检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Xiao, Yan Guo, Jiafeng Fan, Yixing Lan, Yanyan Xu, Jun Cheng, Xueqi University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: If the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Waveform Modelling for the Laser Interferometer Space Antenna

arXiv

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arXiv 2023年

作者： Afshordi, Niayesh Akçay, Sarp Seoane, Pau Amaro Antonelli, Andrea Aurrekoetxea, Josu C. Barack, Leor Barausse, Enrico Benkel, Robert Bernard, Laura Bernuzzi, Sebastiano Berti, Emanuele Bonetti, Matteo Bonga, Béatrice Bozzola, Gabriele Brito, Richard Buonanno, Alessandra Cárdenas-Avendaño, Alejandro Casals, Marc Chernoff, David F. Chua, Alvin J.K. Clough, Katy Colleoni, Marta Compère, Geoffrey Dhesi, Mekhi Druart, Adrien Durkan, Leanne Faye, Guillaume Ferguson, Deborah Field, Scott E. Gabella, William E. García-Bellido, Juan Gracia-Linares, Miguel Gerosa, Davide Green, Stephen R. Haney, Maria Hannam, Mark Heffernan, Anna Hinderer, Tanja Helfer, Thomas Hughes, Scott A. Husa, Sascha Isoyama, Soichiro Katz, Michael L. Kavanagh, Chris Khanna, Gaurav Kidder, Larry E. Korol, Valeriya Küchler, Lorenzo Laguna, Pablo Larrouturou, François Le Tiec, Alexandre Leather, Benjamin Lim, Eugene A. Lim, Hyun Littenberg, Tyson B. Long, Oliver Lousto, Carlos O. Lovelace, Geoffrey Lukes-Gerakopoulos, Georgios Lynch, Philip Macedo, Rodrigo P. Markakis, Charalampos Maggio, Elisa Mandel, Ilya Maselli, Andrea Mathews, Josh Mourier, Pierre Neilsen, David Nagar, Alessandro Nichols, David A. Novák, Jan Okounkova, Maria O’Shaughnessy, Richard Oshita, Naritaka O’Toole, Conor Pan, Zhen Pani, Paolo Pappas, George Paschalidis, Vasileios Pfeiffer, Harald P. Pompili, Lorenzo Pound, Adam Pratten, Geraint Rüter, Hannes R. Ruiz, Milton Sam, Zeyd Sberna, Laura Shapiro, Stuart L. Shoemaker, Deirdre M. Sopuerta, Carlos F. Spiers, Andrew Sundar, Hari Tamanini, Nicola Thompson, Jonathan E. Toubiana, Alexandre Tsokaros, Antonios Upton, Samuel D. van de Meent, Maarten Vernieri, Daniele Wachter, Jeremy M. Warburton, Niels Wardell, Barry Witek, Helvi Witzany, Vojtěch Yang, Huan Zilhão, Miguel Albertini, Angelica Arun, K.G. Bezares, Miguel Bonilla, Alexander Chapman-Bird, Christian Cownden, Bradley Cunningham, Kevin Devitt, Chris Dolan, Sam Duque, Francisco Dyson, Conor Fryer, Chris L. Gair, Jonathan R. Giacomazzo, Bruno Gupta, Priti Han, Wen-Biao Haas, Roland Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada School of Mathematics and Statistics University College Dublin Belfield Dublin 4 Ireland Universitat Politècnica de València València Spain Max Planck Institute for Extraterrestrial Physics Garching Germany Higgs Centre for Theoretical Physics Edinburgh United Kingdom Kavli Institute for Astronomy and Astrophysics Beijing100871 China William H. Miller III Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Astrophysics University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom School of Mathematical Sciences and STAG Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom SISSA Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Max Planck Institute for Gravitational Physics Albert Einstein Institute PotsdamD-14476 Germany Laboratoire Univers et Théories CNRS Observatoire de Paris Université PSL Université Paris Cité Meudon92190 France Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena07743 Germany Dipartimento di Fisica "G. Occhialini" Universitá degli Studi di Milano-Bicocca Piazza della Scienza 3 Milano20126 Italy INFN Sezione di Milano-Bicocca Piazza della Scienza 3 Milano20126 Italy Institute for Mathematics Astrophysics and Particle Physics Radboud University Nijmegen6525 AJ Netherlands The University of Arizona Department of Astronomy TucsonAZ85721 United States CENTRA Departamento de Física Instituto Superior Técnico – IST Universidade de Lisboa – UL Avenida Rovisco Pais 1 Lisboa1049-001 Portugal Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States Institut für Theoretische Physik Universität Leipzig Brüderstraße 16 Leipzig04103 Germany Rio de JaneiroCEP 22290-180 Brazil Department of Astronomy Cornell Univers

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances;from the beginnings of inspirals that will venture into the ground-based detectors’ view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA’s discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome. © 2023, CC BY.

关键词： Gravity waves

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VC-TWJoin: A Stream Join Algorithm Based on Variable Update Cycle Time Window

VC-TWJoin: A Stream Join Algorithm Based on Variable Update ...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Yimu Ji Shangdong Liu Lili Lu Xianbo Lang Haichang Yao Ruchuan Wang Institute of High Performance Computing and Big Data Processing Nanjing University of Posts and Telecommunications School of Computer Science Nanjing College of Information and Technology School of Computer Science Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781538614839

Stream join is one of the key operations for real-time stream data query and calculation. In light of changeable velocity of stream data, traditional static stream join methods are not so adaptive that stream data computing performance will be affected. Based on the large quantity and constantly changing velocity of stream data, by considering traditional stream join algorithm, this paper proposes an optimized algorithm for variable update cycle stream based on time window (VC-TWJoin, Variable Cycle Time Window Join). For unsteady stream calculated in stream join, the optimal update cycle will be calculated to reduce the response time of stream join and improve join efficiency and real-time capability. Both theoretical analysis and experiments demonstrate that the algorithm is better than traditional join algorithms in terms of real-time capability, join response time and throughput.

关键词： Microsoft Windows Real-time systems data models Semantics Computer science Telecommunications Wireless sensor networks

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SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets

引用

Physical Review D 2024年第8期110卷 083509页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon B. Ansarinejad D. Bacon M. Bayliss K. Bechtol M. R. Becker B. A. Benson G. M. Bernstein M. Brodwin D. Brooks A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero R. Cawthon C. Chang R. Chen A. Choi J. Cordero M. Costanzi L. N. da Costa M. E. S. Pereira C. Davis J. DeRose S. Desai T. de Haan J. De Vicente H. T. Diehl S. Dodelson P. Doel C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. García-Bellido M. Gatti G. Giannini M. D. Gladders D. Gruen R. A. Gruendl I. Harrison W. G. Hartley K. Herner S. R. Hinton D. L. Hollowood W. L. Holzapfel K. Honscheid N. Huang E. M. Huff D. J. James M. Jarvis G. Khullar K. Kim R. Kraft K. Kuehn N. Kuropatkin F. Kéruzoré S. Lee P.-F. Leget N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. Mena-Fernández R. Miquel J. Myles A. Navarro-Alsina R. L. C. Ogando A. Palmese S. Pandey A. Pieres A. A. Plazas Malagón J. Prat M. Raveri C. L. Reichardt J. Roberson R. P. Rollins A. K. Romer C. Romero A. Roodman A. J. Ross E. S. Rykoff L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro T. Schrabback M. Schubnell L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin M. Smith T. Somboonpanyakul B. Stalder A. A. Stark V. Strazzullo E. Suchyta M. E. C. Swanson G. Tarle C. To M. A. Troxel I. Tutusaus T. N. Varga A. von der Linden N. Weaverdyck J. Weller P. Wiseman B. Yanny B. Yin M. Young Y. Zhang J. Zuntz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 6020 Innsbruck Austria Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstr. 1 85748 Garching Germany Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ-20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia Institute of Cosmology and Gravitation University of Portsmouth Portsmouth PO1 3FX United Kingdom Department of Physics University of Cincinnati Cincinnati Ohio 45221 USA Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue Madison Wisconsin 53706-1390 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Fermi National Accelerator Laboratory Batavia Illinois 60510-0500 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Physics and Astronomy University of Missouri 5110 Rockhill Road Kansas City Missouri 64110 USA Department of Physics and Astronomy University Coll

We present a Bayesian population modeling method to analyze the abundance of galaxy clusters identified by the South Pole Telescope (SPT) with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). We discuss and validate the modeling choices with a particular focus on a robust, weak-lensing-based mass calibration using DES data. For the DES Year 3 data, we report a systematic uncertainty in weak-lensing mass calibration that increases from 1% at z=0.25 to 10% at z=0.95, to which we add 2% in quadrature to account for uncertainties in the impact of baryonic effects. We implement an analysis pipeline that joins the cluster abundance likelihood with a multiobservable likelihood for the Sunyaev-Zel’dovich effect, optical richness, and weak-lensing measurements for each individual cluster. We validate that our analysis pipeline can recover unbiased cosmological constraints by analyzing mocks that closely resemble the cluster sample extracted from the SPT-SZ, SPTpol ECS, and SPTpol 500d surveys and the DES Year 3 and HST-39 weak-lensing datasets. This work represents a crucial prerequisite for the subsequent cosmological analysis of the real dataset.

关键词： Cosmological parameters Gravitational lenses Large scale structure of the Universe

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GPRInvNet: Deep learning-based ground penetrating radar data inversion for tunnel lining

arXiv

引用

arXiv 2019年

作者： Liu, Bin Ren, Yuxiao Liu, Hanchi Xu, Hui Wang, Zhengfang Cohn, Anthony G. Jiang, Peng The School of Qilu Transportation Shandong University Jinan250061 China The Geotechnical and Structural Engineering Techniques Research Center Shandong University Jinan250061 China The Data Science Institute Shandong University Jinan250061 China The School of Control Science and Engineering Shandong University Jinan250061 China The School of Computing University of Leeds LeedsLS2 9JT United Kingdom The School of Qilu Transportation Shandong University

A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第5期10卷 2002-2011页

作者： Hang, Renlong Liu, Qingshan Sun, Yubao Yuan, Xiaotong Pei, Hucheng Plaza, Javier Plaza, Antonio Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Beijing Electro-Mechanical Engineering Institute Beijing China Hyperspectral Computing Laboratory University of Extremadura Caceres Spain

Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial-spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.

关键词： Hyperspectral image (HSI) classification multiscale fusion robust matrix discriminative analysis (RMDA) spatial-spectral feature extraction (FE)

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Learning knowledge representation across knowledge graphs 31

Learning knowledge representation across knowledge graphs

引用

31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Cai, Pengshan Li, Wei Feng, Yansong Wang, Yuanzhuo Jia, Yantao CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781577357865

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge- graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significandy outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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Efficient Algorithms for the Summed Area Tables Primitive on GPUs

Efficient Algorithms for the Summed Area Tables Primitive on...

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IEEE International Conference on Cluster computing

作者： Peng Chen Mohamed Wahib Shinichiro Takizawa Ryousei Takano Satoshi Matsuoka AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology National Institute of Advanced Industrial Science and Technology Tokyo Japan Dept. of Mathematical and Computing Science Tokyo Institute of Technology Tokyo Japan

Two-dimensional Summed Area Tables (SAT) is a fundamental primitive used in image processing and machine learning applications. We present a collection of optimization methods for computing SAT on CUDA-enabled GPUs. Conventional approaches rely on computing the prefix sum in one dimension in parallel, transposing the matrix, then computing the prefix sum for the other dimension in parallel. Additionally, conventional methods use the scratchpad memory as cache. We propose a collection of algorithms that are scalable with respect to problem size. We use the register cache technique instead of the scratchpad memory and also employ a naive serial scan on the thread level for computing the prefix sum for one of the dimensions. Using a novel transpose-in-registers method we increase the inter-thread parallelism and outperform conventional SAT implementations. In addition, we significantly reduce both the communication between threads and the number of arithmetic instructions. On an Nvidia Pascal P100 GPU and Volta V100, our evaluations demonstrate that our implementations outperform state of the art libraries and yield up to 2.3x and 3.2x speedup over OpenCV and Nvidia NPP libraries, respectively.

关键词： Registers Graphics processing units Instruction sets Two dimensional displays Computer architecture Libraries Acceleration

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Neural or statistical: An empirical study on language models for Chinese input recommendation on mobile 23rd

Neural or statistical: An empirical study on language models...

引用

23rd China conference on Information Retrieval, CCIR 2017

作者： Zhang, Hainan Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e.n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement. © Springer International Publishing AG 2017.

关键词： Recurrent neural networks

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Robust Mathematical Formulation and Probabilistic Description of Agent-Based Computational Economic Market Models

arXiv

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arXiv 2019年

作者： Beikirch, Maximilian Cramer, Simon Frank, Martin Otte, Philipp Pabich, Emma Trimborn, Torsten Rwth Aachen University Templergraben 55 Aachen52056 Germany Rwth Aachen University Production Metrology and Quality Management at Wzl Germany Steinbuch Center for Computing Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Forschungszentrum Julich GmbH Julich Supercomputing Centre Institute for Advanced Simulation Julich52425 Germany Institute for Data Science in Mechanical Engineering Rwth Aachen University Germany NRW.BANK Kavalleriestrae 22 Dusseldorf40213 Germany

In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic market models: The Levy-Levy-Solomon model and the Franke-Westerhoff model. We derive time-continuous formulations of both models, and in particular we discuss the impact of the time-scaling on the model behavior for the Levy-Levy-Solomon model. For the Franke-Westerhoff model, we proof that a constraint required in the original model is not necessary for stability of the time-continuous model. It is shown that a semi-implicit discretization of the time-continuous system preserves this unconditional stability. In addition, this semi-implicit discretization can be computed at cost comparable to the original model. Furthermore, we discuss possible probabilistic descriptions of time continuous agent-based computational economic market models. Especially, we present the potential advantages of kinetic theory in order to derive mesoscopic desciptions of agent-based models. Exemplified, we show two probabilistic descriptions of the Levy-Levy-Solomon and Franke-Westerhoff *** Codes 91B69, 91-10, 91B80, 82C40, 37A50 Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

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