Publications

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

David Bergeron

Christian Iorio-Morin

Marco Bonizzato

Guillaume Lajoie

Nathalie Orr Gaucher

Éric Racine

Alexander G. Weil

2023-04-27

Journal of Child Neurology (published)

doi.org

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Gabriel Tseng

Ivan Zvonkov

Ruben Cartuyvels

Mirali Purohit

David Rolnick

Hannah Kerner

Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficu… (see more)lt or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data, including the temporal dimension (which is critical for many applications, such as monitoring crop growth) and availability of data from many complementary sensors (which can significantly improve a model's predictive performance). We present Presto (the Pretrained Remote Sensing Transformer), a model pre-trained on remote sensing pixel-timeseries data. By designing Presto specifically for remote sensing data, we can create a significantly smaller but performant model. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.

2023-04-26

ArXiv (preprint)

doi.org

openreview.net

The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement

Seyedmostafa Mousavi

Dana Tannenbaum Greenberg

Ruth Ndjaboué

Michelle Greiver

Olivia Drescher

Selma Chipenda Dansokho

Denis Boutin

Jean-Marc Chouinard

Sylvie Dostie

Robert Fenton

Marley Greenberg

Jonathan McGavock

Adhiyat Najam

Monia Rekik

Tom Weisz

Donald J Willison

Audrey Durand

Holly O Witteman

2023-04-26

JMIR Diabetes (published)

doi.org

Controllable Image Generation via Collage Representations

Arantxa Casanova

Marlene Careil

Adriana Romero

Christopher Pal

Jakob Verbeek

Michal Drozdzal

2023-04-25

ArXiv (preprint)

doi.org

openreview.net

Towards ethical multimodal systems

Alexis Roger

Esma Aimeur

Irina Rish

2023-04-25

ArXiv (preprint)

doi.org

arxiv.org

Tri-process model of interpersonal mindfulness: theoretical framework and study protocol

Bassam Khoury

Viktoriya Manova

Lena Adel

Guillaume Dumas

Michael Lifshitz

Rodrigo C. Vergara

Harmehr Sekhon

Soham Rej

According to the Center for Disease Control and Prevention, over 14% of the US population practice mindfulness meditation. The effects of mi… (see more)ndfulness training on physical and mental health have been consistently documented, but its effects on interpersonal relationships are not yet fully understood or investigated. Interpersonal relationships play a crucial role in the wellbeing of individuals and society, and therefore, warrants further study. The aim of this paper is to present a tri-process theoretical model of interpersonal mindfulness and a study protocol to validate the proposed model. Specifically, according to the proposed model, mindfulness meditation training increases the self-awareness, self-regulation, and prosociality of those receiving the training, which ameliorates the quality of interpersonal interactions and the socioemotional support provided to other individuals. Finally, better socioemotional support increases the support receiver’s ability to regulate their emotions. Using a multiphasic longitudinal design involving 640 participants randomized into 480 dyads, the proposed protocol aims to validate the tri-process model and to investigate its mechanisms of actions. The proposed study has important theoretical and social implications and will allow devising new and more effective interpersonal mindfulness programs with applications in multiple fields.

2023-04-25

Frontiers in Psychology (published)

doi.org

Understanding the normative leadership of the world health organization (who): a mixed-method approach

Catherine Régis

Miriam Cohen

Jean-Louis Denis

Pierre Larouche

Gaëlle Foucault

Marie-Andree Girard

2023-04-25

Population Medicine (published)

doi.org

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Junde Wu

Rao Fu

Huihui Fang

Yuanpei Liu

Zhao-Yang Wang

Yanwu Xu

Yueming Jin

Tal Arbel

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in var… (see more)ious segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2\% of the parameters. Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.

2023-04-24

ArXiv (preprint)

doi.org

arxiv.org

Performative Prediction with Neural Networks

Mehrnaz Mofakhami

Ioannis Mitliagkas

Gauthier Gidel

Performative prediction is a framework for learning models that influence the data they intend to predict. We focus on finding classifiers t… (see more)hat are performatively stable, i.e. optimal for the data distribution they induce. Standard convergence results for finding a performatively stable classifier with the method of repeated risk minimization assume that the data distribution is Lipschitz continuous to the model's parameters. Under this assumption, the loss must be strongly convex and smooth in these parameters; otherwise, the method will diverge for some problems. In this work, we instead assume that the data distribution is Lipschitz continuous with respect to the model's predictions, a more natural assumption for performative systems. As a result, we are able to significantly relax the assumptions on the loss function. In particular, we do not need to assume convexity with respect to the model's parameters. As an illustration, we introduce a resampling procedure that models realistic distribution shifts and show that it satisfies our assumptions. We support our theory by showing that one can learn performatively stable classifiers with neural networks making predictions about real data that shift according to our proposed procedure.

2023-04-24

International Conference on Artificial Intelligence and Statistics (Accept (Poster))

doi.org

proceedings.mlr.press

Ranking code clones to support maintenance activities

Osama Ehsan

Foutse Khomh

Ying Zou

Dong Qiu

2023-04-24

Empirical Software Engineering (published)

doi.org