Institution
University of Waterloo
Education•Waterloo, Ontario, Canada•
About: University of Waterloo is a education organization based out in Waterloo, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 36093 authors who have published 93906 publications receiving 2948139 citations. The organization is also known as: UW & uwaterloo.
Papers published on a yearly basis
Papers
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TL;DR: A comprehensive review of the flow-field layouts developed by different companies and research groups and the pros and cons associated with these designs is presented in this article, where the authors also present a comprehensive analysis of the performance and economic advantages of these layouts.
816 citations
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TL;DR: A new database search tool, PEAKS DB, has been developed by incorporating the de novo sequencing results into the database search, and achieves significantly improved accuracy and sensitivity over two other commonly used software packages.
815 citations
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TL;DR: This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
Abstract: Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL). Then, we outline a few important applications of UQ methods. Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field.
809 citations
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TL;DR: In this article, a large library of models based on general relativistic magnetohydrodynamic (GRMHD) simulations and synthetic images produced by GRS was constructed and compared with the observed visibilities.
Abstract: The Event Horizon Telescope (EHT) has mapped the central compact radio source of the elliptical galaxy M87 at 1.3 mm with unprecedented angular resolution. Here we consider the physical implications of the asymmetric ring seen in the 2017 EHT data. To this end, we construct a large library of models based on general relativistic magnetohydrodynamic (GRMHD) simulations and synthetic images produced by general relativistic ray tracing. We compare the observed visibilities with this library and confirm that the asymmetric ring is consistent with earlier predictions of strong gravitational lensing of synchrotron emission from a hot plasma orbiting near the black hole event horizon. The ring radius and ring asymmetry depend on black hole mass and spin, respectively, and both are therefore expected to be stable when observed in future EHT campaigns. Overall, the observed image is consistent with expectations for the shadow of a spinning Kerr black hole as predicted by general relativity. If the black hole spin and M87's large scale jet are aligned, then the black hole spin vector is pointed away from Earth. Models in our library of non-spinning black holes are inconsistent with the observations as they do not produce sufficiently powerful jets. At the same time, in those models that produce a sufficiently powerful jet, the latter is powered by extraction of black hole spin energy through mechanisms akin to the Blandford-Znajek process. We briefly consider alternatives to a black hole for the central compact object. Analysis of existing EHT polarization data and data taken simultaneously at other wavelengths will soon enable new tests of the GRMHD models, as will future EHT campaigns at 230 and 345 GHz.
808 citations
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TL;DR: Nanocrystalline cellulose (NCC) is an emerging renewable nanomaterial that holds promise in many different applications, such as in personal care, chemicals, foods, pharmaceuticals, etc as discussed by the authors.
Abstract: Nanocrystalline cellulose (NCC) is an emerging renewable nanomaterial that holds promise in many different applications, such as in personal care, chemicals, foods, pharmaceuticals, etc. By appropriate modification of NCC, various functional nanomaterials with outstanding properties, or significantly improved physical, chemical, biological, as well as electronic properties can be developed. The nanoparticles are stabilised in aqueous suspension by negative charges on the surface, which are produced during the acid hydrolysis process. NCC suspensions can form a chiral nematic ordered phase beyond a critical concentration, i.e. NCC suspensions transform from an isotropic to an anisotropic chiral nematic liquid crystalline phase. Due to its nanoscale dimension and intrinsic physicochemical properties, NCC is a promising renewable biomaterial that can be used as a reinforcing component in high performance nanocomposites. Many new nanocomposite materials with attractive properties were obtained by the physical incorporation of NCC into a natural or synthetic polymeric matrix. Simple chemical modification on NCC surface can improve its dispersability in different solvents and expand its utilisation in nano-related applications, such as drug delivery, protein immobilisation, and inorganic reaction template. This review paper provides an overview on this emerging nanomaterial, focusing on the surface modification, properties and applications of NCC.
808 citations
Authors
Showing all 36498 results
Name | H-index | Papers | Citations |
---|---|---|---|
John J.V. McMurray | 178 | 1389 | 184502 |
David A. Weitz | 178 | 1038 | 114182 |
David Taylor | 131 | 2469 | 93220 |
Lei Zhang | 130 | 2312 | 86950 |
Will J. Percival | 129 | 473 | 87752 |
Trevor Hastie | 124 | 412 | 202592 |
Stephen Mann | 120 | 669 | 55008 |
Xuan Zhang | 119 | 1530 | 65398 |
Mark A. Tarnopolsky | 115 | 644 | 42501 |
Qiang Yang | 112 | 1117 | 71540 |
Wei Zhang | 112 | 1189 | 93641 |
Hans-Peter Seidel | 112 | 1213 | 51080 |
Theodore S. Rappaport | 112 | 490 | 68853 |
Robert C. Haddon | 112 | 577 | 52712 |
David Zhang | 111 | 1027 | 55118 |