Publications

An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects

Kaveh Shahedi

Nana Gyambrah

Heng Li

Maxime Lamothe

Performance is a critical quality attribute in software development, yet the impact of method-level code changes on performance evolution re… (see more)mains poorly understood. While developers often make intuitive assumptions about which types of modifications are likely to cause performance regressions or improvements, these beliefs lack empirical validation at a fine-grained level. We conducted a large-scale empirical study analyzing performance evolution in 15 mature open-source Java projects hosted on GitHub. Our analysis encompassed 739 commits containing 1,499 method-level code changes, using Java Microbenchmark Harness (JMH) for precise performance measurement and rigorous statistical analysis to quantify both the significance and magnitude of performance variations. We employed bytecode instrumentation to capture method-specific execution metrics and systematically analyzed four key aspects: temporal performance patterns, code change type correlations, developer and complexity factors, and domain-size interactions. Our findings reveal that 32.7% of method-level changes result in measurable performance impacts, with regressions occurring 1.3 times more frequently than improvements. Contrary to conventional wisdom, we found no significant differences in performance impact distributions across code change categories, challenging risk-stratified development strategies. Algorithmic changes demonstrate the highest improvement potential but carry substantial regression risk. Senior developers produce more stable changes with fewer extreme variations, while code complexity correlates with increased regression likelihood. Domain-size interactions reveal significant patterns, with web server + small projects exhibiting the highest performance instability. Our study provides empirical evidence for integrating automated performance testing into continuous integration pipelines.

2025-08-01

arXiv (published)

doi.org

arxiv.org

From Technical Excellence to Practical Adoption: Lessons Learned Building an ML-Enhanced Trace Analysis Tool

Kaveh Shahedi

Matthew Khouzam

Heng Li

Maxime Lamothe

Foutse Khomh

2025-08-01

arXiv (published)

doi.org

arxiv.org

Low-dimensional embeddings of high-dimensional data

Cyril de Bodt

Alex Diaz-Papkovich

Michael Bleher

Kerstin Bunte

Corinna Coupette

Sebastian Damrich

Enrique Fita Sanmartin

Fred Hamprecht

Emőke-Ágnes Horvát

Dhruv Kohli

Smita Krishnaswamy

John A. Lee

Boudewijn P. F. Lelieveldt

Leland McInnes

Ian T. Nabney

Maximilian Noichl

Pavlin G. Poličar

Bastian Rieck

Guy Wolf

Gal Mishne … (see 1 more)

Dmitry Kobak

Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from b… (see more)iology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field.

2025-08-01

arXiv (published)

doi.org

Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification

Taha Mustapha Nehdi

Nairouz Mrabah

Atif Belal

Marco Pedersoli

Eric Granger

2025-08-01

arXiv (published)

doi.org

arxiv.org

MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training

Muhammad Osama Zeeshan

Natacha Gillet

Alessandro Lameiras Koerich

Marco Pedersoli

Francois Bremond

Eric Granger

2025-08-01

arXiv (published)

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.

2025-08-01

arXiv (published)

doi.org

arxiv.org

Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging

Zahra Tehrani Nasab

Amar Kumar

Tal Arbel

Advancements in diffusion-based foundation models have improved text-to-image generation, yet most efforts have been limited to low-resoluti… (see more)on settings. As high-resolution image synthesis becomes increasingly essential for various applications, particularly in medical imaging domains, fine-tuning emerges as a crucial mechanism for adapting these powerful pre-trained models to task-specific requirements and data distributions. In this work, we present a systematic study, examining the impact of various fine-tuning techniques on image generation quality when scaling to high resolution 512x512 pixels. We benchmark a diverse set of fine-tuning methods, including full fine-tuning strategies and parameter-efficient fine-tuning (PEFT). We dissect how different fine-tuning methods influence key quality metrics, including Fréchet Inception Distance (FID), Vendi score, and prompt-image alignment. We also evaluate the utility of generated images in a downstream classification task under data-scarce conditions, demonstrating that specific fine-tuning strategies improve both generation fidelity and downstream performance when synthetic images are used for classifier training and evaluation on real images. Our code is accessible through the project website - https://tehraninasab.github.io/PixelUPressure/.

2025-08-01

arXiv (published)

doi.org

Posttraumatic Growth in Intensive Care Unit Health Care Professionals After COVID-19

Elie Azoulay

Laurent Argaud

Vincent Labbé

Guillaume Dumas

Fabrice Bruneel

Mercé Jourdain

Christophe Guitton

Amélie Seguin

Samir Jaber

David Schnell

Isabelle Vinatier

Fanny Ardisson

Michel Ramakers

Antoine Herault

Olivier Lesieur

Alain Cariou

Antoine Vieillard-Baron

Olivier Guisset

Frédéric Pochard

Michael Darmon … (see 1 more)

Nancy Kentish-Barnes

2025-08-01

JAMA Network Open (published)

doi.org

Posttraumatic Growth in Intensive Care Unit Health Care Professionals After COVID-19

Elie Azoulay

Laurent Argaud

Vincent Labbé

Guillaume Dumas

Fabrice Bruneel

Mercé Jourdain

Christophe Guitton

Amélie Seguin

Samir Jaber

David Schnell

Isabelle Vinatier

Fanny Ardisson

Michel Ramakers

Antoine Herault

Olivier Lesieur

Alain Cariou

Antoine Vieillard-Baron

Olivier Guisset

Frédéric Pochard

Michael Darmon … (see 1 more)

Nancy Kentish-Barnes

This cross-sectional study investigates positive psychological changes after adversity, known as posttraumatic growth, in health care profes… (see more)sionals who worked in intensive care units (ICUs) during the COVID-19 pandemic in France and Belgium.

2025-08-01

JAMA Network Open (published)

doi.org

Posttraumatic Growth in Intensive Care Unit Health Care Professionals After COVID-19

Elie Azoulay

Laurent Argaud

Vincent Labbé

Guillaume Dumas

Fabrice Bruneel

Mercé Jourdain

Christophe Guitton

Amélie Seguin

Samir Jaber

David Schnell

Isabelle Vinatier

Fanny Ardisson

Michel Ramakers

Antoine Herault

Olivier Lesieur

Alain Cariou

Antoine Vieillard-Baron

Olivier Guisset

Frédéric Pochard

Michael Darmon … (see 1 more)

Nancy Kentish-Barnes

This cross-sectional study investigates positive psychological changes after adversity, known as posttraumatic growth, in health care profes… (see more)sionals who worked in intensive care units (ICUs) during the COVID-19 pandemic in France and Belgium.

2025-08-01

JAMA Network Open (published)

doi.org

Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments

Gian Mario Favero

Ge Ya Luo

Douglas Arnold

Image-based personalized medicine has the potential to transform healthcare, particularly for diseases that exhibit heterogeneous progressio… (see more)n such as Multiple Sclerosis (MS). In this work, we introduce the first treatment-aware spatio-temporal diffusion model that is able to generate future masks demonstrating lesion evolution in MS. Our voxel-space approach incorporates multi-modal patient data, including MRI and treatment information, to forecast new and enlarging T2 (NET2) lesion masks at a future time point. Extensive experiments on a multi-centre dataset of 2131 patient 3D MRIs from randomized clinical trials for relapsing-remitting MS demonstrate that our generative model is able to accurately predict NET2 lesion masks for patients across six different treatments. Moreover, we demonstrate our model has the potential for real-world clinical applications through downstream tasks such as future lesion count and location estimation, binary lesion activity classification, and generating counterfactual future NET2 masks for several treatments with different efficacies. This work highlights the potential of causal, image-based generative models as powerful tools for advancing data-driven prognostics in MS.

2025-08-01

arXiv (published)

doi.org

Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments

Gian Mario Favero

Ge Ya Luo

Douglas Arnold