Publications

An Exact Framework for Solving the Space-Time Dependent TSP
Isaac Rudich
Manuel López-Ibáñez
Michael Romer
Quentin Cappart
Louis-Martin Rousseau
Many real-world scenarios involve solving bilevel optimization problems in which there is an outer discrete optimization problem and an inne… (voir plus)r problem involving expensive or black box computation. This arises in space-time–dependent variants of the traveling salesman problem, such as when planning space missions that visit multiple astronomical objects. Planning these missions presents significant challenges due to the constant relative motion of the objects involved. There is an outer combinatorial problem of finding the optimal order to visit the objects and an inner optimization problem that requires finding the optimal departure time and trajectory to travel between each pair of objects. The constant motion of the objects complicates the inner problem, making it computationally expensive. This paper introduces a novel framework utilizing decision diagrams (DDs) and a DD-based branch-and-bound technique, peel-and-bound, to achieve exact solutions for such bilevel optimization problems, assuming sufficient inner problem optimizer quality. The framework leverages problem-specific knowledge to expedite search processes and minimize the number of expensive evaluations required. As a case study, we apply this framework to the asteroid routing problem, a benchmark problem in global trajectory optimization. Experimental results demonstrate the framework’s scalability and ability to generate robust heuristic solutions for tested instances. Many of these solutions are exact, contingent on the assumed quality of the inner problem’s optimizer. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0866 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0866 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
2026-04-01
INFORMS Journal on Computing (publié)
doi.org
arxiv.org
Lipidomics Identifies HFpEF Phenogroups and a High-Risk Metabolic Signature - The BElgian and CAnadian MEtabolomics in HFpEF (BECAME-HF) project.
Julie Hussin
Nassiba Menghoum
Anik Forest
Pamela Mehanna
Olivier Tastet
Julie Legault
Isabelle Robillard Frayne
Sibille Lejeune
David Vancraeynest
Clotilde Roy
Galadriel Briere
Gabrielle Boucher
L Bertrand
Sandrine Horman
David Rhainds
J.‐C. Tardif
Christophe Beauloye
Anne-Catherine Pouleur
Christine Des Rosiers
Rationale: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome with substantial unmet diagnostic and therapeu… (voir plus)tic needs. Circulating lipid metabolism is increasingly implicated in HFpEF pathophysiology but has not been systematically leveraged for molecular stratification. Objective: To determine whether plasma lipidomics can identify molecular phenogroups of HFpEF associated with distinct clinical characteristics and outcomes. Methods and Results: Untargeted plasma lipidomics was performed in non-HF subjects and HFpEF patients from a primary Belgian cohort and an independent Canadian cohort (n=177 in each cohort). In the Belgian cohort, 235 unique lipids spanning 19 subclasses were annotated, including 96 significantly associated with HFpEF (q<0.02). Unsupervised analyses revealed marked lipidomic heterogeneity, with a distinct HFpEF subgroup separable from non-HF subjects. Hierarchical clustering identified three phenogroups with divergent lipid profiles and clinical features. One phenogroup exhibited severe atrial dysfunction, congestion-related biomarkers, elevated indices of cardiac and liver fibrosis, and markedly reduced survival, a second was characterized by prominent metabolic syndrome features, and a third by preserved renal function. Cross-cohort comparison using a supervised classifier trained on 158 shared lipids confirmed analogous lower-risk phenogroups in the Canadian cohort, while the high-risk phenotype was underrepresented. A signature of 10 lipids across six subclasses, including long-chain acylcarnitines, ether phosphatidylcholines, and oxidized sphingomyelins, discriminated the high-risk group and correlated with markers of disease severity. Conclusion: Our findings demonstrate that HFpEF comprises metabolically distinct patient subgroups across cohorts, revealing specific lipidomic dysfunctions that deepen our understanding of HFpEF heterogeneity and underlying pathophysiology.
2026-04-01
medRxiv (accepté)
doi.org
AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languages
Israel Abebe Azime
Jesujoba O. Alabi
Crystina Zhang
Iffat Maab
Atnafu Lambebo Tonja
Tadesse Destaw Belay
Folasade Alabi
Salomey Osei
Saminu Muhammad Aliyu
Nkechinyere Faith Aguobi
Bontu Fufa Balcha
Blessing Sibanda
Davis David
Mouhamadane Mboup
Daud Abolade
Neo Putini
Philipp Slusallek
David Ifeoluwa Adelani
Dietrich Klakow
Assessing the veracity of a claim made online is a complex and important task with real-world implications. When these claims are directed a… (voir plus)t communities with limited access to information and the content concerns issues such as healthcare and culture, the consequences intensify, especially in low-resource languages. In this work, we introduce AfrIFact, a dataset that covers the necessary steps for automatic fact-checking (i.e., information retrieval, evidence extraction, and fact checking), in ten African languages and English. Our evaluation results show that even the best embedding models lack cross-lingual retrieval capabilities, and that cultural and news documents are easier to retrieve than healthcare-domain documents, both in large corpora and in single documents. We show that LLMs lack robust multilingual fact-verification capabilities in African languages, while few-shot prompting improves performance by up to 43% in AfriqueQwen-14B, and task-specific fine-tuning further improves fact-checking accuracy by up to 26%. These findings, along with our release of the AfrIFact dataset, encourage work on low-resource information retrieval, evidence retrieval, and fact checking.
2026-03-31
arXiv (prépublication)
doi.org
arxiv.org
Assessment of differentially private fine-tuning of large language models for synthetic clinical note generation
Atiquer Rahman Sarkar
Fatima Jahan Sarmin
Djedjiga Mouheb
Benjamin C. M. Fung
Noman Mohammed
2026-03-31
Intelligent Systems with Applications (publié)
doi.org
A Capacitated Collection-and-Delivery-Point Location Problem with Random Utility Maximizing Customers
David Pinzon Ulloa
Ammar Metnani
Emma Frejinger
2026-03-31
Computers & Operations Research (publié)
doi.org
arxiv.org
CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe
Tara Saba
Anne Ouyang
Xujie Si
Fan Long
High-performance GPU kernels are critical to modern machine learning systems, yet developing efficient implementations remains a challenging… (voir plus), expert-driven process due to the tight coupling between algorithmic structure, memory hierarchy usage, and hardware-specific optimizations. Recent work has explored using large language models (LLMs) to generate GPU kernels automatically, but generated implementations often struggle to maintain correctness and achieve competitive performance across iterative refinements. We present CuTeGen, an agentic framework for automated generation and optimization of GPU kernels that treats kernel development as a structured generate--test--refine workflow. Unlike approaches that rely on one-shot generation or large-scale search over candidate implementations, CuTeGen focuses on progressive refinement of a single evolving kernel through execution-based validation, structured debugging, and staged optimization. A key design choice is to generate kernels using the CuTe abstraction layer, which exposes performance-critical structures such as tiling and data movement while providing a more stable representation for iterative modification. To guide performance improvement, CuTeGen incorporates workload-aware optimization prompts and delayed integration of profiling feedback. Experimental results on matrix multiplication and activation workloads demonstrate that the framework produces functionally correct kernels and achieves competitive performance relative to optimized library implementations.
2026-03-31
arXiv (prépublication)
doi.org
arxiv.org
Model Merging via Data-Free Covariance Estimation
Marawan Gamal Abdel Hameed
Derek Tam
Pascal Jr Tikeng Notsawo
Colin Raffel
Guillaume Rabusseau
Model merging provides a way of cheaply combining individual models to produce a model that inherits each individual's capabilities. While s… (voir plus)ome merging methods can approach the performance of multitask training, they are often heuristically motivated and lack theoretical justification. A principled alternative is to pose model merging as a layer-wise optimization problem that directly minimizes interference between tasks. However, this formulation requires estimating per-layer covariance matrices from data, which may not be available when performing merging. In contrast, many of the heuristically-motivated methods do not require auxiliary data, making them practically advantageous. In this work, we revisit the interference minimization framework and show that, under certain conditions, covariance matrices can be estimated directly from difference matrices, eliminating the need for data while also reducing computational costs. We validate our approach across vision and language benchmarks on models ranging from 86M parameters to 7B parameters, outperforming previous data-free state-of-the-art merging methods
2026-03-31
arXiv (prépublication)
doi.org
arxiv.org
Primary large-cell neuroendocrine carcinoma of the prostate and its nursing care: A systematic review
Mingli Wang
Xuemei Zhang
Lifang Pan
Jun Ding
2026-03-31
Asian Journal of Surgery (publié)
doi.org
Self-Routing: Parameter-Free Expert Routing from Hidden States
Jama Hussein Mohamud
Drew Wagner
Mirco Ravanelli
Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely on a lear… (voir plus)ned router to map hidden states to expert assignments. In this work, we ask whether a dedicated learned router is strictly necessary for MoE routing. We propose Self-Routing, a parameter-free routing mechanism that uses a designated subspace of the token hidden state directly as expert logits, eliminating the router projection entirely while leaving the rest of the MoE layer unchanged. We evaluate Self-Routing on language modeling across different expert counts and model scales, and on ImageNet-1K classification by comparing it against a standard learned router, random-routing baselines, and dense non-MoE baselines. Our results show that Self-Routing remains competitive with the learned-router baseline while removing all dedicated routing parameters, and yields more balanced expert utilization, with about 17\% higher average normalized routing entropy and no explicit load-balancing loss. On ImageNet-1K with DeiT-S/16, Self-Routing also slightly improves over the corresponding learned-router MoE. These findings suggest that effective MoE routing can emerge from the hidden representation itself without requiring a separate learned router module.
2026-03-31
arXiv (prépublication)
doi.org
arxiv.org
The performance of different propensity score methods for estimating the effects of multiple treatments or exposures: a neutral comparison study
P C Austin
David Austin
BACKGROUND: The generalized propensity score is an extension of the conventional propensity score to settings with a categorical exposure wi… (voir plus)th more than two levels of treatment or exposure. Six different methods of using the generalized propensity score have been used in the general internal medical literature. However, no studies have evaluated the relative performance of these methods. METHODS: We used Monte Carlo simulations to evaluate the performance of seven methods for using the generalized propensity score to estimate the effect of three levels of exposure when outcomes are continuous or binary. We examined estimation of both the average treatment effect and the average treatment effect for the treated. These methods for using the generalized propensity score included: regression and weighting-based approaches proposed by Imbens, regression and weighting-based approaches proposed by McCaffrey, a regression-based approached proposed by Spreeuwenberg, Rubin’s pairwise comparison method, three-way matching, matching weights, and overlap weights. We illustrated the application of these methods by estimating the effect of smoking status (current smoker vs. former smoker vs. never smoker) on death within one year of hospitalization for acute myocardial infarction. RESULTS: No method had consistently superior performance across all scenarios and target estimands. CONCLUSION: We make recommendations for the preferred method depending on the nature of the outcome and the target estimand.
2026-03-31
BMC Medical Research Methodology (publié)
doi.org
Unsteady flow behavior and fluid-induced noise characteristics of T-junction pipe with a closed main branch
Jianchao Yu
Yinqi Wu
Xiating Jiang
Bang Liu
Hui Huang
YuZheng Li
Fuqi Li
2026-03-31
Ocean Engineering (publié)
doi.org
CLIP-AUTT: Test-Time Personalization with Action Unit Prompting for Fine-Grained Video Emotion Recognition
Muhammad Zeeshan
Masoumeh Sharafi
Benoit Savary
Alessandro L. Koerich
Marco Pedersoli
Eric Granger
Personalization in emotion recognition (ER) is essential for an accurate interpretation of subtle and subject-specific expressive patterns. … (voir plus)Recent advances in vision-language models (VLMs) such as CLIP demonstrate strong potential for leveraging joint image-text representations in ER. However, CLIP-based methods either depend on CLIP's contrastive pretraining or on LLMs to generate descriptive text prompts, which are noisy, computationally expensive, and fail to capture fine-grained expressions, leading to degraded performance. In this work, we leverage Action Units (AUs) as structured textual prompts within CLIP to model fine-grained facial expressions. AUs encode the subtle muscle activations underlying expressions, providing localized and interpretable semantic cues for more robust ER. We introduce CLIP-AU, a lightweight AU-guided temporal learning method that integrates interpretable AU semantics into CLIP. It learns generic, subject-agnostic representations by aligning AU prompts with facial dynamics, enabling fine-grained ER without CLIP fine-tuning or LLM-generated text supervision. Although CLIP-AU models fine-grained AU semantics, it does not adapt to subject-specific variability in subtle expressions. To address this limitation, we propose CLIP-AUTT, a video-based test-time personalization method that dynamically adapts AU prompts to videos from unseen subjects. By combining entropy-guided temporal window selection with prompt tuning, CLIP-AUTT enables subject-specific adaptation while preserving temporal consistency. Our extensive experiments on three challenging video-based subtle ER datasets, BioVid, StressID, and BAH, indicate that CLIP-AU and CLIP-AUTT outperform state-of-the-art CLIP-based FER and TTA methods, achieving robust and personalized subtle ER. Our code is publicly available at: https://github.com/osamazeeshan/CLIP-AUTT.
2026-03-29
arXiv (prépublication)
doi.org
arxiv.org