检索结果-内蒙古大学图书馆

Generalized Quantum Subspace Expansion

Physical Review Letters 2022年第2期129卷 020502-020502页

作者： Nobuyuki Yoshioka Hideaki Hakoshima Yuichiro Matsuzaki Yuuki Tokunaga Yasunari Suzuki Suguru Endo Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako-shi Saitama 351-0198 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan

One of the major challenges for erroneous quantum computers is undoubtedly the control over the effect of noise. Considering the rapid growth of available quantum resources that are not fully fault tolerant, it is crucial to develop practical hardware-friendly quantum error mitigation (QEM) techniques to suppress unwanted errors. Here, we propose a novel generalized quantum subspace expansion method which can handle stochastic, coherent, and algorithmic errors in quantum computers. By fully exploiting the substantially extended subspace, we can efficiently mitigate the noise present in the spectra of a given Hamiltonian, without relying on any information of noise. The performance of our method is discussed under two highly practical setups: the quantum subspaces are mainly spanned by powers of the noisy state ρm and a set of error-boosted states, respectively. We numerically demonstrate in both situations that we can suppress errors by orders of magnitude, and show that our protocol inherits the advantages of previous error-agnostic QEM techniques as well as overcoming their drawbacks.

关键词： Quantum algorithms Quantum computation Quantum error correction Quantum information processing Variational approach

来源：评论

学校读者我要写书评

暂无评论

Developing an intelligent virtual reality interactive system based on the ADDIE model for learning pour-over coffee brewing

引用

computers and Education: Artificial Intelligence 2021年 2卷

作者： Yu, Shih-Jou Hsueh, Yu-Ling Sun, Jerry Chih-Yuan Liu, Hao-Ze Institute of Education National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Education National Chiao Tung University Hsinchu30010 Taiwan Department of Computer Science & Information Engineering National Chung Cheng University Chiayi62102 Taiwan National Chung Cheng University Chiayi62102 Taiwan National Chung Cheng University Chiayi62102 Taiwan National Center for High-performance Computing National Applied Research Laboratories Hsinchu Science Park Hsinchu30076 Taiwan

This study aims to use the analysis, design, development, implementation, and evaluation (ADDIE) model to develop an intelligent virtual reality (VR) interactive system for learning pour-over coffee brewing. The operation of pour-over coffee brewing is complex. An intelligent system is a concrete actuator that realizes artificial intelligence (AI) decisions. When learners face the challenge of learning complex skills, an intelligent system can actively analyze their learning behaviors and provide corresponding intelligent feedback to correct them. Furthermore, head-mounted-display VR can structure an immersive environment to improve the learning experience and knowledge transfer. This research integrates these two techniques into an intelligent VR interactive system and utilizes the ADDIE model to enable the system to achieve the ideal educational framework. The participants were 103 students who experienced and evaluated this system. The results indicated that the intelligent VR interactive system matched the participants' learning requirements and enhanced their learning performance. In addition, the learners proposed some suggestions to optimize the system, such as adding detailed content to the intelligent feedback and note function. Overall, this system increases the effectiveness of learning. The study suggests that instructors can use an intelligent VR interactive system to strengthen learners’ manipulative ability and knowledge. © 2021 The Authors

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

引用

Neural computing and Applications 2020年第3期32卷 625-638页

作者： Abd Ghani, Mohd Khanapi Mohammed, Mazin Abed Arunkumar, N. Mostafa, Salama A. Ibrahim, Dheyaa Ahmed Abdullah, Mohamad Khir Jaber, Mustafa Musa Abdulhay, Enas Ramirez-Gonzalez, Gustavo Burhanuddin, M.A. Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia Planning and Follow Up Department University Headquarter University of Anbar Anbar Iraq School of EEE Sastra University Thanjavur India Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia Department of Otorinolaringologi Hospital Pakar Sultanah Fatimah Muar Muar Johor Malaysia Dijlah University College Baghdad Iraq Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Departamento de Telemática Universidad del Cauca Popayán Cauca 190002 Colombia

Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple score-level fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors’ algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decision-based fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

One-shot quantum state redistribution and quantum Markov chains

arXiv

引用

arXiv 2021年

作者： Anshu, Anurag Hadiashar, Shima Bab Jain, Rahul Nayak, Ashwin Touchette, Dave School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Combinatorics and Optimization Institute for Quantum Computing University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Department of Computer Science Center for Quantum Technologies National University of Singapore 21 Lower Kent Ridge Rd Singapore119077 Singapore Department of Computer Science Institut Quantique Université de Sherbrooke 2500 Boulevard de l’Université SherbrookeQCJ1K 2R1 Canada

We revisit the task of quantum state redistribution in the one-shot setting, and design a protocol for this task with communication cost in terms of a measure of distance from quantum Markov chains. More precisely, the distance is defined in terms of quantum max-relative entropy and quantum hypothesis testing entropy. Our result is the first to operationally connect quantum state redistribution and quantum Markov chains, and can be interpreted as an operational interpretation for a possible one-shot analogue of quantum conditional mutual information. The communication cost of our protocol is lower than all previously known ones and asymptotically achieves the well-known rate of quantum conditional mutual information. Thus, our work takes a step towards an optimal characterization of the resources required for one-shot quantum state redistribution, an important open problem in quantum Shannon theory. © 2021, CC BY.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Guiding students towards an understanding of the electromotive force concept in electromagnetic phenomena through a teaching-learning sequence

引用

Physical Review Physics Education Research 2020年第2期16卷 020110-020110页

作者： Kristina Zuza Mieke De Cock Paul van Kampen Thomas Kelly Jenaro Guisasola Department of Applied Physics & DoPER University of the Basque Country (UPV/EHU) San Sebastian E-20018 Spain Department of Physics and Astronomy & LESEC KU Leuven Leuven B-3000 Belgium Centre for the Advancement of STEM Teaching and Learning & School of Physical Sciences Dublin City University (dcU) Dublin 9 Ireland Department of Computer Science & Applied Physics School of Science & Computing Galway-Mayo Institute of Technology Galway Ireland

In this work we present the application of design based research (DBR) methodology to conduct a systematic iterative study of the design and implementation of a teaching-learning sequence (TLS) on emf (electromotive force). This work is the final part of a broader study that started with the analysis of students’ difficulties with emf in the contexts of transitory current, direct current, and electromagnetic induction. We complete our research by investigating to what extent students see emf as a cross-cutting concept. We establish the design parameters based on an epistemological analysis and the empirical data, and describe how we devised and implemented the first version of the TLS that takes 4.5 h of classroom time. We analyze the data obtained in the first implementation and describe the redesign process that resulted in a second version of the TLS that takes 1.5 h of classroom time. We conclude that both versions of the TLS effect a similar boost in student attainment of learning objectives compared to the control group.

关键词： Concepts & principles Epistemology, attitudes, & beliefs Instructional materials development Scientific reasoning & problem solving

来源：评论

学校读者我要写书评

暂无评论

Picosecond Synchronization of Photon Pairs through a Fiber Link between Fermilab and Argonne National Laboratories

arXiv

引用

arXiv 2022年

作者： Kapoor, Keshav Xie, Si Chung, Joaquin Valivarthi, Raju Peña, Cristián Narváez, Lautaro Sinclair, Neil Allmaras, Jason P. Beyer, Andrew D. Davis, Samantha I. Fabre, Gabriel Iskander, George Kanter, Gregory S. Kettimuthu, Rajkumar Korzh, Boris Kumar, Prem Lauk, Nikolai Mueller, Andrew Shaw, Matthew Spentzouris, Panagiotis Spiropulu, Maria Thomas, Jordan M. Wollman, Emma E. The Fermi National Accelerator Laboratory BataviaIL60510 United States California Institute of Technology PasadenaCA91125 United States The Argonne National Laboratory LemontIL60439 United States The University of Chicago Physics Department ChicagoIL60637 United States The John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States The Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States The Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208 United States NuCrypt LLC 1460 Renaissance Dr #205 Park RidgeIL60068 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States The Division of Engineering and Applied Science The Alliance for Quantum Technologies and the Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States

We demonstrate a three-node quantum network for C-band photon pairs using 2 pairs of 59 km of deployed fiber between Fermi and Argonne National Laboratories. The C-band pairs are directed to nodes using a standard telecommunication switch and synchronized to picosecond-scale timing resolution using a coexisting O-or L-band optical clock distribution system. We measure a reduction of coincidence-to-accidental ratio (CAR) of the C-band pairs from 51 ± 2 to 5.3 ± 0.4 due to Raman scattering of the O-band clock pulses. Despite this reduction, the CAR is nevertheless suitable for quantum networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum communication

来源：评论

学校读者我要写书评

暂无评论

Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

引用

arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI’s Extraction in Real Time Traffic Signs Recognition System

引用

Journal of computer science Research 2019年第1期1卷 10-15页

作者： Fareed Qararyah Yousef-Awwad Daraghmi Eman Yasser Daraghmi Department of Computer Engineering KoçUniversityTurkey Computer Systems Engineering Department College of engineering and TechnologyPalestine Technical University-KaddoriePalestine Applied Computing Department College of Applied SciencePalestine Technical University-KaddoriePalestine

Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation *** sign recognition systems consist of an initial detection phase where images transportaand colors are segmented and fed to the recognition *** most challenging process in such systems in terms of time consumption is the detection *** trade off in previous studies,which proposed different methods for detecting traffic signs,is between accuracy and computation time,Therefore,this paper presents a novel accurate and time-efficient color segmentation approach based on logistic *** used RGB color space as the domain to extract the features of our hypothesis;this has boosted the speed of our approach since no color conversion is *** trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting *** results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.

关键词： Traffic sign recognition systems Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Democratizing Uncertainty Quantification

arXiv

引用

arXiv 2024年

作者： Seelinger, Linus Reinarz, Anne Lykkegaard, Mikkel B. Akers, Robert Alghamdi, Amal M.A. Aristoff, David Bangerth, Wolfgang Bénézech, Jean Diez, Matteo Frey, Kurt Jakeman, John D. Jørgensen, Jakob S. Kim, Ki-Tae Kent, Benjamin M. Martinelli, Massimiliano Parno, Matthew Pellegrini, Riccardo Petra, Noemi Riis, Nicolai A.B. Rosenfeld, Katherine Serani, Andrea Tamellini, Lorenzo Villa, Umberto Dodwell, Tim J. Scheichl, Robert Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science Durham University Durham United Kingdom digiLab Exeter United Kingdom UK Atomic Energy Authority Oxford United Kingdom Lyngby Denmark Department of Mathematics Colorado State University Fort CollinsCO United States Department of Mathematics Department of Geosciences Colorado State University Fort CollinsCO United States Centre for Integrated Materials Processes and Structures Department of Mechanical Engineering University of Bath Bath United Kingdom National Research Council Institute of Marine Engineering Rome Italy Institute for Disease Modeling Global Health Division Bill & Melinda Gates Foundation United States Optimization and Uncertainty Quantification Sandia National Laboratories AlbuquerqueNM United States University of California Merced United States National Research Council-Institute for Applied Mathematics and Information Technologies "E. Magenes" Pavia Italy Solea Energy ThetfordVT United States Copenhagen Imaging ApS Herlev Denmark The University of Texas Austin United States Heidelberg University Germany

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support. © 2024, CC BY-NC-ND.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

Understanding metric-related pitfalls in image analysis validation

arXiv

引用

arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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：