU-Net achieved the highest performance on all internal datasets and external testing. Macro-Dice reached 0.761, 0.746, and 0.814 for nnU-Net on UMD, endometrial cancer, and uterine mass datasets, respectively, versus 0.722, 0.726, and 0.789 for MedNeXt. The nnU-Net-MedNeXt gap was largest for multiclass UMD segmentation and smaller in binary tasks. External testing degraded all models; nnU-Net remained highest (0.542), followed by MedNeXt (0.490), U-Net (0.396), and Swin-UNETR (0.287). Conclusions Uterine MRI segmentation performance depended on task, architecture, and evaluation domain. MedNeXt supported modern convolutional design as a strong manual baseline, but nnU-Net remained the most robust overall, emphasizing the importance of dataset-adaptive configuration and external validation.
2026-07-23
medRxiv (prépublication)
doi.org
1D Pre‐Acquisition Navigator Correcting Respiratory‐Induced Field Fluctuations in Multi‐Echo Gradient‐Echo Imaging of the Thoracic Spinal Cord
Alicia E. Cronin
Alexandre D’Astous
Nathan Williams
Antoine Guénette
Aimee Salakhov
Seth Stubblefield
Colin D. Mcknight
Lipika Narisetti
Subramaniam Sriram
Seth A. Smith
Ryan K. Robison
Guillaume Gilbert
Julien Cohen‐Adad
Kristin P. O’Grady
PURPOSE: In the spinal cord (SC), multi-echo gradient echo (ME-GRE) increases gray (GM) and white matter (WM) contrast and improves sensitiv… (voir plus)ity to lesions in people with multiple sclerosis (pwMS). However, SC ME-GRE is susceptible to breathing-induced field fluctuations, causing ghosting artifacts and signal loss. Recent work introduced a 1D phase navigator following the last echo to measure field variations; however, susceptibility to phase wrapping increases at longer echo times. We propose a 1D phase navigator preceding the first echo, reducing phase accumulation and eliminating the need for respiratory monitoring. METHODS: ME-GRE data covering the lower (T9-T12 vertebrae) and upper (T4-T8 vertebrae) thoracic SC were acquired in 20 healthy volunteers and 3 pwMS at 3T. Standard and navigator-corrected images were acquired in the same acquisition. To evaluate image quality, WM and GM signal-to-noise ratio (SNR), WM/GM contrast-to-noise ratio (CNR), and background ghosting signals were measured and compared between the two reconstructions. Both were blindly assessed for artifacts, structural delineation, and diagnostic confidence in pwMS. RESULTS: Navigator correction significantly increased GM and WM SNR and CNR, reduced posterior ghosting across both thoracic regions, and significantly reduced artifacts while increasing structural delineation. Preliminary evaluation in three pwMS showed consistent improvements in artifact mitigation, structural delineation, and lesion conspicuity with navigator correction, providing proof-of-concept for potential clinical application. CONCLUSION: A 1D navigator prior to the first echo reduces ghosting and improves thoracic SC image quality without respiratory monitoring. This approach could improve the diagnostic value and enhance the reliability of thoracic SC ME-GRE.
2026-07-23
Magnetic Resonance in Medicine (publié)
doi.org
Controllable and Content-Based Recommendations
Fırat Öncel
Jihoon Jeong
Emiliano Penaloza
Mirco Ravanelli
Laurent Charlin
Cem Subakan
Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Co… (voir plus)ntrollable and Content-Based Recommendations (CCBR) framework, which builds its recommendations from textual user profile representations. CCBR plugs into collaborative filtering models and introduces controllability via text bottlenecks. We show that CCBR enables text-based and multimodal interventions, allowing users to steer the model towards the directions they prefer. Different from existing controllable recommendation systems, CCBR infers the text summaries directly from item contents (images, audio or video). Across image-, audio-, and video-based datasets, we demonstrate that the proposed framework obtains competitive model performance with standard (latent-representation) models while providing controllable model summaries via text. The model also outperforms TEARS, a recent baseline for controllable recommendation systems. Through systematic interventions, we demonstrate the efficacy of the user steering mechanism.
2026-07-22
arXiv (prépublication)
doi.org
arxiv.org
Cortical microstructural integrity predicts an exploitation bias in older adulthood
Patrick Hewan
Alfie Wearn
Jeremy Hogeveen
Kayla Williams
R Nathan Spreng
Gary R. Turner
Sylvia Villeneuve
Judes Poirier
John C S Breitner
Sylvain Baillet
Andrée-Ann Baril
Bellec Pierre
Véronique Bohbot
Danilo Bzdok
Mallar Chakravarty
D Louis Collins
Mahsa Dadar
Simon Ducharme
Alan Evans
Claudine Gauthier … (voir 80 de plus)
Maiya R Geddes
Rick Hoge
Yasser Ituria‐Medina
Gerhard Multhaup
Lisa-Marie Münter
Natasha Rajah
Pedro Rosa-Neto
Taylor Schmitz
Soucy Jp
Nathan Spreng
Christine Tardif
Etienne Vachon-Presseau
Mohammadali Javanray
Meishan Ai
Philippe Amouyel
Nicholas Ashton
Gabriel Aumont‐Rodrigue
Julie Bailly
Guilia Baracchini
Kaj Blennow
Christian Bocti
Lianne Boisvert
Sophie Boutin
Ann Brinkmalm Westman
A P Dagher
Xing Dai
Samir Das
Marina Dauar‐Tedeschi
Louis De Beaumont
Christine Déry
Maxime Descoteaux
Elena Drobotea
M Elie
Alfonso Fajardo Valdez
Vladimir Fonov
David Morgan
Jonathan Gallago
Greco Cr
Louise Hudon
Gabriel Jean
Anne Labonté
Robert Laforce
Marc Lalancette
Jean-Charles Lambert
Jeannie‐Marie Leoutsakos
Danaé Lussier Dumouchel
B Misic
Béry Mohammediyan
Holly NewboldFox
Eugenia Nita Capota
Alix Noly‐Gandon
Adrian Eduardo Noriega de la Colina
Pierre Orban
Valentin Ourry
Cynthia Picard
Alexa Pichet Binette
A. L. Poirier
Nathalie Prenevost
Ting Qiu
Marc James Quesnel
Charles Ramassamy
Jean‐Michel Raoult
Jordana Remz
Safa Sanami
Frederic St‐Onge
Cherie Strikwerda‐Brown
Elisabeth Sylvain
Andràs Tikàsz
Christina Tremblay
Stefanie Tremblay
Jennifer Tremblay‐Mercier
Stéphanie Tullo
Irem Ulku
Paolo Vitali
Yara Yakoub
Robert Zatorre
Henrik Zetterberg
Pierre Bellec
Jean-Paul Soucy
Claudia Greco
OBJECTIVES: Prefrontal regions are implicated in explore-exploit decision-making during foraging. Older adults often show an exploitation bi… (voir plus)as, and this age period is also marked by deteriorating prefrontal myelination. To investigate whether these phenomena are linked, we examined whether lower magnetization transfer saturation (MTsat), a myelin-sensitive quantitative MRI (qMRI) measure, in these regions predicts greater exploitation bias during foraging, and whether cortical microstructure is a better predictor of bias than macrostructure (i.e., cortical thickness). METHODS: Cognitively healthy older adults with familial risk of Alzheimer's disease (AD) (N=118, 60-88 years) completed a foraging task indexing explore-exploit decision-making. qMRI was used to derive MTsat values for the frontopolar cortex (FPC), medial orbitofrontal cortex (OFC), rostral middle frontal gyrus (rMFG), dorsal anterior cingulate cortex (dACC), as well as the locus coeruleus (LC), a core subcortical region strongly implicated in explore-exploit decision-making. Secondary analyses examined associations between available AD risk markers and foraging. RESULTS: Lower MTsat in the FPC, OFC, rMFG, and LC was associated with an exploitation bias, with LC and FPC emerging as the strongest predictors. No relationship was observed for the dACC. MTsat remained a significant predictor of foraging after controlling for cortical thickness. Observed associations were largely unrelated to AD risk markers. DISCUSSION: Individual differences in cortical microstructural integrity within a well-defined explore-exploit circuit are associated with an exploitative decision-making bias in older adults. These findings highlight the value of qMRI microstructural integrity markers, beyond standard macrostructural assays, in characterizing the neural correlates of exploitation biases in later life.
2026-07-22
Journals of Gerontology Series B: Psychological Sciences and Social Sciences (publié)
doi.org
High-resolution dissection of concept acquisition in different families of protein language models
Shawn Whitfield
Tom Marty
Robert M. Vernon
Christopher J. Langmead
Dhanya Sridhar
Quentin Fournier
Protein language models have been increasingly successful on tasks ranging from fitness prediction to functional design, yet what biological… (voir plus) knowledge they acquire and where it is encoded within their internal representations remain underexplored. Through a high-resolution layer-by-layer interpretability analysis of 8 models from the ESM2 and AMPLIFY families on 22 concepts from human proteome annotations, we found that these models encode concepts of increasing levels of complexity along their depth: basic physicochemical properties and linear motifs are best captured by early-layer embeddings, secondary structure from subsequent layers, and domain-level semantics from middle layers. Principal component projections of these embeddings showed that they separate biologically meaningful protein groupings, and molecular-biology-inspired interventions demonstrated that pLM embeddings can discriminate phosphomimic-active from inactive mutants. Perhaps surprisingly, we observed that pretraining data and compute had a greater impact on the linear emergence of biological concepts than scaling up parameters. By revealing where biological knowledge is captured in pLMs and which choices shape its emergence, our work offers insights to develop more robust, biologically grounded protein language models.
2026-07-22
bioRxiv (prépublication)
doi.org
Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering
Zhuohan Xie
Yuyang Dai
Rania Elbadry
Vanshikaa Jani
Georgi Georgiev
Dimitar Dimitrov
Fan Zhang
Xueqing Peng
Lingfei Qian
Jimin Huang
Jiahui Geng
Yankai Chen
Ye Yuan
Haolun Wu
Yuxia Wang
Ivan Koychev
Veselin Stoyanov
Mingzi Song
Yu Chen
Xue Liu … (voir 1 de plus)
Preslav Nakov
FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task t… (voir plus)ests whether systems can select the correct answer to finance questions involving domain terminology, numerical interpretation, and conceptual financial reasoning across languages and scripts. The final-test set contains 800 questions, with 200 questions per language; gold answers were withheld during submission, and each language was ranked independently by accuracy. The final leaderboards contain 13 English, 11 Chinese, 11 Arabic, and 10 Hindi ranked submissions. Top accuracies range from 92.0% in Hindi to 97.5% in English and Arabic, with the same leading teams appearing near the top across all four languages. The documented systems used retrieval augmentation, direct answer-option scoring, language-specific prompting, selective self-consistency, confidence checks, and LLM-based review stages.
2026-07-21
arXiv (prépublication)
doi.org
arxiv.org
Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering
Zhuohan Xie
Xueqing Peng
Georgi Georgiev
Dimitar Dimitrov
Yuyang Dai
Rania Elbadry
Vanshikaa Jani
Lingfei Qian
Fan Zhang
Jimin Huang
Jiahui Geng
Yankai Chen
Ye Yuan
Haolun Wu
Yuxia Wang
Ivan Koychev
Veselin Stoyanov
Mingzi Song
Yu Chen
Xue Liu … (voir 1 de plus)
Preslav Nakov
FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence. Each final-test item pairs an English … (voir plus)question with financial statements and news in English, Chinese, Japanese, Spanish, and Greek. Participating systems submit one concise answer per item in JSONL format. The final-test set contains 256 items, split evenly between easy and expert tiers; each tier contains four question templates instantiated over 32 company-report groups. Gold answers were withheld during submission, and systems were ranked by macro-averaged item-level ROUGE-1 F1 against organizer-held reference answers. The final leaderboard includes 12 ranked submissions. The strongest systems are closely clustered, with the top four separated by less than one percentage point in ROUGE-1 F1. The submitted system papers document retrieval-augmented generation, cross-lingual evidence handling, structured prompting, answer compression, and validation strategies.
2026-07-21
arXiv (prépublication)
doi.org
arxiv.org
SpatialJEPA: JEPA-inspired graph-context distillation for spatially aware multiomics integration
Dylan Mann-Krzisnik
Yue Li
Abstract Computational frameworks for integrating spatial genomics modalities extend cell-based representation learning across molecular lay… (voir plus)ers, but many paired RNA–ATAC datasets are dissociated and lack spatial coordinates. We introduce SpatialJEPA , a JEPA-inspired teacher–student framework for transferring spatial context from spatial multiomics data to non-spatial multiome data. In contrast to patch- or feature-masking objectives, SpatialJEPA masks spatial context by replacing the teacher’s spatial neighborhood graph with a self-only identity graph during student training, making the spatial sample appear dissociated to the student. The student learns to match teacher embeddings from this graph-context-restricted view and can therefore be applied to dissociated RNA–ATAC data at inference time. In mouse brain multiomics, the resulting representation supports source–target alignment, recovers spatially organized transcriptomic and chromatin-accessibility programs, and shows concordance with ligand–receptor pathway structure compared with non-spatial references.
2026-07-21
bioRxiv (accepté)
doi.org