Institution
Nanjing University of Information Science and Technology
Education•Nanjing, China•
About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.
Papers published on a yearly basis
Papers
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TL;DR: A Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs) and was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP andST regions.
403 citations
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TL;DR: The basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized and the derived models of GAns are classified and introduced one by one.
Abstract: Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.
401 citations
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Abstract: Abstract. Fine particulate matter (PM2.5) is a severe air pollution
problem in China. Observations of PM2.5 have been available since 2013
from a large network operated by the China National Environmental Monitoring
Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean
PM2.5 across China over the 2013–2018 period, averaging at −5.2 µg m−3 a−1. Trends in the five megacity cluster regions targeted by
the government for air quality control are -9.3±1.8 µg m−3 a−1 (±95 % confidence interval) for Beijing–Tianjin–Hebei,
-6.1±1.1 µg m−3 a−1 for the Yangtze River Delta, -2.7±0.8 µg m−3 a−1 for the Pearl River Delta, -6.7±1.3 µg m−3 a−1 for the Sichuan Basin, and -6.5±2.5 µg m−3 a−1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018
observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that
the declines in PM2.5 are qualitatively consistent with drastic
controls of emissions from coal combustion. However, there is also a large
meteorologically driven interannual variability in PM2.5 that
complicates trend attribution. We used a stepwise multiple linear regression
(MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5
anomalies to wind speed, precipitation, relative humidity, temperature, and
850 hPa meridional wind velocity (V850). The meteorology-corrected
PM2.5 trends after removal of the MLR meteorological contribution can
be viewed as being driven by trends in anthropogenic emissions. The mean
PM2.5 decrease across China is −4.6 µg m−3 a−1 in the
meteorology-corrected data, 12 % weaker than in the original data, meaning
that 12 % of the PM2.5 decrease in the original data is
attributable to meteorology. The trends in the meteorology-corrected data
for the five megacity clusters are -8.0±1.1 µg m−3 a−1 for Beijing–Tianjin–Hebei (14 % weaker than in the original
data), -6.3±0.9 µg m−3 a−1 for the Yangtze River Delta
(3 % stronger), -2.2±0.5 µg m−3 a−1 for the Pearl River
Delta (19 % weaker), -4.9±0.9 µg m−3 a−1 for
the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m−3 a−1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of
flattening PM2.5 in the Pearl River Delta and increases in the Fenwei
Plain can be attributed to meteorology rather than to relaxation of emission
controls.
398 citations
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16 Jun 2012
TL;DR: This work presents GRASTA, Grassmannian Robust Adaptive Subspace Tracking Algorithm, an online algorithm for robust subspace estimation from randomly subsampled data, and considers the specific application of background and foreground separation in video.
Abstract: It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems that utilize low-dimensional subspaces, demonstrating that subsampling can improve computation speed while still allowing for accurate subspace learning. We present GRASTA, Grassmannian Robust Adaptive Subspace Tracking Algorithm, an online algorithm for robust subspace estimation from randomly subsampled data. We consider the specific application of background and foreground separation in video, and we assess GRASTA on separation accuracy and computation time. In one benchmark video example [16], GRASTA achieves a separation rate of 46.3 frames per second, even when run in MATLAB on a personal laptop.
398 citations
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TL;DR: Inspired by the fiber-reinforced microstructures and mechano-transduction systems of human muscles, a self-healing, long-lasting thermal tolerant and dual-sensory hydrogel-based sensor is proposed, with high gauge factor and a flexible touch keyboard for signature identification and a "fever indicator" for human forehead's temperature detection can be realized by this Hydrogel bioelectronic device.
Abstract: Recently, self-healing hydrogel bioelectronic devices have raised enormous interest for their tissue-like mechanical compliance, desirable biocompatibility, and tunable adhesiveness on bioartificial organs. However, the practical applications of these hydrogel-based sensors are generally limited by their poor fulfillment of stretchability and sensitivity, brittleness under subzero temperature, and single sensory function. Inspired by the fiber-reinforced microstructures and mechano-transduction systems of human muscles, a self-healing (90.8%), long-lasting thermal tolerant and dual-sensory hydrogel-based sensor is proposed, with high gauge factor (18.28) within broad strain range (268.9%), low limit of detection (5% strain), satisfactory thermosensation (-0.016 °C-1), and highly discernible temperature resolution (2.7 °C). Especially by introducing a glycerol/water binary solvent system, desirable subzero-temperature self-healing performance, high water-retaining, and durable adhesion feature can be achieved, resulting from the ice crystallization inhibition and highly dynamic bonding. On account of the advantageous mechanoreception and thermosensitive capacities, a flexible touch keyboard for signature identification and a "fever indicator" for human forehead's temperature detection can be realized by this hydrogel bioelectronic device.
395 citations
Authors
Showing all 14448 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ashok Kumar | 151 | 5654 | 164086 |
Lei Zhang | 135 | 2240 | 99365 |
Bin Wang | 126 | 2226 | 74364 |
Shuicheng Yan | 123 | 810 | 66192 |
Zeshui Xu | 113 | 752 | 48543 |
Xiaoming Li | 113 | 1932 | 72445 |
Qiang Yang | 112 | 1117 | 71540 |
Yan Zhang | 107 | 2410 | 57758 |
Fei Wang | 107 | 1824 | 53587 |
Yongfa Zhu | 105 | 355 | 33765 |
James C. McWilliams | 104 | 535 | 47577 |
Zhi-Hua Zhou | 102 | 626 | 52850 |
Tao Li | 102 | 2483 | 60947 |
Lei Liu | 98 | 2041 | 51163 |
Jian Feng Ma | 97 | 305 | 32310 |