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Heterogeneous-length text topic modeling for reader-Aware multi-document summarization

ACM Transactions on knowledge Discovery from data 2019年第4期13卷 42-42页

作者： Qiang, Jipeng Chen, Ping Ding, Wei Wang, Tong Xie, Fei Wu, Xindong Department of Computer Science Yangzhou University China Department of Computer Science University of Massachusetts Boston United States Department of Computer Science and Technology Hefei Normal University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-Aware multi-document summarization (RA-MDS).We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms. © 2019 Association for Computing Machinery. All rights reserved.

关键词： heterogeneous-length text LDA multi-document summarization Topic modeling

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Impact of Prior knowledge and data Correlation on Privacy Leakage: A Unified Analysis

arXiv

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arXiv 2019年

作者： Li, Yanan Ren, Xuebin Yang, Shusen Yang, Xinyu National Engineering Laboratory for Big Data Analytics [NEL-BDA Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Ministry of Education Key Lab for Intelligent Networks and Network Security [MOE KLINNS Lab Xi’an Jiaotong University Xi’an Shaanxi710049 China

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Wide-Bandgap Perovskite Solar Cell Using a Fluoride-Assisted Surface Gradient Passivation Strategy

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Angewandte Chemie 2023年第11期135卷

作者： Nan Yan Yan Gao Junjie Yang Zhimin Fang Jiangshan Feng Prof. Xiaojun Wu Prof. Tao Chen Prof. Shengzhong (Frank) Liu Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University Xi'an 710119 China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) Writing - review & editing (lead) Hefei National Laboratory for Physical Sciences at Microscale Department of Materials Science and Engineering University of Science and Technology of China Hefei 230026 China Contribution: Software (lead) Contribution: Methodology (equal) Validation (supporting) Contribution: Formal analysis (equal) Funding acquisition (lead) Investigation (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Funding acquisition (supporting) Investigation (supporting) Writing - original draft (equal) Writing - review & editing (equal) Dalian National Laboratory for Clean Energy iChEM Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 China Contribution: Formal analysis (supporting) Funding acquisition (lead) Investigation (equal) Project administration (lead) Resources (lead) Supervision (lead) Writing - original draft (supporting) Writing - review & editing (supporting)

Wide-band gap (1.68 eV) perovskite solar cells (PSCs) are important components of perovskite/Si tandem devices. However, the efficiency of wide band gap PSCs has been limited by their huge open-circuit voltage ( V oc ) deficit due to non-radiative recombination. Deep-level acceptor defects are identified as the major killers of V oc , and they can be effectively improved by passivation with ammonium salts. Theoretical calculation predicts that increasing the distance between F and −NH 3 + of fluorinated ammonium can dramatically enhance the electropositivity of −NH 3 + terminals, thus providing strong adsorption onto the negatively charged I A and I Pb anti-site defects. Characterizations further confirm that surface gradient passivation employing p -FPEAI demonstrates the most efficient passivation effect. Consequently, a record-efficiency of 21.63 % with the smallest V oc deficit of 441 mV is achieved for 1.68 eV-band gap inverted PSCs. Additionally, a flexible PSC and 1 cm 2 opaque device also deliver the highest PCEs of 21.02 % and 19.31 %, respectively.

关键词： Defects Fluoride Open-Circuit Voltage Deficit Perovskite Wide Band Gap

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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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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Weiyang Zhong Shanxi Jiang Wei Wu Danhong Peng Tian Xu Jun Wang Gongdao Wang Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing PR China China Tower Corporation Limited Nantong Branch PR China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing PR China Zhongda Hospital Southeast University NanjingSchool of Science China Pharmaceutical University Nanjing PR China

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network.

关键词： Classification algorithms Training Neural networks Diabetes Pregnancy Genetic algorithms Sociology

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NaCdSb: An Orthorhombic Zintl Phase with Exceptional Intrinsic Thermoelectric Performance

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Angewandte Chemie 2022年第3期135卷

作者： Kai Guo Yuting Zhang Song Yuan Qinghang Tang Chen Lin Pengfei Luo Jiong Yang Shusheng Pan Li-Dong Zhao Guofeng Cheng Jiye Zhang Jun Luo School of Physics and Materials Science Guangzhou University Guangzhou 510006 China Research Center for Advanced Information Materials (CAIM) Huangpu Research & Graduate School of Guangzhou University Sino-Singapore Guangzhou Knowledge City Huangpu District Guangzhou 510555 China Contribution: Conceptualization (lead) Formal analysis (equal) Writing - original draft (lead) School of Materials Science and Engineering Shanghai University 99 Shangda Road Shanghai 200444 China Contribution: Data curation (equal) Investigation (equal) Validation (lead) Contribution: Data curation (lead) Formal analysis (equal) Materials Genome Institute Shanghai University 99 Shangda Road Shanghai 200444 China Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education and School of Physics and Technology Wuhan University Wuhan 430072 China Contribution: Investigation (equal) Contribution: Investigation (equal) Methodology (supporting) Contribution: Investigation (equal) Writing - review & editing (supporting) School of Materials Science and Engineering Beihang University Beijing 100191 China Contribution: Methodology (equal) Writing - review & editing (equal) Analysis & Testing Center for Inorganic Materials Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai 200050 China Contribution: Formal analysis (equal) Investigation (supporting) Contribution: Data curation (equal) Writing - review & editing (supporting) Contribution: Supervision (lead) Writing - review & editing (equal)

Many Zintl phases are promising thermoelectric materials owning to their features like narrow band gaps, multiband behaviors, ideal charge transport tunnels, and loosely bound cations. Herein we show a new Zintl phase NaCdSb with exceptional intrinsic thermoelectric performance. Pristine NaCdSb exhibits semiconductor behaviors with an experimental hole concentration of 2.9×10 18 cm −3 and a calculated band gap of 0.5 eV. As the temperature increases, the hole concentration rises gradually and approaches its optimal one, leading to a high power factor of 11.56 μW cm −1 K −2 at 673 K. The ultralow thermal conductivity is derived from the small phonon group velocity and short phonon lifetime, ascribed to the structural anharmonicity of Cd−Sb bonds. As a consequence, a maximum zT of 1.3 at 673 K has been achieved without any doping optimization or structural modification, demonstrating that NaCdSb is a remarkable thermoelectric compound with great potential for performance improvement.

关键词： Electron-Crystal NaCdSb Phonon-Glass Thermoelectric Properties Zintl Phase

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Biaxially-Strained Phthalocyanine at Polyoxometalate@Carbon Nanotube Heterostructure Boosts Oxygen Reduction Catalysis

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Angewandte Chemie 2023年第42期135卷

作者： Prof. Sheng Zhu Lingtong Ding Xuehuan Zhang Kun Wang Prof. Xiao Wang Prof. Feng Yang Prof. Gaoyi Han Institute of Molecular Science Key Lab of Materials for Energy Conversion and Storage of Shanxi Province Key Laboratory of Chemical Biology and Molecular Engineering of Education Ministry Shanxi University Taiyuan 030006 China Institute for Carbon-Based Thin Film Electronics Peking University Shanxi (ICTFE-PKU) Taiyuan 030012 China Contribution: Formal analysis (lead) Funding acquisition (lead) Methodology (lead) Resources (lead) Writing - original draft (lead) Writing - review & editing (lead) Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Contribution: Data curation (equal) Methodology (equal) Validation (equal) Visualization (equal) Contribution: Formal analysis (supporting) Investigation (supporting) Department of Chemistry Southern University of Science and Technology Shenzhen 518055 China Contribution: Formal analysis (equal) Funding acquisition (supporting) Methodology (supporting) Visualization (equal) Writing - review & editing (supporting) Contribution: Funding acquisition (supporting) Investigation (equal) Methodology (equal) Writing - review & editing (supporting) Contribution: Project administration (supporting) Supervision (equal)

Iron phthalocyanine (FePc) with unique FeN 4 site has attracted increasing interests as a promising non-precious catalyst. However, the plane symmetric structure endows FePc with undesired catalytic performance toward the oxygen reduction reaction (ORR). Here, we report a novel one-dimensional heterostructured ORR catalyst by coupling FePc at polyoxometalate-encapsulated carbon nanotubes (FePc-{PW 12 }@NTs) using host-guest chemistry. The encapsulation of polyoxometalates can induce a local tensile strain of single-walled NTs to strengthen the interactions with FePc. Both the strain and curvature effects of {PW 12 }@NT scaffold tune the geometric structure and electronic localization of FeN 4 centers to enhance the ORR catalytic performance. As expected, such a heterostructured FePc-{PW 12 }@NT electrocatalyst exhibits prominent durability, methanol tolerance, and ORR activity with a high half-wave potential of 0.90 V and a low Tafel slope of 30.9 mV dec −1 in alkaline medium. Besides, the assembled zinc-air battery demonstrates an ultrahigh power density of 280 mW cm −2 , excellent charge/discharge ability and long-term stability over 500 h, outperforming that of the commercial Pt/C+IrO 2 cathode. This study offers a new strategy to design novel heterostructured catalysts and opens a new avenue to regulate the electrocatalytic performance of phthalocyanine molecules.

关键词： Carbon Nanotubes Electrocatalyst Oxygen Reduction Phthalocyanine Polyoxometalates

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H2O2-Responsive NIR-II AIE Nanobomb for Carbon Monoxide Boosting Low-Temperature Photothermal Therapy

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Angewandte Chemie 2022年第36期134卷

作者： Gongcheng Ma Zhongke Liu Chunguang Zhu Prof. Huajie Chen Prof. Ryan T. K. Kwok Prof. Pengfei Zhang Ben Zhong Tang Prof. Lintao Cai Prof. Ping Gong Guangdong Key Laboratory of Nanomedicine Shenzhen Engineering Laboratory of Nanomedicine and Nanoformulations CAS-HK Joint Lab for Biomaterials Research Laboratory for Biomedical Optics and Molecular Imaging Shenzhen Key Laboratory for Molecular Imaging CAS Key Lab for Health Informatics Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China Contribution: Data curation (lead) Validation (lead) Writing - original draft (lead) Nano Science and Technology Institute University of Science & Technology of China Suzhou 215123 China Contribution: Data curation (equal) Investigation (equal) Key Laboratory of Environmentally Friendly Chemistry and Application of the Ministry of Education College of Chemistry Xiangtan University Xiangtan 411105 China Contribution: Data curation (supporting) Methodology (supporting) Contribution: Project administration (supporting) Resources (supporting) Supervision (supporting) Writing - review & editing (equal) Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction and Institute for Advanced Study The Hong Kong University of Science and Technology (HKUST) Clear Water Bay Kowloon Hong Kong China Contribution: Funding acquisition (equal) Supervision (equal) Writing - review & editing (equal) Contribution: Conceptualization (lead) Formal analysis (lead) Methodology (equal) Project administration (lead) Resources (equal) Supervision (equal) Writing - original draft (equal) Writing - review & editing (lead) School of Science and Engineering Shenzhen Institute of Aggregate Science and Technology The Chinese University of Hong Kong Shenzhen Guangdong 518172 China Contribution: Formal analysis (supporting) Funding acquisition (equal) Writing - review & editing (supporting) Contribution: Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - revi

Low-temperature photothermal therapy (PTT), which circumvents the limitations of conventional PTT (e.g., thermotolerance and adverse effects), is an emerging therapeutic strategy which shows great potential for future clinical applications. The expression of heat shock proteins (HSPs) can dramatically impair the therapeutic efficacy of PTT. Thus, inhibition of HSPs repair and reducing the damage of nearby normal cells is crucial for improving the efficiency of low-temperature PTT. Herein, we developed a nanobomb based on the self-assembly of NIRII AIE polymer PBPTV and carbon monoxide (CO) carrier polymer mPEG(CO). This smart nanobomb can be exploded in a tumor microenvironment in which hydrogen peroxide is overexpressed and release CO into cancer cells to significantly inhibit the expression of HSPs and hence improve the antitumor efficiency of the low-temperature PTT.

关键词： Aggregation Induced Emission Gas Therapy Heat Shock Protein NIR-II Photothermal Therapy

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Fast & scalable distributed set similarity joins for big data analytics 33

Fast & scalable distributed set similarity joins for big dat...

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33rd IEEE International Conference on data engineering, ICDE 2017

作者： Rong, Chuitian Lin, Chunbin Silva, Yasin N. Wang, Jianguo Lu, Wei Du, Xiaoyong Tianjin Polytechnic University China Arizona State University United States University of California San Diego United States Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781509065431

Set similarity join is an essential operation in big data analytics, e.g., data integration and data cleaning, that finds similar pairs from two collections of sets. To cope with the increasing scale of the data, distributed algorithms are called for to support large-scale set similarity joins. Multiple techniques have been proposed to perform similarity joins using MapReduce in recent years. These techniques, however, usually produce huge amounts of duplicates in order to perform parallel processing successfully as MapReduce is a shared-nothing framework. The large number of duplicates incurs on both large shuffle cost and unnecessary computation cost, which significantly decrease the performance. Moreover, these approaches do not provide a load balancing guarantee, which results in a skewness problem and negatively affects the scalability properties of these techniques. To address these problems, in this paper, we propose a duplicate free framework, called FS-Join, to perform set similarity joins efficiently by utilizing an innovative vertical partitioning technique. FS-Join employs three powerful filtering methods to prune dissimilar string pairs without computing their similarity scores. To further improve the performance and scalability, FS-Join integrates horizontal partitioning. Experimental results on three real datasets show that FS-Join outperforms the state-of-Theart methods by one order of magnitude on average, which demonstrates the good scalability and performance qualities of the proposed technique. © 2017 IEEE.

关键词： MapReduce

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PIE: A Personalized Incentive for Location-aware Mobile Crowd Sensing

PIE: A Personalized Incentive for Location-aware Mobile Crow...

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IEEE Symposium on Computers and Communications

作者： Yao Wu Yuncheng Wu Juru Zeng Hong Chen Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China

Mobile crowd sensing has the potential to acquire massive data from places and address large-scale societal problems. However, most currently existing crowd sensing systems suffer from insufficient participants. Therefore, incentive design for crowd sensing is essential and urgent. In this paper, different from the auction-based and server-dominant incentives, we design a personalized incentive, PIE, with partiality for neither the server nor the participants with budget constraint. The total payment for all the participants accords to their collective participation level, and the individual reward for each participant depends on individual contribution. We measure the individual contribution and participation level based on Voronoi diagram and Shannon entropy. Both offline and online incentives are proposed with budget constraint. Experimental study shows that our incentives are participation-aware and contribution-dependent, which encourages participants' active join, balanced distribution and flexible reward.

关键词： Sensors Servers Atmospheric measurements Particle measurements Entropy Mobile communication Computational modeling

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