Institution
Edith Cowan University
Education•Perth, Western Australia, Australia•
About: Edith Cowan University is a education organization based out in Perth, Western Australia, Australia. It is known for research contribution in the topics: Population & Tourism. The organization has 4040 authors who have published 13529 publications receiving 339582 citations. The organization is also known as: Edith Cowan & ECU.
Topics: Population, Tourism, Isometric exercise, Higher education, Health care
Papers published on a yearly basis
Papers
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TL;DR: PM2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing, and the estimated effects were robust after adjusting for SO2, O3, CO and NO2.
Abstract: Background
Heavy fine particulate matter (PM2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing.
162 citations
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13 May 2013TL;DR: This paper empirically shows the capabilities of current automated evaluation tools and investigates the effectiveness of 6 state-of-the-art tools by analysing their coverage, completeness and correctness with regard to WCAG 2.0 conformance.
Abstract: The use of web accessibility evaluation tools is a widespread practice. Evaluation tools are heavily employed as they help in reducing the burden of identifying accessibility barriers. However, an over-reliance on automated tests often leads to setting aside further testing that entails expert evaluation and user tests. In this paper we empirically show the capabilities of current automated evaluation tools. To do so, we investigate the effectiveness of 6 state-of-the-art tools by analysing their coverage, completeness and correctness with regard to WCAG 2.0 conformance. We corroborate that relying on automated tests alone has negative effects and can have undesirable consequences. Coverage is very narrow as, at most, 50% of the success criteria are covered. Similarly, completeness ranges between 14% and 38%; however, some of the tools that exhibit higher completeness scores produce lower correctness scores (66-71%) due to the fact that catching as many violations as possible can lead to an increase in false positives. Therefore, relying on just automated tests entails that 1 of 2 success criteria will not even be analysed and among those analysed, only 4 out of 10 will be caught at the further risk of generating false positives.
161 citations
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TL;DR: It is demonstrated that plasma GFAP levels are elevated in cognitively normal older adults at risk of AD, and observations suggest that astrocytic damage or activation begins from the pre-symptomatic stage of AD and is associated with brain Aβ load.
Abstract: Glial fibrillary acidic protein (GFAP), an astrocytic cytoskeletal protein, can be measured in blood samples, and has been associated with Alzheimer’s disease (AD). However, plasma GFAP has not been investigated in cognitively normal older adults at risk of AD, based on brain amyloid-β (Aβ) load. Cross-sectional analyses were carried out for plasma GFAP and plasma Aβ1–42/Aβ1–40 ratio, a blood-based marker associated with brain Aβ load, in participants (65–90 years) categorised into low (Aβ−, n = 63) and high (Aβ+, n = 33) brain Aβ load groups via Aβ positron emission tomography. Plasma GFAP, Aβ1–42, and Aβ1–40 were measured using the Single molecule array (Simoa) platform. Plasma GFAP levels were significantly higher (p < 0.00001), and plasma Aβ1–42/Aβ1–40 ratios were significantly lower (p < 0.005), in Aβ+ participants compared to Aβ− participants, adjusted for covariates age, sex, and apolipoprotein E-e4 carriage. A receiver operating characteristic curve based on a logistic regression of the same covariates, the base model, distinguished Aβ+ from Aβ− (area under the curve, AUC = 0.78), but was outperformed when plasma GFAP was added to the base model (AUC = 0.91) and further improved with plasma Aβ1–42/Aβ1–40 ratio (AUC = 0.92). The current findings demonstrate that plasma GFAP levels are elevated in cognitively normal older adults at risk of AD. These observations suggest that astrocytic damage or activation begins from the pre-symptomatic stage of AD and is associated with brain Aβ load. Observations from the present study highlight the potential of plasma GFAP to contribute to a diagnostic blood biomarker panel (along with plasma Aβ1–42/Aβ1–40 ratios) for cognitively normal older adults at risk of AD.
161 citations
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TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end dual-path convolutional network to learn the image and text representations, which is based on an unsupervised assumption that each image/text group can be viewed as a class.
Abstract: Matching images and sentences demands a fine understanding of both modalities. In this article, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image/text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss on heterogeneous features (i.e., text and image features) is less effective, because it is hard to find appropriate triplets at the beginning. So the naive way of using the ranking loss may compromise the network from learning inter-modal relationship. To address this problem, we propose the instance loss, which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image/text group can be viewed as a class. So the network can learn the fine granularity from every image/text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this article constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language-based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available.
161 citations
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TL;DR: John Klironomos, Martin Zobel, Mark Tibbett, William D. Stock, Matthias C. Rillig, Jeri L. Parrent, Mari Moora, Alexander M. Koch, Jose M.Facelli, Evelina Facelli, Ian A. Dickie and James D. Bever
Abstract: John Klironomos, Martin Zobel, Mark Tibbett, William D. Stock, Matthias C. Rillig, Jeri L. Parrent, Mari Moora, Alexander M. Koch, Jose M. Facelli, Evelina Facelli, Ian A. Dickie and James D. Bever
160 citations
Authors
Showing all 4128 results
Name | H-index | Papers | Citations |
---|---|---|---|
Paul Jackson | 141 | 1372 | 93464 |
William J. Kraemer | 123 | 755 | 54774 |
D. Allan Butterfield | 115 | 504 | 43528 |
Kerry S. Courneya | 112 | 608 | 49504 |
Robert U. Newton | 109 | 753 | 42527 |
Roger A. Barker | 101 | 620 | 39728 |
Ralph N. Martins | 95 | 630 | 35394 |
Wei Wang | 95 | 3544 | 59660 |
David W. Dunstan | 91 | 403 | 37901 |
Peter E.D. Love | 90 | 546 | 24815 |
Andrew Jones | 83 | 695 | 28290 |
Hongqi Sun | 81 | 265 | 20354 |
Leon Flicker | 79 | 465 | 22669 |
Mark A. Jenkins | 79 | 472 | 21100 |
Josep M. Gasol | 77 | 313 | 22638 |