Publications

Hybrid Scattering Transform - Long Short-Term Memory Networks for Intrapartum Fetal Heart Rate Classification
"Derek Kweku DEGBEDZUI
Michael W Kuzniewicz
Marie-Coralie Cornet
Yvonne Wu
Heather Forquer
Lawrence Gerstley
Emily F. Hamilton
Doina Precup
P. Warrick
Robert E. Kearney
This study assessed the early detection of the increased risk of hypoxic ischemic encephalopathy using raw fetal heart rate and its transfor… (see more)mation with scattering transform and a long short-term memory recurrent neural network. There was no significant difference between the two approaches. However, the use of scattering transform produced lower computational demands. Considering scalability to the large data in our database and computational efficiency, the experiments involving scattering transform coefficients will be selected to conduct subsequent experiments. Future works will address the limitations of this study, including the low model performance.
2023-10-01
2023 Computing in Cardiology (CinC) (published)
doi.org
L'éthique au cœur de l'IA
Lyse Langlois
Marc-Antoine Dilhac
Jim Dratwa
Thierry Ménissier
Jean-gabriel Ganascia
Daniel Weinstock
L. Bégin
Allison Marchildon
Issu d’un travail collaboratif regroupant des spécialistes de l’éthique, de la philosophie, de l’informatique et de l’économie, l… (see more)e rapport « L’éthique au cœur de l’IA » vise à préciser et clarifier le rôle que doit occuper l’éthique à l’ère de l’intelligence artificielle (IA), et à mettre en lumière comment cette notion peut être appliquée et mise en œuvre de manière efficace et fructueuse. S’adressant à l’ensemble des individus engagés, de près ou de loin, dans le développement de l’IA, ce document met de l’avant une éthique centrée sur la réflexivité et le dialogue. Dans une volonté de traduire plus concrètement cette vision, il met en lumière l’approche méthodologique utilisée pour construire la Déclaration de Montréal et propose également quelques pistes de recommandation. En somme, le présent texte plaide pour l’inclusion d’une réelle réflexion éthique dans l’ensemble des étapes du processus de développement de l’IA. Il se veut ainsi une main tendue, un appel à la collaboration entre éthiciennes et éthiciens, développeuses et développeurs et membres de l’industrie afin de véritablement intégrer l’éthique au cœur de l’IA.
2023-10-01
(published)
doi.org
One-4-All: Neural Potential Fields for Embodied Navigation
Sacha Morin
Miguel Saavedra-Ruiz
Liam Paull
A fundamental task in robotics is to navigate between two locations. In particular, real-world navigation can require long-horizon planning … (see more)using high-dimensional RGB images, which poses a substantial challenge for end-to-end learning-based approaches. Current semi-parametric methods instead achieve long-horizon navigation by combining learned modules with a topological memory of the environment, often represented as a graph over previously collected images. However, using these graphs in practice requires tuning a number of pruning heuristics. These heuristics are necessary to avoid spurious edges, limit runtime memory usage and maintain reasonably fast graph queries in large environments. In this work, we present One-4-All (O4A), a method leveraging self-supervised and manifold learning to obtain a graph-free, end-to-end navigation pipeline in which the goal is specified as an image. Navigation is achieved by greedily minimizing a potential function defined continuously over image embeddings. Our system is trained offline on non-expert exploration sequences of RGB data and controls, and does not require any depth or pose measurements. We show that 04A can reach long-range goals in 8 simulated Gibson indoor environments and that resulting embeddings are topologically similar to ground truth maps, even if no pose is observed. We further demonstrate successful real-world navigation using a Jackal UGV platform.aaProject page https://montrealrobotics.ca/o4a/.
2023-10-01
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)
doi.org
arxiv.org
PRACTICAL APPLICATIONS OF MEDICAL GENETICS AND GENOMICS FOR PSYCHIATRISTS
Yannis Trakadis
2023-10-01
European Neuropsychopharmacology (published)
doi.org
Robustness assessment of hyperspectral image CNNs using metamorphic testing
Rached Bouchoucha
Houssem Ben Braiek
Foutse Khomh
Sonia Bouzidi
Rania Zaatour
2023-10-01
Information and Software Technology (published)
doi.org
Torque-Based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer
Donghyeon Kim
Glen Berseth
Mathew Schwartz
Jaeheung Park
In this letter, we review the question of which action space is best suited for controlling a real biped robot in combination with Sim2Real … (see more)training. Position control has been popular as it has been shown to be more sample efficient and intuitive to combine with other planning algorithms. However, for position control, gain tuning is required to achieve the best possible policy performance. We show that, instead, using a torque-based action space enables task-and-robot agnostic learning with less parameter tuning and mitigates the sim-to-reality gap by taking advantage of torque control's inherent compliance. Also, we accelerate the torque-based-policy training process by pre-training the policy to remain upright by compensating for gravity. The letter showcases the first successful sim-to-real transfer of a torque-based deep reinforcement learning policy on a real human-sized biped robot.
2023-10-01
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
Towards More General Loss and Setting in Unsupervised Domain Adaptation
Changjian Shui
Ruizhi Pu
Gezheng Xu
Jun Wen
Fan Zhou
Christian Gagné
Charles Ling
Boyu Wang
In this article, we present an analysis of unsupervised domain adaptation with a series of theoretical and algorithmic results. We derive a … (see more)novel Rényi-
2023-10-01
IEEE Transactions on Knowledge and Data Engineering (published)
doi.org
W56. UNRAVELING THE IMPACT OF GENOMIC VARIATIONS ON COGNITIVE ABILITY ACROSS THE HUMAN CORTEX: INSIGHTS FROM GENE EXPRESSION AND COPY NUMBER VARIANTS
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Thomas Renne
Bank Engchuan
Omar Shanta
Bhooma Thiruvahindrapuram
J. MacDonald
Marieke Klein
Stephen W Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Guillaume Dumas
Sébastien Jacquemont
2023-10-01
European Neuropsychopharmacology (published)
doi.org
Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning
Hanqing Zhao
Xue (Steve) Liu
Gregory Dudek
We consider the detection of faults in robotic manipulators, with particular emphasis on faults that have not been observed or identified in… (see more) advance, which naturally includes those that occur very infrequently. Recent studies indicate that the reward function obtained through Inverse Reinforcement Learning (IRL) can help detect anomalies caused by faults in a control system (i.e. fault detection). Current IRL methods for fault detection, however, either use a linear reward representation or require extensive sampling from the environment to estimate the policy, rendering them inappropriate for safety-critical situations where sampling of failure observations via fault injection can be expensive and dangerous. To address this issue, this paper proposes a zero-shot and exogenous fault detector based on an approximate variational reward imitation learning (AVRIL) structure. The fault detector recovers a reward signal as a function of externally observable information to describe the normal operation, which can then be used to detect anomalies caused by faults. Our method incorporates expert knowledge through a customizable reward prior distribution, allowing the fault detector to learn the reward solely from normal operation samples, without the need for a simulator or costly interactions with the environment. We evaluate our approach for exogenous partial fault detection in multi-stage robotic manipulator tasks, comparing it with several baseline methods. The results demonstrate that our method more effectively identifies unseen faults even when they occur within just three controller time steps.
2023-10-01
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)
doi.org
Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord
Haykel Snoussi
Julien Cohen-Adad
Olivier Commowick
Benoit Combes
Elise Bannier
Slimane Tounekti
Anne Kerbrat
Christian Barillot
Emmanuel Caruyer
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is… (see more) widely used for MS diagnosis and clinical follow‐up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball‐and‐Stick models.
2023-09-29
Brain and Behavior (published)
doi.org
arxiv.org
PEACE: Prompt Engineering Automation for CLIPSeg Enhancement in Aerial Robotics
Haechan Mark Bong
Rongge Zhang
Ricardo de Azambuja
Giovanni Beltrame
From industrial to space robotics, safe landing is an essential component for flight operations. With the growing interest in artificial int… (see more)elligence, we direct our attention to learning based safe landing approaches. This paper extends our previous work, DOVESEI, which focused on a reactive UAV system by harnessing the capabilities of open vocabulary image segmentation. Prompt-based safe landing zone segmentation using an open vocabulary based model is no more just an idea, but proven to be feasible by the work of DOVESEI. However, a heuristic selection of words for prompt is not a reliable solution since it cannot take the changing environment into consideration and detrimental consequences can occur if the observed environment is not well represented by the given prompt. Therefore, we introduce PEACE (Prompt Engineering Automation for CLIPSeg Enhancement), powering DOVESEI to automate the prompt generation and engineering to adapt to data distribution shifts. Our system is capable of performing safe landing operations with collision avoidance at altitudes as low as 20 meters using only monocular cameras and image segmentation. We take advantage of DOVESEI's dynamic focus to circumvent abrupt fluctuations in the terrain segmentation between frames in a video stream. PEACE shows promising improvements in prompt generation and engineering for aerial images compared to the standard prompt used for CLIP and CLIPSeg. Combining DOVESEI and PEACE, our system was able improve successful safe landing zone selections by 58.62% compared to using only DOVESEI. All the source code is open source and available online.
2023-09-29
ArXiv (preprint)
doi.org
arxiv.org
Tree Cross Attention
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2023-09-29
ArXiv (preprint)
doi.org
arxiv.org