Publications

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

Erick 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

Michael 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