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MONTE-CARLO METHOD FOR SPIN MODELS WITH LONG-RANGE INTERACTIONS

INTERNATIONAL JOURNAL OF MODERN PHYSICS C-PHYSICS AND COMPUTERS 1995年第3期6卷 359-370页

作者： LUIJTEN, E BLOTE, HWJ Faculty of Applied Physics Delft University of Technology P.O. Box 5046 2600 GA Delft The Netherlands

We introduce a Monte Carlo method for the simulation of spin models with ferromagnetic long-range interactions in which the amount of time per spin-flip operation is independent of the system size, in spite of the fact that the interactions between each spin and all other spins are taken into account. We work out two algorithms for the q-state Potts model and discuss the generalization to systems with other interactions and to O(n) models. We illustrate the method with a simulation of the mean-field Ising model, for which we have also analytically calculated the leading finite-size correction to the dimensionless amplitude ratio [m(2)](2)/[m(4)] at the critical temperature.

关键词： LONG-RANGE INTERACTIONS ISING MODEL cluster algorithm

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UAV-Assisted cluster-Based Task Allocation for Mobile Crowdsensing in a Space-Air-Ground-Sea Integrated Network

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SENSORS 2024年第1期24卷 208-208页

作者： Liu, Yang Li, Yong Cheng, Wei Wang, Weiguang Yang, Junhua Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China Henan Univ Sci & Technol Sch Informat Engn Luoyang 471000 Peoples R China Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China

Mobile crowdsensing (MCS), which is a grassroots sensing paradigm that utilizes the idea of crowdsourcing, has attracted the attention of academics. More and more researchers have devoted themselves to adopting MCS in space-air-ground-sea integrated networks (SAGSINs). Given the dynamics of the environmental conditions in SAGSINs and the uncertainty of the sensing capabilities of mobile people, the quality and coverage of the sensed data change periodically. To address this issue, we propose a novel UAV-assisted cluster-based task allocation (UCTA) algorithm for MCS in SAGSINs in a two-stage process. We first introduce the edge nodes and establish a three-layer hierarchical system with UAV-assistance, called "Platform-Edge cluster-Participants". Moreover, an edge-aided attribute-based cluster algorithm is designed, aiming at organizing tasks into clusters, which significantly diminishes both the communication overhead and computational complexity while enhancing the efficiency of task allocation. Subsequently, a greedy selection algorithm is proposed to select the final combination that performs the sensing task in each cluster. Extensive simulations are conducted comparing the developed algorithm with the other three benchmark algorithms, and the experimental results unequivocally endorse the superiority of our proposed UCTA algorithm.

关键词： mobile crowdsensing SAGSIN edge computing cluster algorithm task allocation

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Spatio-temporal hierarchical cluster analysis of mining-induced seismicity in coal mines using Ward's minimum variance method

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JOURNAL OF APPLIED GEOPHYSICS 2021年 184卷 104249-104249页

作者： Lurka, Adam Cent Min Inst GIG Lab Min Geophys Katowice Poland

Mining-induced seismicity is one of the most dangerous physical phenomenon's accompanying coal and ore extraction in underground mines and often resulting in rock burst. In many cases, this seismicity can be grouped together, forming spatial clusters of events related to some structures in mines such as faults or pillars. However, as shown in this paper, the usual seismicity in coal mines is mainly related to current mining process and there are no distinct spatial or temporal groups of seismic events, but rather they form continuous cloud of events related to mining activity. Therefore, we introduce new spatio-temporal metric together with Ward's minimum variance method to obtain hierarchical clustering analysis of mining-induced seismicity. Constructing metric in this way made it possible to assess seismic hazard concurrently in space and time. The clustering method is utilized to a case study of mining-induced seismicity from coal mine with high seismic hazard and where high energy mainshock occurred. In each cluster, magnitude distribution of seismic activity is calculated, showing in some cases, a significant departure from Gutenberg-Richter distribution of seismic events, which can be important in seismic hazard analysis. As a measure of accuracy of each cluster locations we have introduced bootstrapping procedure. We have shown that location errors of seismic activity affect each cluster center location of the order of a few tens of meters. The presented cluster analysis of mining seismicity provides new measures to determine group of seismic sources and high stressed areas in the rock mass in mines allowing for determination of seismic and rockburst hazard. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mining induced seismicity cluster cluster algorithm Magnitude distribution Seismic event Seismic hazard Seismic event location Bootstrapping

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cluster Monte Carlo method for the 3D Ashkin-Teller model

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2020年第0期500卷 166365-000页

作者： Wojtkowiak, Zbigniew Musial, Grzegorz Adam Mickiewicz Univ Fac Phys Ul Uniwersytetu Poznanskiego 2 PL-61614 Poznan Poland

This paper presents a cluster Monte Carlo method suitable for both, the first- and the second-order phase transitions in the 3D Ashkin-Teller (AT) model. A cluster algorithm is necessary to verify correctness of the results obtained so far using Metropolis algorithm exhibiting significant critical slowing down. Moreover, metastable states have recently been investigated in this model which can affect the results, especially obtained using the Metropolis algorithm. Our Wolff type algorithm is described and its dynamic critical behavior is demonstrated. Our computer experiments exploit the properties of Binder and Challa cumulants and additionally the one proposed by Lee and Kosterlitz, the last two adapted by us to give clear results for the AT model. The energy distribution histogram method is also independently applied for the first time for the 3D AT model using the Wolff type algorithm. For validation of the previous results and of our algorithm, it is demonstrated that the results of our computations along the line between Ising and Potts points, which are resealed to their thermodynamic limits, are consistent with those obtained using the Metropolis algorithm. It is also shown that the presented cluster algorithm of the Wolff type significantly reduces the problem of critical slowing down for the 3D AT model, and the dynamic critical exponent reaches values close to zero. As the best strategy, it is suggested to use the cluster algorithm in the critical region and the Metropolis one beyond.

关键词： Monte Carlo method cluster algorithm Critical behavior Phase transitions Lattice spin systems 3D Ashkin-Teller model

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From co-expression to co-regulation: how many microarray experiments do we need?

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GENOME BIOLOGY 2004年第7期5卷 1-11页

作者： Yeung, KY Medvedovic, M Bumgarner, RE Univ Washington Dept Microbiol Seattle WA 98195 USA Univ Cincinnati Med Ctr Dept Environm Hlth Ctr Genome Informat Cincinnati OH 45267 USA

Background: cluster analysis is often used to infer regulatory modules or biological function by associating unknown genes with other genes that have similar expression patterns and known regulatory elements or functions. However, clustering results may not have any biological relevance. Results: We applied various clustering algorithms to microarray datasets with different sizes, and we evaluated the clustering results by determining the fraction of gene pairs from the same clusters that share at least one known common transcription factor. We used both yeast transcription factor databases (SCPD, YPD) and chromatin immunoprecipitation (ChIP) data to evaluate our clustering results. We showed that the ability to identify co-regulated genes from clustering results is strongly dependent on the number of microarray experiments used in cluster analysis and the accuracy of these associations plateaus at between 50 and 100 experiments on yeast data. Moreover, the model-based clustering algorithm MCLUST consistently outperforms more traditional methods in accurately assigning co-regulated genes to the same clusters on standardized data. Conclusions: Our results are consistent with respect to independent evaluation criteria that strengthen our confidence in our results. However, when one compares ChIP data to YPD, the false-negative rate is approximately 80% using the recommended p-value of 0.001. In addition, we showed that even with large numbers of experiments, the false-positive rate may exceed the true-positive rate. In particular, even when all experiments are included, the best results produce clusters with only a 28% true-positive rate using known gene transcription factor interactions.

关键词： cluster algorithm Microarray Experiment Additional Data File Microarray Dataset Gene Subset

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GENERALIZATION OF THE FORTUIN-KASTELEYN TRANSFORMATION AND ITS APPLICATION TO QUANTUM SPIN SIMULATIONS

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JOURNAL OF STATISTICAL PHYSICS 1995年第1-2期80卷 169-221页

作者： KAWASHIMA, N GUBERNATIS, JE LOS ALAMOS NATL LAB DIV THEORETLOS ALAMOSNM 87545

We generalize the Fortuin-Kasteleyn (FK) cluster representation of the partition function of the Ising model to represent the partition function of quantum spin models with an arbitrary spin magnitude in arbitrary dimensions. This generalized representation enables us to develop a new cluster algorithm for the simulation of quantum spin systems by the worldline Monte Carlo method. Because the Swendsen-Wang algorithm is based on the FK representation, the new cluster algorithm naturally includes it as a special case. As well as the general description of the new representation, we present an illustration of our new algorithm for some special interesting cases: the Ising model, the antiferromagnetic Heisenberg model with S=1, and a general Heisenberg model. The new algorithm is applicable to models with any range of the exchange interaction, any lattice geometry, and any dimensions.

关键词： QUANTUM MONTE CARLO cluster algorithm XXZ MODEL HEISENBERG MODEL XY MODEL

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Optimal delineation of joint sets using a fuzzy clustering algorithm

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International Journal of Rock Mechanics and Mining Sciences 1998年第4-5期35卷 495-496页

作者： Hammah, R.E. Curran, J.H. Rock Engineering Group Department of Civil Engineering University of Toronto Toronto Ont. M5S 1A4 Canada

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PCA-based population structure inference with generic clustering algorithms

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BMC BIOINFORMATICS 2009年第Sup1期10卷 1-13页

作者： Lee, Chih Abdool, Ali Huang, Chun-Hsi Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA

Background: Handling genotype data typed at hundreds of thousands of loci is very time-consuming and it is no exception for population structure inference. Therefore, we propose to apply PCA to the genotype data of a population, select the significant principal components using the Tracy-Widom distribution, and assign the individuals to one or more subpopulations using generic clustering algorithms. Results: We investigated K-means, soft K-means and spectral clustering and made comparison to STRUCTURE, a model-based algorithm specifically designed for population structure inference. Moreover, we investigated methods for predicting the number of subpopulations in a population. The results on four simulated datasets and two real datasets indicate that our approach performs comparably well to STRUCTURE. For the simulated datasets, STRUCTURE and soft K-means with BIC produced identical predictions on the number of subpopulations. We also showed that, for real dataset, BIC is a better index than likelihood in predicting the number of subpopulations. Conclusion: Our approach has the advantage of being fast and scalable, while STRUCTURE is very time-consuming because of the nature of MCMC in parameter estimation. Therefore, we suggest choosing the proper algorithm based on the application of population structure inference.

关键词： cluster algorithm Markov Chain Monte Carlo Genotype Data Real Dataset Spectral cluster

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Binary tree summation Monte Carlo simulation for Potts models

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2003年第1-2期321卷 351-358页

作者： Wang, JS Natl Univ Singapore Singapore MIT Alliance Singapore 119260 Singapore Natl Univ Singapore Dept Computat Sci Singapore 119260 Singapore

In this talk, we briefly comment on Sweeny and Gliozzi methods. cluster Monte Carlo method, and recent transition matrix Monte Carlo for Potts models. We mostly concentrate on a new algorithm known as 'binary tree summation'. Some of the most interesting features of this method will be highlighted-such as simulating fractional number of Potts states, as well as offering the partition function and thermodynamic quantities as functions of temperature in a single run. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： Sweeny algorithm Gliozzi algorithm cluster algorithm binary tree summation Ising model

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CHOOSING THE R-DIMENSION FOR THE FCV FAMILY OF clusterING algorithmS

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BIT 1982年第2期22卷 140-149页

作者： GUNDERSON, RW UTAH STATE UNIV DEPT MATHLOGANUT 84322

A strategy is given for selecting the dimensionr of the linear variety which is used to define the criterion functionalJ vrm and which determines the shape of the data clusters detected by the correspondingc-Varieties... 详细信息

关键词： Computational Mathematic cluster algorithm Linear Variety Data cluster

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