Institution
University of Western Australia
Education•Perth, Western Australia, Australia•
About: University of Western Australia is a education organization based out in Perth, Western Australia, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 29613 authors who have published 87405 publications receiving 3064466 citations. The organization is also known as: UWA & University of WA.
Papers published on a yearly basis
Papers
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TL;DR: This paper systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems, and presents a flexible toolkit for constructing well-designed test problems.
Abstract: When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not
1,567 citations
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TL;DR: In this paper, the authors extended previous research on perceived value by including the role of perceived risk within a model of the antecedents and consequences of perceived value and found that perceived risk played an important role in the perceived product and service quality-value for money relationship and was a significant mediator of this relationship.
1,566 citations
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TL;DR: The many new candidate genes suggested by these results include IL10, IL19, IL20, GLIS3, CD69 and IL27.
Abstract: Type 1 diabetes (T1D) is a common autoimmune disorder that arises from the action of multiple genetic and environmental risk factors. We report the findings of a genome-wide association study of T1D, combined in a meta-analysis with two previously published studies. The total sample set included 7,514 cases and 9,045 reference samples. Forty-one distinct genomic locations provided evidence for association with T1D in the meta-analysis (P < 10(-6)). After excluding previously reported associations, we further tested 27 regions in an independent set of 4,267 cases, 4,463 controls and 2,319 affected sib-pair (ASP) families. Of these, 18 regions were replicated (P < 0.01; overall P < 5 × 10(-8)) and 4 additional regions provided nominal evidence of replication (P < 0.05). The many new candidate genes suggested by these results include IL10, IL19, IL20, GLIS3, CD69 and IL27.
1,547 citations
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TL;DR: A comprehensive survey on adversarial attacks on deep learning in computer vision can be found in this paper, where the authors review the works that design adversarial attack, analyze the existence of such attacks and propose defenses against them.
Abstract: Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas, deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently led to a large influx of contributions in this direction. This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.
1,542 citations
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Nicholas J Kassebaum1, Megha Arora1, Ryan M Barber1, Zulfiqar A Bhutta2 +679 more•Institutions (268)
TL;DR: In this paper, the authors used the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015.
1,533 citations
Authors
Showing all 29972 results
Name | H-index | Papers | Citations |
---|---|---|---|
Nicholas G. Martin | 192 | 1770 | 161952 |
Cornelia M. van Duijn | 183 | 1030 | 146009 |
Kay-Tee Khaw | 174 | 1389 | 138782 |
Steven N. Blair | 165 | 879 | 132929 |
David W. Bates | 159 | 1239 | 116698 |
Mark E. Cooper | 158 | 1463 | 124887 |
David Cameron | 154 | 1586 | 126067 |
Stephen T. Holgate | 142 | 870 | 82345 |
Jeremy K. Nicholson | 141 | 773 | 80275 |
Xin Chen | 139 | 1008 | 113088 |
Graeme J. Hankey | 137 | 844 | 143373 |
David Stuart | 136 | 1665 | 103759 |
Joachim Heinrich | 136 | 1309 | 76887 |
Carlos M. Duarte | 132 | 1173 | 86672 |
David Smith | 129 | 2184 | 100917 |