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Institution

Australian National University

EducationCanberra, Australian Capital Territory, Australia
About: Australian National University is a education organization based out in Canberra, Australian Capital Territory, Australia. It is known for research contribution in the topics: Population & Galaxy. The organization has 34419 authors who have published 109261 publications receiving 4315448 citations. The organization is also known as: The Australian National University & ANU.
Topics: Population, Galaxy, Stars, Zircon, Politics


Papers
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Journal ArticleDOI
TL;DR: In this article, the physics of the 21 cm transition were reviewed, focusing on processes relevant at high redshifts, and the insights to be gained from such observations were described.

1,315 citations

Journal ArticleDOI
TL;DR: It is argued that the visual cortex representation corresponds closely to the Gabor scheme owing to its advantages in treating the subsequent problem of pattern recognition.
Abstract: On the basis of measured receptive field profiles and spatial frequency tuning characteristics of simple cortical cells, it can be concluded that the representation of an image in the visual cortex must involve both spatial and spatial frequency variables. In a scheme due to Gabor, an image is represented in terms of localized symmetrical and antisymmetrical elementary signals. Both measured receptive fields and measured spatial frequency tuning curves conform closely to the functional form of Gabor elementary signals. It is argued that the visual cortex representation corresponds closely to the Gabor scheme owing to its advantages in treating the subsequent problem of pattern recognition.

1,307 citations

Posted Content
TL;DR: In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values and yields consistent improvement over strong baselines in image classification, object detection and person re-identification.
Abstract: In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person re-identification. Code is available at: this https URL.

1,305 citations

Journal ArticleDOI
TL;DR: The Modules for Experiments in Stellar Astrophysics (MESA) Isochrones and Stellar Tracks (MIST) project as discussed by the authors provides a set of stellar evolutionary tracks and isochrones computed using MESA, a state-of-the-art 1D stellar evolution package.
Abstract: This is the first of a series of papers presenting the Modules for Experiments in Stellar Astrophysics (MESA) Isochrones and Stellar Tracks (MIST) project, a new comprehensive set of stellar evolutionary tracks and isochrones computed using MESA, a state-of-the-art open-source 1D stellar evolution package. In this work, we present models with solar-scaled abundance ratios covering a wide range of ages ($5 \leq \rm \log(Age)\;[yr] \leq 10.3$), masses ($0.1 \leq M/M_{\odot} \leq 300$), and metallicities ($-2.0 \leq \rm [Z/H] \leq 0.5$). The models are self-consistently and continuously evolved from the pre-main sequence to the end of hydrogen burning, the white dwarf cooling sequence, or the end of carbon burning, depending on the initial mass. We also provide a grid of models evolved from the pre-main sequence to the end of core helium burning for $-4.0 \leq \rm [Z/H] < -2.0$. We showcase extensive comparisons with observational constraints as well as with some of the most widely used existing models in the literature. The evolutionary tracks and isochrones can be downloaded from the project website at this http URL

1,301 citations

Journal ArticleDOI
08 Nov 2001-Nature
TL;DR: An overview of the current state of knowledge of global and regional patterns of carbon exchange by terrestrial ecosystems is provided, confirming that the terrestrial biosphere was largely neutral with respect to net carbon exchange during the 1980s, but became a net carbon sink in the 1990s.
Abstract: Knowledge of carbon exchange between the atmosphere, land and the oceans is important, given that the terrestrial and marine environments are currently absorbing about half of the carbon dioxide that is emitted by fossil-fuel combustion. This carbon uptake is therefore limiting the extent of atmospheric and climatic change, but its long-term nature remains uncertain. Here we provide an overview of the current state of knowledge of global and regional patterns of carbon exchange by terrestrial ecosystems. Atmospheric carbon dioxide and oxygen data confirm that the terrestrial biosphere was largely neutral with respect to net carbon exchange during the 1980s, but became a net carbon sink in the 1990s. This recent sink can be largely attributed to northern extratropical areas, and is roughly split between North America and Eurasia. Tropical land areas, however, were approximately in balance with respect to carbon exchange, implying a carbon sink that offset emissions due to tropical deforestation. The evolution of the terrestrial carbon sink is largely the result of changes in land use over time, such as regrowth on abandoned agricultural land and fire prevention, in addition to responses to environmental changes, such as longer growing seasons, and fertilization by carbon dioxide and nitrogen. Nevertheless, there remain considerable uncertainties as to the magnitude of the sink in different regions and the contribution of different processes.

1,291 citations


Authors

Showing all 34925 results

NameH-indexPapersCitations
Cyrus Cooper2041869206782
Nicholas G. Martin1921770161952
David R. Williams1782034138789
Krzysztof Matyjaszewski1691431128585
Anton M. Koekemoer1681127106796
Robert G. Webster15884390776
Ashok Kumar1515654164086
Andrew White1491494113874
Bernhard Schölkopf1481092149492
Paul Mitchell146137895659
Liming Dai14178182937
Thomas J. Smith1401775113919
Michael J. Keating140116976353
Joss Bland-Hawthorn136111477593
Harold A. Mooney135450100404
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023280
2022773
20215,261
20205,464
20195,109
20184,825