Publications

Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies
Rashid A. Mushkani
Hugo Berard
Shin Koseki
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.
Sandrine Bédard
Jan Valosek
Maryam Seif
Armin Curt
Simon Schading-Sassenhausen
Nikolai Pfender
P. Freund
Markus Hupp
Julien Cohen-Adad
2025-02-28
The spine journal (publié)
doi.org
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Parham Saremi
Amar Kumar
Mohammed Mohammed
Zahra Tehrani Nasab
Tal Arbel
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Self-adaptive cyber defense for sustainable IoT: A DRL-based IDS optimizing security and energy efficiency
Saeid Jamshidi
Ashkan Amirnia
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Samira Keivanpour
2025-02-28
Journal of Network and Computer Applications (publié)
doi.org
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Shamsuddeen Hassan Muhammad
Nedjma OUSIDHOUM
Idris Abdulmumin
Seid Muhie Yimam
Jan Philip Wahle
Terry Lima Ruas
Meriem Beloucif
Christine de Kock
Tadesse Belay
Ibrahim Ahmad
Nirmal Surange
Daniela Teodorescu
David Ifeoluwa Adelani
Alham Fikri Aji
Felermino Ali
Vladimir Araujo
Abinew Ayele
Oana Ignat
Alexander Panchenko
Yi Zhou … (voir 1 de plus)
Saif M. Mohammad
2025-02-28
arXiv (publié)
doi.org
arxiv.org
A Taxonomy of Inefficiencies in LLM-Generated Python Code
Altaf Allah Abbassi
Leuson Da Silva
Amin Nikanjam
Foutse Khomh
Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed L… (voir plus)LM-generated code and identified various quality issues- such as redundancy, poor maintainability, and sub-optimal performance- a systematic understanding and categorization of these inefficiencies remain unexplored. Therefore, we empirically investigate inefficiencies in LLM-generated Python code by state-of-the-art models, i.e., CodeLlama, DeepSeek-Coder, and CodeGemma. To do so, we manually analyze 492 generated Python code snippets in the HumanEval+ dataset. We then construct a taxonomy of inefficiencies in LLM-generated Python code that includes 5 categories (General Logic, Performance, Readability, Maintainability, and Errors) and 19 subcategories of inefficiencies. We validate the obtained taxonomy through an online survey with 58 LLM practitioners and researchers. The surveyed participants affirmed the completeness of the proposed taxonomy, and the relevance and the popularity of the identified code inefficiency patterns. Our qualitative findings indicate that inefficiencies are diverse and interconnected, affecting multiple aspects of code quality, with logic and performance-related inefficiencies being the most frequent and often co-occurring while impacting overall code quality. Our taxonomy provides a structured basis for evaluating the quality of LLM-generated code and guiding future research to improve code generation efficiency.
2025-02-28
arXiv (publié)
doi.org
arxiv.org
A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions (preprint)
Dirk Douwes-Schultz
Alexandra M. Schmidt
Yannan Shen
David L Buckeridge
2025-02-28
The Annals of Applied Statistics (publié)
doi.org
arxiv.org
Tractable Representations for Convergent Approximation of Distributional HJB Equations
Julie Alhosh
Harley Wiltzer
David Meger
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Assessing the adoption of security policies by developers in terraform across different cloud providers
Alexandre Verdet
Mohammad Hamdaqa
Leuson Da Silva
Foutse Khomh
Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to manage and … (voir plus)configure cloud infrastructure using scripts. However, the scripting process does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks. As a result, ensuring security relies on practitioners’ understanding and the adoption of explicit policies. To understand how practitioners deal with this problem, we perform an empirical study analyzing the adoption of scripted security best practices present in Terraform files, applied on AWS, Azure, and Google Cloud. We assess the adoption of these practices by analyzing a sample of 812 open-source GitHub projects. We scan each project’s configuration files, looking for policy implementation through static analysis (Checkov and Tfsec). The category Access policy emerges as the most widely adopted in all providers, while Encryption at rest presents the most neglected policies. Regarding the cloud providers, we observe that AWS and Azure present similar behavior regarding attended and neglected policies. Finally, we provide guidelines for cloud practitioners to limit infrastructure vulnerability and discuss further aspects associated with policies that have yet to be extensively embraced within the industry.
2025-02-26
Empirical Software Engineering (publié)
doi.org
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan
Xiaojing Zhu
Cantay Caliskan
Dino P. Christenson
Konstantinos Spiliopoulos
Dylan Walker
Eric D. Kolaczyk
2025-02-26
Journal of Computational and Graphical Statistics (publié)
doi.org
arxiv.org
Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems
Vojko Pjanovic
Jacob Zavatone-Veth
Paul Masset
Sander Keemink
Michele Nardin
Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior e… (voir plus)ffectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions—including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples—derived from noisy inputs—with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity “bump” representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.
2025-02-25
bioRxiv (prépublication)
doi.org
Considerations and recommendations from the <scp>ISMRM</scp> diffusion study group for preclinical diffusion <scp>MRI</scp>: Part 1: In vivo small‐animal imaging
Ileana O. Jelescu
Francesco Grussu
Andrada Ianus
Brian Hansen
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler
Andreea Hertanu … (voir 37 de plus)
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Kurt G Schilling
2025-02-25
Magnetic Resonance in Medicine (publié)
doi.org