Publications

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

Kaveh Shahedi

Matthew Khouzam

Heng Li

Maxime Lamothe

System tracing has become essential for understanding complex software behavior in modern systems, yet sophisticated trace analysis tools fa… (voir plus)ce significant adoption gaps in industrial settings. Through a year-long collaboration with Ericsson Montr\'eal, developing TMLL (Trace-Server Machine Learning Library, now in the Eclipse Foundation), we investigated barriers to trace analysis adoption. Contrary to assumptions about complexity or automation needs, practitioners struggled with translating expert knowledge into actionable insights, integrating analysis into their workflows, and trusting automated results they could not validate. We identified what we called the Excellence Paradox: technical excellence can actively impede adoption when conflicting with usability, transparency, and practitioner trust. TMLL addresses this through adoption-focused design that embeds expert knowledge in interfaces, provides transparent explanations, and enables incremental adoption. Validation through Ericsson's experts'feedback, Eclipse Foundation's integration, and a survey of 40 industry and academic professionals revealed consistent patterns: survey results showed that 77.5% prioritize quality and trust in results over technical sophistication, while 67.5% prefer semi-automated analysis with user control, findings supported by qualitative feedback from industrial collaboration and external peer review. Results validate three core principles: cognitive compatibility, embedded expertise, and transparency-based trust. This challenges conventional capability-focused tool development, demonstrating that sustainable adoption requires reorientation toward adoption-focused design with actionable implications for automated software engineering tools.

2025-08-02

ArXiv (prépublication)

arxiv.org

Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models

Darcy Elizabeth Wagner

Hani N. Alsafadi

Nilay Mitash

Aurelien Justet

Qianjiang Hu

Ricardo Pineda

Claudia Staab-Weijnitz

Martina Korfei

Nika Gvazava

Kristin Wannemo

Ugochi Onwuka

Molly Mozurak

Adriana Estrada-Bernal

Juan Cala Garcia

Katrin Mutze

Rita Costa

Deniz Bölükbas

John Stegmayr

Wioletta Skronska-Wasek

Stephan Klee … (voir 14 de plus)

Chiharu Ota

Hoeke A. Baarsma

Jingtao Wang

John Sembrat

Anne Hilgendorff

Jun Ding

Andreas Günther

Rachel Chambers

Ivan O Rosas

Stijn de Langhe

Naftali Kaminski

Mareike Lehmann

Oliver Eickelberg

Melanie Königshoff

Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (voir plus)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.

2025-08-02

Nature Communications (publié)

doi.org

Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs

William St-Arnaud

Margarida Carvalho

Golnoosh Farnadi

In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (voir plus)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.

2025-08-01

European Journal of Operational Research (publié)

doi.org

arxiv.org

Audio Prototypical Network for Controllable Music Recommendation

Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While thes… (voir plus)e models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system’s modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, tempo, or instrumentation. In this paper, we propose an audio prototypical network for controllable music recommendation. This network expresses user preferences in terms of prototypes representative of semantically meaningful features pertaining to musical qualities. We show that the model obtains competitive recommendation performance compared to popular baseline models while also providing interpretable and controllable user profiles.

2025-08-01

arXiv (publié)

doi.org

openreview.net

CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations

Hager Radi

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 res… (voir plus)earch 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.

2025-08-01

arXiv (publié)

doi.org

arxiv.org

Co-Producing AI: Toward an Augmented, Participatory Lifecycle

Rashid A. Mushkani

Hugo Berard

Toumadher Ammar

Cassandre Chatonnier

Shin (Alexandre) Koseki

Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionatel… (voir plus)y impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.

2025-08-01

arXiv (publié)

doi.org

arxiv.org

Efficient Deep Reinforcement Learning-Based Supplementary Damping Control With a Coordinated RMS Training and EMT Testing Scheme

Tao Xue

Mingxuan Zhao

Ilhan Kocar

Mohsen Ghafouri

Antoine Lesage-Landry

Siqi Bu

Ziqing Zhu

Inverter-based resources (IBRs) can cause instability in weak AC grids. While supplementary damping controllers (SDCs) effectively mitigate … (voir plus)this instability, they are typically designed for specific resonance frequencies but struggle with large shifts caused by changing grid conditions. This paper proposes a deep reinforcement learning-based agent (DRL Agent) as an adaptive SDC to handle shifted resonance frequencies. To address the time-consuming nature of training DRL Agents in electromagnetic transient (EMT) simulations, we coordinate fast root mean square (RMS) and EMT simulations. Resonance frequencies of the weak grid instability are accurately reproduced by RMS simulations to support the training process. The DRL Agent’s efficacy is tested in unseen scenarios outside the training dataset. We then iteratively improve the DRL Agent’s performance by modifying the reward function and hyper-parameters.

2025-08-01

IEEE Transactions on Power Delivery (publié)

doi.org

Efficient Deep Reinforcement Learning-Based Supplementary Damping Control With a Coordinated RMS Training and EMT Testing Scheme

Tao Xue

Mingxuan Zhao

Ilhan Kocar

Mohsen Ghafouri

Antoine Lesage-Landry

Siqi Bu

Ziqing Zhu

Inverter-based resources (IBRs) can cause instability in weak AC grids. While supplementary damping controllers (SDCs) effectively mitigate … (voir plus)this instability, they are typically designed for specific resonance frequencies but struggle with large shifts caused by changing grid conditions. This paper proposes a deep reinforcement learning-based agent (DRL Agent) as an adaptive SDC to handle shifted resonance frequencies. To address the time-consuming nature of training DRL Agents in electromagnetic transient (EMT) simulations, we coordinate fast root mean square (RMS) and EMT simulations. Resonance frequencies of the weak grid instability are accurately reproduced by RMS simulations to support the training process. The DRL Agent’s efficacy is tested in unseen scenarios outside the training dataset. We then iteratively improve the DRL Agent’s performance by modifying the reward function and hyper-parameters.

2025-08-01

IEEE Transactions on Power Delivery (publié)

doi.org

Efficient Deep Reinforcement Learning-Based Supplementary Damping Control with a Coordinated RMS Training and EMT Testing Scheme

Tao Xue

Mingxuan Zhao

Ilhan Kocar

Mohsen Ghafouri

Antoine Lesage-Landry

Siqi Bu

Ziqing Zhu

2025-08-01

IEEE Transactions on Power Delivery (publié)

doi.org

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

Kaveh Shahedi

Nana Gyambrah

Heng Li

Maxime Lamothe

Foutse Khomh

Performance is a critical quality attribute in software development, yet the impact of method-level code changes on performance evolution re… (voir plus)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 (publié)

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 (publié)