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arXiv 2020年

作者： Shi, Jiangbo Gao, Zeyu Zhang, Haichuan Puttapirat, Pargorn Wang, Chunbao Zhang, Xiangrong Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Xi'an Shaanxi710049 China School of Electronic and Information Engineering Xi'an Jiao tong University Xi'an Shaanxi710049 China Shaan xi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Xi'an Shaanxi710049 China Department of Pathology First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi710061 China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi710071 China

Pathological examination is an important step in cancer diagnosis. Pathologists make diagnosis and pathology report based on observed cell and tissue structure on pathological slides. With the development of statistical machine learning, especially deep learning, automated classifiers are being used to analyze histopathological slides and assist pathologists in diagnosis. Currently, commonly used annotation methods in histopathological slides include image-wise, bounding box, ellipse-wise, pixel-wise. In order to verify the influence of annotation in pathological slide on deep learning model, we design corresponding experiments to test the performance based on annotations with different granularity annotation. In classification, all state-of-the-art deep learning based classifiers perform better when they are trained by the dataset with pixel-wise annotation. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows that finer-grained annotation can not only improve the performance of deep learning models, but help deep learning model extract more accurate phenotypic information from histopathological slides. An accurate acquisitions of phenotypic information can help pathologists to enquire the model based on which regions and features in the slide were mainly used to calculate the prediction, improve the reliability of the model prediction. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Identification of cotton using random forest based on wide-band imaging spectrometer data of Tiangong-2

Identification of cotton using random forest based on wide-b...

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Tiangong-2 Remote Sensing Application Conference on Technology, Method and Application

作者： She, Xiaojun Fu, Kangyu Wang, Jie Qi, Wenchao Li, Xiaolu Fu, Shuangling Chongqing Engineering Research Center for Remote Sensing Big Data Application School of Geographical Sciences Southwest University Chongqing400715 China Research Base of Karst Ecoenvironments at Nanchuan in Chongqing Ministry of Nature Resources School of Geographical Sciences Southwest University Chongqing400715 China State Key Laboratory of Remote Sensing Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China China Coal Technology and Engineering Group Chongqing Research Institute Chongqing400039 China

ISBN: (纸本)9789811335006

Crop identification needs the relatively high spatial, temporal and spectral resolution due to the similarity spectral or spatial features of crops. Tiangong-2 (TG-2) Space laboratory which payloads the Wide-band Imaging Spectrometer (WIS) can provide 14 bands covering from visible to near-infrared (VIS-NIR) wavelength with spatial resolution of 100 m and a repeat circle of 3 days. The high resolution of spatial-spectral-temporal is of promising advantage in precision crops identification. Random forest (RF) is a powerful machine learning classifier and has a good potential in the remote sensing classification. In this paper, TG-2 WIS image was used to extract the cotton in Xinjiang area with field experimental validation. RF along with support vector machine (SVM) and maximum likelihood classification (MLC) were utilized and compared in the cotton classification study. Three types of features of principal component, texture feature and spectral index are extracted for the three classifiers. A total of 29 features are used as input bands for MLC, SVM and RF. The results showed that RF was superior to the other methods. The overall accuracy and Kappa coefficients of RF are 91.2% and 0.871, respectively. This paper proves that the RF classification method has a good effect in the vegetation extraction of TG-2 data and positive significance for expanding the application of TG-2 data. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Extraction

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Elastic-plastic time-history analysis of steel-tube stiff-frame arch bridge without inner filling concrete

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IOP Conference Series: Earth and Environmental Science 2021年第1期638卷

作者： Ben Zhang Li Li Wenming Su School of Civil Engineering Chongqing Jiaotong University Chongqing 400074 China China Railway Bridge Science Research Institude Ltd. Wuhan 430034 China State Key Laborary for Health and Safety of Bridge Structures Wuhan 430034 China Shandong Hi-Speed Engineering Testing Co. Ltd Jinan 250000 China Key Laboratory for Bridge Structure Big Data Performance Diagnosis and Treatment Improvement of Transportation Industry Jinan 250000 China

Based on a case study of a typical arch bridge with hollow steel tube and stiffness frame located in the high intensity region of western China, this paper adopts the onlinear time- history method to analyze the structural response of the overall bridge elastic-plastic model under seismic action. Under the force of Wolong wave in Wenchuan Earthquake, the main load-bearing members of the structure have a good seismic performance. Only part of the columns on the main arch ring enter the plastic hinge state for energy dissipation, and the lower columns have a more obvious energy dissipation effect than the higher ones. The result indicates that the design idea of structure which is similar to that of strong column and weak beam in ductility design, has the characteristics of 'strong arch and weak beam' and can therefore improve the ability of structure to resist collapse under earthquake action.

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393 DECONVOLUTING THE COMPLEXITY OF LONG NON-CODING RNAS AND ESOPHAGEAL CARCINOMA FOR POTENTIAL CLINICAL IMPLICATIONS

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Diseases of the Esophagus 2021年第SUPPLEMENT_1期34卷

作者： Luan, Siyuan Yang, Yushang Zhang, Shouyue Zeng, Xiaoxi Xiao, Xin Fu, Leilei Liu, Bo Yuan, Yong Department of Thoracic Surgery West China Hospital Sichuan University Chengdu China State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University Chengdu China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China School of Life Science and Engineering Southwest Jiaotong University Chengdu China

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RETRACTED ARTICLE: An enhanced pairing-based authentication scheme for smart grid communications

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Journal of Ambient Intelligence and Humanized Computing 2021年第1期15卷 165-165页

作者： Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Tian, Yuan Al-Nabhan, Najla Abdulrahman College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Computer Engineering Nanjing Institute of Technology Nanjing China Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Kingdom of Saudi Arabia

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An Advanced Quantum-Resistant Signature Scheme for Cloud Based on Eisenstein Ring

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Computers, Materials & Continua 2018年第7期56卷 19-34页

作者： Faguo Wu Xiao Zhang Wang Yao Zhiming Zheng Lipeng Xiang Wanpeng Li Key Laboratory of Mathematics Informatics and Behavioral SemanticsMinistry of EducationSchool of Mathematics and Systems ScienceBeihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100191China The Fifth Electronics Research Institute of Ministry of Industry and Information Technology Guangzhou510610China Department of Electrical and Electronic Engineering City University of LondonUK

Signature,widely used in cloud environment,describes the work as readily identifying its *** existing signature schemes in the literature mostly rely on the Hardness assumption which can be easily solved by quantum *** this paper,we proposed an advanced quantum-resistant signature scheme for Cloud based on Eisenstein Ring(ETRUS)which ensures our signature scheme proceed in a lattice with higher *** proved that ETRUS highly improve the performance of traditional lattice signature ***,the Norm of polynomials decreases significantly in ETRUS which can effectively reduce the amount of polynomials convolution ***,storage complexity of ETRUS is smaller than classical ***,according to all convolution of ETRUS enjoy lower degree polynomials,our scheme appropriately accelerate 56.37%speed without reducing its security level.

关键词： Signature quantum-resistant Eisenstein Ring ETRUS

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Corrigendum to: Multi-Swarm Cuckoo Search Algorithm withQ-Learning Model

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The Computer Journal 2022年第7期65卷 1938-1938页

作者： Li, Juan Xiao, Dan-dan Zhang, Ting Liu, Chun Li, Yuan-xiang Wang, Gai-ge School of Artificial Intelligence Wuhan Technology and Business University Wuhan 430065 China School of Artificial Intelligence Wuchang University of Technology Wuhan 430223 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Gaoxin District Changchun Jilin China 130012 School of Computer Wuhan University No. 299 Bayi Street Wuchang District Wuhan Hubei China 430072 Department of Computer Science and Technology Ocean University of China No. 238 Songling Street Laoshan District Qingdao Shandong China 266100 Institute of Algorithm and Big Data Analysis Northeast Normal University No. 5268 Renmin Street Nanguan District Changchun Jilin China 130117

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Correction: Identification of a novel bile marker clusterin and a public online prediction platform based on deep learning for cholangiocarcinoma

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BMC medicine 2023年第1期21卷 368页

作者： Long Gao Yanyan Lin Ping Yue Shuyan Li Yong Zhang Ningning Mi Mingzhen Bai Wenkang Fu Zhili Xia Ningzu Jiang Jie Cao Man Yang Yanni Ma Fanxiang Zhang Chao Zhang Joseph W Leung Shun He Jinqiu Yuan Wenbo Meng Xun Li The First School of Clinical Medicine Lanzhou University Lanzhou 730030 Gansu China. Department of General Surgery The First Hospital of Lanzhou University Lanzhou 730030 Gansu China. Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine Transformation Lanzhou 730030 Gansu China. School of Medical Information and Engineering Xuzhou Medical University Xuzhou 221004 Jiangsu China. Clinical Research Center Big Data Center The Seventh Affiliated Hospital Sun Yat-Sen University Shenzhen 518107 Guangdong China. Division of Gastroenterology UC Davis Medical Center and Sacramento VA Medical Center Sacramento CA 95817 USA. Department of Endoscopy National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. heshuns@***. Clinical Research Center Big Data Center The Seventh Affiliated Hospital Sun Yat-Sen University Shenzhen 518107 Guangdong China. yuanjq5@***. The First School of Clinical Medicine Lanzhou University Lanzhou 730030 Gansu China. mengwb@***. Department of General Surgery The First Hospital of Lanzhou University Lanzhou 730030 Gansu China. mengwb@***. Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine Transformation Lanzhou 730030 Gansu China. mengwb@***.

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A performance evaluation of correspondence grouping methods for 3D rigid data matching

arXiv

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arXiv 2019年

作者： Yang, Jiaqi Xian, Ke Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710129 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts including shape retrieval, 3D object recognition, and point cloud registration together with various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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Improved Upper Bound on the Network Function Computing Capacity

Improved Upper Bound on the Network Function Computing Capac...

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作者： Guang, Xuan Yeung, Raymond W. Yang, Shenghao Li, Congduan School of Mathematical Sciences and LPMC Nankai University Tianjin300071 China Institute of Network Coding Chinese University of Hong Kong Hong Kong Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China

The problem of network function computation over a directed acyclic network is investigated in this paper. In such a network, a sink node desires to compute with zero error a target function, of which the inputs are generated at multiple source nodes. The edges in the network are assumed to be error-free and have limited capacity. The nodes in the network are assumed to have unbounded computing capability and be able to perform network coding. The computing rate of a network code that can compute the target function over the network is the average number of times that the target function is computed with zero error for one use of the network. In this paper, we obtain an improved upper bound on the computing capacity, which is applicable to arbitrary target functions and arbitrary network topologies. This improved upper bound not only is an enhancement of the previous upper bounds but also is the first tight upper bound on the computing capacity for computing an arithmetic sum over a certain non-tree network, which has been widely studied in the literature. We also introduce a multi-dimensional array approach that facilitates evaluation of the improved upper bound. Furthermore, we apply this bound to the problem of computing a vector-linear function over a network. With this bound, we are not only able to enhance a previous result on computing a vector-linear function over a network but also simplify the proof significantly. Finally, we prove that for computing the binary maximum function over the reverse butterfly network, our improved upper bound is not achievable. This result establishes that in general our improved upper bound is non-achievable, but whether it is asymptotically achievable or not remains open. © 1963-2012 IEEE.

关键词： Network coding

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