检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Dong, Bin Hou, Jikai Lu, Yiping Zhang, Zhihua Beijing International Center for Mathematical Research Peking University Center for Data Science Peking University Beijing Institute of Big Data Research Beijing China School of Mathematical Sciences Peking University Beijing China Institute for Computational & Mathematical Engineering Stanford University

Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation. Our answer is "early stopping". Assuming that the teacher network is overparameterized, we argue that the teacher network is essentially harvesting dark knowledge from the data via early stopping. This can be justified by a new concept, Anisotropic Information Retrieval (AIR), which means that the neural network tends to fit the informative information first and the non-informative information (including noise) later. Motivated by the recent development on theoretically analyzing overparameterized neural networks, we can characterize AIR by the eigenspace of the Neural Tangent Kernel(NTK). AIR facilities a new understanding of distillation. With that, we further utilize distillation to refine noisy labels. We propose a self-distillation algorithm to sequentially distill knowledge from the network in the previous training epoch to avoid memorizing the wrong labels. We also demonstrate, both theoretically and empirically, that self-distillation can benefit from more than just early stopping. Theoretically, we prove convergence of the proposed algorithm to the ground truth labels for randomly initialized overparameterized neural networks in terms of `2 distance, while the previous result was on convergence in 0-1 loss. The theoretical result ensures the learned neural network enjoy a margin on the training data which leads to better generalization. Empirically, we achieve better testing accuracy and entirely avoid early stopping which makes the algorithm more user-friendly. Copyright © 2019, The Authors. All rights reserved.

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

Chimera states in hybrid coupled neuron populations

arXiv

引用

arXiv 2020年

作者： Calim, Ali Torres, Joaquin J. Ozer, Mahmut Uzuntarla, Muhammet Department of Biomedical Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Department of Electromagnetism and Physics of the Matter Institute Carlos I for Theoretical and Computational Physics University of Granada GranadaE-18071 Spain Ministry of National Education Ankara Turkey Center for Artificial Intelligence and Data Science Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey

Here we study the emergence of chimera states, a recently reported phenomenon referring to the coexistence of synchronized and unsynchronized dynamical units, in a population of Morris-Lecar neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the relative abundance of chemical and electrical synapses affects the features of chimera and different synchrony states (i.e. incoherent, traveling wave and coherent) and the regions in the space of relevant parameters for their emergence. Additionally, we show that, when the relative population of chemical synapses increases further, a new intriguing chaotic dynamical behavior appears above the region for chimera states. This is characterized by the coexistence of two distinct synchronized states with different amplitude, and an unsynchronized state, that we denote as a chaotic amplitude chimera. We also discuss about the computational implications of such state. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

What affects the performance of POD for the simulation of heat transfer through building component?

引用

Journal of Physics: Conference Series 2021年第1期2069卷

作者： Tianfeng Hou Staf Roels Hans Janssen KU Leuven Department of Civil Engineering Building Physics Section 3001 Leuven Belgium Data Assimilation Research Team RIKEN Center for Computational Science Kobe Japan RIKEN Prediction Science Laboratory Kobe Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program Kobe Japan

The capability of the proper orthogonal decomposition for the simulation of heat transfer in building components is investigated via three applications: linear heat transfer (not coupled to mass transfer), mildly non-linear heat transfer (coupled to air and moisture transfer (hygroscopic)) and highly non-linear heat transfer (coupled to moisture transfer (capillary)). It is shown that increasing non-linearity leads to an increasing number of required construction modes. To further investigate the reason for this degrading performance of POD, the singular values' decay progress from the different training snapshots is addressed in this paper. The results confirm that a fast decay of the singular values implies a high interrelation of the snapshots and a better performance of the POD method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

arXiv

引用

arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe Terrien, Ryan Holcomb, Rae Ford, Eric Zawadzki, Brianna Bowler, Brendan P. Bender, Chad Cochran, William Diddams, Scott Endl, Michael Fredrick, Connor Halverson, Samuel Hearty, Fred Hill, Gary J. Lin, Andrea Metcalf, Andrew Monson, Andrew Ramsey, Lawrence Roy, Arpita Schwab, Christian Wright, Jason Zeimann, Gregory Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

来源：评论

学校读者我要写书评

暂无评论

THE TEMPO SURVEY II: science CASES LEVERAGED FROM A PROPOSED 30-DAY TIME DOMAIN SURVEY OF THE ORION NEBULA WITH THE NANCY GRACE ROMAN SPACE TELESCOPE

arXiv

引用

arXiv 2024年

作者： Soares-Furtado, Melinda Limbach, Mary Anne Vanderburg, Andrew Bally, John Becker, Juliette Rosen, Anna L. Bouma, Luke G. Vos, Johanna M. Howell, Steve B. Beatty, Thomas G. Best, William M.J. Cody, Anne Marie Distler, Adam D’Onghia, Elena Heller, René Hensley, Brandon S. Hinkel, Natalie R. Jackson, Brian Kounkel, Marina Kraus, Adam Mann, Andrew W. Marston, Nicholas T. Robberto, Massimo Rodriguez, Joseph E. Steffen, Jason H. Teske, Johanna K. Townsend, Richard Yarza, Ricardo Youngblood, Allison Department of Astronomy University of Wisconsin-Madison 475 N. Charter St. MadisonWI53706 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Center for Astrophysics and Space Astronomy Astrophysical and Planetary Sciences Department University of Colorado BoulderCO80389 United States Department of Astronomy San Diego State University San DiegoCA92182 United States Computational Science Research Center San Diego State University San DiegoCA92182 United States Department of Astronomy MC 249-17 California Institute of Technology PasadenaCA91125 United States School of Physics Trinity College Dublin The University of Dublin Dublin 2 Ireland NASA Ames Research Center Moffett FieldCA94035 United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States SETI Institute 189 N. Bernardo Ave #200 Mountain ViewCA94043 United States Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Physics & Astronomy Department Louisiana State University Baton RougeLA70803 United States Department of Physics Boise State University BoiseID83725-1570 United States Department of Physics & Astronomy Vanderbilt University NashvilleTN37235 United States Department of Physics & Astronomy The University of North Carolina at Chapel Hill Chapel HillNC27599-3255 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Johns Hopkins University 3400 N. Charles St. BaltimoreMD21218 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Un

The TEMPO (Transiting Exosatellites, Moons, and Planets in Orion) Survey is a proposed 30-day observational campaign using the Nancy Grace Roman Space Telescope. By providing deep, high-resolution, short-cadence infrared photometry of a dynamic star-forming region, TEMPO will investigate the demographics of exosatellites orbiting free-floating planets and brown dwarfs — a largely unexplored discovery space. Here, we present the simulated detection yields of three populations: extrasolar moon analogs orbiting free-floating planets, exosatellites orbiting brown dwarfs, and exoplanets orbiting young stars. Additionally, we outline a comprehensive range of anticipated scientific outcomes accompanying such a survey. These science drivers include: obtaining observational constraints to test prevailing theories of moon, planet, and star formation;directly detecting widely separated exoplanets orbiting young stars;investigating the variability of young stars and brown dwarfs;constraining the low-mass end of the stellar initial mass function;constructing the distribution of dust in the Orion Nebula and mapping evolution in the near-infrared extinction law;mapping emission features that trace the shocked gas in the region;constructing a dynamical map of Orion members using proper motions;and searching for extragalactic sources and transients via deep extragalactic observations reaching a limiting magnitude of mAB = 29.7 mag (F146 filter). © 2024, CC BY.

关键词： Orbits

来源：评论

学校读者我要写书评

暂无评论

Scaling up the lattice dynamics of amorphous materials by orders of magnitude

arXiv

引用

arXiv 2020年

作者： Kriuchevskyi, Ivan Palyulin, Vladimir V. Milkus, Rico Elder, Robert M. Sirk, Timothy W. Zaccone, Alessio Department of Physics "A. Pontremoli" University of Milan via Celoria 16 Milan20133 Italy Centre for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobelya Ulitsa 3 Moscow121205 Russia Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Polymers Branch U.S. Army Research Laboratory Aberdeen Proving Ground MD United States Bennett Aerospace Inc. CaryNC27518 United States Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD20903 United States

We generalise the non-affine theory of viscoelasticity for use with large, well-sampled systems of arbitrary chemical complexity. Having in mind predictions of mechanical and vibrational properties of amorphous systems with atomistic resolution, we propose an extension of the Kernel Polynomial Method (KPM) for the computation of the vibrational density of states (VDOS) and the eigenmodes, including the Γ-correlator of the affine force-field, which is a key ingredient of lattice-dynamic calculations of viscoelasticity. We show that the results converge well to the solution obtained by direct diagonalization (DD) of the Hessian (dynamical) matrix. As is well known, the DD approach has prohibitively high computational requirements for systems with N = 104 atoms or larger. Instead, the KPM approach developed here allows one to scale up lattice dynamic calculations of real materials up to 106 atoms, with a hugely more favorable (linear) scaling of computation time and memory consumption with N. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscoelasticity

来源：评论

学校读者我要写书评

暂无评论

Lessons Learned from Assessing Trustworthy AI in Practice

引用

Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Open-Source Knowledge Graph Ecosystem for the Life sciences

arXiv

引用

arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Multi-scale simulations of electron and ion dynamics in self-irradiated silicon

arXiv

引用

arXiv 2020年

作者： Lee, Cheng-Wei Stewart, James A. Foiles, Stephen M. Dingreville, Rémi Schleife, André Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Integrated Nanotechnologies Sandia National Laboratories AlbuquerqueNM87185 United States Computational Materials and Data Science Department Sandia National Laboratories AlbuquerqueNM87185 United States Materials Research Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

The interaction of energetic ions with the electronic and ionic system of target materials is an interesting but challenging multi-scale problem and understanding of the early stages after impact of heavy, initially charged ions is particularly poor. At the same time, energy deposition during these early stages determines later formation of damage cascades. We address the multi-scale character by combining real-time time-dependent density functional theory for electron dynamics with molecular dynamics simulations of damage cascades. Our first-principles simulations prove that core electrons affect electronic stopping and have an unexpected influence on the charge state of the projectile. We show that this effect is absent for light projectiles, but dominates the stopping physics for heavy projectiles. By parameterizing an inelastic energy loss friction term in the molecular dynamics simulations using our first-principles results, we also show a qualitative influence of electronic stopping physics on radiation-damage cascades. Copyright © 2020, The Authors. All rights reserved.

关键词： Projectiles

来源：评论

学校读者我要写书评

暂无评论

Vec2SPARQL: Integrating SPARQL queries and knowledge graph embeddings 11

Vec2SPARQL: Integrating SPARQL queries and knowledge graph e...

引用

11th International Conference Semantic Web Applications and Tools for Life sciences, SWAT4LS 2018

作者： Kulmanov, Maxat Kafkas, Senay Karwath, Andreas Malic, Alexander Gkoutos, Georgios V Dumontier, Michel Hoehndorf, Robert Computer Electrical and Mathematical Science and Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Centre for Computational Biology University of Birmingham Birmingham United Kingdom Institute of Data Science Maastricht University Maastricht Netherlands

Recent developments in machine learning have led to a rise of large number of methods for extracting features from structured data. The features are represented as vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://***/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://***/. © 2018 CEUR Workshop Proceedings. All rights reserved.

关键词： Vector spaces

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：