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: In this article, the authors examined the how new manufacturing firms choose locations in response to the environmental regulations in Jiangsu, China and found that firms' relocation patterns are common and consistent, i.e., new polluting firms are more likely to be driven by the government environmental mandates to northern Jiangsu.
100 citations
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Nanjing University of Science and Technology1, Chinese Academy of Sciences2, Nanjing University of Information Science and Technology3, Jiangsu University4, Beihang University5, Norwegian University of Science and Technology6, Nanjing University7, King Abdullah University of Science and Technology8, University of Macau9, Southern Medical University10, Fudan University11, University of Southern California12
TL;DR: A large and diverse abdominal CT organ segmentation dataset with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases is presented and a simple and effective method is developed for each benchmark, which can be used as out-of-the-box methods and strong baselines.
Abstract: With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. The datasets, codes, and trained models are publicly available at this https URL.
100 citations
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TL;DR: In this article, a substantial spatial and temporal variations of aerosols and their compositions in North China Plain (NCP) poses a good testbed for the validation of model simulations.
100 citations
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TL;DR: The proposed BSC descriptor obtained high descriptiveness, strong robustness, and high efficiency in both time and memory and achieved high recognition rates on the considered UWA, Queen, and WHU datasets.
Abstract: 3D local surface description is now at the core of many computer vision technologies, such as 3D object recognition, intelligent driving, and 3D model reconstruction. However, most of the existing 3D feature descriptors still suffer from low descriptiveness, weak robustness, and inefficiency in both time and memory. To overcome these challenges, this paper presents a robust and descriptive 3D Binary Shape Context (BSC) descriptor with high efficiency in both time and memory. First, a novel BSC descriptor is generated for 3D local surface description, and the performance of the BSC descriptor under different settings of its parameters is analyzed. Next, the descriptiveness, robustness, and efficiency in both time and memory of the BSC descriptor are evaluated and compared to those of several state-of-the-art 3D feature descriptors. Finally, the performance of the BSC descriptor for 3D object recognition is also evaluated on a number of popular benchmark datasets, and an urban-scene dataset is collected by a terrestrial laser scanner system. Comprehensive experiments demonstrate that the proposed BSC descriptor obtained high descriptiveness, strong robustness, and high efficiency in both time and memory and achieved high recognition rates of 94.8%, 94.1% and 82.1% on the considered UWA, Queen, and WHU datasets, respectively.
99 citations
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TL;DR: It is proved that the non-homogenous discrete grey model (abbreviated as NDGM) with first accumulated generating operator violates the principle of new information priority and principle of minimal information of grey system theory.
Abstract: It is proved that the non-homogenous discrete grey model (abbreviated as NDGM) with first accumulated generating operator violates the principle of new information priority and principle of minimal information of grey system theory. A new NDGM with the fractional-order accumulation is put forward. The first value is effective when the accumulation order number is not 1, and the priority of new information can be better reflected when the accumulation order number becomes smaller. Three real case studies show that the proposed grey model has higher performances not only on model fitting but also on forecasting.
99 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 |