Publications

Combining virtual reality and hypnosis to alleviate chronic pain in elderly with hand arthritis: protocol for a randomised phase II clinical trial

Valentyn Fournier

Marie-Fania Simard

Sai Yan Yuen

Joséphine Guiné

Floriane Rousseaux

Julie Lebeau

Karim Jerbi

Philippe Richebé

Mathieu Landry

Pierre Rainville

David Ogez

Chronic pain is a common health condition that significantly impacts the quality of life of those affected, affecting one in five people in … (voir plus)Canada. The prevalence of this condition tends to increase with age, making it a major health issue given the ageing population. However, its management remains inadequate and requires significant mobilisation of healthcare professionals as well as the development of multiple therapeutic solutions. Among these, non-pharmacological interventions such as hypnosis and virtual reality have proven effective. Nevertheless, while the existing literature seems promising, it presents methodological limitations. Therefore, this study aims to assess the effectiveness of an intervention combining virtual reality and hypnosis in an ageing population suffering from a widespread chronic pain condition, that is, hand arthritis. This study will be a single-centre randomised clinical trial. Participants will be randomly assigned to one of two conditions: one receiving an intervention combining virtual reality and hypnosis, and the other receiving only virtual reality. The effectiveness of the intervention on current perceived pain before and after the intervention (primary outcome) will be evaluated. Secondary outcomes will include anxiety and depressive symptoms, quality of life, relaxation and fatigue. Exploratory analyses will also be conducted to contribute to the emerging literature by examining physiological variables such as heart rate variability, respiratory rate and electrodermal activity during the intervention, and their relationship with primary and secondary outcomes. The project was approved by the Research Ethical Committee of the Hospital Maisonneuve-Rosemont (Project no 2024-3539). Participants will be asked to provide written consent for their participation. Results from this study will be shared through peer-reviewed publications, as well as oral and poster presentations at scientific events. The protocol for this study was preregistered on Open Science Framework and raw anonymised data will be available on this platform ( https://osf.io/vbh72/?view_only=1d17c5708f894faab6669d85e1fde75d ). NCT06833905 .

2025-09-30

BMJ Open (publié)

doi.org

Current landscape of clinical genetics knowledge and attitudes among Non-Geneticist Physicians - the McGill genetics education survey (McGES).

Sarah Abdullah-Maklan

Yannis Trakadis

2025-09-30

Journal of Community Genetics (publié)

doi.org

Egalitarian Gradient Descent: A Simple Approach to Accelerated Grokking

Ali Saheb Pasand

Elvis Dopgima Dohmatob

Grokking is the phenomenon whereby, unlike the training performance, which peaks early in the training process, the test/generalization perf… (voir plus)ormance of a model stagnates over arbitrarily many epochs and then suddenly jumps to usually close to perfect levels. In practice, it is desirable to reduce the length of such plateaus, that is to make the learning process"grok"faster. In this work, we provide new insights into grokking. First, we show both empirically and theoretically that grokking can be induced by asymmetric speeds of (stochastic) gradient descent, along different principal (i.e singular directions) of the gradients. We then propose a simple modification that normalizes the gradients so that dynamics along all the principal directions evolves at exactly the same speed. Then, we establish that this modified method, which we call egalitarian gradient descent (EGD) and can be seen as a carefully modified form of natural gradient descent, groks much faster. In fact, in some cases the stagnation is completely removed. Finally, we empirically show that on classical arithmetic problems such as modular addition and sparse parity problem which this stagnation has been widely observed and intensively studied, that our proposed method eliminates the plateaus.

2025-09-30

arXiv (publié)

doi.org

arxiv.org

Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets

David R. Johnson

Rishabh Anand

Smita Krishnaswamy

Michael Perlmutter

2025-09-30

ArXiv (prépublication)

doi.org

arxiv.org

Gaussian Embeddings: How JEPAs Secretly Learn Your Data Density

Randall Balestriero

Nicolas Ballas

Michael G. Rabbat

Yann Lecun

Joint Embedding Predictive Architectures (JEPAs) learn representations able to solve numerous downstream tasks out-of-the-box. JEPAs combine… (voir plus) two objectives: (i) a latent-space prediction term, i.e., the representation of a slightly perturbed sample must be predictable from the original sample's representation, and (ii) an anti-collapse term, i.e., not all samples should have the same representation. While (ii) is often considered as an obvious remedy to representation collapse, we uncover that JEPAs' anti-collapse term does much more--it provably estimates the data density. In short, any successfully trained JEPA can be used to get sample probabilities, e.g., for data curation, outlier detection, or simply for density estimation. Our theoretical finding is agnostic of the dataset and architecture used--in any case one can compute the learned probabilities of sample

2025-09-30

arXiv (publié)

doi.org

arxiv.org

Improving GUI Grounding with Explicit Position-to-Coordinate Mapping

Christopher Pal

2025-09-30

arXiv (publié)

doi.org

arxiv.org

Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?

Kevin Kasa

Graham W. Taylor

Krishnamurthy Dj Dvijotham

Alexandre Lacoste

AI agents are vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external content or tool outputs cau… (voir plus)se unintended or harmful behavior. Inspired by the well-established concept of firewalls, we show that a simple, modular and model-agnostic defense operating at the agent--tool interface achieves perfect security (0% or the lowest possible attack success rate) with high utility (task success rate) across four public benchmarks: AgentDojo, Agent Security Bench, InjecAgent and tau-Bench, while achieving a state-of-the-art security-utility tradeoff compared to prior results. Specifically, we employ a defense based on two firewalls: a Tool-Input Firewall (Minimizer) and a Tool-Output Firewall (Sanitizer). Unlike prior complex approaches, this firewall defense makes minimal assumptions on the agent and can be deployed out-of-the-box, while maintaining strong performance without compromising utility. However, our analysis also reveals critical limitations in these existing benchmarks, including flawed success metrics, implementation bugs, and most importantly, weak attacks, hindering significant progress in the field. To foster more meaningful progress, we present targeted fixes to these issues for AgentDojo and Agent Security Bench while proposing best-practices for more robust benchmark design. Further, we demonstrate that although these firewalls push the state-of-the-art on existing benchmarks, it is still possible to bypass them in practice, underscoring the need to incorporate stronger attacks in security benchmarks. Overall, our work shows that existing agentic security benchmarks are easily saturated by a simple approach and highlights the need for stronger agentic security benchmarks with carefully chosen evaluation metrics and strong adaptive attacks.

2025-09-30

arXiv (publié)

doi.org

arxiv.org

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid Mushkani

Shin Koseki

2025-09-30

Habitat International (publié)

doi.org

Just-in-time Episodic Feedback Hinter: Leveraging Offline Knowledge to Improve LLM Agents Adaptation

Hadi Nekoei