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776 results on '"Wu Xiaotian"'

1. Effects of Different Aerosols on Cloud-to-ground Lightning Activity in the Yangtze River Delta

Author

Wu Xiaotian, Wang Xiaoyan, Zheng Dong, and Zhang Yijun

Subjects
cloud-to-ground lightning activity, aerosol, cloud microphysics, Meteorology. Climatology, QC851-999
Abstract

Lightning activity poses threats to human life and property safety. A large volume studies indicate that aerosol plays a significant role in lightning activity. To explore the effect of different types of aerosols on lightning activity in the Yangtze River Delta and its surrounding areas, cloud-to-ground (CG) lightning location data and aerosol optical depth from reanalysis dataset is analyzed in the target area(27.5°-35°N, 115°-122.5°E) during 2015-2021.The spatial distribution of aerosol optical depth (AOD) with CG lightning condition and without CG lightning condition, and monthly variation of aerosol concentration under two conditions are investigated. All the grids in the target region show that sulfate AOD is higher, while dust AOD is lower with CG lightning. In summer there are little differences between the AOD with CG lightning and without CG lightning conditions. In other months, more sulfate aerosol and less dust aerosol are found with CG lightning condition.The spatial distribution of CG lightning density difference under higher and lower AOD conditions is given. The results show that when the sulfate AOD is high, the CG lightning density of most grids is higher. The CG lightning density is significantly lower when the dust AOD is high.The correlation coefficient between CG lightning density and the AOD for different types of aerosols is calculated for the months when CG lightning is active. When the concentration of sulfate aerosol is low, the correlation coefficient between sulfate AOD and CG lightning density is significant. When the sulfate aerosol concentration exceeds a certain threshold, there is no significant correlation between CG lightning density and sulfate AOD. The positive correlation may be due to the fact that the cloud microphysical effect of sulfate aerosols can promote the development of convection. With sufficient water vapors, sulfate aerosols form cloud condensation nuclei and promote CG lightning activity. When the concentration of sulfate aerosols is higher than the threshold value, the cloud microphysical effect and the radiation effect of aerosol may be counterbalanced, which may lead to weak correlation between CG lightning density and aerosol concentration. The results also show a weak negative correlation between dust aerosol and CG lightning density in April, May, June, and no significant correlation in July, August, September. Considering the low concentration of dust aerosols in the Yangtze River Delta and its surrounding areas, the inhibitory effect of dust aerosols on CG lightning activity may not be just explained by the radiation effect. The large particle size of dust aerosol may play a significant role in suppressing CG lightning activity.

Published
2023
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2. A De-singularity Subgradient Approach for the Extended Weber Location Problem

Author

Lai, Zhao-Rong, Wu, Xiaotian, Fang, Liangda, and Chen, Ziliang

Subjects
Computer Science - Machine Learning
Abstract

The extended Weber location problem is a classical optimization problem that has inspired some new works in several machine learning scenarios recently. However, most existing algorithms may get stuck due to the singularity at the data points when the power of the cost function $1\leqslant q<2$, such as the widely-used iterative Weiszfeld approach. In this paper, we establish a de-singularity subgradient approach for this problem. We also provide a complete proof of convergence which has fixed some incomplete statements of the proofs for some previous Weiszfeld algorithms. Moreover, we deduce a new theoretical result of superlinear convergence for the iteration sequence in a special case where the minimum point is a singular point. We conduct extensive experiments in a real-world machine learning scenario to show that the proposed approach solves the singularity problem, produces the same results as in the non-singularity cases, and shows a reasonable rate of linear convergence. The results also indicate that the $q$-th power case ($1

Published
2024

27. Select Good Regions for Deblurring based on Convolutional Neural Networks

Author

Yang, Hang, Wu, Xiaotian, and Sun, Xinglong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The goal of blind image deblurring is to recover sharp image from one input blurred image with an unknown blur kernel. Most of image deblurring approaches focus on developing image priors, however, there is not enough attention to the influence of image details and structures on the blur kernel estimation. What is the useful image structure and how to choose a good deblurring region? In this work, we propose a deep neural network model method for selecting good regions to estimate blur kernel. First we construct image patches with labels and train a deep neural networks, then the learned model is applied to determine which region of the image is most suitable to deblur. Experimental results illustrate that the proposed approach is effective, and could be able to select good regions for image deblurring.

Published
2020

28. Computational Performance of a Germline Variant Calling Pipeline for Next Generation Sequencing

Author

Liu, Jie, Wu, Xiaotian, Zhang, Kai, Liu, Bing, Bao, Renyi, Chen, Xiao, Cai, Yiran, Shen, Yiming, He, Xinjun, Yan, Jun, and Ji, Weixing

Subjects
Quantitative Biology - Genomics, Computer Science - Performance, cs.PF, q-bio.GN, C.4, D.4.8, J.3
Abstract

With the booming of next generation sequencing technology and its implementation in clinical practice and life science research, the need for faster and more efficient data analysis methods becomes pressing in the field of sequencing. Here we report on the evaluation of an optimized germline mutation calling pipeline, HummingBird, by assessing its performance against the widely accepted BWA-GATK pipeline. We found that the HummingBird pipeline can significantly reduce the running time of the primary data analysis for whole genome sequencing and whole exome sequencing while without significantly sacrificing the variant calling accuracy. Thus, we conclude that expansion of such software usage will help to improve the primary data analysis efficiency for next generation sequencing., Comment: 6 pages, 6 figures, 3 tables

Published
2020

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