Publications

Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation.
Da-Wei Jaw
Shih-Chia Huang
Zhihui Lu
Benjamin Fung
Sy-Yen Kuo
Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with suffic… (see more)ient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low-and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low-and sufficient-luminance domains.
2023-09-07
IEEE Transactions on Cybernetics (published)
doi.org
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piché
Valentin Thomas
Joseph Marino
Rafael Pardinas
Gian Maria Marconi
Chris Pal
Mohammad Emtiyaz Khan
2023-09-06
TMLR (accepted)
doi.org
openreview.net
Cardiomyocyte orientation recovery at micrometer scale reveals long‐axis fiber continuum in heart walls
Drisya Dileep
Tabish A Syed
Tyler FW Sloan
Perundurai S Dhandapany
Kaleem Siddiqi
Minhajuddin Sirajuddin
2023-09-06
The EMBO Journal (published)
doi.org
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy
Sékou-Oumar Kaba
Carmelo Gonzales
Santiago Miret
Siamak Ravanbakhsh
2023-09-06
ArXiv (preprint)
doi.org
arxiv.org
Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface
Atieh Taheri
Mohammad Izadi
Gururaj Shriram
Negar Rostamzadeh
Shaun Kane
Text-to-image generation models have grown in popularity due to their ability to produce high-quality images from a text prompt. One use for… (see more) this technology is to enable the creation of more accessible art creation software. In this paper, we document the development of an alternative user interface that reduces the typing effort needed to enter image prompts by providing suggestions from a large language model, developed through iterative design and testing within the project team. The results of this testing demonstrate how generative text models can support the accessibility of text-to-image models, enabling users with a range of abilities to create visual art.
2023-09-05
ArXiv (preprint)
doi.org
arxiv.org
Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures
Option Pricing
Saeed Marzban
Érick Delage
Jonathan Yu-Meng Li
2023-09-05
Quantitative Finance (published)
doi.org
Tidying Up the Conversational Recommender Systems' Biases
Armin Moradi
Golnoosh Farnadi
The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research… (see more) circles. However, concerns regarding biases in these systems have emerged. While individual components of CRS have been subject to bias studies, a literature gap remains in understanding specific biases unique to CRS and how these biases may be amplified or reduced when integrated into complex CRS models. In this paper, we provide a concise review of biases in CRS by surveying recent literature. We examine the presence of biases throughout the system's pipeline and consider the challenges that arise from combining multiple models. Our study investigates biases in classic recommender systems and their relevance to CRS. Moreover, we address specific biases in CRS, considering variations with and without natural language understanding capabilities, along with biases related to dialogue systems and language models. Through our findings, we highlight the necessity of adopting a holistic perspective when dealing with biases in complex CRS models.
2023-09-05
ArXiv (preprint)
doi.org
arxiv.org
xSA: A Binary Cross-Entropy Simulated Annealing Polar Decoder
Ryan Seah
Huayi Zhou
Marwan Jalaleddine
Warren Gross
Polar decoders such as successive-cancellation and successive-cancellation list decoders are limited by their sequential nature, which leads… (see more) to a linear increase in latency with the codeword length. Heuristic based decoders such as quantum annealing have been proposed to overcome this limitation. However, these decoders have shown poor performance when decoding polar codes with more than eight bits. In this paper, we developed new meta-heuristic based polar decoder, called xSA, which uses a new receiver constraint modeled by the binary cross-entropy function. We also propose a method to determine the weights used in a quadratic unconstrained binary optimization (QUBO) function. The polar code is assumed to have been sent across an AWGN channel and we conducted our experiments and simulations using PyQUBO and dwave-neal. Our results show that xSA is able to decode codes of length 16 and 32 with a near-ML FER performance, presenting itself as a promising alternative to traditional polar decoders for real world applications and next generation cellular communications.
2023-09-04
International Symposium on Turbo Codes and Iterative Information Processing (published)
doi.org
Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images
Pedro Vianna
Merve Kulbay
Pamela Boustros
Sara-Ivana Calce
Cassandra Larocque-Rigney
Laurent Patry-Beaudoin
Yi Hui Luo
Muawiz Chaudary
Samuel Kadoury
Bich Nguyen
Emmanuel Montagnon
Eugene Belilovsky
Guy Wolf
Michaël Chassé
An Tang
Guy Cloutier
Early detection of nonalcoholic fatty liver disease (NAFLD) is crucial to avoid further complications. Ultrasound is often used for screenin… (see more)g and monitoring of hepatic steatosis, however it is limited by the subjective interpretation of images. Computer assisted diagnosis could aid radiologists to achieve objective grading, and artificial intelligence approaches have been tested across various medical applications. In this study, we evaluated the performance of a two-stage hepatic steatosis detection deep learning framework, with a first step of liver segmentation and a subsequent step of hepatic steatosis classification. We evaluated the models on internal and external datasets, aiming to understand the generalizability of the framework. In the external dataset, our segmentation model achieved a Dice score of 0.92 (95% CI: 0.78, 1.00), and our classification model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.89). Our findings highlight the potential benefits of applying artificial intelligence models in NAFLD assessment.
2023-09-03
IUS (published)
doi.org
122 Is Selenium-75 a Feasible HDR Brachytherapy Source?
Jake Reid
Shirin A. Enger
Jonathan Kalinowski
A. Armstrong
John Munro
2023-09-01
Radiotherapy and Oncology (published)
doi.org
124 Development of a Novel Dosimetry Software for Patient-Specific Intravascular Brachytherapy Treatment Planning on Optical Coherence Tomography Images
Maryam Rahbaran
Jonathan Kalinowski
James Man Git Tsui
Joseph DeCunha
Kevin Croce
Brian Bergmark
Philip Devlin
Shirin A. Enger
2023-09-01
Radiotherapy and Oncology (published)
doi.org
125 Toward the Translation of Rectal Intensity Modulated Brachytherapy for Feasibility and Safety Studies
Jonathan Kalinowski
Shirin A. Enger
2023-09-01
Radiotherapy and Oncology (published)
doi.org