Publications

How does hemispheric specialization contribute to human-defining cognition?
Gesa Hartwigsen
Yoshua Bengio
Danilo Bzdok
2021-05-11
Neuron (published)
doi.org
Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting
Brennan Nichyporuk
Justin Szeto
Douglas Arnold
Tal Arbel
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patie… (see more)nt images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.
2021-05-11
MIDL.io/2021/Conference/Short (poster)
openreview.net
openreview.net
Graph Attention Networks with Positional Embeddings
Liheng Ma
Reihaneh Rabbany
Adriana Romero Soriano
2021-05-09
Advances in Knowledge Discovery and Data Mining (published)
doi.org
arxiv.org
SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks
Farimah Poursafaei
Reihaneh Rabbany
Željko Žilić
2021-05-09
Advances in Knowledge Discovery and Data Mining (published)
doi.org
Facilitating Asynchronous Participatory Design of Open Source Software: Bringing End Users into the Loop
Jazlyn Hellman
Jinghui Cheng
Jin Guo
2021-05-08
Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (published)
doi.org
arxiv.org
Interprofessional collaboration and health policy: results from a Quebec mixed method legal research
Marie-Andree Girard
Catherine Régis
Jean-Louis Denis
2021-05-06
Journal of Interprofessional Care (published)
doi.org
Interprofessional collaboration and health policy: results from a Quebec mixed method legal research
Marie-Andree Girard
Catherine Régis
Jean-Louis Denis
ABSTRACT Interprofessional collaboration (IPC) is central to effective care. This practice is structured by an array of laws, regulations an… (see more)d policies but the literature on their impact on IPC is scarce. This study aims to illustrate the gap between the texts and clinicians’ knowledge of the legal framework using an anonymous web-based survey. The survey, sent to nurses and physicians in Quebec, Canada, focused on the IPC legal framework, legal knowledge sources and IPC perceptions or beliefs. The primary outcome was to determine the gap between the law and understanding of the law. The secondary outcome was to identify legal knowledge sources for clinicians in Quebec. A total of 267 participants filled in the survey. For knowledge acquisition, 40% of physicians turned to insurers whereas 43% of nurses turned to their regulatory body. Only 30% of physicians correctly identified what activity is reserved for physicians while 39% of nurses correctly identified their reserved activity. Regarding legal perceptions, 28% of physicians and 39% of nurses thought IPC could increase their liability. These participants have a higher tendency to name liability-related issues as barriers to IPC. These results show an important discrepancy between clinicians’ knowledge about law and policies, and the actual texts themselves. This gap can lead to misinterpretations of the law by clinicians, ineffective policy changes by policymakers and can perpetuate ineffective implementation of IPC.
2021-05-06
Journal of Interprofessional Care (published)
doi.org
Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders
Sophie-Camille Hogue
Flora Chen
Geneviève Brassard
Denis Lebel
Jean-François Bussières
Audrey Durand
Maxime Thibault
2021-05-06
J. Am. Medical Informatics Assoc. (published)
doi.org
PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks
Olivier Tessier-Lariviere
Luke Y. Prince
Pascal Fortier-Poisson
Lorenz Wernisch
Oliver Armitage
Emil Hewage
Guillaume Lajoie
Blake Richards
Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover k… (see more)nown ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.
2021-05-04
International IEEE/EMBS Conference on Neural Engineering (published)
doi.org
Toward Causal Representation Learning
Bernhard Schölkopf
Francesco Locatello
Stefan Bauer
Nan Rosemary Ke
Nal Kalchbrenner
Anirudh Goyal
Yoshua Bengio
The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and… (see more) increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.
2021-05-01
Proceedings of the IEEE (published)
doi.org
Design and Implementation of Smooth Renewable Power in Cloud Data Centers
Xinxin Liu
Yu Hua
Xue (Steve) Liu
Ling Yang
Yuanyuan Sun
The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts o… (see more)n environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88 percent on average. We have released the source codes for public use.
2021-04-30
IEEE Transactions on Cloud Computing (published)
doi.org
Gradient Masked Federated Optimization
Irene Tenison
Sreya Francis
Irina Rish
2021-04-21
ArXiv (preprint)
arxiv.org
arxiv.org