检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Budding, Edwin Southworth, John Pavlovski, Krešimir Rhodes, Michael D. Zihao, Wu Love, Tom Blackford, Mark G. Banks, Timothy S. Alexander, Murray Carter Observatory 40 Salamanca Road Kelburn Wellington6012 New Zealand School of Chemical & Physical Sciences Victoria University of Wellington PO Box 600 Wellington6140 New Zealand Astrophysics Group Keele University StaffordshireST5 5BG United Kingdom Department of Physics Faculty of Science University of Zagreb Bijenička cesta 32 Zagreb10000 Croatia Brigham Young University ProvoUT84602 United States Dept. Statistics & Data Science National University of Singapore 6 Science Drive 2 Singapore117546 Singapore Variable Stars South RASNZ PO Box 3181 Wellington6011 New Zealand Variable Stars South Congarinni Observatory CongarinniNSW2447 Australia Nielsen 675 6th Ave New YorkNY10011 United States Dept. of Physical Science & Engineering Harper College 1200 W Algonquin Rd PalatineIL60067 United States Physics Department University of Winnipeg 515 Portage Avenue WinnipegR3B 2E9 Canada

New photometry, including TESS data, have been combined with recent spectroscopic observations of the Orion Ib pulsating triple-star system VV Ori. This yields a revised set of absolute parameters with increased precision. Two different programs were utilized for the light curve analysis, with results in predictably close agreement. The agreement promotes confidence in the analysis procedures. The spectra were analysed using the FDBinary program. The main parameters are as follows: M1 = 11.6 ± 0.14 and M2 = 4.8 ± 0.06 (M). We estimate an approximate mass of the wide companion as M3 = 2.0 ± 0.3 M. Similarly, R1 = 5.11±0.03, R2 = 2.51±0.02, R3 = 1.8±0.1 (R);Te1 = 26600±300, Te2 = 16300±400 and Te3 = 10000 ± 1000 (K). The close binary’s orbital separation is a = 13.91 (R);its age is 8 ± 2 (Myr) and its photometric distance is 396 ± 7 pc. The primary’s β Cep type oscillations support these properties and confirm our understanding of its evolutionary status. Examination of the well-defined λ6678 He I profiles reveals the primary to have a significantly low projected rotation: some 80% of the synchronous value. This can be explained on the basis of the precession of an unaligned spin axis. This proposal can resolve also observed variations of the apparent inclination and address other longer-term irregularities of the system reported in the literature. This topic invites further observations and follow-up theoretical study of the dynamics of this intriguing young multiple star. Copyright © 2023, The Authors. All rights reserved.

关键词： Photometry

来源：评论

学校读者我要写书评

暂无评论

Optimal resampling for learning small models

arXiv

引用

arXiv 2019年

作者： Ghose, Abhishek Ravindran, Balaraman Dept. of Computer Science and Engineering IIT Madras Dept. of Computer Science and Engineering Robert Bosch Centre for Data Science and AI IIT Madras

Models often need to be constrained to a certain size for them to be considered interpretable, for e.g., a decision tree of dept. 5 is much easier to make sense of than one of dept. 30. This suggests a trade-off between interpretability and accuracy. Our work tries to minimize this trade-off by suggesting the optimal distribution of the data to learn from, that surprisingly, may be different from the original distribution. We use an Infinite Beta Mixture Model (IBMM) to represent a specific set of sampling schemes. The parameters of the IBMM are learned using a Bayesian Optimizer (BO). While even under simplistic assumptions a distribution in the original d-dimensional space would need to optimize for O(d) variables - cumbersome for most real-world data - our technique lowers this number significantly to a fixed set of 8 variables at the cost of some additional preprocessing. The proposed technique is model-agnostic;it can be applied to any classifier. It also admits a general notion of model size. We demonstrate its effectiveness using multiple real-world datasets to construct decision trees, linear probability models and gradient boosted models. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Argumentative topology: finding loop(holes) in logic

arXiv

引用

arXiv 2020年

作者： Tymochko, Sarah New, Zachary Bynum, Lucius Purvine, Emilie Doster, Timothy Chaput, Julien Emerson, Tegan Dept. of Computational Mathematics Science and Engineering Michigan State University United States Pacific Northwest National Laboratory United States Department of Geological Sciences University of Texas El Paso United States

Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the introduction of increasingly sophisticated text representations. While many of the new word embedding techniques can be shown to capture particular notions of sentiment or associative structures, we explore the ability of two different word embeddings to uncover or capture the notion of logical shape in text. To this end we present a novel framework that we call Topological Word Embeddings which leverages mathematical techniques in dynamical system analysis and data driven shape extraction (i.e. topological data analysis). In this preliminary work we show that using a topological delay embedding we are able to capture and extract a different, shape-based notion of logic aimed at answering the question "Can we find a circle in a circular argument?" Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Public Kaggle Competition "IceCube – Neutrinos in Deep Ice"

arXiv

引用

arXiv 2023年

作者： Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Tjus, J. Becker Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Meneguolo, C. Boscolo Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Classen, L. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, D.F. Dave, P. De Clercq, C. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, Philipp Ellinger, E. El Mentawi, S. Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Fox, D. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, L. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.D. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. Jeong, M. Technical University of Munich Munich Data Science Institute Walther-von-Dyck-Strasse 10 Garching85748 Germany III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen Copenh

The reconstruction of neutrino events in the IceCube experiment is crucial for many scientific analyses, including searches for cosmic neutrino sources. The Kaggle competition "IceCube – Neutrinos in Deep ice" was a public machine learning challenge designed to encourage the development of innovative solutions to improve the accuracy and efficiency of neutrino event reconstruction. Participants worked with a dataset of simulated neutrino events and were tasked with creating a suitable model to predict the direction vector of incoming neutrinos. From January to April 2023, hundreds of teams competed for a total of $50k prize money, which was awarded to the best performing few out of the many thousand submissions. In this contribution I will present some insights into the organization of this large outreach project, and summarize some of the main findings, results and takeaways. Copyright © 2023, The Authors. All rights reserved.

关键词： Neutrons

来源：评论

学校读者我要写书评

暂无评论

Persistent Homology of Complex Networks for Dynamic State Detection

arXiv

引用

arXiv 2019年

作者： Myers, Audun Munch, Elizabeth Khasawneh, Firas A. Department of Mechanical Engineering Michigan State University Dept. of Computational Mathematics Science and Engineering Dept. of Mathematics Michigan State University

In this paper we develop a novel Topological data Analysis (TDA) approach for studying graph representations of time series of dynamical systems. Specifically, we show how persistent homology, a tool from TDA, can be used to yield a compressed, multi-scale representation of the graph that can distinguish between dynamic states such as periodic and chaotic behavior. We show the approach for two graph constructions obtained from the time series. In the first approach the time series is embedded into a point cloud which is then used to construct an undirected k-nearest neighbor graph. The second construct relies on the recently developed ordinal partition framework. In either case, a pairwise distance matrix is then calculated using the shortest path between the graph’s nodes, and this matrix is utilized to define a filtration of a simplicial complex that enables tracking the changes in homology classes over the course of the filtration. These changes are summarized in a persistence diagram—a two-dimensional summary of changes in the topological features. We then extract existing as well as new geometric and entropy point summaries from the persistence diagram and compare to other commonly used network characteristics. Our results show that persistence-based point summaries yield a clearer distinction of the dynamic behavior and are more robust to noise than existing graph-based scores, especially when combined with ordinal graphs. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Tidal Disruption: An Unforgettable Encounter with a Black Hole

arXiv

引用

arXiv 2023年

作者： Hosale, Mark-David Madsen, Jim Basu, Vedant Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Tjus, J. Becker Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Meneguolo, C. Boscolo Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Classen, L. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, D.F. Dave, P. De Clercq, C. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. Mentawi, S. El Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Fox, D. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, L. Halzen, F. Hamdaoui, H. Minh, M. Ha Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.D. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Computational Arts School of Art Media Performance and Design York University Toronto Canada WIPAC UW-Madison United States III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Cop

The annual GLEAM outdoor art exhibition features curated, large-scale light installations in the Olbrich Botanical Gardens in Madison, Wisconsin. With submissions from local, regional, and international artists, the annual two-month long event draws tens of thousands of visitors each fall. "Tidal Disruption," an art-science light sculpture representative of the form and behavior of a tidal disruption event, debuted at GLEAM 2022. The installation was a collaboration between artists Hosale and Abdu’Allah, and astrophysicist Madsen. The immersive light and sound exhibit stylistically depicted the death of a star as it spirals into a black hole in a roughly 4 minute sequence. Designed primarily by Hosale, the components were custom fabricated using clear PVC pipe at the Physical sciences Laboratory at UW-Madison. In this poster, we will describe the process of developing, fabricating, and installing "Tidal Disruption" and the response from the viewers. Copyright © 2023, The Authors. All rights reserved.

关键词： Gravitation

来源：评论

学校读者我要写书评

暂无评论

Self-attention capsule networks for object classification

arXiv

引用

arXiv 2019年

作者： Hoogi, Assaf Wilcox, Brian Gupta, Yachee Rubin, Daniel L. Dept. of Biomedical Data Science Stanford University Dept. of Computer Science and Applied Math Weizmann Institute of Science Dept. of Electrical Engineering Stanford University

We propose a novel architecture for object classification, called Self-Attention Capsule Networks (SACN). SACN is the first model that incorporates the Self-Attention mechanism as an integral layer within the Capsule Network (CapsNet). While the Self-Attention mechanism supplies a longrange dependencies, results in selecting the more dominant image regions to focus on, the CapsNet analyzes the relevant features and their spatial correlations inside these regions only. The features are extracted in the convolutional layer. Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task. The attention map is then fed into the CapsNet primary layer that is followed by a classification layer. The proposed SACN model was designed to solve two main limitations of the baseline CapsNet - analysis of complex data and significant computational load. In this work, we use a shallow CapsNet architecture and compensates for the absence of a deeper network by using the Self-Attention module to significantly improve the results. The proposed Self-Attention CapsNet architecture was extensively evaluated on six different datasets, mainly on three different medical sets, in addition to the natural MNIST, SVHN and CIFAR10. The model was able to classify images and their patches with diverse and complex backgrounds better than the baseline CapsNet. As a result, the proposed Self-Attention CapsNet significantly improved classification performance within and across different datasets and outperformed the baseline CapsNet, ResNet-18 and DenseNet-40 not only in classification accuracy but also in robustness. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Adaptive Partitioning for Template Functions on Persistence Diagrams

arXiv

引用

arXiv 2019年

作者： Tymochko, Sarah Munch, Elizabeth Khasawneh, Firas A. Dept. of Computational Mathematics Science & Engineering Michigan State University East LansingMI Dept. of Computational Mathematics Science & Engineering Dept. of Mathematics Michigan State University East LansingMI Dept. of Mechanical Engineering Michigan State University East LansingMI United States

—As the field of Topological data Analysis continues to show success in theory and in applications, there has been increasing interest in using tools from this field with methods for machine learning. Using persistent homology, specifically persistence diagrams, as inputs to machine learning techniques requires some mathematical creativity. The space of persistence diagrams does not have the desirable properties for machine learning, thus methods such as kernel methods and vectorization methods have been developed. One such featurization of persistence diagrams by Perea, Munch and Khasawneh in [1] uses continuous, compactly supported functions, referred to as "template functions," which results in a stable vector representation of the persistence diagram. In this paper, we provide a method of adaptively partitioning persistence diagrams to improve these featurizations based on localized information in the diagrams. Additionally, we provide a framework to adaptively select parameters required for the template functions in order to best utilize the partitioning method. We present results for application to example data sets comparing classification results between template function featurizations with and without partitioning, in addition to other methods from the literature. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Chatter Classification in Turning using Machine Learning and Topological data Analysis

引用

IFAC-PapersOnLine 2018年第14期51卷 195-200页

作者： Khasawneh, Firas A. Munch, Elizabeth Perea, Jose A. Dept. of Mechanical Engineering Michigan State University East LansingMI48824 United States Dept. of Computational Mathematics Science and Engineering Dept. of Mathematics Michigan State University East LansingMI48824 United States

Chatter identification and detection in machining processes has been an active area of research in the past two decades. Part of the challenge in studying chatter is that machining equations that describe its occurrence are often nonlinear delay differential equations. The majority of the available tools for chatter identification rely on defining a metric that captures the characteristics of chatter, and a threshold that signals its occurrence. The difficulty in choosing these parameters can be somewhat alleviated by utilizing machine learning techniques. However, even with a successful classification algorithm, the transferability of typical machine learning methods from one data set to another remains very limited. In this paper we combine supervised machine learning with Topological data Analysis (TDA) to obtain a descriptor of the process which can detect chatter. The features we use are derived from the persistence diagram of an attractor reconstructed from the time series via Takens embedding. We test the approach using deterministic and stochastic turning models, where the stochasticity is introduced via the cutting coefficient term. Our results show a 97% successful classification rate on the deterministic model labeled by the stability diagram obtained using the spectral element method. The features gleaned from the deterministic model are then utilized for characterization of chatter in a stochastic turning model where there are very limited analysis methods. © 2018

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

LEARNING GEOGRAPHICAL DISTRIBUTION of VACANT HOUSES USING CLOSED MUNICIPAL data: A CASE STUDY of WAKAYAMA CITY, JAPAN 5

LEARNING GEOGRAPHICAL DISTRIBUTION of VACANT HOUSES USING CL...

引用

5th International Conference on Smart data and Smart Cities, SDSC 2020

作者： Baba, H. Akiyama, Y. Tokudomi, T. Takahashi, Y. Center for Spatial Information Science The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba Japan Dept. of Urban and Civil Engineering Tokyo City University 1-28-1 Tamazutsumi Setagaya-ku Tokyo Japan Wakayama Data Utilization Promotion Center 3-17 Higashikuramaecho Wakayama Wakayama Japan Statistics Bureau of Japan 3-17 Higashikuramaecho Wakayama Wakayama Japan

Vacant housing detection is an urgent problem that needs to be addressed. It is also a suitable example to promote utilisation of smart data that are stored in municipalities. This study proposes a vacant housing detection model that uses closed municipal data and considers accelerating the use of public data to promote smart cities. Employing a machine learning technique, this study ensures high predictive power for vacant housing detection. The model enables us to handle complex municipal data that include non-linear feature characteristics and substantial missing data. In particular, handling missing data is important in the practical use of closed municipal data because not all of the data are necessarily absorbed to a building unit. Consequently, the model in this analysis showed that the accuracy and false positive rate are 95.4 percent and 3.7 percent, respectively, which are high enough to detect vacant houses. However, the true positive rate is 77.0 percent. Although the rate is not low to some extent, selection of features and further collection of extra samples may improve the rate. Geographic distribution of vacant houses further enabled us to check the difference between the actual and estimated number of vacant houses, and more than 80 percent of 500-meter grid data are with below 10 errors, which we think, provides city planners with informative data to roughly grasp geographical tendencies. © 2020 Copernicus GmbH. All rights reserved.

关键词： Machine learning

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：