Publications

Simultaneous detection and estimation in olfactory sensing
Chen Jiang
Matthew Y. He
Venkatesh N. Murthy
Cengiz Pehlevan
Jacob A. Zavatone-Veth
Paul Masset
The mammalian olfactory system shows an exceptional ability for rapid and accurate decoding of both the identity and concentration of odoran… (voir plus)ts. Previous works have used the theory of compressed sensing to elucidate the algorithmic basis for this capability: decoding odor information from the responses of a restricted repertoire of receptors is possible because only a few relevant odorants are present in any given sensory scene. However, existing circuit models for olfactory decoding still cannot contend with the complexity of naturalistic olfactory scenes; they are limited to detection of a handful of odorants. Here, we propose a model for olfactory compressed sensing inspired by simultaneous localization and mapping algorithms in navigation: the set of odors that are present in a given scene, and the concentration of those present odors, are inferred separately. To enable rapid inference of odor presence in a biologically-plausible recurrent circuit, our model leverages the framework of Mirrored Langevin Dynamics, which gives a general recipe for sampling from constrained distributions using rate-based dynamics. This results in a recurrent circuit model that can accurately infer presence and concentration at scale and can be mapped onto the primary cell types of the olfactory bulb. This frame-work offers a path towards circuit models—for olfactory sensing and beyond—that both perform well in naturalistic environments and make experimentally-testable predictions for neural response dynamics.
2025-11-02
bioRxiv (prépublication)
doi.org
Survey on <scp>AI</scp> Ethics: A Socio‐Technical Perspective
Dave Mbiazi
Meghana Bhange
Maryam Babaei
Ivaxi Sheth
Patrik Joslin Kenfack
Samira Ebrahimi Kahou
Abstract The past decade has observed a significant advancement in AI, with deep learning‐based models being deployed in diverse scenarios… (voir plus), including safety‐critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x‐risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.
2025-11-01
Computational Intelligence (publié)
doi.org
arxiv.org
Characterization of astrocytic primary cilia in the adult mouse cortex and hippocampus
Sylvie C. Lahaie
Albert HK. Fok
Jessica M. Nicholls
Hannah Lee
Tabish A. Syed
Sabrina Chierzi
Sayuri Hatada
Naomi Egawa
Alex L. Schober
Tak Yi Mayumi Wong
Robert Royston
Kaleem Siddiqi
Yoshiyuki Kubota
Keith K. Murai
2025-10-31
Heliyon (publié)
doi.org
Combining Virtual Reality and Hypnosis? A User Experience Study in Patients with Multiple Myeloma Following Stem Cell Transplantation
Jade Véronneau
Alexandra Chevestrier-Lefeuvre
Valentyn Fournier
Audrey Laurin
Rémi Caron-Trahan
Mathieu Landry
Joséphine Guiné
Odile Dubey-Harispe
Nadia Godin
Idrissi Moulay
Danny Wade
Sandie Oberoi
Caroline Arbour
Philippe Richebé
Karim Jerbi
Pierre Rainville
Richard LeBlanc
Floriane Rousseaux
David Ogez
Multiple myeloma (MM) and stem cell transplantation (SCT) significantly impact patients’ quality of life. Virtual reality with hypnosis (V… (voir plus)RH) has emerged as a promising nonpharmacological intervention to address these challenges, yet data on its acceptability and user experience remain scarce. This study assessed the acceptability and user experience of a VRH intervention among adult patients with MM who had undergone allogeneic SCT. Participants used a VRH application and rated their experience through standardized questionnaires and semistructured interviews. Quantitative data were analyzed descriptively, and qualitative data underwent descriptive content analysis. Findings indicated high patients’ satisfaction, strong perceived relevance, and low cybersickness. Qualitative analysis revealed perceived emotional and psychological benefits. VRH was deemed particularly suitable during hospitalization and treatment periods. This study shows the potential of combining virtual reality and hypnosis for MM patients following SCT. Indeed, they showed high satisfaction levels, paving the way for further studies evaluating the clinical efficacy of such interventions.
2025-10-31
Journal of Patient Experience (publié)
doi.org
Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs
Mina Taraghi
Yann Batiste Pequignot
Amin Nikanjam
Mohamed Amine Merzouk
Foutse Khomh
Organizations are increasingly adopting and adapting Large Language Models (LLMs) hosted on public repositories such as HuggingFace. Althoug… (voir plus)h these adaptations often improve performance on specialized downstream tasks, recent evidence indicates that they can also degrade a model's safety or fairness. Since different fine-tuning techniques may exert distinct effects on these critical dimensions, this study undertakes a systematic assessment of their trade-offs. Four widely used Parameter-Efficient Fine-Tuning methods, LoRA, IA3, Prompt-Tuning, and P-Tuning, are applied to four instruction-tuned model families (Meta-Llama-3-8B, Qwen2.5-7B, Mistral-7B, and Gemma-7B). In total, 235 fine-tuned variants are evaluated across eleven safety hazard categories and nine demographic fairness dimensions. The results show that adapter-based approaches (LoRA, IA3) tend to improve safety scores and are the least disruptive to fairness, retaining higher accuracy and lower bias scores. In contrast, prompt-based methods (Prompt-Tuning and P-Tuning) generally reduce safety and cause larger fairness regressions, with decreased accuracy and increased bias. Alignment shifts are strongly moderated by base model type: LLaMA remains stable, Qwen records modest gains, Gemma experiences the steepest safety decline, and Mistral, which is released without an internal moderation layer, displays the greatest variance. Improvements in safety do not necessarily translate into improvements in fairness, and no single configuration optimizes all fairness metrics simultaneously, indicating an inherent trade-off between these objectives. These findings suggest a practical guideline for safety-critical deployments: begin with a well-aligned base model, favour adapter-based PEFT, and conduct category-specific audits of both safety and fairness.
2025-10-31
ArXiv (prépublication)
doi.org
arxiv.org
Evaluating WMT 2025 Metrics Shared Task Submissions on the SSA-MTE African Challenge Set
Senyu Li
Felermino Dario Mario Ali
Jiayi Wang
Rui Sousa-Silva
Henrique Lopes Cardoso
Pontus Stenetorp
Colin Cherry
David Ifeoluwa Adelani
2025-10-31
Proceedings of the Tenth Conference on Machine Translation (publié)
doi.org
Findings of the WMT25 Shared Task on Automated Translation Evaluation Systems: Linguistic Diversity is Challenging and References Still Help
Alon Lavie
Greg Hanneman
Sweta Agrawal
Diptesh Kanojia
Chi-kiu Lo
Vilém Zouhar
Frédéric Blain
Chrysoula Zerva
Eleftherios Avramidis
Sourabh Dattatray Deoghare
Archchana Sindhujan
Jiayi Wang
David Ifeoluwa Adelani
Brian Thompson
Tom Kocmi
Markus Freitag
Daniel Deutsch
2025-10-31
Proceedings of the Tenth Conference on Machine Translation (publié)
doi.org
Intersectionality in Surgical Care in LMICs: A Systematic Scoping Review
Ayla Gerk
Shreenik Kundu
Elena Guadagno
Justina Seyi-Olajide
Dunya Moghul
Joaquim Bustorff-Silva
Cristina Camargo
Dan Poenaru
2025-10-31
American Journal of Surgery (publié)
doi.org
Simulate intelligently: Causal incremental reinforcement learning for streamlined industrial chemical process design optimization
Eslam G. Al-Sakkari
Ahmed Ragab
Mohamed Ali
Hanane Dagdougui
Daria C. Boffito
2025-10-31
Journal of Environmental Chemical Engineering (publié)
doi.org
The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities
Saeid Jamshidi
Negar Shahabi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Carol Fung
2025-10-31
Internet of Things (publié)
doi.org
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation
Rabiul Awal
Mahsa Massoud
Aarash Feizi
Zichao Li
Suyuchen Wang
Christopher Pal
Aishwarya Agrawal
David Vázquez
Siva Reddy
Juan A. Rodriguez
Perouz Taslakian
Spandana Gella
Sai Rajeswar
We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing inv… (voir plus)olving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models'abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.
2025-10-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
openreview.net
Discovery of Sustainable Refrigerants through Physics-Informed RL Fine-Tuning of Sequence Models
Adrien Goldszal
Diego Calanzone
Vincent Taboga
Pierre-Luc Bacon
Most refrigerants currently used in air-conditioning systems, such as hydrofluorocarbons, are potent greenhouse gases and are being phased d… (voir plus)own. Large-scale molecular screening has been applied to the search for alternatives, but in practice only about 300 refrigerants are known, and only a few additional candidates have been suggested without experimental validation. This scarcity of reliable data limits the effectiveness of purely data-driven methods. We present Refgen, a generative pipeline that integrates machine learning with physics-grounded inductive biases. Alongside fine-tuning for valid molecular generation, Refgen incorporates predictive models for critical properties, equations of state, thermochemical polynomials, and full vapor compression cycle simulations. These models enable reinforcement learning fine-tuning under thermodynamic constraints, enforcing consistency and guiding discovery toward molecules that balance efficiency, safety, and environmental impact. By embedding physics into the learning process, Refgen leverages scarce data effectively and enables de novo refrigerant discovery beyond the known set of compounds.
2025-10-30
SIMBIOCHEM @ Neural Information Processing Systems (publié)
doi.org
openreview.net