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Institution

University of Sydney

EducationSydney, New South Wales, Australia
About: University of Sydney is a education organization based out in Sydney, New South Wales, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 61532 authors who have published 187345 publications receiving 6114218 citations. The organization is also known as: Sydney University & USyd.


Papers
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Journal ArticleDOI
TL;DR: In this new approach, the nanoengineering efficiency of living cells is harnessed and natural organisms such as diatoms and viruses are used to make nanostructures that could have commercial applications.
Abstract: Biomimetics is the extraction of good design from nature. One approach to optical biomimetics focuses on the use of conventional engineering methods to make direct analogues of the reflectors and anti-reflectors found in nature. However, recent collaborations between biologists, physicists, engineers, chemists and materials scientists have ventured beyond experiments that merely mimic what happens in nature, leading to a thriving new area of research involving biomimetics through cell culture. In this new approach, the nanoengineering efficiency of living cells is harnessed and natural organisms such as diatoms and viruses are used to make nanostructures that could have commercial applications.

588 citations

Journal ArticleDOI
TL;DR: To meet the challenge of chronic diseases, primary health care will have to be strengthened substantially and research on scaling-up should be embedded in large-scale delivery programmes for chronic diseases with a strong emphasis on assessment.

588 citations

Journal ArticleDOI
TL;DR: In this paper, Johnson V and Gunn I photometry for a large number of Local Group galaxies using the Isaac Newton Telescope Wide Field Camera (INT WFC) was obtained for the top few magnitudes of the red giant branch in each system.
Abstract: We have obtained Johnson V and Gunn i photometry for a large number of Local Group galaxies using the Isaac Newton Telescope Wide Field Camera (INT WFC). The majority of these galaxies are members of the M31 subgroup and the observations are deep enough to study the top few magnitudes of the red giant branch in each system. We previously measured the location of the tip of the red giant branch (TRGB) for Andromeda I, Andromeda II and M33 to within systematic uncertainties of typically <0.05 mag. As the TRGB acts as a standard candle in old, metal-poor stellar populations, we were able to derive distances to each of these galaxies. Here we derive TRGB distances to the giant spiral galaxy M31 and 13 additional dwarf galaxies ‐ NGC 205, 185, 147, Pegasus, WLM, LGS3, Cetus, Aquarius, And III, V, VI, VII and the newly discovered dwarf spheroidal And IX. The observations for each of the dwarf galaxies were intentionally taken in photometric conditions. In addition to the distances, we also self-consistently derive the median metallicity of each system from the colour of their red giant branches. This allows us to take into account the small metallicity variation of the absolute I magnitude of the TRGB. The homogeneous nature of our data and the identical analysis applied to each of the 17 Local Group galaxies ensures that these estimates form a reliable set of distance and metallicity determinations that are ideal for comparative studies of Local Group galaxy properties. Ke yw ords: galaxies: general ‐ Local Group ‐ galaxies: stellar content.

587 citations

Book Chapter
01 Dec 2009
TL;DR: A novel test of the independence hypothesis for one particular kernel independence measure, the Hilbert-Schmidt independence criterion (HSIC), which outperforms established contingency table and functional correlation-based tests, and is greater for multivariate data.
Abstract: Although kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically significant dependence. We provide a novel test of the independence hypothesis for one particular kernel independence measure, the Hilbert-Schmidt independence criterion (HSIC). The resulting test costs O(m2), wherem is the sample size. We demonstrate that this test outperforms established contingency table and functional correlation-based tests, and that this advantage is greater for multivariate data. Finally, we show the HSIC test also applies to text (and to structured data more generally), for which no other independence test presently exists.

587 citations


Authors

Showing all 62240 results

NameH-indexPapersCitations
Salim Yusuf2311439252912
David J. Hunter2131836207050
Rob Knight2011061253207
Eric B. Rimm196988147119
Michael Marmot1931147170338
Nicholas G. Martin1921770161952
Jing Wang1844046202769
David R. Williams1782034138789
Jasvinder A. Singh1762382223370
Rory Collins162489193407
David W. Johnson1602714140778
Tien Yin Wong1601880131830
Barbara E.K. Klein16085693319
Peter B. Reich159790110377
Nicholas J. Talley158157190197
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023316
20221,185
202114,815
202014,013
201912,834
201811,456