Publications

Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model

Faraz Lotfi

Khalil Virji

Farnoosh Faraji

Lucas Berry

Andrew Holliday

David Meger

Gregory Dudek

In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning … (see more)(RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle's model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making. The code and the evaluation data are available at https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).

2024-05-12

2024 IEEE International Conference on Robotics and Automation (ICRA) (published)

doi.org

arxiv.org

Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology

Alexis Nolin-Lapalme, BSc

Denis Corbin, MSc

Olivier Tastet, MSc

Robert Avram, MD, MSc

Julie G. Hussin, PhD

2024-05-10

Canadian Journal of Cardiology (published)

doi.org

An AI-Resilient Text Rendering Technique for Reading and Skimming Documents

Ziwei Gu

Ian Arawjo

Kenneth Li

Jonathan K. Kummerfeld

Elena L. Glassman

2024-05-10

Proceedings of the CHI Conference on Human Factors in Computing Systems (published)

doi.org

arxiv.org

ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing

Ian Arawjo

Chelse Swoopes

Priyan Vaithilingam

Martin Wattenberg

Elena L. Glassman

Evaluating outputs of large language models (LLMs) is challenging, requiring making -- and making sense of -- many responses. Yet tools that… (see more) go beyond basic prompting tend to require knowledge of programming APIs, focus on narrow domains, or are closed-source. We present ChainForge, an open-source visual toolkit for prompt engineering and on-demand hypothesis testing of text generation LLMs. ChainForge provides a graphical interface for comparison of responses across models and prompt variations. Our system was designed to support three tasks: model selection, prompt template design, and hypothesis testing (e.g., auditing). We released ChainForge early in its development and iterated on its design with academics and online users. Through in-lab and interview studies, we find that a range of people could use ChainForge to investigate hypotheses that matter to them, including in real-world settings. We identify three modes of prompt engineering and LLM hypothesis testing: opportunistic exploration, limited evaluation, and iterative refinement.

2024-05-10

Proceedings of the CHI Conference on Human Factors in Computing Systems (published)

doi.org

arxiv.org

DirectGPT: A Direct Manipulation Interface to Interact with Large Language Models

Damien Masson

Sylvain Malacria

Géry Casiez

Daniel Vogel

2024-05-10

Proceedings of the CHI Conference on Human Factors in Computing Systems (published)

doi.org

arxiv.org

Schrödinger's Update: User Perceptions of Uncertainties in Proprietary Large Language Model Updates

Zilin Ma

Yiyang Mei

Krzysztof Z. Gajos

Ian Arawjo

2024-05-10

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (published)

doi.org

Calibration‐free parallel transmission of the cervical, thoracic, and lumbar spinal cord at <scp>7T</scp>

Christoph S. Aigner

Manuel F. Sánchez Alarcon

Alexandre D'Astous

Eva Alonso‐Ortiz

Julien Cohen‐Adad

Sebastian Schmitter

The development of universal shims represents a significant advance by eliminating time‐consuming subject‐specific pTx adjustments. This… (see more) approach is expected to make UHF spinal cord imaging more accessible and user‐friendly, particularly for non‐pTx experts.

2024-05-09

Magnetic Resonance in Medicine (published)

doi.org

Exploring the digital divide: results of a survey informing mobile application development

Maira Corinne Claudio

Zachary Rehany

Katerina Stachtari

Elena Guadagno

Esli Osmanlliu

Dan Poenaru

Introduction Mobile health apps risk widening health disparities if they overlook digital inclusion. The digital divide, encompassing access… (see more), familiarity, and readiness, poses a significant barrier to medical interventions. Existing literature lacks exploration of the digital divide's contributing factors. Hence, data are needed to comprehend the challenges in developing inclusive health apps. Methods We created a survey to gauge internet and smartphone access, smartphone familiarity, and readiness for using mobile health apps among caregivers of pediatric patients in tertiary care. Open-ended questions solicited feedback and suggestions on mobile health applications. Responses were categorized by similarity and compared. Developed with patient partners, the survey underwent cognitive testing and piloting for accuracy. Results Data from 209 respondents showed that 23% were affected by the digital divide, mainly due to unfamiliarity with digital skills. Among 49 short text responses about health app concerns, 31 mentioned security and confidentiality, with 7 mentioning the impersonal nature of such apps. Desired features included messaging healthcare providers, scheduling, task reminders, and simplicity. Conclusions This study underscores a digital divide among caregivers of pediatric patients, with nearly a quarter affected primarily due to a lack of digital comfort. Respondents emphasized user-friendliness and online security for health apps. Future apps should prioritize digital inclusion by addressing the significant barriers and carefully considering patient and family concerns.

2024-05-09

Frontiers Digit. Health (published)

doi.org

G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks

Zhaoyu Li

Jinpei Guo

Xujie Si

2024-05-09

TMLR (accepted)

doi.org

openreview.net

Repeat it without me: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge

Mathieu Boudreau

Agah Karakuzu

Julien Cohen‐Adad

Ecem Bozkurt

Madeline Carr

Marco Castellaro

Luis Concha

Mariya Doneva

Seraina A. Dual

Alex Ensworth

Alexandru Foias

Véronique Fortier

Refaat E. Gabr

Guillaume Gilbert

Carri K. Glide‐Hurst

Matthew Grech‐Sollars

Siyuan Hu

Oscar Jalnefjord

Jorge Jovicich

Kübra Keskin … (see 22 more)

Peter Koken

Anastasia Kolokotronis

Simran Kukran

Nam G. Lee

Ives R. Levesque

Bochao Li

Dan Ma

Burkhard Mädler

Nyasha G. Maforo

Jamie Near

Erick Pasaye

Alonso Ramirez‐Manzanares

Ben Statton

Christian Stehning

Stefano Tambalo

Ye Tian

Chenyang Wang

Kilian Weiss

Niloufar Zakariaei

Shuo Zhang

Ziwei Zhao

Nikola Stikov

Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). … (see more)The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org.The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

2024-05-09

Magnetic Resonance in Medicine (published)

doi.org

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems

David Dalrymple

David

Joar Max Viktor Skalse

Yoshua Bengio

Stuart Russell

Max Tegmark

Sanjit A. Seshia

Steve Omohundro

Christian Szegedy

Ben Goldhaber

Nora Ammann

Alessandro Abate

Joe Halpern

Clark Barrett

Ding Zhao

Zhi-Xuan Tan

Jeannette Wing

Joshua B. Tenenbaum

Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with … (see more)a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.

2024-05-09

ArXiv (preprint)

doi.org

arxiv.org

Interacting with a Visuotactile Countertop

M. Jenkin

Francois Hogan

Kaleem Siddiqi

Jean-François Tremblay

Bobak H. Baghi

Gregory Dudek

2024-05-07