Tianwei Xu

Yunnan Normal University

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Juxiang Zhou

Yunnan Normal University

Fengqi Nie

Second Affiliated Hospital of Nanjing Medical University

Shanxun Yu

Nanjing Medical University

Hangjiang Fu

Jinling Institute of Technology

Ming Sun

Salk Institute for Biological Studies

Pei Ma

National University Health System

Qinnan Chen

Xiamen University of Technology

Zhaoxia Wang

Chongqing Medical University

Shuai Yan

Jilin University

Zhenyao Chen

Shanghai Chest Hospital

Cooperative Institutions

Yunnan Normal University

Chinese Academy of Sciences

Hainan University

Peking University

Nanjing University

Xinjiang Institute of Ecology and Geography

Shanghai Jiao Tong University

Ministry of Agriculture and Rural Affairs

Sun Yat-sen University

University of Chinese Academy of Sciences

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TIGHT TOUGHNESS CONDITION FOR FRACTIONAL (g, f, n)-CRITICAL GRAPHS

Journal of the Korean Mathematical Society (2014)

Wei Gao Liang Li Tianwei Xu Juxiang Zhou

A graph G is called a fractional (g, f, n)-critical graph if any n vertices are removed from G, then the resulting graph admits a fractional (g, f)-factor. In this paper, we determine the new toughness condition for fractional (g, f, n)-critical graphs. It is proved that G is fractional (g, f, n)-critical if $t(G){\geq}\frac{b^2-1+bn}{a}$. This bound is sharp in some sense. Furthermore, the best toughness condition for fractional (a, b, n)-critical graphs is given.

10.4134/jkms.2014.51.1.055

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Citations (26)

New Multi-dividing Ontology Learning Algorithm Using Special Loss Functions

The Open Cybernetics & Systemics Journal (2015)

Wei Gao Liang Li Tianwei Xu

As an important data structure model, ontology has become one of the core contents in information science.Multi-dividing ontology algorithm combines the advantages of graph structure and learning algorithms proved to have high efficiency.In this paper, in terms of multi-dividing proper loss functions, we propose new multi-dividing ontology learning algorithms for similarity measure and ontology mapping construction.Several theoretical statistical characteristics supporting the new learning model are given.Finally, four experiments on different scientific fields verify that our multi-dividing ontology algorithm has high accuracy and efficiency in application implements.

10.2174/1874110x01408010259

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Citations (2)

Figure S2 from Long Noncoding RNA LINC01234 Functions as a Competing Endogenous RNA to Regulate CBFB Expression by Sponging miR-204-5p in Gastric Cancer

Xin Chen Zhenyao Chen Shanxun Yu Fengqi Nie Shuai Yan

<p>Effect of miR-204-5p on GC cells proliferation.</p>

Competing Endogenous RNA

10.1158/1078-0432.22464111

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Figure S4 from Long Noncoding RNA LINC01234 Functions as a Competing Endogenous RNA to Regulate CBFB Expression by Sponging miR-204-5p in Gastric Cancer

Xin Chen Zhenyao Chen Shanxun Yu Fengqi Nie Shuai Yan

<p>The effect of CBFB on GC cells proliferation in vitro,and tumor growth in vivo</p>

Competing Endogenous RNA

10.1158/1078-0432.22464105.v1

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A Multi-Layered Energy-Efficient and Delay-Reduced Chain-Based Data Gathering Protocol for Wireless Sensor Network

Lingyun Yuan Yunlong Zhu Tianwei Xu

Aiming at the limitations of LEACH, PEGASIS and PEDAP, we present a multi-layer energy-efficient and delay-reducing chain-based data gathering protocol (MEDC) for wireless sensor network, under consideration of the characteristics of wireless sensing traffic monitoring network (WSTMN) such as band monitoring area, high-speed target, long-distance base station and restricted network density. The protocol puts forward the idea of multi-layer chain, and uses the minimum total energy algorithm to construct the chain. Moreover, the maximum residual energy of nodes is the standard for selection of leaders. The experimental results show that MEDC works for WSTMN better than LEACH and PEGASIS, which can not only prolong the network lifetime, but also reduce the network delay remarkably.

10.1109/mesa.2008.4735735

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Citations (14)

Minority costume image retrieval by fusion of color histogram and edge orientation histogram

Xu-mei Shen Juxiang Zhou Tianwei Xu

It has very important practical significance to analyze and research minority costume from the perspective of computer vision for minority culture protection and inheritance. As first exploration in minority costume image retrieval, this paper proposed a novel image feature representation method to describe the rich information of minority costume image. Firstly, the color histogram and edge orientation histogram are calculated for divided sub-blocks of minority costume image. Then, the final feature vector for minority costume image is formed by effective fusion of color histogram and edge orientation histogram. Finally, the improved Canberra distance is introduced to measure the similarity between query image and retrieval image. We have evaluated the performances of the proposed algorithm on self-build minority costume image dataset, and the experimental results show that our method can effectively express the integrated feature of minority costume images, including color, texture, shape and spatial information. Compared with some conventional methods, our method has higher and stable retrieval accuracy.

Color histogram

Feature (linguistics)

Image histogram

10.1109/icis.2016.7550786

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Citations (3)

Boosted non-linear and non-negative sparse learning for single image super-resolution

Yungang Zhang Tianwei Xu

In image super-resolution area, the effectiveness of sparse learning methods have been demonstrated, especially in single image super-resolution applications. A novel sparse learning method is proposed in this paper for the task of single image super-resolution. The proposed non-linear and nonnegative sparsity-based learning model is used to capture the nonlinear data distributions in images. Moreover, a boosting ensemble strategy is used to improve the learned `weak' sparse model. In each round of boosting, the training images are chosen according to the reconstruction errors of the learned models from previous rounds. The obtained dictionary ensemble contains dictionaries which have different reconstruction capability on different types of image patches. Then the support vector regression is applied for obtaining the final result image. The proposed non-negative kernel sparse model was tested on popular benchmark images, the experimental results illustrate the competitiveness of the proposed method.

Boosting

Kernel (algebra)

Benchmark (surveying)

Ensemble Learning

10.1109/fskd.2017.8393189

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Analysis of User Behavior of Libraries at Home and Abroad Based on Citespace

Yanli Pan Tianwei Xu Juxiang Zhou

This article mainly uses Citespace analysis software to visually analyze the journal articles on library user behavior analysis included in the web of science and CNKI. The research is visualized in these aspects: keyword co-occurrence analysis, keyword cluster analysis, hot topics analysis, topic hot spot analysis, and finally it is concluded that there are four aspects of library user behavior trend analysis: digital reading, user needs, precision and personalization, and data-driven.

10.1109/csei51395.2021.9477643

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全日制专业硕士学位研究生“双导师制”建设的协同机制探索——基于利益相关者的视角

研究生教育研究 (2018)

Chao Yang Yang Chao 徐天伟 Tianwei Xu

'双导师制'建设是推进专业硕士学位研究生教育综合改革的关键环节，也是一项综合性工作，涉及到政府、高校、企业行业部门、校内外导师和硕士生等首要利益相关主体的利益。'双导师制'实施过程中，由于各首要利益相关主体利益诉求不同以及之间相互博弈，导致学术逻辑与市场逻辑、教育内部发展规律与外部发展环境、个人价值和社会价值等矛盾的普遍存在并产生一系列现实问题。基于此，需各首要利益相关主体参与'双导师制'建设，明确各自侧重点以及之间的关系，在交流合作、激励考核、聘任培训等方面构建有效的协同机制，从而为双导师提升专业硕士学位研究生培养能力提供机制保障。

Source

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ImageRank: A novel sorting algorithm with relevance feedback in application of national costume image retrieval

Baiyou Ma Tianwei Xu Juxiang Zhou

It is difficult to obtain fairly satisfactory result for national costume images retrieval in real application using existing search engine due to the complexity of visual features and semantic information implied in national costume. Given the fact, a novel sorting algorithm ImageRank is proposed for image retrieval with relevance feedback in this paper, inspired by the PageRank algorithm used by Google Search to rank websites or webpages in their search engine results. ImageRank is realized based on Lucene Image Retrieval (LIRE) toolkit, which is a Java library that provides a simple way to retrieve images and photos based on color and texture characteristics. And it assimilates a good deal of PageRank to accomplish efficient image retrieval by ensuring accurate level of images correlation in resource repository through update the indexical (points-to) relationship timely according to user's behavior dynamically. Experimental results investigate that the performance of ImageRank is better than LIRE. In addition, the conservativeness and convergence of IR value in ImageRank are also verified.

Relevance Feedback

PageRank

Relevance

Rank (graph theory)

10.1109/siprocess.2017.8124526

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Citations (3)