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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Structure of n-Lie algebras with involutive derivations

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International Journal of Mathematics and Mathematical Sciences 2018年第1期2018卷 1-9页

作者： Bai, Ruipu Hou, Shuai Gao, Yansha College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding 071002 China College of Mathematics and Information Science Hebei University Baoding 071002 China

We study the structure of n-Lie algebras with involutive derivations for n≥2. We obtain that a 3-Lie algebra A is a two-dimensional extension of Lie algebras if and only if there is an involutive derivation D on A=A1 A-1 such that dimA1=2 or dimA-1=2, where A1 and A-1 are subspaces of A with eigenvalues 1 and-1, respectively. We show that there does not exist involutive derivations on nonabelian n-Lie algebras with n=2s for s≥1. We also prove that if A is a (2s+2)-dimensional (2s+1)-Lie algebra with dimA1=r, then there are involutive derivations on A if and only if r is even, or r satisfies 1≤r≤s+2. We discuss also the existence of involutive derivations on (2s+3)-dimensional (2s+1)-Lie algebras. © 2018 Ruipu Bai et al.

关键词： LIE algebras NONABELIAN groups MATHEMATICAL proofs DERIVATIVES (Mathematics) SUBSPACES (Mathematics)

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The SCUBA-2 Large eXtragalactic Survey: 850 μm map, catalogue and the bright-end number counts of the XMM-LSS field

arXiv

引用

arXiv 2023年

作者： Garratt, T.K. Geach, J.E. Tamura, Y. Coppin, K.E.K. Franco, M. Ao, Y. Chen, C.-C. Cheng, C. Clements, D.L. Dai, Y.S. Dannerbauer, H. Greve, T.R. Hatsukade, B. Hwang, H.S. Jiang, L. Kohno, K. Koprowski, M.P. Michalowski, M.J. Sawicki, M. Scott, D. Shim, H. Takeuchi, T.T. Wang, W.-H. Xue, Y.Q. Yang, C. Centre for Astrophysics Research University of Hertfordshire HatfieldAL10 9AB United Kingdom Division of Particle and Astrophysical Science Nagoya University Aichi Nagoya464-8602 Japan Purple Mountain Observatory Key Laboratory for Radio Astronomy Chinese Academy of Sciences 10 Yuanhua Road Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Anhui Hefei230026 China Academia Sinica Institute of Astronomy and Astrophysics No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Chinese Academy of Sciences South America Center for Astronomy National Astronomical Observatories CAS Beijing100101 China Imperial College London Blackett Lab Prince Consort Road LondonSW7 2AZ United Kingdom National Astronomical Observatories Chinese Academy of Sciences 20A Datun RoadChaoyang District Beijing100101 China Tenerife La LagunaE-38205 Spain Universidad de La Laguna Dpto. Astrofísica Tenerife La LagunaE-38206 Spain National Space Institute DTU Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK-2800 Denmark Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Tokyo Mitaka181-0015 Japan Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Astronomy Program Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Kavli Institute for Astronomy and Astrophysics Peking University No. 5 Yiheyuan Road Haidian District Beijing100871 China Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Torun87-100 Poland Astronomical Observatory Institute Faculty of Physics Adam Mickiewicz University ul. Sloneczna 36 Poznań60-286 Poland Department of Astronomy and P

We present 850 μm imaging of the XMM-LSS field observed for 170 hours as part of the James Clerk Maxwell Telescope SCUBA-2 Large eXtragalactic Survey (S2LXS). S2LXS XMM-LSS maps an area of 9 deg2, reaching a moderate depth of 1σ 4 mJy beam−1. This is the largest contiguous area of extragalactic sky mapped by JCMT at 850 μm to date. The wide area of the S2LXS XMM-LSS survey allows us to probe the ultra-bright (S850μm & 15 mJy), yet rare submillimetre population. We present the S2LXS XMM-LSS catalogue, which comprises 40 sources detected at >5σ significance, with deboosted flux densities in the range of 7 mJy to 48 mJy. We robustly measure the bright-end of the 850 μm number counts at flux densities >7 mJy, reducing the Poisson errors compared to existing measurements. The S2LXS XMM-LSS observed number counts show the characteristic upturn at bright fluxes, expected to be motivated by local sources of submillimetre emission and high-redshift strongly lensed galaxies. We find that the observed 850 μm number counts are best reproduced by model predictions that include either strong lensing or source blending from a 15 arcsec beam, indicating that both may make an important contribution to the observed over-abundance of bright single dish 850 μm selected sources. We make the S2LXS XMM-LSS 850 μm map and >5σ catalogue presented here publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Blending

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Correntropy based self-organizing map

Correntropy based self-organizing map

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2016 International Conference on machine learning and Cybernetics, ICMLC 2016

作者： Shang, Qing-Zhen Xing, Hong-Jie Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China

ISBN: (纸本)9781509003891

Self-organizing map (SOM) is regarded as a type of feedfoward neural network. It has been successfully used for unsupervised learning. However, the objective function of the traditional SOM relies on the mean squared error (MSE) criterion, which makes the performance of SOM become poor in the presence of noise. In the paper, correntropy based measure is proposed to substitute MSE to enhance the anti-noise ability of SOM. Moreover, the half-quadratic optimization technique is utilized to deal with the corresponding optimization problem. Experimental results demonstrate that the proposed method achieves better anti-noise performance in comparison with the traditional SOM. © 2016 IEEE.

关键词： Self organizing maps

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SKA Science Data Challenge 2: analysis and results

arXiv

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arXiv 2023年

作者： Hartley, P. Bonaldi, A. Braun, R. Aditya, J.N.H.S. Aicardi, S. Alegre, L. Chakraborty, A. Chen, X. Choudhuri, S. Clarke, A.O. Coles, J. Collinson, J.S. Cornu, D. Darriba, L. Veneri, M. Delli Forbrich, J. Fraga, B. Galan, A. Garrido, J. Gubanov, F. Håkansson, H. Hardcastle, M.J. Heneka, C. Herranz, D. Hess, K.M. Jagannath, M. Jaiswal, S. Jurek, R.J. Korber, D. Kitaeff, S. Kleiner, D. Lao, B. Lu, X. Mazumder, A. Moldón, J. Mondal, R. Ni, S. Önnheim, M. Parra, M. Patra, N. Peel, A. Salomé, P. Sánchez-Expósito, S. Sargent, M. Semelin, B. Serra, P. Shaw, A.K. Shen, A.X. Sjöberg, A. Smith, L. Soroka, A. Stolyarov, V. Tolley, E. Toribio, M.C. van der Hulst, J.M. Sadr, A. Vafaei Verdes-Montenegro, L. Westmeier, T. Yu, K. Yu, L. Zhang, L. Zhang, X. Zhang, Y. Alberdi, A. Ashdown, M. Bom, C.R. Brüggen, M. Cannon, J. Chen, R. Combes, F. Conway, J. Courbin, F. Ding, J. Fourestey, G. Freundlich, J. Gao, L. Gheller, C. Guo, Q. Gustavsson, E. Jirstrand, M. Jones, M.G. Józsa, G. Kamphuis, P. Kneib, J.-P. Lindqvist, M. Liu, B. Liu, Y. Mao, Y. Marchal, A. Márquez, I. Meshcheryakov, A. Olberg, M. Oozeer, N. Pandey-Pommier, M. Pei, W. Peng, B. Sabater, J. Sorgho, A. Starck, J.L. Tasse, C. Wang, A. Wang, Y. Xi, H. Yang, X. Zhang, H. Zhang, J. Zhao, M. Zuo, S. SKA Observatory Jodrell Bank Lower Withington MacclesfieldSK11 9FT United Kingdom Jodrell Bank Centre for Astrophysics Department of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Shanghai Astronomical Observatory Key Laboratory of Radio Astronomy CAS 80 Nandan Road Shanghai200030 China DIO Observatoire de Paris CNRS PSL Paris75104 France Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 SHJL United Kingdom Department of Astronomy Astrophysics and Space Engineering Indian Institute of Technology Indore Indore453552 India National Astronomical Observatory Chinese Academy of Sciences 20A Datun Road Beijing100101 China School of Physics and Astronomy Queen Mary University of London LondonE1 4NS United Kingdom Centre for Strings Gravitation and Cosmology Department of Physics Indian Institute of Technology Madras Chennai600036 India Department of Physics Institute of Astronomy University of Cambridge Cambridge United Kingdom LERMA Observatoire de Paris PSL research Université CNRS Sorbonne Université Paris75104 France Glorieta de la Astronomía s/n Granada18008 Spain Department of Information Technology and Electrical Engineering University of Naples Federico II 21 Via Claudio NapoliI-80125 Italy Centre for Astrophysics Research University of Hertfordshire Hertfordshire Hatfield United Kingdom URCA RJ Rio de Janeiro22290-180 Brazil Observatoire de Sauverny Versoix1290 Switzerland Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University Moscow Russia Fraunhofer-Chalmers Centre & Fraunhofer Center for Machine Learning GothenburgSE-412 88 Sweden University of Hamburg Hamburg Observatory Gojenbergsweg 112 Hamburg21029 Germany Instituto de Física de Cantabria CSIC-UC Av. de Los Castros s/n SantanderE-39005 Spain ASTRON The Netherlands Institute for Radio Astronomy Postbus 2 Dwingeloo7990 AA Netherlands Kapteyn

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarise the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterise 233245 neutral hydrogen (Hi) sources in a simulated data product representing a 2000 h SKA MID spectral line observation from redshifts 0.25 to 0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, ‘reproducibility awards’ were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy – which combined predictions from two independent machine learning techniques to yield a 20 percent improvement in overall performance – underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

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Correntropy based self-organizing map

Correntropy based self-organizing map

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Qing-Zhen Shang Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Province China

ISBN: (纸本)9781509003914

关键词： Neurons Distributed databases Optimization Topology Kernel Cybernetics Self-organizing feature maps

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Recent developments in the PySCF program package

arXiv

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arXiv 2020年

作者： Sun, Qiming Zhang, Xing Banerjee, Samragni Bao, Peng Barbry, Marc Blunt, Nick S. Bogdanov, Nikolay A. Booth, George H. Chen, Jia Cui, Zhi-Hao Eriksen, Janus Juul Gao, Yang Guo, Sheng Hermann, Jan Hermes, Matthew R. Koh, Kevin Koval, Peter Lehtola, Susi Li, Zhendong Liu, Junzi Mardirossian, Narbe McClain, James D. Motta, Mario Mussard, Bastien Pham, Hung Q. Pulkin, Artem Purwanto, Wirawan Robinson, Paul J. Ronca, Enrico Sayfutyarova, Elvira Scheurer, Maximilian Schurkus, Henry F. Smith, James E.T. Sun, Chong Sun, Shi-Ning Upadhyay, Shiv Wagner, Lucas K. Wang, Xiao White, Alec Whitfield, James Daniel Williamson, Mark J. Wouters, Sebastian Yang, Jun Yu, Jason M. Zhu, Tianyu Berkelbach, Timothy C. Sharma, Sandeep Sokolov, Alexander Yu. Chan, Garnet Kin-Lic AxiomQuant Investment Management LLC Shanghai200120 China Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry and Biochemistry The Ohio State University ColumbusOH43210 United States Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of Chemistry Chinese Academy of Sciences Beijing100190 China Simbeyond B.V. P.O. Box 513 EindhovenNL-5600 MB Netherlands Department of Chemistry Lensfield Road CambridgeCB2 1EW United Kingdom Max Planck Institute for Solid State Research Heisenbergstraße 1 Stuttgart70569 Germany Department of Physics King's College London Strand LondonWC2R 2LS United Kingdom Department of Physics University of Florida GainesvilleFL32611 United States Quantum Theory Project University of Florida GainesvilleFL32611 United States School of Chemistry University of Bristol Cantock's Close BristolBS8 1TS United Kingdom Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Google Inc. Mountain ViewCA94043 United States FU Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany TU Berlin Machine Learning Group Marchstr. 23 Berlin10587 Germany Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemistry and Biochemistry The University of Notre Dame du Lac 251 Nieuwland Science Hall Notre DameIN46556 United States Simune Atomistics S.L. Avenida Tolosa 76 Donostia-San Sebastian Spain HelsinkiFI-00014 Finland Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Chemistry The Johns Hopkins University BaltimoreMD21218 United States AMGEN

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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An improved hybrid model for order quantity allocation and supplier risk exposure

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International Journal of Fuzzy System Applications 2016年第3期5卷 120-147页

作者： Ng, Peh Sang Zhang, Feng Department of Physical and Mathematical Science Universiti Tunku Abdul Rahman Jalan Universiti Perak Malaysia Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Hebei China

This paper investigates the risk exposure arising from the supplier evaluation criteria of cost, quality, delivery, and flexibility. An integrated method of Fuzzy Decision Making Trial and Evaluation laboratory (FDEMATEL) and Fuzzy Analytical Network Process (FANP), which is able to address interaction and feedback effects between the criteria and subjectivity in the decision rating, is used to generate the importance weights of the objectives for a network relationship problem. Different weights of the objectives are then incorporated into a Fuzzy Multi-Objective Programming (FMOP) model for determining the optimal order quantity allocated to the suppliers. The proposed method provides a more realistic decision making scenario by considering different importance weights of criteria from individual priority. Therefore, it provides a practical method to solve the real world problems. The validity of the proposed method is illustrated by a case study from a trading company. © 2016, IGI Global.

关键词： Risk assessment

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Index construction for mathematical expression retrieval based on Trie tree 4th

Index construction for mathematical expression retrieval bas...

引用

4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Sun, Jing Tian, Xuedong Liu, Dazhong Zhang, Zhiming School of Computer Science and Technology Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9781138028302

With two-dimensional structures, mathematical expressions deliver more information than normal text not only with symbols but also their spatial arrangements. Users of mathematical retrieval systems need more search modes, such as fuzzy matching mode, rather than only exact matching mode to fit the features of mathematical expressions. Therefore, more and more researchers focus on how to retrieve mathematical expressions on the levels of structural similarity matching and synonyms matching. In this paper, an index structure based on Trie tree is designed oriented to measure the structural similarity of mathematical expressions. Firstly, the existing math indexing and matching method are introduced. Then, a stack algorithm of extracting information of math operators in LaTeX format is given. Finally, an index method based on Trie Tree is proposed for structural matching of mathematical expressions. The experimental analysis shows the effectiveness of the index method in this paper. © 2015 Taylor & Francis Group, London.

关键词： Search engines

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