检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Sharif, Kashif Liu, Ximeng Du, Xiaojiang Guizani, Mohsen Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Information Systems Singapore Management University College of Mathematics and Computer Science Fuzhou University Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiquitous computing environment, customers release different computing or sensing tasks, and people, also known as data processors, participate in these tasks and get paid for providing their idle computing and communication resources. Thus, how to select an appropriate and reliable customer while not disclosing processors' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving customer selection scheme in ubiquitous computing, called TPCS, to enable potential processors select the customers with good reputation. The basic concept of TPCS is that each data processor holds a trust value, and the reputation score of the customer is calculated based on processors' trust values and feedbacks via a truth discovery process. To preserve processors' privacy, pseudonyms and Paillier cryptosystem are applied to conceal each processor's real identity. In addition, three authentication protocols are designed to ensure that only the valid data processors (i.e., the processors registering in the system, holding the truthful trust values, and joining the computing tasks) can pass the authentication. A comprehensive security analysis is conducted to prove that our proposed TPCS scheme is secure and can defend against several sophisticated attacks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Ubiquitous computing

来源：评论

学校读者我要写书评

暂无评论

Commentaries on Viewpoint: Using V̇o2max as a marker of training status in athletes - can we do better?

引用

Journal of applied physiology (Bethesda, Md. : 1985) 2022年第1期133卷 148-164页

作者： Pedro L Valenzuela Manuel Mateo-March Xabier Muriel Mikel Zabala Alejandro Lucia David Barranco-Gil Grégoire P Millet Franck Brocherie Johannes Burtscher Martin Burtscher Benjamin J Ryan Rachel A Gioscia-Ryan Stephane Perrey Víctor Rodrigo-Carranza Fernando González-Mohíno José María González-Ravé Jordan Santos-Concejero Benedito S Denadai Camila C Greco Arturo Casado Carl Foster Raffaele Mazzolari Giulia Nicole Baldrighi Elisa Pastorio Davide Malatesta Aurélien Patoz Fabio Borrani Stephen J Ives Justin A DeBlauw Ricardo Dantas de Lucas Fernando Klitzke Borszcz Eduardo Marcel Fernandes Nascimento Luiz Guilherme Antonacci Guglielmo Tiago Turnes Richard T Jaspers Stephan van der Zwaard Romuald Lepers Julien Louis Anderson Meireles Hiago L. R. de Souza Géssyca T de Oliveira Marcelo P dos Santos Rhaí A Arriel Moacir Marocolo B Hunter S Meyler D Muniz-Pumares Renato M Ferreira Abigail S Sogard Stephen J Carter Timothy D Mickleborough Guilherme Pereira Saborosa Raphael Dinalli de Oliveira Freitas Paula Souza Alves dos Santos João Pedro de Souza Ferreira Francisco de Assis Manoel Sandro Fernandes da Silva Christoph Triska Bettina Karsten Dajo Sanders Elliot S Lipksi David J Spindler Matthijs K. C. Hesselink Rodrigo Zacca Márcio Fagundes Goethel David Bruce Pyne Brayden M Wood Peyton E Allen Jaden L Gabelhausen Alexandra M Keller Mast T Lige Alicia S Oumsang Greg L Smart Hunter L Paris Arthur H Dewolf Guillaume Toffoli Borja Martinez-Gonzalez Samuele M Marcora Daniela Terson de Paleville Ricardo J Fernandes Susana M Soares J. Arturo Abraldes Guilherme Matta Arthur Henrique Bossi D G McCarthy W Bostad J Gibala Mary Vagula Grupo de Investigación en Actividad física y Salud (PaHerg) Instituto de Investigación Hospital 12 de Octubre (imas12) Madrid Spain Faculty of Sport Sciences Universidad Europea de Madrid Madrid Spain Sport Science Department. Universidad Miguel Hernández Elche Spain Human Performance and Sports Science Laboratory Faculty of Sport Sciences University of Murcia Murcia Spain Department of Physical Education & Sport Faculty of Sport Sciences University of Granada Granada Spain Institute of Sport Sciences University of Lausanne Lausanne Switzerland Laboratory Sport Expertise and Performance (EA 7370) French Institute of Sport (INSEP) Paris France Department of Sport Science University of Innsbruck Innsbruck Austria Thermal and Mountain Medicine Division US Army Research Institute of Environmental Medicine Natick Massachusetts Department of Anesthesiology University of Michigan Ann Arbor Michigan EuroMov Digital Health in Motion University of Montpellier Montpellier France Sport Training Lab University of Castilla-La Mancha Toledo Spain Facultad de Ciencias de la Vida y de la Naturaleza Universidad Nebrija Madrid Spain Department of Physical Education and Sport University of the Basque Country UPV/EHU Vitoria-Gasteiz Spain Human Performance Laboratory São Paulo State University Rio Claro Brazil Center for Sport Studies Rey Juan Carlos University Madrid Spain University of Wisconsin-La Crosse La Crosse Wisconsin Department of Physical Education and Sport University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain Department of Molecular Medicine University of Pavia Pavia Italy Department of Brain and Behavioural Sciences − Medical and Genomic Statistics Unit University of Pavia Pavia Italy Department of Biomedical Sciences for Health University of Milan Milan Italy Institute of Sport Sciences of University of Lausanne (ISSUL) University of Lausanne Lausanne Switzerland Health and Human Physiological Sciences Skidmore College Saratoga Springs New

来源：评论

学校读者我要写书评

暂无评论

Development of Virtual Learning Factory Toolkit for Production engineering Education

引用

IOP Conference Series: Materials science and engineering 2021年第1期1140卷

作者： Kashif Mahmood Tauno Otto Vladimir Kuts Walter Terkaj Marcello Urgo Geza Haidegger Department of Mechanical and Industrial Engineering Tallinn University of Technology Tallinn Estonia STIIMA-CNR Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council Milan Italy Manufacturing and Production Systems Division Department of Mechanical Engineering Politecnico di Milano Milan Italy SZTAKI – Institute for Computer Science and Control Budapest Hungary

The advent of industry 4.0 and the continuous digitalization of production ask for the enhancement of human skills and competences in the field of information and communication technology (ICT). Therefore, higher education has to keep pace with the global market needs for the necessary ICT skills and the overall understanding of the complexity of industries in 21st century. This paper focuses on the development and integration of Virtual Learning Factory (VLF) tools that can be used in production management and engineering education. The digital tools integrated in VLF toolkit can help students to exploit enabling technologies like simulation and virtual reality in their manufacturing studies and practical projects with industrial companies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SPINBIS: Spintronics based Bayesian Inference System with Stochastic Computing

arXiv

引用

arXiv 2019年

作者： Jia, Xiaotao Yang, Jianlei Dai, Pengcheng Liu, Runze Chen, Yiran Zhao, Weisheng Advanced Innovation Center for Big Data and Brain Computing Fert Beijing Research Institute School of microelectronics Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Fert Beijing Research Institute School of Computer Science and Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering Duke University NC Durham27708 United States

Bayesian inference is an effective approach for solving statistical learning problems, especially with uncertainty and incompleteness. However, Bayesian inference is a computing-intensive task whose efficiency is physically limited by the bottlenecks of conventional computing platforms. In this work, a spintronics based stochastic computing approach is proposed for efficient Bayesian inference. The inherent stochastic switching behaviors of spintronic devices are exploited to build stochastic bitstream generator (SBG) for stochastic computing with hybrid CMOS/MTJ circuits design. Aiming to improve the inference efficiency, an SBG sharing strategy is leveraged to reduce the required SBG array scale by integrating a switch network between SBG array and stochastic computing logic. A device-to-architecture level framework is proposed to evaluate the performance of spintronics based Bayesian inference system (SPINBIS). Experimental results on data fusion applications have shown that SPINBIS could improve the energy efficiency about 12× than MTJ-based approach with 45% design area overhead and about 26× than FPGA-based approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

The First 18 Months of JGR: MLC

Journal of Geophysical Research: Machine Learning and Comput...

引用

Journal of Geophysical research: Machine Learning and Computation 2025年第2期2卷

作者： Enrico Camporeale Raffaele Marino John Rundle Doris Folini Yangkang Chen Donald D. Lucas Xiaofeng Li Thomas E. Berger Geoffrey C. Fox Yixin Wen Chaopeng Shen Renata Wentzcovitch School of Physical and Chemical Sciences Queen Mary University of London London UK Space Weather Technology Research and Education Center University of Colorado Boulder CO USA Laboratoire de Mécanique des Fluides et d’Acoustique CNRS - École Centrale de Lyon Écully France Department of Physics and Astronomy Department of Earth and Planetary Sciences University of California Davis CA USA Department of Environmental System Science Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland Bureau of Economic Geology Jackson School of Geosciences The University of Texas at Austin Austin TX USA Atmospheric Earth and Energy Division Lawrence Livermore National Laboratory Livermore CA USA Institute of Oceanology Chinese Academy of Sciences Qingdao P. R. China Biocomplexity Institute and Computer Science Department University of Virginia Charlottesville VA USA Department of Geography University of Florida Gainesville FL USA Civil and Environmental Engineering The Pennsylvania State University University Park PA USA Department of Applied Physics and Applied Mathematics Department of Earth and Environmental Sciences Columbia University New York NY USA

As editors of Journal of Geophysical research: Machine Learning and Computation , we are delighted to celebrate the successes of our first 18 months. The journal's editors reflect on the first year and a half since its founding They express concern for the restriction or removal of publicly funded data sources Our effort focuses on ensuring that software shared in FAIR repositories is not only available, but also functional and citable

关键词：

来源：评论

学校读者我要写书评

暂无评论

SVM-based deep stacking networks

arXiv

引用

arXiv 2019年

作者： Wang, Jingyuan Feng, Kai Wu, Junjie MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations School of Economics and Management Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

CHIME/FRB Discovery of 25 Repeating Fast Radio Burst Sources

arXiv

引用

arXiv 2023年

作者： Andersen, Bridget C. Bandura, Kevin Bhardwaj, Mohit Boyle, P.J. Brar, Charanjot Cassanelli, Tomas Chatterjee, S. Chawla, Pragya Cook, Amanda M. Curtin, Alice P. Dobbs, Matt Dong, Fengqiu Adam Faber, Jakob T. Fandino, Mateus Fonseca, Emmanuel Gaensler, B.M. Giri, Utkarsh Herrera-Martin, Antonio Hill, Alex S. Ibik, Adaeze Josephy, Alexander Kaczmarek, Jane F. Kader, Zarif Kaspi, Victoria Landecker, T.L. Lanman, Adam E. Lazda, Mattias Leung, Calvin Lin, Hsiu-Hsien Masui, Kiyoshi W. McKinven, Ryan Mena-Parra, Juan Meyers, Bradley W. Michilli, D. Ng, Cherry Pandhi, Ayush Pearlman, Aaron B. Pen, Ue-Li Petroff, Emily Pleunis, Ziggy Rafiei-Ravandi, Masoud Rahman, Mubdi Ransom, Scott M. Renard, Andre Sand, Ketan R. Sanghavi, Pranav Scholz, Paul Shah, Vishwangi Shin, Kaitlyn Siegel, Seth Smith, Kendrick Stairs, Ingrid Su, Jianing Tendulkar, Shriharsh P. Vanderlinde, Keith Wang, Haochen Wulf, Dallas Zwaniga, Andrew Department of Physics McGill University 3600 rue University MontréalQCH3A 2T8 Canada Trottier Space Institute McGill University 3550 rue University MontréalQCH3A 2A7 Canada Lane Department of Computer Science and Electrical Engineering 1220 Evansdale Drive PO Box 6109 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8370451 Chile Department of Astronomy Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Canada Cahill Center for Astronomy and Astrophysics MC 249-17 California Institute of Technology PasadenaCA91125 United States Department of Physical Sciences Thompson Rivers University KamloopsBC Canada Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Department of Computer Science Math Physics & Statistics University of British Columbia KelownaBCV1V 1V7 Canada Dominion Radio Astrophysical Observatory Herzberg Research Centre for Astronomy and Astrophysics National Research Council Canada PO Box 248 PentictonBCV2A 6J9 Canada MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute

We present the discovery of 25 new repeating fast radio burst (FRB) sources found among CHIME/FRB events detected between 2019 September 30 and 2021 May 1. The sources were found using a new clustering algorithm that looks for multiple events co-located on the sky having similar dispersion measures (DMs). The new repeaters have DMs ranging from ∼220 pc cm−3 to ∼1700 pc cm−3, and include sources having exhibited as few as two bursts to as many as twelve. We report a statistically significant difference in both the DM and extragalactic DM (eDM) distributions between repeating and apparently nonrepeating sources, with repeaters having lower mean DM and eDM, and we discuss the implications. We find no clear bimodality between the repetition rates of repeaters and upper limits on repetition from apparently nonrepeating sources after correcting for sensitivity and exposure effects, although some active repeating sources stand out as anomalous. We measure the repeater fraction over time and find that it tends to an equilibrium of 2.6+2−296% over our total time-on-sky thus far. We also report on 14 more sources which are promising repeating FRB candidates and which merit follow-up observations for confirmation. Copyright © 2023, The Authors. All rights reserved.

关键词： Astrophysics

来源：评论

学校读者我要写书评

暂无评论

2023 Astrophotonics Roadmap: pathways to realizing multi-functional integrated astrophotonic instruments

arXiv

引用

arXiv 2023年

作者： Jovanovic, Nemanja Gatkine, Pradip Anugu, Narsireddy Amezcua-Correa, Rodrigo Thakur, Ritoban Basu Beichman, Charles Bender, Chad Berger, Jean-Philippe Bigioli, Azzurra Bland-Hawthorn, Joss Bourdarot, Guillaume Bradford, Charles M. Broeke, Ronald Bryant, Julia Bundy, Kevin Cheriton, Ross Cvetojevic, Nick Diab, Momen Diddams, Scott A. Dinkelaker, Aline N. Duis, Jeroen Eikenberry, Stephen Ellis, Simon Endo, Akira Figer, Donald F. Fitzgerald, Michael Gris-Sanchez, Itandehui Gross, Simon Grossard, Ludovic Guyon, Olivier Haffert, Sebastiaan Y. Halverson, Samuel Harris, Robert J. He, Jinping Herr, Tobias Hottinger, Philipp Huby, Elsa Ireland, Michael Jenson-Clem, Rebecca Jewell, Jeffrey Jocou, Laurent Kraus, Stefan Labadie, Lucas Lacour, Sylvestre Laugier, Romain Lawniczuk, Katarzyna Lin, Jonathan Leifer, Stephanie Leon-Saval, Sergio Martin, Guillermo Martinache, Frantz Martinod, Marc-Antoine Mazin, Benjamin A. Minardi, Stefano Monnier, John D. Moreira, Reinan Mourard, Denis Nayak, Abani Shankar Norris, Barnaby Obrzud, Ewelina Perraut, Karine Reynaud, François Sallum, Steph Schiminovich, David Schwab, Christian Serbayn, Eugene Soliman, Sherif Stoll, Andreas Tang, Liang Tuthill, Peter Vahala, Kerry Vasisht, Gautam Veilleux, Sylvain Walter, Alexander B. Wollack, Edward J. Xin, Yinzi Yang, Zongyin Yerolatsitis, Stephanos Zhang, Yang Zou, Chang-Ling Department of Astronomy California Institute of Technology PasadenaCA United States The CHARA Array of Georgia State University Mount Wilson Observatory Mount WilsonCA91203 United States CREOL The College of Optics and Photonics University of Central Florida OrlandoFL United States IPAC NASA Exoplanet Science Institute Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Steward Observatory University of Arizona TucsonAZ United States Institut de Planétologie et d'Astrophysique UGA CNRS France Katholieke Universiteit Leuven Belgium School of Physics The University of Sydney Australia Max Planck Institute for Extraterrestrial Physics Garching Germany Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Bright Photonics BV Eindhoven Netherlands Department of Astronomy and Astrophysics University of California Santa CruzCA United States Advanced Electronics and Photonics Research Centre National Research Council Canada Ottawa Canada Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange France Dunlap Institute for Astronomy and Astrophysics University of Toronto Toronto Canada Electrical Computer and Energy Engineering Department of Physics University of Colorado BoulderCO United States Potsdam Germany PHIX Photonics Assembly Enschede Netherlands Australian Astronomical Optics Macquarie University NSW Australia Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Center for Detectors Rochester Institute of Technology RochesterNY United States UCLA Physics & Astronomy Department Los AngelesCA United States ITEAM Research Institute Universitat Politècnica de València Valencia Spain MQ Photonics Research Centre Macquarie University NSW Australia XLIM Research Institute University of Limoges France Subaru Telescope National Astronomical Observatory of Japan National Institute of Natural Scien

Photonic technologies offer numerous functionalities that can be used to realize astrophotonic instruments. The most spectacular example to date is the ESO Gravity instrument at the Very Large Telescope in Chile that combines the light-gathering power of four 8 m telescopes through a complex photonic interferometer. Fully integrated astrophotonic devices stand to offer critical advantages for instrument development, including extreme miniaturization when operating at the diffraction-limit, as well as integration, superior thermal and mechanical stabilization owing to the small footprint, and high replicability offering significant cost savings. Numerous astrophotonic technologies have been developed to address shortcomings of conventional instruments to date, including for example the development of photonic lanterns to convert from multimode inputs to single mode outputs, complex aperiodic fiber Bragg gratings to filter OH emission from the atmosphere, complex beam combiners to enable long baseline interferometry with for example, ESO Gravity, and laser frequency combs for high precision spectral calibration of spectrometers. Despite these successes, the facility implementation of photonic solutions in astronomical instrumentation is currently limited because of 1) low throughputs from coupling to fibers, coupling fibers to chips, propagation and bend losses, device losses, etc., 2) difficulties with scaling to large channel count devices needed for large bandwidths and high resolutions, and 3) efficient integration of photonics with detectors, to name a few. In this roadmap, we identify 23 key areas that need further development. We outline the challenges and advances needed across those areas covering design tools, simulation capabilities, fabrication processes, the need for entirely new components, integration and hybridization and the characterization of devices. To realize these advances the astrophotonics community will have to work cooperatively with industr

关键词： Fiber Bragg gratings

来源：评论

学校读者我要写书评

暂无评论

Data-driven model reduction for multilinear control systems via tensor trains

arXiv

引用

arXiv 2019年

作者： Chen, Can Surana, Amit Bloch, Anthony Rajapakse, Indika Department of Mathematics Department of Electrical Engineering and Computer Science University of Michigan United Technologies Research Center 411 Silver Lane East HartfordCT06108 United States Department of Mathematics University of Michigan Department of Computational Medicine & Bioinformatics Medical School and Department of Mathematics University of Michigan

In this paper, we explore the role of tensor algebra in balanced truncation (BT) based model reduction/identification for high-dimensional multilinear/linear time invariant systems. In particular, we employ tensor train decomposition (TTD), which provides a good compromise between numerical stability and level of compression, and has an associated algebra that facilitates computations. Using TTD, we propose a new BT approach which we refer to as higher-order balanced truncation, and consider different data-driven variations including higher-order empirical gramians, higher-order balanced proper orthogonal decomposition and a higher-order eigensystem realization algorithm. We perform computational and memory complexity analysis for these different flavors of TTD based BT methods, and compare with the corresponding standard BT methods in order to develop insights into where the proposed framework may be beneficial. We provide numerical results on simulated and experimental datasets showing the efficacy of the proposed framework. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

First encounters on Bethe Lattices and Cayley trees

arXiv

引用

arXiv 2020年

作者： Peng, Junhao Sandev, Trifce Kocarev, Ljupco School of Math and Information Science Guangzhou University Guangzhou510006 China Guangdong Provincial Key Laboratory co-sponsored by province and city of Information Security Technology Guangzhou University Guangzhou510006 China Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 Skopje1000 Macedonia Institute of Physics & Astronomy University of Potsdam Potsdam-GolmD-14776 Germany Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 Skopje1000 Macedonia Faculty of Computer Science and Engineering Ss. Cyril and Methodius University P.O. Box 393 Skopje1000 Macedonia

In this work we consider the first encounter problems between a fixed and/or mobile target A and a moving trap B on Bethe Lattices and Cayley trees. The survival probability (SP) of the target A on the both kinds of structures are analyzed analytically and compared. On Bethe Lattices, the results show that the fixed target will still prolong its survival time, whereas, on Cayley trees, there are some initial positions where the target should move to prolong its survival time. The mean first encounter time (MFET) for mobile target A is evaluated numerically and compared with the mean first passage time (MFPT) for the fixed target A. Different initial settings are addressed and clear boundaries are obtained. These findings are helpful for optimizing the strategy to prolong the survival time of the target or to speed up the search process on Cayley trees, in relation to the target’s movement and the initial position configuration of the two walkers. We also present a new method, which uses a small amount of memory, for simulating random walks on Cayley trees. Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：