Cluster-in-molecule local correlation method for large systems
LI Wei; LI ShuHua
2014-01-01
A linear scaling local correlation method,cluster-in-molecule（CIM）method,was developed in the last decade for large systems.The basic idea of the CIM method is that the electron correlation energy of a large system,within the M ller-Plesset perturbation theory（MP）or coupled cluster（CC）theory,can be approximately obtained from solving the corresponding MP or CC equations of various clusters.Each of such clusters consists of a subset of localized molecular orbitals（LMOs）of the target system,and can be treated independently at various theory levels.In the present article,the main idea of the CIM method is reviewed,followed by brief descriptions of some recent developments,including its multilevel extension and different ways of constructing clusters.Then,some applications for large systems are illustrated.The CIM method is shown to be an efficient and reliable method for electron correlation calculations of large systems,including biomolecules and supramolecular complexes.
The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis
Chen Yidong
2004-01-01
Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.
Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes
Kok Gerdalize
2009-04-01
Full Text Available Abstract Background Mpumalanga Province, South Africa is a low malaria transmission area that is subject to malaria epidemics. SaTScan methodology was used by the malaria control programme to detect local malaria clusters to assist disease control planning. The third season for case cluster identification overlapped with the first season of implementing an outbreak identification and response system in the area. Methods SaTScan™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using definitively confirmed individual cases in seven towns over three malaria seasons. Following passive case reporting at health facilities during the 2002 to 2005 seasons, active case detection was carried out in the communities, this assisted with determining the probable source of infection. The distribution and statistical significance of the clusters were explored by means of Monte Carlo replication of data sets under the null hypothesis with replications greater than 999 to ensure adequate power for defining clusters. Results and discussion SaTScan detected five space-clusters and two space-time clusters during the study period. There was strong concordance between recognized local clustering of cases and outbreak declaration in specific towns. Both Albertsnek and Thambokulu reported malaria outbreaks in the same season as space-time clusters. This synergy may allow mutual validation of the two systems in confirming outbreaks demanding additional resources and cluster identification at local level to better target resources. Conclusion Exploring the clustering of cases assisted with the planning of public health activities, including mobilizing health workers and resources. Where appropriate additional indoor residual spraying, focal larviciding and health promotion activities, were all also carried out.
Lee Yun-Shien
2008-03-01
Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.
Singan, Vasanth R
2012-06-08
AbstractBackgroundThe localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization.FindingsWe have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells.ConclusionsWe show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.
Local Correlation Calculations Using Standard and Renormalized Coupled-Cluster Methods
Piecuch, Piotr; Li, Wei; Gour, Jeffrey
2009-03-01
Local correlation variants of the coupled-cluster (CC) theory with singles and doubles (CCSD) and CC methods with singles, doubles, and non-iterative triples, including CCSD(T) and the completely renormalized CR-CC(2,3) approach, are developed. The main idea of the resulting CIM-CCSD, CIM-CCSD(T), and CIM-CR-CC(2,3) methods is the realization of the fact that the total correlation energy of a large system can be obtained as a sum of contributions from the occupied orthonormal localized molecular orbitals and their respective occupied and unoccupied orbital domains. The CIM-CCSD, CIM-CCSD(T), and CIM-CR-CC(2,3) algorithms are characterized by the linear scaling of the total CPU time with the system size and embarrassing parallelism. By comparing the results of the canonical and CIM-CC calculations for normal alkanes and water clusters, it is demonstrated that the CIM-CCSD, CIM-CCSD(T), and CIM-CR-CC(2,3) approaches recover the corresponding canonical CC correlation energies to within 0.1 % or so, while offering savings in the computer effort by orders of magnitude. By examining the dissociation of dodecane into C11H23 and CH3 and several lowest-energy structures of the (H2O)n clusters, it is shown that the CIM-CC methods accurately reproduce the relative energetics of the corresponding canonical CC calculations.
A Bayesian cluster analysis method for single-molecule localization microscopy data.
Griffié, Juliette; Shannon, Michael; Bromley, Claire L; Boelen, Lies; Burn, Garth L; Williamson, David J; Heard, Nicholas A; Cope, Andrew P; Owen, Dylan M; Rubin-Delanchy, Patrick
2016-12-01
Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.
Krause, Christine; Werner, Hans-Joachim
2012-06-07
Explicitly correlated local coupled-cluster (LCCSD-F12) methods with pair natural orbitals (PNOs), orbital specific virtual orbitals (OSVs), and projected atomic orbitals (PAOs) are compared. In all cases pair-specific virtual subspaces (domains) are used, and the convergence of the correlation energy as a function of the domain sizes is studied. Furthermore, the performance of the methods for reaction energies of 52 reactions involving 58 small and medium sized molecules is investigated. It is demonstrated that for all choices of virtual orbitals much smaller domains are needed in the explicitly correlated methods than without the explicitly correlated terms, since the latter correct a large part of the domain error, as found previously. For PNO-LCCSD-F12 with VTZ-F12 basis sets on the average only 20 PNOs per pair are needed to obtain reaction energies with a root mean square deviation of less than 1 kJ mol(-1) from complete basis set estimates. With OSVs or PAOs at least 4 times larger domains are needed for the same accuracy. A new hybrid method that combines the advantages of the OSV and PNO methods is proposed and tested. While in the current work the different local methods are only simulated using a conventional CCSD program, the implications for low-order scaling local implementations of the various methods are discussed.
An efficient and near linear scaling pair natural orbital based local coupled cluster method
Riplinger, Christoph; Neese, Frank
2013-01-01
In previous publications, it was shown that an efficient local coupled cluster method with single- and double excitations can be based on the concept of pair natural orbitals (PNOs) [F. Neese, A. Hansen, and D. G. Liakos, J. Chem. Phys. 131, 064103 (2009), 10.1063/1.3173827]. The resulting local pair natural orbital-coupled-cluster single double (LPNO-CCSD) method has since been proven to be highly reliable and efficient. For large molecules, the number of amplitudes to be determined is reduced by a factor of 105-106 relative to a canonical CCSD calculation on the same system with the same basis set. In the original method, the PNOs were expanded in the set of canonical virtual orbitals and single excitations were not truncated. This led to a number of fifth order scaling steps that eventually rendered the method computationally expensive for large molecules (e.g., >100 atoms). In the present work, these limitations are overcome by a complete redesign of the LPNO-CCSD method. The new method is based on the combination of the concepts of PNOs and projected atomic orbitals (PAOs). Thus, each PNO is expanded in a set of PAOs that in turn belong to a given electron pair specific domain. In this way, it is possible to fully exploit locality while maintaining the extremely high compactness of the original LPNO-CCSD wavefunction. No terms are dropped from the CCSD equations and domains are chosen conservatively. The correlation energy loss due to the domains remains below 8800 basis functions and >450 atoms. In all larger test calculations done so far, the LPNO-CCSD step took less time than the preceding Hartree-Fock calculation, provided no approximations have been introduced in the latter. Thus, based on the present development reliable CCSD calculations on large molecules with unprecedented efficiency and accuracy are realized.
Wen Liu
2016-12-01
Full Text Available Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS. Due to the absence of satellite signal in Global Navigation Satellite System (GNSS, various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP, which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC, is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1 and the XiDan Joy City (Floors 1 and 2, as Test-bed 2, and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.
Liu, Wen; Fu, Xiao; Deng, Zhongliang
2016-12-02
Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.
Localized attack on clustering networks
Dong, Gaogao; Du, Ruijin; Shao, Shuai; Stanley, H Eugene; Shlomo, Havlin
2016-01-01
Clustering network is one of which complex network attracting plenty of scholars to discuss and study the structures and cascading process. We primarily analyzed the effect of clustering coefficient to other various of the single clustering network under localized attack. These network models including double clustering network and star-like NON with clustering and random regular (RR) NON of ER networks with clustering are made up of at least two networks among which exist interdependent relation among whose degree of dependence is measured by coupling strength. We show both analytically and numerically, how the coupling strength and clustering coefficient effect the percolation threshold, size of giant component, critical coupling point where the behavior of phase transition changes from second order to first order with the increase of coupling strength between the networks. Last, we study the two types of clustering network: one type is same with double clustering network in which each subnetwork satisfies ...
Rastgarpour, Maryam; Shanbehzadeh, Jamshid; Soltanian-Zadeh, Hamid
2014-08-01
medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.
Ronesh Sharma
2013-06-01
Full Text Available An automatic container code recognition system is of a great importance to the logistic supply chain management. Techniques have been proposed and implemented for the ISO container code region identification and recognition, however those systems have limitations on the type of container images with illumination factor and marks present on the container due to handling in the mass environmental condition. Moreover the research is not limited for differentiating between different formats of code and color of code characters. In this paper firstly an object clustering method is proposed to localize each line of the container code region. Secondly, the localizing algorithm is implemented with opencv and visual studio to perform localization and then recognition. Thus for real time application, the implemented system has added advantage of being easily integrated with other web application to increase the efficiency of the supply chain management. The experimental results and the application demonstrate the effectiveness of the proposed system for practical use.
Spectral clustering based on local linear approximations
Arias-Castro, Ery; Lerman, Gilad
2010-01-01
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface, possibly contaminated with outliers. We consider a prototype for a higher-order spectral clustering method based on the residual from a local linear approximation. In an asymptotic setting where the number of points becomes large, we obtain theoretical guaranties for this algorithm and show that, both in terms of separation and robustness to outliers, it outperforms the standard spectral clustering algorithm based on pairwise distances of Ng, Jordan and Weiss (NIPS, 2001). Under some conditions on the dimension of, and the incidence angle at, an intersection, the algorithm is able to recover the intersecting clusters. The optimal choice for some of the tuning parameters depends on the dimension and thickness of the clusters. We provide estimators that come close enough for our purposes. We discuss the cases of clusters of mixed dimensions and of clus...
Zhiqin Zhu
2016-11-01
Full Text Available The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods.
Unconventional methods for clustering
Kotyrba, Martin
2016-06-01
Cluster analysis or clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. The topic of this paper is one of the modern methods of clustering namely SOM (Self Organising Map). The paper describes the theory needed to understand the principle of clustering and descriptions of algorithm used with clustering in our experiments.
Spectral Clustering with Local Projection Distance Measurement
Chen Diao
2015-01-01
Full Text Available Constructing a rational affinity matrix is crucial for spectral clustering. In this paper, a novel spectral clustering via local projection distance measure (LPDM is proposed. In this method, the Local-Projection-Neighborhood (LPN is defined, which is a region between a pair of data, and other data in the LPN are projected onto the straight line among the data pairs. Utilizing the Euclidean distance between projective points, the local spatial structure of data can be well detected to measure the similarity of objects. Then the affinity matrix can be obtained by using a new similarity measurement, which can squeeze or widen the projective distance with the different spatial structure of data. Experimental results show that the LPDM algorithm can obtain desirable results with high performance on synthetic datasets, real-world datasets, and images.
Niching method using clustering crowding
GUO Guan-qi; GUI Wei-hua; WU Min; YU Shou-yi
2005-01-01
This study analyzes drift phenomena of deterministic crowding and probabilistic crowding by using equivalence class model and expectation proportion equations. It is proved that the replacement errors of deterministic crowding cause the population converging to a single individual, thus resulting in premature stagnation or losing optional optima. And probabilistic crowding can maintain equilibrium multiple subpopulations as the population size is adequate large. An improved niching method using clustering crowding is proposed. By analyzing topology of fitness landscape using hill valley function and extending the search space for similarity analysis, clustering crowding determines the locality of search space more accurately, thus greatly decreasing replacement errors of crowding. The integration of deterministic and probabilistic replacement increases the capacity of both parallel local hill climbing and maintaining multiple subpopulations. The experimental results optimizing various multimodal functions show that,the performances of clustering crowding, such as the number of effective peaks maintained, average peak ratio and global optimum ratio are uniformly superior to those of the evolutionary algorithms using fitness sharing, simple deterministic crowding and probabilistic crowding.
The SMART CLUSTER METHOD - adaptive earthquake cluster analysis and declustering
Schaefer, Andreas; Daniell, James; Wenzel, Friedemann
2016-04-01
Earthquake declustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity with usual applications comprising of probabilistic seismic hazard assessments (PSHAs) and earthquake prediction methods. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation. Various methods have been developed to address this issue from other researchers. These have differing ranges of complexity ranging from rather simple statistical window methods to complex epidemic models. This study introduces the smart cluster method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal identification. Hereby, an adaptive search algorithm for data point clusters is adopted. It uses the earthquake density in the spatio-temporal neighbourhood of each event to adjust the search properties. The identified clusters are subsequently analysed to determine directional anisotropy, focussing on a strong correlation along the rupture plane and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010/2011 Darfield-Christchurch events, an adaptive classification procedure is applied to disassemble subsequent ruptures which may have been grouped into an individual cluster using near-field searches, support vector machines and temporal splitting. The steering parameters of the search behaviour are linked to local earthquake properties like magnitude of completeness, earthquake density and Gutenberg-Richter parameters. The method is capable of identifying and classifying earthquake clusters in space and time. It is tested and validated using earthquake data from California and New Zealand. As a result of the cluster identification process, each event in
Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA
2009-12-22
Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.
Local Clusters in a Globalized World
Reinau, Kristian Hegner
Currently there is growing focus on how cluster internal and cluster external relations affect the creation of knowledge in companies placed in clusters. However, the current theories on this topic are too simple and the interplay between internal and external relations is relatively unknown....... This paper presents a case study that with the basis in the theory about tacit and explicit knowledge, the theory about communities of practice as well as parts of the theory on networks focuses on how knowledge is developed in high-tech companies placed in a cluster. Thus this case study illuminates how...... internal and external relations and factors affect the knowledge development. The findings are that most of the formal relations of the case companies studied are global in reach, while most informal relations are anchored locally in the cluster. The amount of externally anchored informal relations...
Semi-supervised clustering methods
Bair, Eric
2013-01-01
Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830
Cheng, Junying; Mei, Yingjie; Liu, Biaoshui; Guan, Jijing; Liu, Xiaoyun; Wu, Ed X; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu
2017-03-01
To develop and evaluate a novel 2D phase-unwrapping method that works robustly in the presence of severe noise, rapid phase changes, and disconnected regions. The MR phase map usually varies rapidly in regions adjacent to wraps. In contrast, the phasors can vary slowly, especially in regions distant from tissue boundaries. Based on this observation, this paper develops a phase-unwrapping method by using a pixel clustering and local surface fitting (CLOSE) approach to exploit different local variation characteristics between the phase and phasor data. The CLOSE approach classifies pixels into easy-to-unwrap blocks and difficult-to-unwrap residual pixels first, and then sequentially performs intrablock, interblock, and residual-pixel phase unwrapping by a region-growing surface-fitting method. The CLOSE method was evaluated on simulation and in vivo water-fat Dixon data, and was compared with phase region expanding labeler for unwrapping discrete estimates (PRELUDE). In the simulation experiment, the mean error ratio by CLOSE was less than 1.50%, even in areas with signal-to-noise ratio equal to 0.5, phase changes larger than π, and disconnected regions. For 350 in vivo knee and ankle images, the water-fat swap ratio of CLOSE was 4.29%, whereas that of PRELUDE was 25.71%. The CLOSE approach can correctly unwrap phase with high robustness, and benefit MRI applications that require phase unwrapping. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Localization technique in VANets using Clustering (LVC
Nasreddine Lagraa
2010-07-01
Full Text Available Relative location information is an important aspect in vehicular Ad hoc networks .It helps to build vehicle topology maps, also provides location information of nearby vehicles. Due to the characteristics of VANet, the existing relative positioning techniques developed initially for Ad hoc or sensors networks are not directly applicable to vehicular networks. In this paper, we propose a protocol of localization in VANet when no GPS information is available, based on clustering and has the advantage to use a single coordinates system. We study its impact on the performances of the network, by using the network simulator NS-2.
Local matrix learning in clustering and applications for manifold visualization.
Arnonkijpanich, Banchar; Hasenfuss, Alexander; Hammer, Barbara
2010-05-01
Electronic data sets are increasing rapidly with respect to both, size of the data sets and data resolution, i.e. dimensionality, such that adequate data inspection and data visualization have become central issues of data mining. In this article, we present an extension of classical clustering schemes by local matrix adaptation, which allows a better representation of data by means of clusters with an arbitrary spherical shape. Unlike previous proposals, the method is derived from a global cost function. The focus of this article is to demonstrate the applicability of this matrix clustering scheme to low-dimensional data embedding for data inspection. The proposed method is based on matrix learning for neural gas and manifold charting. This provides an explicit mapping of a given high-dimensional data space to low dimensionality. We demonstrate the usefulness of this method for data inspection and manifold visualization.
Gene Expression Data Knowledge Discovery using Global and Local Clustering
H, Swathi
2010-01-01
To understand complex biological systems, the research community has produced huge corpus of gene expression data. A large number of clustering approaches have been proposed for the analysis of gene expression data. However, extracting important biological knowledge is still harder. To address this task, clustering techniques are used. In this paper, hybrid Hierarchical k-Means algorithm is used for clustering and biclustering gene expression data is used. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. A validation technique, Figure of Merit is used to determine the quality of clustering results. Appropriate knowledge is mined from the clusters by embedding a BLAST similarity search program into the clustering and biclustering process. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. To determine the quality of clustering results, a validation technique, Figure of Merit is used. Appropriate ...
Scoring methods used in cluster analysis
Sirota, Sergej
2014-01-01
The aim of the thesis is to compare methods of cluster analysis correctly classify objects in the dataset into groups, which are known. In the theoretical section first describes the steps needed to prepare a data file for cluster analysis. The next theoretical section is dedicated to the cluster analysis, which describes ways of measuring similarity of objects and clusters, and dedicated to description the methods of cluster analysis used in practical part of this thesis. In practical part a...
A local search for a graph clustering problem
Navrotskaya, Anna; Il'ev, Victor
2016-10-01
In the clustering problems one has to partition a given set of objects (a data set) into some subsets (called clusters) taking into consideration only similarity of the objects. One of most visual formalizations of clustering is graph clustering, that is grouping the vertices of a graph into clusters taking into consideration the edge structure of the graph whose vertices are objects and edges represent similarities between the objects. In the graph k-clustering problem the number of clusters does not exceed k and the goal is to minimize the number of edges between clusters and the number of missing edges within clusters. This problem is NP-hard for any k ≥ 2. We propose a polynomial time (2k-1)-approximation algorithm for graph k-clustering. Then we apply a local search procedure to the feasible solution found by this algorithm and hold experimental research of obtained heuristics.
Analysis of forest fires spatial clustering using local fractal measure
Kanevski, Mikhail; Rochat, Mikael; Timonin, Vadim
2013-04-01
The research deals with an application of local fractal measure - local sandbox counting or mass counting, for the characterization of patterns of spatial clustering. The main application concerns the simulated (random patterns within validity domain in forest regions) and real data (forest fires in Ticino, Switzerland) case studies. The global patterns of spatial clustering of forest fires were extensively studied using different topological (nearest-neighbours, Voronoi polygons), statistical (Ripley's k-function, Morisita diagram) and fractal/multifractal measures (box-counting, sandbox counting, lacunarity) (Kanevski, 2008). Generalizations of these measures to functional ones can reveal the structure of the phenomena, e.g. burned areas. All these measures are valuable and complementary tools to study spatial clustering. Moreover, application of the validity domain (complex domain where phenomena is studied) concept helps in understanding and interpretation of the results. In the present paper a sandbox counting method was applied locally, i.e. each point of ignition was considered as a centre of events counting with an increasing search radius. Then, the local relationships between the radius and the number of ignition points within the given radius were examined. Finally, the results are mapped using an interpolation algorithm for the visualization and analytical purposes. Both 2d (X,Y) and 3d (X,Y,Z) cases were studied and compared. Local "fractal" study gives an interesting spatially distributed picture of clustering. The real data case study was compared with a reference homogeneous pattern - complete spatial randomness. The difference between two patterns clearly indicates the regions with important spatial clustering. An extension to the local functional measure was applied taking into account the surface of burned area, i.e. by analysing only data with the fires above some threshold of burned area. Such analysis is similar to marked point processes and
Convex Decomposition Based Cluster Labeling Method for Support Vector Clustering
Yuan Ping; Ying-Jie Tian; Ya-Jian Zhou; Yi-Xian Yang
2012-01-01
Support vector clustering (SVC) is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes. However,SVC's popularity is degraded by its highly intensive time complexity and poor label performance.To overcome such problems,we present a novel efficient and robust convex decomposition based cluster labeling (CDCL) method based on the topological property of dataset.The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors (SVs).According to a robust algorithm applied in the nearest neighboring convex hulls,the adjacency matrix of convex hulls is built up for finding the connected components; and the remaining data points would be assigned the label of the nearest convex hull appropriately.The approach's validation is guaranteed by geometric proofs.Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.
Local Community Detection Algorithm Based on Minimal Cluster
Yong Zhou
2016-01-01
Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.
Cluster-based global firms' use of local capabilities
Andersen, Poul Houman; Bøllingtoft, Anne
2011-01-01
knowledge base as a mediating variable, the purpose of this paper is to examine how globalization affected the studied firms’ use of local cluster-based knowledge, integration of local and global knowledge, and networking capabilities. Design/methodology/approach – Qualitative case studies of nine firms...... knowledge were highly active in local knowledge use, whereas CBFs characterized by a more implicit knowledge base did not use localized knowledge. Research limitations/implications – The study is exploratory and covers three clusters in one small and open developed economy. Further corroboration through...... takes a micro-oriented perspective and focus on clusters in Denmark, a small and mature economy...
An Overview on Clustering Methods
Madhulatha, T Soni
2012-01-01
Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.
A Continuous Clustering Method for Vector Fields
Garcke, H.; Preußer, T.; Rumpf, M.; Telea, A.; Weikard, U.; Wijk, J. van
2000-01-01
A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hillard model which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly
Single pass kernel -means clustering method
T Hitendra Sarma; P Viswanath; B Eswara Reddy
2013-06-01
In unsupervised classiﬁcation, kernel -means clustering method has been shown to perform better than conventional -means clustering method in identifying non-isotropic clusters in a data set. The space and time requirements of this method are $O(n^2)$, where is the data set size. Because of this quadratic time complexity, the kernel -means method is not applicable to work with large data sets. The paper proposes a simple and faster version of the kernel -means clustering method, called single pass kernel k-means clustering method. The proposed method works as follows. First, a random sample $\\mathcal{S}$ is selected from the data set $\\mathcal{D}$. A partition $\\Pi_{\\mathcal{S}}$ is obtained by applying the conventional kernel -means method on the random sample $\\mathcal{S}$. The novelty of the paper is, for each cluster in $\\Pi_{\\mathcal{S}}$, the exact cluster center in the input space is obtained using the gradient descent approach. Finally, each unsampled pattern is assigned to its closest exact cluster center to get a partition of the entire data set. The proposed method needs to scan the data set only once and it is much faster than the conventional kernel -means method. The time complexity of this method is $O(s^2+t+nk)$ where is the size of the random sample $\\mathcal{S}$, is the number of clusters required, and is the time taken by the gradient descent method (to ﬁnd exact cluster centers). The space complexity of the method is $O(s^2)$. The proposed method can be easily implemented and is suitable for large data sets, like those in data mining applications. Experimental results show that, with a small loss of quality, the proposed method can signiﬁcantly reduce the time taken than the conventional kernel -means clustering method. The proposed method is also compared with other recent similar methods.
Cluster expansion for ground states of local Hamiltonians
Bastianello, Alvise; Sotiriadis, Spyros
2016-08-01
A central problem in many-body quantum physics is the determination of the ground state of a thermodynamically large physical system. We construct a cluster expansion for ground states of local Hamiltonians, which naturally incorporates physical requirements inherited by locality as conditions on its cluster amplitudes. Applying a diagrammatic technique we derive the relation of these amplitudes to thermodynamic quantities and local observables. Moreover we derive a set of functional equations that determine the cluster amplitudes for a general Hamiltonian, verify the consistency with perturbation theory and discuss non-perturbative approaches. Lastly we verify the persistence of locality features of the cluster expansion under unitary evolution with a local Hamiltonian and provide applications to out-of-equilibrium problems: a simplified proof of equilibration to the GGE and a cumulant expansion for the statistics of work, for an interacting-to-free quantum quench.
Cluster expansion for ground states of local Hamiltonians
Alvise Bastianello
2016-08-01
Full Text Available A central problem in many-body quantum physics is the determination of the ground state of a thermodynamically large physical system. We construct a cluster expansion for ground states of local Hamiltonians, which naturally incorporates physical requirements inherited by locality as conditions on its cluster amplitudes. Applying a diagrammatic technique we derive the relation of these amplitudes to thermodynamic quantities and local observables. Moreover we derive a set of functional equations that determine the cluster amplitudes for a general Hamiltonian, verify the consistency with perturbation theory and discuss non-perturbative approaches. Lastly we verify the persistence of locality features of the cluster expansion under unitary evolution with a local Hamiltonian and provide applications to out-of-equilibrium problems: a simplified proof of equilibration to the GGE and a cumulant expansion for the statistics of work, for an interacting-to-free quantum quench.
Cluster expansion for ground states of local Hamiltonians
Bastianello, Alvise, E-mail: abastia@sissa.it [SISSA, via Bonomea 265, 34136 Trieste (Italy); INFN, Sezione di Trieste (Italy); Sotiriadis, Spyros [SISSA, via Bonomea 265, 34136 Trieste (Italy); INFN, Sezione di Trieste (Italy); Institut de Mathématiques de Marseille (I2M), Aix Marseille Université, CNRS, Centrale Marseille, UMR 7373, 39, rue F. Joliot Curie, 13453, Marseille (France); University of Roma Tre, Department of Mathematics and Physics, L.go S.L. Murialdo 1, 00146 Roma (Italy)
2016-08-15
A central problem in many-body quantum physics is the determination of the ground state of a thermodynamically large physical system. We construct a cluster expansion for ground states of local Hamiltonians, which naturally incorporates physical requirements inherited by locality as conditions on its cluster amplitudes. Applying a diagrammatic technique we derive the relation of these amplitudes to thermodynamic quantities and local observables. Moreover we derive a set of functional equations that determine the cluster amplitudes for a general Hamiltonian, verify the consistency with perturbation theory and discuss non-perturbative approaches. Lastly we verify the persistence of locality features of the cluster expansion under unitary evolution with a local Hamiltonian and provide applications to out-of-equilibrium problems: a simplified proof of equilibration to the GGE and a cumulant expansion for the statistics of work, for an interacting-to-free quantum quench.
Using an Improved Clustering Method to Detect Anomaly Activities
LI Han; ZHANG Nan; BAO Lihui
2006-01-01
In this paper, an improved k-means based clustering method (IKCM) is proposed. By refining the initial cluster centers and adjusting the number of clusters by splitting and merging procedures, it can avoid the algorithm resulting in the situation of locally optimal solution and reduce the number of clusters dependency. The IKCM has been implemented and tested. We perform experiments on KDD-99 data set. The comparison experiments with H-means+also have been conducted. The results obtained in this study are very encouraging.
Kernel method-based fuzzy clustering algorithm
Wu Zhongdong; Gao Xinbo; Xie Weixin; Yu Jianping
2005-01-01
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
Localized helium excitations in 4He_N-benzene clusters
Huang, P; Huang, Patrick
2003-01-01
We compute ground and excited state properties of small helium clusters 4He_N containing a single benzene impurity molecule. Ground-state structures and energies are obtained for N=1,2,3,14 from importance-sampled, rigid-body diffusion Monte Carlo (DMC). Excited state energies due to helium vibrational motion near the molecule surface are evaluated using the projection operator, imaginary time spectral evolution (POITSE) method. We find excitation energies of up to ~23 K above the ground state. These states all possess vibrational character of helium atoms in a highly anisotropic potential due to the aromatic molecule, and can be categorized in terms of localized and collective vibrational modes. These results appear to provide precursors for a transition from localized to collective helium excitations at molecular nanosubstrates of increasing size. We discuss the implications of these results for analysis of anomalous spectral features in recent spectroscopic studies of large aromatic molecules in helium clu...
Clustering method based on data division and partition
卢志茂; 刘晨; 张春祥; 王蕾
2014-01-01
Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets (VLDS). In this work, a novel division and partition clustering method (DP) was proposed to solve the problem. DP cut the source data set into data blocks, and extracted the eigenvector for each data block to form the local feature set. The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector. Ultimately according to the global eigenvector, the data set was assigned by criterion of minimum distance. The experimental results show that it is more robust than the conventional clusterings. Characteristics of not sensitive to data dimensions, distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.
2015-01-01
The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion ...
Cluster-based localization and tracking in ubiquitous computing systems
Martínez-de Dios, José Ramiro; Torres-González, Arturo; Ollero, Anibal
2017-01-01
Localization and tracking are key functionalities in ubiquitous computing systems and techniques. In recent years a very high variety of approaches, sensors and techniques for indoor and GPS-denied environments have been developed. This book briefly summarizes the current state of the art in localization and tracking in ubiquitous computing systems focusing on cluster-based schemes. Additionally, existing techniques for measurement integration, node inclusion/exclusion and cluster head selection are also described in this book.
Rees, Johanna Susan; Li, Xue-Wen; Perrett, Sarah; Lilley, Kathryn Susan; Jackson, Antony Philip
2017-04-03
This manuscript describes a new and general method to identify proteins localized into spatially restricted membrane microenvironments. Horseradish peroxidase (HRP) is brought into contact with a target protein by being covalently linked to a primary or secondary antibody, an antigen or substrate, a drug, or a toxin. A biotinylated tyramide-based reagent is then added. In the presence of HRP and hydrogen peroxide, the reagent is converted into a free radical that only diffuses a short distance before covalently labeling proteins within a few tens to hundreds of nanometers from the target. The biotinylated proteins can then be isolated by standard affinity chromatography and identified by liquid chromatography (LC) and mass spectrometry (MS). The assay can be made quantitative by using stable isotope labeling with amino acids in cell culture (SILAC) or isobaric tagging at the peptide level. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.
Data Reduction Method for Categorical Data Clustering
Sánchez Garreta, José Salvador; Rendón, Eréndira; García, Rene A.; Abundez, Itzel; Gutiérrez, Citlalih; Gasca, Eduardo
2008-01-01
Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes the database. This is done using a structure called CM-tree. In order to test our metho...
Probing molecular spin clusters by local measurements
Troiani, Filippo; Paris, Matteo G. A.
2016-09-01
We address the characterization of molecular nanomagnets at the quantum level and analyze the performance of local measurements in estimating the physical parameters in their spin Hamiltonians. To this aim, we compute key quantities in quantum estimation theory, such as the classical and the quantum Fisher information, in the prototypical case of a heterometallic antiferromagnetic ring. We show that local measurements, performed only on a portion of the molecule, allow a precise estimate of the parameters related to both magnetic defects and avoided level crossings.
Local Turgor Pressure Reduction via Channel Clustering.
Scher-Zagier, Jonah K; Carlsson, Anders E
2016-12-20
The primary drivers of yeast endocytosis are actin polymerization and curvature-generating proteins, such as clathrin and BAR domain proteins. Previous work has indicated that these factors may not be capable of generating the forces necessary to overcome turgor pressure. Thus local reduction of the turgor pressure, via localized accumulation or activation of solute channels, might facilitate endocytosis. The possible reduction in turgor pressure was calculated numerically, by solving the diffusion equation through a Legendre polynomial expansion. It was found that for a region of increased permeability having radius 45 nm, as few as 60 channels with a spacing of 10 nm could locally decrease the turgor pressure by 50%. We identified a key dimensionless parameter, p = P1a/D, where P1 is the increased permeability, a is the radius of the permeable region, and D is the solute diffusion coefficient. When p > 0.44, the turgor pressure is locally reduced by >50%. An approximate analytic theory was used to generate explicit formulas for the turgor pressure reduction in terms of key parameters. These findings may also be relevant to plants, where the mechanisms that allow endocytosis to proceed despite high turgor pressure are largely unknown. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Quantum Monte Carlo methods and lithium cluster properties. [Atomic clusters
Owen, R.K.
1990-12-01
Properties of small lithium clusters with sizes ranging from n = 1 to 5 atoms were investigated using quantum Monte Carlo (QMC) methods. Cluster geometries were found from complete active space self consistent field (CASSCF) calculations. A detailed development of the QMC method leading to the variational QMC (V-QMC) and diffusion QMC (D-QMC) methods is shown. The many-body aspect of electron correlation is introduced into the QMC importance sampling electron-electron correlation functions by using density dependent parameters, and are shown to increase the amount of correlation energy obtained in V-QMC calculations. A detailed analysis of D-QMC time-step bias is made and is found to be at least linear with respect to the time-step. The D-QMC calculations determined the lithium cluster ionization potentials to be 0.1982(14) (0.1981), 0.1895(9) (0.1874(4)), 0.1530(34) (0.1599(73)), 0.1664(37) (0.1724(110)), 0.1613(43) (0.1675(110)) Hartrees for lithium clusters n = 1 through 5, respectively; in good agreement with experimental results shown in the brackets. Also, the binding energies per atom was computed to be 0.0177(8) (0.0203(12)), 0.0188(10) (0.0220(21)), 0.0247(8) (0.0310(12)), 0.0253(8) (0.0351(8)) Hartrees for lithium clusters n = 2 through 5, respectively. The lithium cluster one-electron density is shown to have charge concentrations corresponding to nonnuclear attractors. The overall shape of the electronic charge density also bears a remarkable similarity with the anisotropic harmonic oscillator model shape for the given number of valence electrons.
Locality-Aware CTA Clustering For Modern GPUs
Li, Ang; Song, Shuaiwen; Liu, Weifeng; Liu, Xu; Kumar, Akash; Corporaal, Henk
2017-04-08
In this paper, we proposed a novel clustering technique for tapping into the performance potential of a largely ignored type of locality: inter-CTA locality. We first demonstrated the capability of the existing GPU hardware to exploit such locality, both spatially and temporally, on L1 or L1/Tex unified cache. To verify the potential of this locality, we quantified its existence in a broad spectrum of applications and discussed its sources of origin. Based on these insights, we proposed the concept of CTA-Clustering and its associated software techniques. Finally, We evaluated these techniques on all modern generations of NVIDIA GPU architectures. The experimental results showed that our proposed clustering techniques could significantly improve on-chip cache performance.
Sequential Combination Methods forData Clustering Analysis
钱 涛; Ching Y.Suen; 唐远炎
2002-01-01
This paper proposes the use of more than one clustering method to improve clustering performance. Clustering is an optimization procedure based on a specific clustering criterion. Clustering combination can be regardedasatechnique that constructs and processes multiple clusteringcriteria.Sincetheglobalandlocalclusteringcriteriaarecomplementary rather than competitive, combining these two types of clustering criteria may enhance theclustering performance. In our past work, a multi-objective programming based simultaneous clustering combination algorithmhasbeenproposed, which incorporates multiple criteria into an objective function by a weighting method, and solves this problem with constrained nonlinear optimization programming. But this algorithm has high computationalcomplexity.Hereasequential combination approach is investigated, which first uses the global criterion based clustering to produce an initial result, then uses the local criterion based information to improve the initial result with aprobabilisticrelaxation algorithm or linear additive model.Compared with the simultaneous combination method, sequential combination haslow computational complexity. Results on some simulated data and standard test data arereported.Itappearsthatclustering performance improvement can be achieved at low cost through sequential combination.
CHANGE DETECTION BY FUSING ADVANTAGES OF THRESHOLD AND CLUSTERING METHODS
M. Tan
2017-09-01
Full Text Available In change detection (CD of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effectiveness of the proposed method.
PERFORMANCE OF SELECTED AGGLOMERATIVE HIERARCHICAL CLUSTERING METHODS
Nusa Erman
2015-01-01
Full Text Available A broad variety of different methods of agglomerative hierarchical clustering brings along problems how to choose the most appropriate method for the given data. It is well known that some methods outperform others if the analysed data have a specific structure. In the presented study we have observed the behaviour of the centroid, the median (Gower median method, and the average method (unweighted pair-group method with arithmetic mean – UPGMA; average linkage between groups. We have compared them with mostly used methods of hierarchical clustering: the minimum (single linkage clustering, the maximum (complete linkage clustering, the Ward, and the McQuitty (groups method average, weighted pair-group method using arithmetic averages - WPGMA methods. We have applied the comparison of these methods on spherical, ellipsoid, umbrella-like, “core-and-sphere”, ring-like and intertwined three-dimensional data structures. To generate the data and execute the analysis, we have used R statistical software. Results show that all seven methods are successful in finding compact, ball-shaped or ellipsoid structures when they are enough separated. Conversely, all methods except the minimum perform poor on non-homogenous, irregular and elongated ones. Especially challenging is a circular double helix structure; it is being correctly revealed only by the minimum method. We can also confirm formerly published results of other simulation studies, which usually favour average method (besides Ward method in cases when data is assumed to be fairly compact and well separated.
Local and global approaches of affinity propagation clustering for large scale data
Xia, Dingyin; Zhang, Xuqing; Zhuang, Yueting
2009-01-01
Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two...
Document Clustering using Sequential Information Bottleneck Method
Gayathri, P J; Punithavalli, M
2010-01-01
This paper illustrates the Principal Direction Divisive Partitioning (PDDP) algorithm and describes its drawbacks and introduces a combinatorial framework of the Principal Direction Divisive Partitioning (PDDP) algorithm, then describes the simplified version of the EM algorithm called the spherical Gaussian EM (sGEM) algorithm and Information Bottleneck method (IB) is a technique for finding accuracy, complexity and time space. The PDDP algorithm recursively splits the data samples into two sub clusters using the hyper plane normal to the principal direction derived from the covariance matrix, which is the central logic of the algorithm. However, the PDDP algorithm can yield poor results, especially when clusters are not well separated from one another. To improve the quality of the clustering results problem, it is resolved by reallocating new cluster membership using the IB algorithm with different settings. IB Method gives accuracy but time consumption is more. Furthermore, based on the theoretical backgr...
Local method for detecting communities
Bagrow, James P.; Bollt, Erik M.
2005-10-01
We propose a method of community detection that is computationally inexpensive and possesses physical significance to a member of a social network. This method is unlike many divisive and agglomerative techniques and is local in the sense that a community can be detected within a network without requiring knowledge of the entire network. A global application of this method is also introduced. Several artificial and real-world networks, including the famous Zachary karate club, are analyzed.
Are Young Massive Star Clusters in the Local Universe Analogous to Globular Clusters Progenitors?
Charbonnel, Corinne
2015-08-01
Several models do compete to reproduce the present-day characteristics of globular clusters (GC) and to explain the origin of the multiple stellar populations these systems are hosting.In parallel, independent clues on GC early evolution may be derived from observations of young massive clusters (YMC) in the Local Group.But are these two populations of clusters related? In this talk, we discuss how and if GC and YMC data can be reconciled.We revisit in particular the impact of massive stars on the early evolution of massive star clusters, as well as the question of early gas expulsion.We propose several tests to probe whether the YMC we are observing today can be considered as the analogues of GC progenitors.
Local theory of extrapolation methods
Kulikov, Gennady
2010-03-01
In this paper we discuss the theory of one-step extrapolation methods applied both to ordinary differential equations and to index 1 semi-explicit differential-algebraic systems. The theoretical background of this numerical technique is the asymptotic global error expansion of numerical solutions obtained from general one-step methods. It was discovered independently by Henrici, Gragg and Stetter in 1962, 1964 and 1965, respectively. This expansion is also used in most global error estimation strategies as well. However, the asymptotic expansion of the global error of one-step methods is difficult to observe in practice. Therefore we give another substantiation of extrapolation technique that is based on the usual local error expansion in a Taylor series. We show that the Richardson extrapolation can be utilized successfully to explain how extrapolation methods perform. Additionally, we prove that the Aitken-Neville algorithm works for any one-step method of an arbitrary order s, under suitable smoothness.
New clustering methods for population comparison on paternal lineages.
Juhász, Z; Fehér, T; Bárány, G; Zalán, A; Németh, E; Pádár, Z; Pamjav, H
2015-04-01
The goal of this study is to show two new clustering and visualising techniques developed to find the most typical clusters of 18-dimensional Y chromosomal haplogroup frequency distributions of 90 Western Eurasian populations. The first technique called "self-organizing cloud (SOC)" is a vector-based self-learning method derived from the Self Organising Map and non-metric Multidimensional Scaling algorithms. The second technique is a new probabilistic method called the "maximal relation probability" (MRP) algorithm, based on a probability function having its local maximal values just in the condensation centres of the input data. This function is calculated immediately from the distance matrix of the data and can be interpreted as the probability that a given element of the database has a real genetic relation with at least one of the remaining elements. We tested these two new methods by comparing their results to both each other and the k-medoids algorithm. By means of these new algorithms, we determined 10 clusters of populations based on the similarity of haplogroup composition. The results obtained represented a genetically, geographically and historically well-interpretable picture of 10 genetic clusters of populations mirroring the early spread of populations from the Fertile Crescent to the Caucasus, Central Asia, Arabia and Southeast Europe. The results show that a parallel clustering of populations using SOC and MRP methods can be an efficient tool for studying the demographic history of populations sharing common genetic footprints.
The Development of Cluster and Histogram Methods
Swendsen, Robert H.
2003-11-01
This talk will review the history of both cluster and histogram methods for Monte Carlo simulations. Cluster methods are based on the famous exact mapping by Fortuin and Kasteleyn from general Potts models onto a percolation representation. I will discuss the Swendsen-Wang algorithm, as well as its improvement and extension to more general spin models by Wolff. The Replica Monte Carlo method further extended cluster simulations to deal with frustrated systems. The history of histograms is quite extensive, and can only be summarized briefly in this talk. It goes back at least to work by Salsburg et al. in 1959. Since then, it has been forgotten and rediscovered several times. The modern use of the method has exploited its ability to efficiently determine the location and height of peaks in various quantities, which is of prime importance in the analysis of critical phenomena. The extensions of this approach to the multiple histogram method and multicanonical ensembles have allowed information to be obtained over a broad range of parameters. Histogram simulations and analyses have become standard techniques in Monte Carlo simulations.
Swarm: robust and fast clustering method for amplicon-based studies
Frédéric Mahé
2014-09-01
Full Text Available Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.
Time-varying clustering for local lighting and material design
HUANG PeiJie; GU YuanTing; WU XiaoLong; CHEN YanYun; WU EnHua
2009-01-01
This paper presents an interactive graphics processing unit (GPU)-based rellghting system in which local lighting condition,surface materials and viewing direction can all be changed on the fly.To support these changes,we simulate the lighting transportation process at run time,which is normally impractical for interactive use due to its huge computational burden.We greatly alleviate this burden by a hierarchical structure named a transportation tree that clusters similar emitting samples together within a perceptually acceptable error bound.Furthermore,by exploiting the coherence in time as well as in space,we incrementally adjust the dusters rather than computing them from scratch in each frame.With a pre-computed visibility map,we are able to efficiently estimate the indirect illumination in parallel on graphlce hardware,by simply summing up the radiance shoots from cluster representatives,plus a small number of operations of merging and splitting on clusters.With relighting based on the time-varying clusters,Interactive update of global illumination effects with multi-bounced indirect lighting is demonstrated in appllcations to msterial animation and scene decoration.
Batista-Romero, Fidel A.; Bernal-Uruchurtu, Margarita I.; Hernández-Lamoneda, Ramón, E-mail: ramon@uaem.mx [Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Cuernavaca, Morelos 62209 (Mexico); Pajón-Suárez, Pedro [Instituto Superior de Tecnologías y Ciencias Aplicadas (InSTEC), Habana 6163 (Cuba)
2015-09-07
The performance of local correlation methods is examined for the interactions present in clusters of bromine with water where the combined effect of hydrogen bonding (HB), halogen bonding (XB), and hydrogen-halogen (HX) interactions lead to many interesting properties. Local methods reproduce all the subtleties involved such as many-body effects and dispersion contributions provided that specific methodological steps are followed. Additionally, they predict optimized geometries that are nearly free of basis set superposition error that lead to improved estimates of spectroscopic properties. Taking advantage of the local correlation energy partitioning scheme, we compare the different interaction environments present in small clusters and those inside the 5{sup 12}6{sup 2} clathrate cage. This analysis allows a clear identification of the reasons supporting the use of local methods for large systems where non-covalent interactions play a key role.
Mapping Cigarettes Similarities using Cluster Analysis Methods
Lorentz JÃƒÂ¤ntschi
2007-09-01
Full Text Available The aim of the research was to investigate the relationship and/or occurrences in and between chemical composition information (tar, nicotine, carbon monoxide, market information (brand, manufacturer, price, and public health information (class, health warning as well as clustering of a sample of cigarette data. A number of thirty cigarette brands have been analyzed. Six categorical (cigarette brand, manufacturer, health warnings, class and four continuous (tar, nicotine, carbon monoxide concentrations and package price variables were collected for investigation of chemical composition, market information and public health information. Multiple linear regression and two clusterization techniques have been applied. The study revealed interesting remarks. The carbon monoxide concentration proved to be linked with tar and nicotine concentration. The applied clusterization methods identified groups of cigarette brands that shown similar characteristics. The tar and carbon monoxide concentrations were the main criteria used in clusterization. An analysis of a largest sample could reveal more relevant and useful information regarding the similarities between cigarette brands.
Comparing the performance of biomedical clustering methods
Wiwie, Christian; Baumbach, Jan; Röttger, Richard
2015-01-01
Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene......-ranging comparison we were able to develop a short guideline for biomedical clustering tasks. ClustEval allows biomedical researchers to pick the appropriate tool for their data type and allows method developers to compare their tool to the state of the art....
Local clustering in scale-free networks with hidden variables.
van der Hofstad, Remco; Janssen, A J E M; van Leeuwaarden, Johan S H; Stegehuis, Clara
2017-02-01
We investigate the presence of triangles in a class of correlated random graphs in which hidden variables determine the pairwise connections between vertices. The class rules out self-loops and multiple edges. We focus on the regime where the hidden variables follow a power law with exponent τ∈(2,3), so that the degrees have infinite variance. The natural cutoff h_{c} characterizes the largest degrees in the hidden variable models, and a structural cutoff h_{s} introduces negative degree correlations (disassortative mixing) due to the infinite-variance degrees. We show that local clustering decreases with the hidden variable (or degree). We also determine how the average clustering coefficient C scales with the network size N, as a function of h_{s} and h_{c}. For scale-free networks with exponent 2vanish only for networks as large as N=10^{9}.
A Survey of Localized Star Clusters in NGC 1427A
Weaver, John R.; Gregg, Michael
2016-01-01
It is well established that galactic clusters provide dynamic environments in which to examine galaxy evolution. The starbursting dwarf irregular NGC 1427A presents an interesting case as it is being pulled into the nearby Fornax cluster at supersonic speeds, producing a visibly exceptional star formation rate and notably blue colors. It has been suggested that the highly deformed structure of NGC 1427A is due to ram pressure stripping as a result of interacting with a super-heated ICM provided by several nearby elliptical galaxies. The gas density profile of its leading edge is similar to a "bow-shock", containing several dozen super-star clusters (SSCs) and thousands of smaller star forming clusters. It is clearly evident that the properties of NGC 1427A change rapidly over relatively short distances. Using dithered HST/ACS images in Sloan equivalent g' r' i' z' and Hα filters, we present a morphological and photometric study of NGC 1427A using a novel approach in which stellar properties are measured from sources grouped within localized regions. Apertures are fitted for ~5000 sources at 4σ using a filter-combined master image. Four characteristic regions are chosen to study stellar properties, selected interactively through DS9. We then introduce COMET, a specially-designed source catalog handler for producing graphical figures of each region, cropping both spatially and photometrically. These are then batch-reviewed and analyzed using synthetic isochrones corresponding of each region. Hα bright sources are indicated to illustrate the significance of SSCs. Secondary analysis is carried out using smoothed color maps of source-subtracted diffuse light, yielding penetrative mapping of underlying stellar populations. We show for the first time how the dynamical stellar populations of NGC 1427A differ as a function of position across the surface of the galaxy, ultimately furthering our understanding of cluster interactions and the evolution of irregular galaxies
RRW: repeated random walks on genome-scale protein networks for local cluster discovery
Can Tolga
2009-09-01
Full Text Available Abstract Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL, and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.
Recent advances in coupled-cluster methods
Bartlett, Rodney J
1997-01-01
Today, coupled-cluster (CC) theory has emerged as the most accurate, widely applicable approach for the correlation problem in molecules. Furthermore, the correct scaling of the energy and wavefunction with size (i.e. extensivity) recommends it for studies of polymers and crystals as well as molecules. CC methods have also paid dividends for nuclei, and for certain strongly correlated systems of interest in field theory.In order for CC methods to have achieved this distinction, it has been necessary to formulate new, theoretical approaches for the treatment of a variety of essential quantities
The polarizable embedding coupled cluster method
Sneskov, Kristian; Schwabe, Tobias; Kongsted, Jacob
2011-01-01
We formulate a new combined quantum mechanics/molecular mechanics (QM/MM) method based on a self-consistent polarizable embedding (PE) scheme. For the description of the QM region, we apply the popular coupled cluster (CC) method detailing the inclusion of electrostatic and polarization effects...... all coupled to a polarizable MM environment. In the process, we identify CC densitylike intermediates that allow for a very efficient implementation retaining a computational low cost of the QM/MM terms even when the number of MM sites increases. The strengths of the new implementation are illustrated...
Local clustering in scale-free networks with hidden variables
van der Hofstad, Remco; Janssen, A. J. E. M.; van Leeuwaarden, Johan S. H.; Stegehuis, Clara
2017-02-01
We investigate the presence of triangles in a class of correlated random graphs in which hidden variables determine the pairwise connections between vertices. The class rules out self-loops and multiple edges. We focus on the regime where the hidden variables follow a power law with exponent τ ∈(2 ,3 ) , so that the degrees have infinite variance. The natural cutoff hc characterizes the largest degrees in the hidden variable models, and a structural cutoff hs introduces negative degree correlations (disassortative mixing) due to the infinite-variance degrees. We show that local clustering decreases with the hidden variable (or degree). We also determine how the average clustering coefficient C scales with the network size N , as a function of hs and hc. For scale-free networks with exponent 2 universality class at hand. We characterize the extremely slow decay of C when τ ≈2 and show that for τ =2.1 , say, clustering starts to vanish only for networks as large as N =109 .
Oxygen vacancy clustering and electron localization in oxygen-deficient SrTiO(3): LDA + U study.
Cuong, Do Duc; Lee, Bora; Choi, Kyeong Mi; Ahn, Hyo-Shin; Han, Seungwu; Lee, Jaichan
2007-03-16
We find, using a local density approximation +Hubbard U method, that oxygen vacancies tend to cluster in a linear way in SrTiO(3), a prototypical perovskite oxide, accompanied by strong electron localization at the 3d state of the nearby Ti transition metal ion. The vacancy clustering and the associated electron localization lead to a profound impact on materials properties, e.g., the reduction in free-carrier densities, the appearance of characteristic optical spectra, and the decrease in vacancy mobility. The high stability against the vacancy migration also suggests the physical reality of the vacancy cluster.
Topologically clustering: a method for discarding mismatches
Wang, Yongtao; Zhang, Dazhi; Gao, Chenqiang; Tian, Jinwen
2007-11-01
Wide baseline stereo correspondence has become a challenging and attractive problem in computer vision and its related applications. Getting high correct ratio initial matches is a very important step of general wide baseline stereo correspondence algorithm. Ferrari et al. suggested a voting scheme called topological filter in [3] to discard mismatches from initial matches, but they didn't give theoretical analysis of their method. Furthermore, the parameter of their scheme was uncertain. In this paper, we improved Ferraris' method based on our theoretical analysis, and presented a novel scheme called topologically clustering to discard mismatches. The proposed method has been tested using many famous wide baseline image pairs and the experimental results showed that the developed method can efficiently extract high correct ratio matches from low correct ratio initial matches for wide baseline image pairs.
Fuzzy Clustering - Principles, Methods and Examples
Kroszynski, Uri; Zhou, Jianjun
1998-01-01
One of the most remarkable advances in the field of identification and control of systems -in particular mechanical systems- whose behaviour can not be described by means of the usual mathematical models, has been achieved by the application of methods of fuzzy theory.In the framework of a study...... about identification of "black-box" properties by analysis of system input/output data sets, we have prepared an introductory note on the principles and the most popular data classification methods used in fuzzy modeling. This introductory note also includes some examples that illustrate the use...... of the methods. The examples were solved by hand and served as a test bench for exploration of the MATLAB capabilities included in the Fuzzy Control Toolbox. The fuzzy clustering methods described include Fuzzy c-means (FCM), Fuzzy c-lines (FCL) and Fuzzy c-elliptotypes (FCE)....
Cluster-localized sparse logistic regression for SNP data.
Binder, Harald; Müller, Tina; Schwender, Holger; Golka, Klaus; Steffens, Michael; Hengstler, Jan G; Ickstadt, Katja; Schumacher, Martin
2012-08-14
The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, these groups of individuals are identified using a clustering approach, where each group may be defined via different SNPs. This allows for representing complex interaction patterns, such as compositional epistasis, that might not be detected by a single main effects model. In a simulation study, the CLR approach results in improved prediction performance, compared to the main effects approach, and identification of important SNPs in several scenarios. Improved prediction performance is also obtained for an application example considering urinary bladder cancer. Some of the identified SNPs are predictive for all individuals, while others are only relevant for a specific group. Together with the sets of SNPs that define the groups, potential interaction patterns are uncovered.
Breaking the hierarchy - a new cluster selection mechanism for hierarchical clustering methods
Zweig Katharina A
2009-10-01
Full Text Available Abstract Background Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the hierarchy by a so-called level selection algorithm. In 2005, a new kind of hierarchical clustering method was introduced by Palla et al. that differs in two ways from Ward's method: it can be used on data on which no full similarity matrix is defined and it can produce overlapping clusters, i.e., allow for multiple membership of items in clusters. These features are optimal for biological and chemical data sets but until now no level selection algorithm has been published for this method. Results In this article we provide a general selection scheme, the level independent clustering selection method, called LInCS. With it, clusters can be selected from any level in quadratic time with respect to the number of clusters. Since hierarchically clustered data is not necessarily associated with a similarity measure, the selection is based on a graph theoretic notion of cohesive clusters. We present results of our method on two data sets, a set of drug like molecules and set of protein-protein interaction (PPI data. In both cases the method provides a clustering with very good sensitivity and specificity values according to a given reference clustering. Moreover, we can show for the PPI data set that our graph theoretic cohesiveness measure indeed chooses biologically homogeneous clusters and disregards inhomogeneous ones in most cases. We finally discuss how the method can be generalized to other hierarchical clustering methods to allow for a level independent cluster selection. Conclusion Using our new cluster selection method together with the method by Palla et al. provides a new interesting clustering mechanism that allows to compute overlapping clusters, which is especially valuable for biological and
Dynamically screened local correlation method using enveloping localized orbitals
Auer, Alexander A.; Nooijen, Marcel
2006-07-01
In this paper we present a local coupled cluster approach based on a dynamical screening scheme, in which amplitudes are either calculated at the coupled cluster level (in this case CCSD) or at the level of perturbation theory, employing a threshold driven procedure based on MP2 energy increments. This way, controllable accuracy and smooth convergence towards the exact result are obtained in the framework of an a posteriori approximation scheme. For the representation of the occupied space a new set of local orbitals is presented with the size of a minimal basis set. This set is atom centered, is nonorthogonal, and has shapes which are fairly independent of the details of the molecular system of interest. Two slightly different versions of combined local coupled cluster and perturbation theory equations are considered. In the limit both converge to the untruncated CCSD result. Benchmark calculations for four systems (heptane, serine, water hexamer, and oxadiazole-2-oxide) are carried out, and decay of the amplitudes, truncation error, and convergence towards the exact CCSD result are analyzed.
Yan, Donghui; Jordan, Michael I
2011-01-01
Inspired by Random Forests (RF) in the context of classification, we propose a new clustering ensemble method---Cluster Forests (CF). Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure $\\kappa$. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis shows that the $\\kappa$ criterion is shown to grow each local clustering in a desirable way---it is "noise-resistant." A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.
Local and global approaches of affinity propagation clustering for large scale data
Ding-yin XIA; Fei WU; Xu-qing ZHANG; Yue-ting ZHUANG
2008-01-01
Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix.The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmarkdata points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.
OmegaWINGS: spectroscopy in the outskirts of local clusters of galaxies
Moretti, A.; Gullieuszik, M.; Poggianti, B.; Paccagnella, A.; Couch, W. J.; Vulcani, B.; Bettoni, D.; Fritz, J.; Cava, A.; Fasano, G.; D'Onofrio, M.; Omizzolo, A.
2017-03-01
Context. Studies of the properties of low-redshift cluster galaxies suffer, in general, from small spatial coverage of the cluster area. WINGS, the most homogeneous and complete study of galaxies in dense environments to date, obtained spectroscopic redshifts for 48 clusters at a median redshift of 0.05, out to an average distance of approximately 0.5 cluster virial radii. The WINGS photometric survey was recently extended by the VST survey OmegaWINGS to cover the outskirts of a subset of the original cluster sample. Aims: In this work, we present the spectroscopic follow-up of 33 of the 46 clusters of galaxies observed with VST over 1 square degree. The aim of this spectroscopic survey is to enlarge the number of cluster members and study the galaxy characteristics and the cluster dynamical properties out to large radii, reaching the virial radius and beyond. Methods: We used the AAOmega spectrograph at AAT to obtain fiber-integrated spectra covering the wavelength region between 3800 and 9000 Å with a spectral resolution of 3.5-6 Å full width at half maximum (FWHM). Observations were performed using two different configurations and exposure times per cluster. We measured redshifts using both absorption and emission lines and used them to derive the cluster redshifts and velocity dispersions. Results: We present here the redshift measurements for 17 985 galaxies, 7497 of which turned out to be cluster members. The sample magnitude completeness is 80% at V = 20. Thanks to the observing strategy, the radial completeness turned out to be relatively constant (90%) within the AAOmega field of view. The success rate in measuring redshifts is 95%, at all radii. Conclusions: We provide redshifts for the full sample of galaxies in OmegaWINGS clusters together with updated and robust cluster redshift and velocity dispersions. These data, publicly accessible through the CDS and VO archives, will enable evolutionary and environmental studies of cluster properties, providing
MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS
Jana Halčinová
2014-06-01
Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.
Fuzzy Clustering Using C-Means Method
Georgi Krastev
2015-05-01
Full Text Available The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described in this paper: the problem about the fuzzy clustering has been discussed and the general formal concept of the problem of the fuzzy clustering analysis has been presented. The formulation of the problem has been specified and the algorithm for solving it has been described.
侯茂章
2012-01-01
跨区域实现国际化扩张是当前地方产业集群发展的必然趋势。国际化过程中的风险界定、风险识别、风险因素分析、风险规避等是地方产业集群风险管理的重要内容。采用模糊层次分析法对地方产业集群国际化发展过程中的主要风险进行定量分析，有助于地方产业集群采取有效措施预防、化解各种风险。%Internationalization is the inevitable trend of local industrial clusters at the present time. Risk definition, risk identification, risk analysis are the vital content of risk management in the process of local industrial clusters' internationalization. By using the Fuzzy AHP method, this paper effectively carries out quantitative analysis on the major risks that local industrial clusters face in its internationalization process, which can help local industrial clusters to take effective measures to prevent and resolve these risks.
Approximating the Expansion Profile and Almost Optimal Local Graph Clustering
Gharan, Shayan Oveis
2012-01-01
Spectral partitioning is a simple, nearly-linear time, algorithm to find sparse cuts, and the Cheeger inequalities provide a worst-case guarantee for the quality of the approximation found by the algorithm. Local graph partitioning algorithms [ST08,ACL06,AP09] run in time that is nearly linear in the size of the output set, and their approximation guarantee is worse than the guarantee provided by the Cheeger inequalities by a polylogarithmic $\\log^{\\Omega(1)} n$ factor. It has been a long standing open problem to design a local graph clustering algorithm with an approximation guarantee close to the guarantee of the Cheeger inequalities and with a running time nearly linear in the size of the output. In this paper we solve this problem; we design an algorithm with the same guarantee (up to a constant factor) as the Cheeger inequality, that runs in time slightly super linear in the size of the output. This is the first sublinear (in the size of the input) time algorithm with almost the same guarantee as the Che...
A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.
Yuan, Feng; Liu, Hong; Chen, Shou-Qiang; Xu, Liang
2016-12-01
This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.
Guo, Yang; Li, Wei; Li, Shuhua
2014-10-02
An improved cluster-in-molecule (CIM) local correlation approach is developed to allow electron correlation calculations of large systems more accurate and faster. We have proposed a refined strategy of constructing virtual LMOs of various clusters, which is suitable for basis sets of various types. To recover medium-range electron correlation, which is important for quantitative descriptions of large systems, we find that a larger distance threshold (ξ) is necessary for highly accurate results. Our illustrative calculations show that the present CIM-MP2 (second-order Møller-Plesser perturbation theory, MP2) or CIM-CCSD (coupled cluster singles and doubles, CCSD) scheme with a suitable ξ value is capable of recovering more than 99.8% correlation energies for a wide range of systems at different basis sets. Furthermore, the present CIM-MP2 scheme can provide reliable relative energy differences as the conventional MP2 method for secondary structures of polypeptides.
Integrated management of thesis using clustering method
Astuti, Indah Fitri; Cahyadi, Dedy
2017-02-01
Thesis is one of major requirements for student in pursuing their bachelor degree. In fact, finishing the thesis involves a long process including consultation, writing manuscript, conducting the chosen method, seminar scheduling, searching for references, and appraisal process by the board of mentors and examiners. Unfortunately, most of students find it hard to match all the lecturers' free time to sit together in a seminar room in order to examine the thesis. Therefore, seminar scheduling process should be on the top of priority to be solved. Manual mechanism for this task no longer fulfills the need. People in campus including students, staffs, and lecturers demand a system in which all the stakeholders can interact each other and manage the thesis process without conflicting their timetable. A branch of computer science named Management Information System (MIS) could be a breakthrough in dealing with thesis management. This research conduct a method called clustering to distinguish certain categories using mathematics formulas. A system then be developed along with the method to create a well-managed tool in providing some main facilities such as seminar scheduling, consultation and review process, thesis approval, assessment process, and also a reliable database of thesis. The database plays an important role in present and future purposes.
Discrete range clustering using Monte Carlo methods
Chatterji, G. B.; Sridhar, B.
1993-01-01
For automatic obstacle avoidance guidance during rotorcraft low altitude flight, a reliable model of the nearby environment is needed. Such a model may be constructed by applying surface fitting techniques to the dense range map obtained by active sensing using radars. However, for covertness, passive sensing techniques using electro-optic sensors are desirable. As opposed to the dense range map obtained via active sensing, passive sensing algorithms produce reliable range at sparse locations, and therefore, surface fitting techniques to fill the gaps in the range measurement are not directly applicable. Both for automatic guidance and as a display for aiding the pilot, these discrete ranges need to be grouped into sets which correspond to objects in the nearby environment. The focus of this paper is on using Monte Carlo methods for clustering range points into meaningful groups. One of the aims of the paper is to explore whether simulated annealing methods offer significant advantage over the basic Monte Carlo method for this class of problems. We compare three different approaches and present application results of these algorithms to a laboratory image sequence and a helicopter flight sequence.
Spatial clustering and local risk of leprosy in São Paulo, Brazil.
Ramos, Antônio Carlos Vieira; Yamamura, Mellina; Arroyo, Luiz Henrique; Popolin, Marcela Paschoal; Chiaravalloti Neto, Francisco; Palha, Pedro Fredemir; Uchoa, Severina Alice da Costa; Pieri, Flávia Meneguetti; Pinto, Ione Carvalho; Fiorati, Regina Célia; Queiroz, Ana Angélica Rêgo de; Belchior, Aylana de Souza; Dos Santos, Danielle Talita; Garcia, Maria Concebida da Cunha; Crispim, Juliane de Almeida; Alves, Luana Seles; Berra, Thaís Zamboni; Arcêncio, Ricardo Alexandre
2017-02-01
Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D) is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method. Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR) of leprosy. Maps considering these risks and their confidence intervals were constructed. A total of 434 cases were identified, including 188 (43.31%) borderline leprosy and 101 (23.28%) lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75%) presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000) contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267). Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984) and 15.24 (95%CI = 10.114-22.919). These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.
Advanced cluster methods for correlated-electron systems
Fischer, Andre
2015-04-27
In this thesis, quantum cluster methods are used to calculate electronic properties of correlated-electron systems. A special focus lies in the determination of the ground state properties of a 3/4 filled triangular lattice within the one-band Hubbard model. At this filling, the electronic density of states exhibits a so-called van Hove singularity and the Fermi surface becomes perfectly nested, causing an instability towards a variety of spin-density-wave (SDW) and superconducting states. While chiral d+id-wave superconductivity has been proposed as the ground state in the weak coupling limit, the situation towards strong interactions is unclear. Additionally, quantum cluster methods are used here to investigate the interplay of Coulomb interactions and symmetry-breaking mechanisms within the nematic phase of iron-pnictide superconductors. The transition from a tetragonal to an orthorhombic phase is accompanied by a significant change in electronic properties, while long-range magnetic order is not established yet. The driving force of this transition may not only be phonons but also magnetic or orbital fluctuations. The signatures of these scenarios are studied with quantum cluster methods to identify the most important effects. Here, cluster perturbation theory (CPT) and its variational extention, the variational cluster approach (VCA) are used to treat the respective systems on a level beyond mean-field theory. Short-range correlations are incorporated numerically exactly by exact diagonalization (ED). In the VCA, long-range interactions are included by variational optimization of a fictitious symmetry-breaking field based on a self-energy functional approach. Due to limitations of ED, cluster sizes are limited to a small number of degrees of freedom. For the 3/4 filled triangular lattice, the VCA is performed for different cluster symmetries. A strong symmetry dependence and finite-size effects make a comparison of the results from different clusters difficult
Local structure of the magnetotail current sheet: 2001 Cluster observations
A. Runov
2006-03-01
Full Text Available Thirty rapid crossings of the magnetotail current sheet by the Cluster spacecraft during July-October 2001 at a geocentric distance of 19 R_{E} are examined in detail to address the structure of the current sheet. We use four-point magnetic field measurements to estimate electric current density; the current sheet spatial scale is estimated by integration of the translation velocity calculated from the magnetic field temporal and spatial derivatives. The local normal-related coordinate system for each case is defined by the combining Minimum Variance Analysis (MVA and the curlometer technique. Numerical parameters characterizing the plasma sheet conditions for these crossings are provided to facilitate future comparisons with theoretical models. Three types of current sheet distributions are distinguished: center-peaked (type I, bifurcated (type II and asymmetric (type III sheets. Comparison to plasma parameter distributions show that practically all cases display non-Harris-type behavior, i.e. interior current peaks are embedded into a thicker plasma sheet. The asymmetric sheets with an off-equatorial current density peak most likely have a transient nature. The ion contribution to the electric current rarely agrees with the current computed using the curlometer technique, indicating that either the electron contribution to the current is strong and variable, or the current density is spatially or temporally structured.
A Latent Variable Clustering Method for Wireless Sensor Networks
Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir
2016-01-01
In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work...... obtain by running simulations of a time dynamic sensor network. The performance of the proposed method outperforms the existing clustering methods, such as the Girvan-Newmans algorithm, the Kargers algorithm and the Spectral Clustering method, in terms of packet acceptance probability and delay....
Fuzzy Clustering Methods and their Application to Fuzzy Modeling
Kroszynski, Uri; Zhou, Jianjun
1999-01-01
Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate...... prediction of outputs. This article presents an overview of some of the most popular clustering methods, namely Fuzzy Cluster-Means (FCM) and its generalizations to Fuzzy C-Lines and Elliptotypes. The algorithms for computing cluster centers and principal directions from a training data-set are described....... A method to obtain an optimized number of clusters is outlined. Based upon the cluster's characteristics, a behavioural model is formulated in terms of a rule-base and an inference engine. The article reviews several variants for the model formulation. Some limitations of the methods are listed...
A New Feature Selection Method for Text Clustering
XU Junling; XU Baowen; ZHANG Weifeng; CUI Zifeng; ZHANG Wei
2007-01-01
Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, a new feature selection method for text clustering based on expectation maximization and cluster validity is proposed. It uses supervised feature selection method on the intermediate clustering result which is generated during iterative clustering to do feature selection for text clustering; meanwhile, the Davies-Bouldin's index is used to evaluate the intermediate feature subsets indirectly. Then feature subsets are selected according to the curve of the DaviesBouldin's index. Experiment is carried out on several popular datasets and the results show the advantages of the proposed method.
Fuzzy Clustering Method for Web User Based on Pages Classification
ZHAN Li-qiang; LIU Da-xin
2004-01-01
A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article.The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log.After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users.Finally, it gets the clustering result through the fuzzy clustering method.The experimental results show the effectiveness of the method.
CCM: A Text Classification Method by Clustering
Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock
2011-01-01
In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results...... show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based...
The Wine Clusters of Mendoza and Serra Gaúcha: A Local Development Perspective
María Verónica ALDERETE
2014-01-01
Full Text Available This paper consists of a descriptive analysis that explains how the successful performance of the wine cluster is followed by improvements in local development indicators. To this end, certain local development indicators are proposed to describe and compare the wine clusters of Mendoza (Argentina and Serra Gaúcha (Brazil. In Argentina, the Mendoza wine cluster has stimulated the local development of the region. For its part, Serra Gaúcha is the most successful wine center in Brazil and regards Mendoza as the benchmark in terms of local development.
A local energy consumption prediction-based clustering protocol for wireless sensor networks.
Yu, Jiguo; Feng, Li; Jia, Lili; Gu, Xin; Yu, Dongxiao
2014-12-03
Clustering is a fundamental and effective technique for utilizing sensor nodes' energy and extending the network lifetime for wireless sensor networks. In this paper, we propose a novel clustering protocol, LECP-CP (local energy consumption prediction-based clustering protocol), the core of which includes a novel cluster head election algorithm and an inter-cluster communication routing tree construction algorithm, both based on the predicted local energy consumption ratio of nodes. We also provide a more accurate and realistic cluster radius to minimize the energy consumption of the entire network. The global energy consumption can be optimized by the optimization of the local energy consumption, and the energy consumption among nodes can be balanced well. Simulation results validate our theoretical analysis and show that LECP-CP has high efficiency of energy utilization, good scalability and significant improvement in the network lifetime.
A graph clustering method for community detection in complex networks
Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn
2017-03-01
Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.
Nonlinear system identification with global and local soft computing methods
Runkler, T.A. [Siemens AG, Muenchen (Germany). Zentralabt. Technik Information und Kommunikation
2000-10-01
An important step in the design of control systems is system identification. Data driven system identification finds functional models for the system's input output behavior. Regression methods are simple and effective, but may cause overshoots for complicated characteristics. Neural network approaches such as the multilayer perceptron yield very accurate models, but are black box approaches which leads to problems in system and stability analysis. In contrast to these global modeling methods crisp and fuzzy rule bases represent local models that can be extracted from data by clustering methods. Depending on the type and number of models different degrees of model accuracy can be achieved. (orig.)
Improved method for the feature extraction of laser scanner using genetic clustering
Yu Jinxia; Cai Zixing; Duan Zhuohua
2008-01-01
Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm (WFCA) instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering al-gorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.
The Cluster Variation Method: A Primer for Neuroscientists
Alianna J. Maren
2016-09-01
Full Text Available Effective Brain–Computer Interfaces (BCIs require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h for the case of an equiprobable distribution of bistate (neural/neural ensemble units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.
Ivanov, Vladimir V.; Zakharov, Anton B.; Adamowicz, Ludwik
2013-12-01
A new semi-empirical π-electron local coupled cluster theory has been developed to calculate static dipole polarisabilities and hyperpolarisabilities of extended π-conjugated systems. The key idea of the approach is the use of the ethylene molecular orbitals as the orbital basis set for π-conjugated compounds (the method is termed the Covalent Unbonded Molecules of Ethylene method, cue). Test calculations of some small model organic conjugated compounds demonstrate high accuracy of the version of the cue local coupled cluster theory developed in this work in comparison with the π-electron full configuration interaction (FCI) method. Calculations of different conjugated carbon-based oligomer chains (polyenes, polyynes, polyacenes, polybenzocyclobutadiene, etc.) demonstrate fast convergence (per π-electron) of the polarisability and hyperpolarisability values in the calculations when more classes of orbital excitations are included in the coupled cluster single and double (CCSD) excitation operator. The results show qualitatively correct dependence on the system size.
Local rewiring algorithms to increase clustering and grow a small world
Alstott, Jeff; Pizza, Pamela B; Radcliffe, Mary
2016-01-01
Many real-world networks have high clustering among vertices: vertices that share neighbors are often also directly connected to each other. A network's clustering can be a useful indicator of its connectedness and community structure. Algorithms for generating networks with high clustering have been developed, but typically rely on adding or removing edges and nodes, sometimes from a completely empty network. Here, we introduce algorithms that create a highly clustered network by starting with an existing network and rearranging edges, without adding or removing them; these algorithms can preserve other network properties even as the clustering increases. These algorithms rely on local rewiring rules, in which a single edge changes one of its vertices in a way that is guaranteed to increase clustering. This greedy algorithm can be applied iteratively to transform a random network into a form with much higher clustering. Additionally, these algorithms grow the network's clustering faster than they increase it...
An adaptive spatial clustering method for automatic brain MR image segmentation
Jingdan Zhang; Daoqing Dai
2009-01-01
In this paper, an adaptive spatial clustering method is presented for automatic brain MR image segmentation, which is based on a competitive learning algorithm-self-organizing map (SOM). We use a pattern recognition approach in terms of feature generation and classifier design. Firstly, a multi-dimensional feature vector is constructed using local spatial information. Then, an adaptive spatial growing hierarchical SOM (ASGHSOM) is proposed as the classifier, which is an extension of SOM, fusing multi-scale segmentation with the competitive learning clustering algorithm to overcome the problem of overlapping grey-scale intensities on boundary regions. Furthermore, an adaptive spatial distance is integrated with ASGHSOM, in which local spatial information is considered in the cluster-ing process to reduce the noise effect and the classification ambiguity. Our proposed method is validated by extensive experiments using both simulated and real MR data with varying noise level, and is compared with the state-of-the-art algorithms.
On Comparison of Clustering Methods for Pharmacoepidemiological Data.
Feuillet, Fanny; Bellanger, Lise; Hardouin, Jean-Benoit; Victorri-Vigneau, Caroline; Sébille, Véronique
2015-01-01
The high consumption of psychotropic drugs is a public health problem. Rigorous statistical methods are needed to identify consumption characteristics in post-marketing phase. Agglomerative hierarchical clustering (AHC) and latent class analysis (LCA) can both provide clusters of subjects with similar characteristics. The objective of this study was to compare these two methods in pharmacoepidemiology, on several criteria: number of clusters, concordance, interpretation, and stability over time. From a dataset on bromazepam consumption, the two methods present a good concordance. AHC is a very stable method and it provides homogeneous classes. LCA is an inferential approach and seems to allow identifying more accurately extreme deviant behavior.
Progeny Clustering: A Method to Identify Biological Phenotypes
Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.
2015-01-01
Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476
A multi-sequential number-theoretic optimization algorithm using clustering methods
XU Qing-song; LIANG Yi-zeng; HOU Zhen-ting
2005-01-01
A multi-sequential number-theoretic optimization method based on clustering was developed and applied to the optimization of functions with many local extrema. Details of the procedure to generate the clusters and the sequential schedules were given. The algorithm was assessed by comparing its performance with generalized simulated annealing algorithm in a difficult instructive example and a D-optimum experimental design problem. It is shown the presented algorithm to be more effective and reliable based on the two examples.
A Grouping Method of Distribution Substations Using Cluster Analysis
Ohtaka, Toshiya; Iwamoto, Shinichi
Recently, it has been considered to group distribution substations together for evaluating the reinforcement planning of distribution systems. However, the grouping is carried out by the knowledge and experience of an expert who is in charge of distribution systems, and a subjective feeling of a human being causes ambiguous grouping at the moment. Therefore, a method for imitating the grouping by the expert has been desired in order to carry out a systematic grouping which has numerical corroboration. In this paper, we propose a grouping method of distribution substations using cluster analysis based on the interconnected power between the distribution substations. Moreover, we consider the geographical constraints such as rivers, roads, business office boundaries and branch boundaries, and also examine a method for adjusting the interconnected power. Simulations are carried out to verify the validity of the proposed method using an example system. From the simulation results, we can find that the imitation of the grouping by the expert becomes possible due to considering the geographical constraints and adjusting the interconnected power, and also the calculation time and iterations can be greatly reduced by introducing the local and tabu search methods.
Localized lipid packing of transmembrane domains impedes integrin clustering.
Mehrdad Mehrbod
Full Text Available Integrin clustering plays a pivotal role in a host of cell functions. Hetero-dimeric integrin adhesion receptors regulate cell migration, survival, and differentiation by communicating signals bidirectionally across the plasma membrane. Thus far, crystallographic structures of integrin components are solved only separately, and for some integrin types. Also, the sequence of interactions that leads to signal transduction remains ambiguous. Particularly, it remains controversial whether the homo-dimerization of integrin transmembrane domains occurs following the integrin activation (i.e. when integrin ectodomain is stretched out or if it regulates integrin clustering. This study employs molecular dynamics modeling approaches to address these questions in molecular details and sheds light on the crucial effect of the plasma membrane. Conducting a normal mode analysis of the intact αllbβ3 integrin, it is demonstrated that the ectodomain and transmembrane-cytoplasmic domains are connected via a membrane-proximal hinge region, thus merely transmembrane-cytoplasmic domains are modeled. By measuring the free energy change and force required to form integrin homo-oligomers, this study suggests that the β-subunit homo-oligomerization potentially regulates integrin clustering, as opposed to α-subunit, which appears to be a poor regulator for the clustering process. If α-subunits are to regulate the clustering they should overcome a high-energy barrier formed by a stable lipid pack around them. Finally, an outside-in activation-clustering scenario is speculated, explaining how further loading the already-active integrin affects its homo-oligomerization so that focal adhesions grow in size.
Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann
2017-07-01
Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.
Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann
2017-03-01
Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.
Application of the cluster variation method to interstitial solid solutions
Pekelharing, M.I.
2008-01-01
A thermodynamic model for interstitial alloys, based on the Cluster Variation Method (CVM), has been developed, capable of incorporating short range ordering (SRO), long range ordering (LRO), and the mutual interaction between the host and the interstitial sublattices. The obtained cluster-based
A Latent Variable Clustering Method for Wireless Sensor Networks
Vasilev, Vladislav; Mihovska, Albena Dimitrova; Poulkov, Vladimir
2016-01-01
In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and...
Clustering Methods Application for Customer Segmentation to Manage Advertisement Campaign
Maciej Kutera
2010-10-01
Full Text Available Clustering methods are recently so advanced elaborated algorithms for large collection data analysis that they have been already included today to data mining methods. Clustering methods are nowadays larger and larger group of methods, very quickly evolving and having more and more various applications. In the article, our research concerning usefulness of clustering methods in customer segmentation to manage advertisement campaign is presented. We introduce results obtained by using four selected methods which have been chosen because their peculiarities suggested their applicability to our purposes. One of the analyzed method – k-means clustering with random selected initial cluster seeds gave very good results in customer segmentation to manage advertisement campaign and these results were presented in details in the article. In contrast one of the methods (hierarchical average linkage was found useless in customer segmentation. Further investigations concerning benefits of clustering methods in customer segmentation to manage advertisement campaign is worth continuing, particularly that finding solutions in this field can give measurable profits for marketing activity.
Object-Oriented Image Clustering Method Using UAS Photogrammetric Imagery
Lin, Y.; Larson, A.; Schultz-Fellenz, E. S.; Sussman, A. J.; Swanson, E.; Coppersmith, R.
2016-12-01
Unmanned Aerial Systems (UAS) have been used widely as an imaging modality to obtain remotely sensed multi-band surface imagery, and are growing in popularity due to their efficiency, ease of use, and affordability. Los Alamos National Laboratory (LANL) has employed the use of UAS for geologic site characterization and change detection studies at a variety of field sites. The deployed UAS equipped with a standard visible band camera to collect imagery datasets. Based on the imagery collected, we use deep sparse algorithmic processing to detect and discriminate subtle topographic features created or impacted by subsurface activities. In this work, we develop an object-oriented remote sensing imagery clustering method for land cover classification. To improve the clustering and segmentation accuracy, instead of using conventional pixel-based clustering methods, we integrate the spatial information from neighboring regions to create super-pixels to avoid salt-and-pepper noise and subsequent over-segmentation. To further improve robustness of our clustering method, we also incorporate a custom digital elevation model (DEM) dataset generated using a structure-from-motion (SfM) algorithm together with the red, green, and blue (RGB) band data for clustering. In particular, we first employ an agglomerative clustering to create an initial segmentation map, from where every object is treated as a single (new) pixel. Based on the new pixels obtained, we generate new features to implement another level of clustering. We employ our clustering method to the RGB+DEM datasets collected at the field site. Through binary clustering and multi-object clustering tests, we verify that our method can accurately separate vegetation from non-vegetation regions, and are also able to differentiate object features on the surface.
Highly localized clustering states in a granular gas driven by a vibrating wall
Livne, Eli; Meerson, Baruch; Sasorov, Pavel V.
2000-01-01
An ensemble of inelastically colliding grains driven by a vibrating wall in 2D exhibits density clustering. Working in the limit of nearly elastic collisions and employing granular hydrodynamics, we predict, by a marginal stability analysis, a spontaneous symmetry breaking of the extended clustering state (ECS). 2D steady-state solutions found numerically describe localized clustering state (LCSs). Time-dependent granular hydrodynamic simulations show that LCSs can develop from natural initia...
Urban Fire Risk Clustering Method Based on Fire Statistics
WU Lizhi; REN Aizhu
2008-01-01
Fire statistics and fire analysis have become important ways for us to understand the law of fire,prevent the occurrence of fire, and improve the ability to control fire. According to existing fire statistics, the weighted fire risk calculating method characterized by the number of fire occurrence, direct economic losses,and fire casualties was put forward. On the basis of this method, meanwhile having improved K-mean clus-tering arithmetic, this paper established fire dsk K-mean clustering model, which could better resolve the automatic classifying problems towards fire risk. Fire risk cluster should be classified by the absolute dis-tance of the target instead of the relative distance in the traditional cluster arithmetic. Finally, for applying the established model, this paper carded out fire risk clustering on fire statistics from January 2000 to December 2004 of Shenyang in China. This research would provide technical support for urban fire management.
An Effective Method of Producing Small Neutral Carbon Clusters
XIA Zhu-Hong; CHEN Cheng-Chu; HSU Yen-Chu
2007-01-01
An effective method of producing small neutral carbon clusters Cn (n = 1-6) is described. The small carbon clusters (positive or negative charge or neutral) are formed by plasma which are produced by a high power 532nm pulse laser ablating the surface of the metal Mn rod to react with small hydrocarbons supplied by a pulse valve, then the neutral carbon clusters are extracted and photo-ionized by another laser (266nm or 355nm) in the ionization region of a linear time-of-flight mass spectrometer. The distributions of the initial neutral carbon clusters are analysed with the ionic species appeared in mass spectra. It is observed that the yield of small carbon clusters with the present method is about 10 times than that of the traditional widely used technology of laser vaporization of graphite.
Fast optimization of binary clusters using a novel dynamic lattice searching method.
Wu, Xia; Cheng, Wen
2014-09-28
Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.
Southern Sky Redshift Survey: Clustering of Local Galaxies
Willmer, Christopher N. A.; da Costa, Luiz Nicolaci; Pellegrini, Paulo S.
1998-03-01
We use the two-point correlation function to calculate the clustering properties of the recently completed SSRS2 survey, which probes two well-separated regions of the sky, allowing one to evaluate the sensitivity of sample-to-sample variations. Taking advantage of the large number of galaxies in the combined sample, we also investigate the dependence of clustering on the internal properties of galaxies. The redshift-space correlation function for the combined magnitude-limited sample of the SSRS2 is given by xi(s) = [s/(5.85 h^-1 Mpc)]^-1.60 for separations in the range 2 h^-1 Mpc b b is the linear biasing factor for optical galaxies. We have used the SSRS2 sample to study the dependence of xi on the internal properties of galaxies, such as luminosity, morphology, and color. We confirm earlier results that luminous galaxies (L > L^*) are more clustered than sub-L^* galaxies and that the luminosity segregation is scale-independent. We also find that early types are more clustered than late types. However, in the absence of rich clusters, the relative bias between early and late types in real space, b_E+S0/b_S ~ 1.2, is not as strong as previously estimated. Furthermore, both morphologies present a luminosity-dependent bias, with the early types showing a slightly stronger dependence on luminosity. We also find that red galaxies are significantly more clustered than blue ones, with a mean relative bias of b_R/b_B ~ 1.4, stronger than that observed for morphology. Finally, by comparing our results with the measurements obtained from the infrared-selected galaxies, we determine that the relative bias between optical and IRAS galaxies in real space is b_o/b_I ~ 1.4. Based on observations obtained at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatories, operated by the Association of Universities for Research in Astronomy, Inc., under cooperative agreement with the National Science Foundation; Complejo Astronómico El Leoncito, operated
EMAS Regulation in Italian Clusters: Investigating the Involvement of Local Stakeholders
Roberto Merli; Michele Preziosi; Ilaria Massa
2014-01-01
The last revision of the EMAS (Eco Management and Audit Scheme) Regulation encouraged a cluster approach to increase the participation of the organizations and to involve local stakeholders in the commitment to sustainability...
Control methods for localization of nonlinear waves
Porubov, Alexey; Andrievsky, Boris
2017-03-01
A general form of a distributed feedback control algorithm based on the speed-gradient method is developed. The goal of the control is to achieve nonlinear wave localization. It is shown by example of the sine-Gordon equation that the generation and further stable propagation of a localized wave solution of a single nonlinear partial differential equation may be obtained independently of the initial conditions. The developed algorithm is extended to coupled nonlinear partial differential equations to obtain consistent localized wave solutions at rather arbitrary initial conditions. This article is part of the themed issue 'Horizons of cybernetical physics'.
Variable cluster analysis method for building neural network model
王海东; 刘元东
2004-01-01
To address the problems that input variables should be reduced as much as possible and explain output variables fully in building neural network model of complicated system, a variable selection method based on cluster analysis was investigated. Similarity coefficient which describes the mutual relation of variables was defined. The methods of the highest contribution rate, part replacing whole and variable replacement are put forwarded and deduced by information theory. The software of the neural network based on cluster analysis, which can provide many kinds of methods for defining variable similarity coefficient, clustering system variable and evaluating variable cluster, was developed and applied to build neural network forecast model of cement clinker quality. The results show that all the network scale, training time and prediction accuracy are perfect. The practical application demonstrates that the method of selecting variables for neural network is feasible and effective.
A dynamic fuzzy clustering method based on genetic algorithm
ZHENG Yan; ZHOU Chunguang; LIANG Yanchun; GUO Dongwei
2003-01-01
A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.
New resampling method for evaluating stability of clusters
Neuhaeuser Markus
2008-01-01
Full Text Available Abstract Background Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in clustering procedures. Statistical methods are required to distinguish between real and random clusters. Several methods for assessing cluster stability have been published, including resampling methods such as the bootstrap. We propose a new resampling method based on continuous weights to assess the stability of clusters in hierarchical clustering. While in bootstrapping approximately one third of the original items is lost, continuous weights avoid zero elements and instead allow non integer diagonal elements, which leads to retention of the full dimensionality of space, i.e. each variable of the original data set is represented in the resampling sample. Results Comparison of continuous weights and bootstrapping using real datasets and simulation studies reveals the advantage of continuous weights especially when the dataset has only few observations, few differentially expressed genes and the fold change of differentially expressed genes is low. Conclusion We recommend the use of continuous weights in small as well as in large datasets, because according to our results they produce at least the same results as conventional bootstrapping and in some cases they surpass it.
JEONG Myeong-ho; JANG Yong-ll; PARK Soon-young; BAE Hae-young
2004-01-01
A shared-nothing spatial database cluster is system that provides continuous service even if some system failure happens in any node. So, an efficient recovery of system failure is very important. Generally, the existing method recovers the failed node by using both cluster log and local log. This method, however, cause several problems that increase communication cost and size of cluster log. This paper proposes novel recovery method using recently updated record information in shared-nothing spatial database cluster. The proposed technique utilizes update information of records and pointers of actual data. This makes a reduction of log size and communication cost.Consequently, this reduces recovery time of failed node due to less processing of update operations.
DNA splice site sequences clustering method for conservativeness analysis
Quanwei Zhang; Qinke Peng; Tao Xu
2009-01-01
DNA sequences that are near to splice sites have remarkable conservativeness,and many researchers have contributed to the prediction of splice site.In order to mine the underlying biological knowledge,we analyze the conservativeness of DNA splice site adjacent sequences by clustering.Firstly,we propose a kind of DNA splice site sequences clustering method which is based on DBSCAN,and use four kinds of dissimilarity calculating methods.Then,we analyze the conservative feature of the clustering results and the experimental data set.
Color Image Segmentation Method Based on Improved Spectral Clustering Algorithm
Dong Qin
2014-01-01
Contraposing to the features of image data with high sparsity of and the problems on determination of clustering numbers, we try to put forward an color image segmentation algorithm, combined with semi-supervised machine learning technology and spectral graph theory. By the research of related theories and methods of spectral clustering algorithms, we introduce information entropy conception to design a method which can automatically optimize the scale parameter value. So it avoids the unstab...
The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies
Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Messa, M.; Pellerin, A.; Ryon, J. E.; Smith, L. J.; Shabani, F.; Thilker, D.; Ubeda, L.
2017-05-01
We present a study of the hierarchical clustering of the young stellar clusters in six local (3-15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ˜40-60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.
VizieR Online Data Catalog: OmegaWINGS local clusters of galaxies redshifts (Moretti+, 2017)
Moretti, A.; Gullieuszik, M.; Poggianti, B.; Paccagnella, A.; Couch, W. J.; Vulcani, B.; Bettoni, D.; Fritz, J.; Cava, A.; Fasaano, G.; D'Onofrio, M.; Omizzolo, A.
2017-02-01
Redshifts, magnitude/radial completeness, and memberships are given for the 17985 galaxies observed as part of the OmegaWINGS survey of local clusters of galaxies over 1 square degree. Redshifts have been measured using both absorption and emission lines features. The sample magnitude completeness is 80% at V=20. Thanks to the observing strategy, the radial completeness turned out to be relatively constant (90%) within the AAOmega field of view. The success rate in measuring redshifts is 95%, at all radii. Cluster members are flagged 1 or 2, depending on the cluster structure/secondary structure, and 0 if they are not cluster members. (1 data file).
Fennel, Thomas; Ramunno, Lora; Brabec, Thomas
2007-12-07
Our molecular dynamics analysis of Xe_{147-5083} clusters identifies two mechanisms that contribute to the yet unexplained observation of extremely highly charged ions in intense laser cluster experiments. First, electron impact ionization is enhanced by the local cluster electric field, increasing the highest charge states by up to 40%; a corresponding theoretical method is developed. Second, electron-ion recombination after the laser pulse is frustrated by acceleration electric fields typically used in ion detectors. This increases the highest charge states by up to 90%, as compared to the usual assumption of total recombination of all cluster-bound electrons. Both effects together augment the highest charge states by up to 120%, in reasonable agreement with experiments.
An Examination of Three Spatial Event Cluster Detection Methods
Hensley H. Mariathas
2015-03-01
Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.
Poggianti, Bianca M; Finn, Rose; Bamford, Steven; De Lucia, Gabriella; Varela, Jesus; Aragon-Salamanca, Alfonso; Halliday, Claire; Noll, Stefan; Saglia, Roberto; Zaritsky, Dennis; Best, Philip; Clowe, Douglas; Milvang-Jensen, Bo; Jablonka, Pascale; Pello, Roser; Rudnick, Gregory; Simard, Luc; von der Linden, Anja; White, Simon
2008-01-01
We investigate how the [OII] properties and the morphologies of galaxies in clusters and groups at z=0.4-0.8 depend on projected local galaxy density, and compare with the field at similar redshifts and clusters at low-z. In both nearby and distant clusters, higher-density regions contain proportionally fewer star-forming galaxies, and the average [OII] equivalent width of star-forming galaxies is independent of local density. However, in distant clusters the average current star formation rate (SFR) in star-forming galaxies seems to peak at densities ~15-40 galaxies Mpc^{-2}. At odds with low-z results, at high-z the relation between star-forming fraction and local density varies from high- to low-mass clusters. Overall, our results suggest that at high-z the current star formation (SF) activity in star-forming galaxies does not depend strongly on global or local environment, though the possible SFR peak seems at odds with this conclusion. We find that the cluster SFR normalized by cluster mass anticorrelate...
A Clustering Method Based on the Maximum Entropy Principle
Edwin Aldana-Bobadilla
2015-01-01
Full Text Available Clustering is an unsupervised process to determine which unlabeled objects in a set share interesting properties. The objects are grouped into k subsets (clusters whose elements optimize a proximity measure. Methods based on information theory have proven to be feasible alternatives. They are based on the assumption that a cluster is one subset with the minimal possible degree of “disorder”. They attempt to minimize the entropy of each cluster. We propose a clustering method based on the maximum entropy principle. Such a method explores the space of all possible probability distributions of the data to find one that maximizes the entropy subject to extra conditions based on prior information about the clusters. The prior information is based on the assumption that the elements of a cluster are “similar” to each other in accordance with some statistical measure. As a consequence of such a principle, those distributions of high entropy that satisfy the conditions are favored over others. Searching the space to find the optimal distribution of object in the clusters represents a hard combinatorial problem, which disallows the use of traditional optimization techniques. Genetic algorithms are a good alternative to solve this problem. We benchmark our method relative to the best theoretical performance, which is given by the Bayes classifier when data are normally distributed, and a multilayer perceptron network, which offers the best practical performance when data are not normal. In general, a supervised classification method will outperform a non-supervised one, since, in the first case, the elements of the classes are known a priori. In what follows, we show that our method’s effectiveness is comparable to a supervised one. This clearly exhibits the superiority of our method.
Ages of Globular Clusters from HIPPARCOS Parallaxes of Local Subdwarfs
Gratton, Raffaele G.; Fusi Pecci, Flavio; Carretta, Eugenio; Clementini, Gisella; Corsi, Carlo E.; Lattanzi, Mario
1997-12-01
We report here initial but strongly conclusive results for absolute ages of Galactic globular clusters (GGCs). This study is based on high-precision trigonometric parallaxes from the HIPPARCOS satellite coupled with accurate metal abundances ([Fe/H], [O/Fe], and [α/Fe]) from high-resolution spectroscopy for a sample of about thirty subdwarfs. Systematic effects due to star selection (Lutz-Kelker corrections to parallaxes) and the possible presence of undetected binaries in the sample of bona fide single stars are examined, and appropriate corrections are estimated. They are found to be small for our sample. The new data allow us to reliably define the absolute location of the main sequence (MS) as a function of metallicity. These results are then used to derive distances and ages for a carefully selected sample of nine globular clusters having metallicities determined from high-dispersion spectra of individual giants according to a procedure totally consistent with that used for the field subdwarfs. Very precise and homogeneous reddening values have also been independently determined for these clusters. Random errors for our distance moduli are +/-0.08 mag, and systematic errors are likely of the same order of magnitude. These very accurate distances allow us to derive ages with internal errors of ~12% (+/-1.5 Gyr). The main results are: 1. HIPPARCOS parallaxes are smaller than corresponding ground-based measurements, leading, in turn, to longer distance moduli (~0.2 mag) and younger ages (~2.8 Gyr). 2. The distance to NGC 6752 derived from our MS fitting is consistent with that determined using the white dwarf cooling sequence. 3. The relation between the zero-age HB (ZAHB) absolute magnitude and metallicity for the nine program clusters is MV(ZAHB)=(0.22+/-0.09)([Fe/H]+1.5)+(0.49+/-0.04) . This relation is fairly consistent with some of the most recent theoretical models. Within quoted errors, the slope is in agreement with that given by the Baade-Wesselink (BW
Dynamic integration of remote cloud resources into local computing clusters
Fleig, Georg; Erli, Guenther; Giffels, Manuel; Hauth, Thomas; Quast, Guenter; Schnepf, Matthias [Institut fuer Experimentelle Kernphysik, Karlsruher Institut fuer Technologie (Germany)
2016-07-01
In modern high-energy physics (HEP) experiments enormous amounts of data are analyzed and simulated. Traditionally dedicated HEP computing centers are built or extended to meet this steadily increasing demand for computing resources. Nowadays it is more reasonable and more flexible to utilize computing power at remote data centers providing regular cloud services to users as they can be operated in a more efficient manner. This approach uses virtualization and allows the HEP community to run virtual machines containing a dedicated operating system and transparent access to the required software stack on almost any cloud site. The dynamic management of virtual machines depending on the demand for computing power is essential for cost efficient operation and sharing of resources with other communities. For this purpose the EKP developed the on-demand cloud manager ROCED for dynamic instantiation and integration of virtualized worker nodes into the institute's computing cluster. This contribution will report on the concept of our cloud manager and the implementation utilizing a remote OpenStack cloud site and a shared HPC center (bwForCluster located in Freiburg).
Formation and local electronic structure of Ge clusters on Si(111)-7×7 surfaces
Ma Hai-Feng; Xu Ming-Chun; Yang Bing; Shi Dong-Xia; Guo Hai-Ming; Pang Shi-Jin; Gao Hong-Jun
2007-01-01
We report the formation and local electronic structure of Ge clusters on the Si(111)-7×7 surface studied by using variable temperature scanning tunnelling microscopy (VT-STM) and low-temperature scanning tunnelling spectroscopy (STS). Atom-resolved STM images reveal that the Ge atoms are prone to forming clusters with 1.0 nm in diameter for coverage up to 0.12 ML. Such Ge clusters preferentially nucleate at the centre of the faulted-half unit cells, leading to the 'dark sites' of Si centre adatoms from the surrounding three unfaulted-half unit cells in filled-state images. Biasdependent STM images show the charge transfer from the neighbouring Si adatoms to Ge clusters. Low-temperature STS of the Ge clusters reveals that there is a band gap on the Ge cluster and the large voltage threshold is about 0.9 V.
A New Method for Medical Image Clustering Using Genetic Algorithm
Akbar Shahrzad Khashandarag
2013-01-01
Full Text Available Segmentation is applied in medical images when the brightness of the images becomes weaker so that making different in recognizing the tissues borders. Thus, the exact segmentation of medical images is an essential process in recognizing and curing an illness. Thus, it is obvious that the purpose of clustering in medical images is the recognition of damaged areas in tissues. Different techniques have been introduced for clustering in different fields such as engineering, medicine, data mining and so on. However, there is no standard technique of clustering to present ideal results for all of the imaging applications. In this paper, a new method combining genetic algorithm and k-means algorithm is presented for clustering medical images. In this combined technique, variable string length genetic algorithm (VGA is used for the determination of the optimal cluster centers. The proposed algorithm has been compared with the k-means clustering algorithm. The advantage of the proposed method is the accuracy in selecting the optimal cluster centers compared with the above mentioned technique.
Cluster Monte Carlo methods for the FePt Hamiltonian
Lyberatos, A., E-mail: lyb@materials.uoc.gr [Materials Science and Technology Department, P.O. Box 2208, 71003 Heraklion (Greece); Parker, G.J. [HGST, A Western Digital Company, 3403 Yerba Buena Road, San Jose, CA 95135 (United States)
2016-02-15
Cluster Monte Carlo methods for the classical spin Hamiltonian of FePt with long range exchange interactions are presented. We use a combination of the Swendsen–Wang (or Wolff) and Metropolis algorithms that satisfies the detailed balance condition and ergodicity. The algorithms are tested by calculating the temperature dependence of the magnetization, susceptibility and heat capacity of L1{sub 0}-FePt nanoparticles in a range including the critical region. The cluster models yield numerical results in good agreement within statistical error with the standard single-spin flipping Monte Carlo method. The variation of the spin autocorrelation time with grain size is used to deduce the dynamic exponent of the algorithms. Our cluster models do not provide a more accurate estimate of the magnetic properties at equilibrium. - Highlights: • A new cluster Monte Carlo algorithm was applied to FePt nanoparticles. • Magnetic anisotropy imposes a restriction on cluster moves. • Inclusion of Metropolis steps is required to satisfy ergodicity. • In the critical region a percolating cluster occurs for any grain size. • Critical slowing down is not solved by the new cluster algorithms.
A new method to prepare colloids of size-controlled clusters from a matrix assembly cluster source
Cai, Rongsheng; Jian, Nan; Murphy, Shane; Bauer, Karl; Palmer, Richard E.
2017-05-01
A new method for the production of colloidal suspensions of physically deposited clusters is demonstrated. A cluster source has been used to deposit size-controlled clusters onto water-soluble polymer films, which are then dissolved to produce colloidal suspensions of clusters encapsulated with polymer molecules. This process has been demonstrated using different cluster materials (Au and Ag) and polymers (polyvinylpyrrolidone, polyvinyl alcohol, and polyethylene glycol). Scanning transmission electron microscopy of the clusters before and after colloidal dispersion confirms that the polymers act as stabilizing agents. We propose that this method is suitable for the production of biocompatible colloids of ultraprecise clusters.
A liquid drop model for embedded atom method cluster energies
Finley, C. W.; Abel, P. B.; Ferrante, J.
1996-01-01
Minimum energy configurations for homonuclear clusters containing from two to twenty-two atoms of six metals, Ag, Au, Cu, Ni, Pd, and Pt have been calculated using the Embedded Atom Method (EAM). The average energy per atom as a function of cluster size has been fit to a liquid drop model, giving estimates of the surface and curvature energies. The liquid drop model gives a good representation of the relationship between average energy and cluster size. As a test the resulting surface energies are compared to EAM surface energy calculations for various low-index crystal faces with reasonable agreement.
Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation
Tushar H Jaware
2013-10-01
Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.
Visualization methods for statistical analysis of microarray clusters
Li Kai
2005-05-01
Full Text Available Abstract Background The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. Results We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets and is available at http://function.princeton.edu/GeneVAnD. Conclusion Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters.
Evolution of Local Microstructures: Spatial Instabilities of Coarsening Clusters
Frazier, Donald O.
1999-01-01
dynamics at various volume fractions. Preliminary results of numerical and experimental investigations, focused on the growth of finite particle clusters, provide important insight into the nature of the transition between the two scaling regimes. The companion microgravity experiment centers on the growth within finite particle clusters, and follows the temporal dynamics driving microstructural evolution, using holography.
Ian T. Kracalik
2012-11-01
Full Text Available We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle and small (sheep and goats domestic ruminants across Kazakhstan. The Getis-Ord (Gi* statistic and a multidirectional optimal ecotope algorithm (AMOEBA were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149 and for small ruminants (n = 9. In contrast, Gi* revealed fewer large ruminant clusters (n = 122 and more small ruminant clusters (n = 61. Significant environmental differences were found between groups using the Kruskall-Wallis and Mann- Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.
Kracalik, Ian T; Blackburn, Jason K; Lukhnova, Larisa; Pazilov, Yerlan; Hugh-Jones, Martin E; Aikimbayev, Alim
2012-11-01
We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle) and small (sheep and goats) domestic ruminants across Kazakhstan. The Getis-Ord (Gi*) statistic and a multidirectional optimal ecotope algorithm (AMOEBA) were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149) and for small ruminants (n = 9). In contrast, Gi* revealed fewer large ruminant clusters (n = 122) and more small ruminant clusters (n = 61). Significant environmental differences were found between groups using the Kruskall-Wallis and Mann-Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.
A novel clustering and supervising users' profiles method
Zhu Mingfu; Zhang Hongbin; Song Fangyun
2005-01-01
To better understand different users' accessing intentions, a novel clustering and supervising method based on accessing path is presented. This method divides users' interest space to express the distribution of users' interests, and directly to instruct the constructing process of web pages indexing for advanced performance.
Ultra-Wideband Geo-Regioning: A Novel Clustering and Localization Technique
Armin Wittneben
2007-12-01
Full Text Available Ultra-wideband (UWB technology enables a high temporal resolution of the propagation channel. Consequently, a channel impulse response between transmitter and receiver can be interpreted as signature for their relative positions. If the position of the receiver is known, the channel impulse response indicates the position of the transmitter and vice versa. This work introduces UWB geo-regioning as a clustering and localization method based on channel impulse response fingerprinting, develops a theoretical framework for performance analysis, and evaluates this approach by means of performance results based on measured channel impulse responses. Complexity issues are discussed and performance dependencies on signal-to-noise ratio, a priori knowledge, observation window, and system bandwidth are investigated.
Spatial abundance and clustering of Culicoides (Diptera: Ceratopogonidae) on a local scale
Kirkeby, Carsten; Bødker, Rene; Stockmarr, Anders;
2013-01-01
Background Biting midges, Culicoides, of the Obsoletus group and the Pulicaris group have been involved in recent outbreaks of bluetongue virus and the former was also involved in the Schmallenberg virus outbreak in northern Europe. Methods For the first time, here we investigate the local...... abundance pattern of these two species groups in the field by intensive sampling with a grid of light traps on 16 catch nights. Neighboring trap catches can be spatially dependent on each other, hence we developed a conditional autoregressive (CAR) model framework to test a number of spatial and non......, and cluster locations shifted between catch nights. No significant temporal autocorrelation was detected. CAR models for both species groups identified a significant positive impact of humidity and significant negative impacts of precipitation and wind turbulence. Temperature was also found to be significant...
Methods for analyzing cost effectiveness data from cluster randomized trials
Clark Allan
2007-09-01
Full Text Available Abstract Background Measurement of individuals' costs and outcomes in randomized trials allows uncertainty about cost effectiveness to be quantified. Uncertainty is expressed as probabilities that an intervention is cost effective, and confidence intervals of incremental cost effectiveness ratios. Randomizing clusters instead of individuals tends to increase uncertainty but such data are often analysed incorrectly in published studies. Methods We used data from a cluster randomized trial to demonstrate five appropriate analytic methods: 1 joint modeling of costs and effects with two-stage non-parametric bootstrap sampling of clusters then individuals, 2 joint modeling of costs and effects with Bayesian hierarchical models and 3 linear regression of net benefits at different willingness to pay levels using a least squares regression with Huber-White robust adjustment of errors, b a least squares hierarchical model and c a Bayesian hierarchical model. Results All five methods produced similar results, with greater uncertainty than if cluster randomization was not accounted for. Conclusion Cost effectiveness analyses alongside cluster randomized trials need to account for study design. Several theoretically coherent methods can be implemented with common statistical software.
An improved local immunization strategy for scale-free networks with a high degree of clustering
Xia, Lingling; Jiang, Guoping; Song, Yurong; Song, Bo
2017-01-01
The design of immunization strategies is an extremely important issue for disease or computer virus control and prevention. In this paper, we propose an improved local immunization strategy based on node's clustering which was seldom considered in the existing immunization strategies. The main aim of the proposed strategy is to iteratively immunize the node which has a high connectivity and a low clustering coefficient. To validate the effectiveness of our strategy, we compare it with two typical local immunization strategies on both real and artificial networks with a high degree of clustering. Simulations on these networks demonstrate that the performance of our strategy is superior to that of two typical strategies. The proposed strategy can be regarded as a compromise between computational complexity and immune effect, which can be widely applied in scale-free networks of high clustering, such as social network, technological networks and so on. In addition, this study provides useful hints for designing optimal immunization strategy for specific network.
Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution
Satish Gajawada; Durga Toshniwal
2012-01-01
Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...
Report of a Workshop on Parallelization of Coupled Cluster Methods
Rodney J. Bartlett Erik Deumens
2008-05-08
The benchmark, ab initio quantum mechanical methods for molecular structure and spectra are now recognized to be coupled-cluster theory. To benefit from the transiiton to tera- and petascale computers, such coupled-cluster methods must be created to run in a scalable fashion. This Workshop, held as a aprt of the 48th annual Sanibel meeting, at St. Simns, Island, GA, addressed that issue. Representatives of all the principal scientific groups who are addressing this topic were in attendance, to exchange information about the problem and to identify what needs to be done in the future. This report summarized the conclusions of the workshop.
Agent-based method for distributed clustering of textual information
Potok, Thomas E [Oak Ridge, TN; Reed, Joel W [Knoxville, TN; Elmore, Mark T [Oak Ridge, TN; Treadwell, Jim N [Louisville, TN
2010-09-28
A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.
Li, Wei; Guo, Yang; Li, Shuhua
2012-06-07
A refined cluster-in-molecule (CIM) method for local correlation calculations of large molecules is presented. In the present work, two new strategies are introduced to further improve the CIM approach: (1) Some medium-range electron correlation energies, which are neglected in the previous CIM approach, are taken into account. (2) A much simpler procedure using only a distance threshold is used to construct various clusters. To cover the medium-range correlation effect as much as possible, some two-atom-centered clusters are built, in addition to one-atom-centered clusters. Our test calculations at the second order perturbation theory (MP2) level show that the refined CIM method can recover about 99.9% of the conventional MP2 correlation energy using an appropriate distance threshold. The accuracy of the present CIM method is capable of providing reliable relative energies of medium-sized systems such as polyalanines with 10 residues, and water molecules with 50 water molecules. For polyalanines with up to 30 residues, we have demonstrated that the computational cost of the CIM-MP2 calculation increases linearly with the molecular size, but the required memory and disc-space do not need to increase for large systems. The improved CIM method has been used to compute the relative energy of ice-like (H(2)O)(96) clusters (with 2400 basis functions) and to predict the dimerization energy of a double-helical foldamer (with 2330 basis functions). The present CIM method is expected to be a practical local correlation method for describing the relative energies of large systems.
Liu, Song; Zhu, Lizhe; Sheong, Fu Kit; Wang, Wei; Huang, Xuhui
2017-01-30
We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Cluster Monte Carlo methods for the FePt Hamiltonian
Lyberatos, A.; Parker, G. J.
2016-02-01
Cluster Monte Carlo methods for the classical spin Hamiltonian of FePt with long range exchange interactions are presented. We use a combination of the Swendsen-Wang (or Wolff) and Metropolis algorithms that satisfies the detailed balance condition and ergodicity. The algorithms are tested by calculating the temperature dependence of the magnetization, susceptibility and heat capacity of L10-FePt nanoparticles in a range including the critical region. The cluster models yield numerical results in good agreement within statistical error with the standard single-spin flipping Monte Carlo method. The variation of the spin autocorrelation time with grain size is used to deduce the dynamic exponent of the algorithms. Our cluster models do not provide a more accurate estimate of the magnetic properties at equilibrium.
Non-hierarchical clustering methods on factorial subspaces
Tortora, Cristina
2011-01-01
Cluster analysis (CA) aims at finding homogeneous group of individuals, where homogeneous is referred to individuals that present similar characteristics. Many CA techniques already exist, among the non-hierarchical ones the most known, thank to its simplicity and computational property, is k-means method. However, the method is unstable when the number of variables is large and when variables are correlated. This problem leads to the development of two-step methods, they perform a linear tra...
Niraj, Shakhakarmi
2012-01-01
In this paper, a robust distributed malicious node detection and precise localization and tracking method is proposed for Cluster based Mobile Ad hoc Network (MANET). Certificate Authority (CA) node is selected as the most stable node among trusted nodes, surrounded by Registration Authority nodes (RAs) in each cluster to generate the Dynamic Demilitarized Zone (DDMZ) to defend CA from probable attackers and mitigate the authentication overhead. The RAs also co-operate with member nodes to detect a target node and determine whether it is malicious or not, by providing the public key certificate and trust value. In addition, Internet Protocol (IP) based Triangulation and multi-lateration method are deployed based on using the average time difference of Time of Arrival (ToA) and Time of Departure (ToD) of the management packets. Triangulation uses three reference nodes which are elected within each cluster based on Best Criterion Function (BCF) to localize each member node inside the cluster in 2D. Multi-latera...
Select and Cluster: A Method for Finding Functional Networks of Clustered Voxels in fMRI
DonGiovanni, Danilo
2016-01-01
Extracting functional connectivity patterns among cortical regions in fMRI datasets is a challenge stimulating the development of effective data-driven or model based techniques. Here, we present a novel data-driven method for the extraction of significantly connected functional ROIs directly from the preprocessed fMRI data without relying on a priori knowledge of the expected activations. This method finds spatially compact groups of voxels which show a homogeneous pattern of significant connectivity with other regions in the brain. The method, called Select and Cluster (S&C), consists of two steps: first, a dimensionality reduction step based on a blind multiresolution pairwise correlation by which the subset of all cortical voxels with significant mutual correlation is selected and the second step in which the selected voxels are grouped into spatially compact and functionally homogeneous ROIs by means of a Support Vector Clustering (SVC) algorithm. The S&C method is described in detail. Its performance assessed on simulated and experimental fMRI data is compared to other methods commonly used in functional connectivity analyses, such as Independent Component Analysis (ICA) or clustering. S&C method simplifies the extraction of functional networks in fMRI by identifying automatically spatially compact groups of voxels (ROIs) involved in whole brain scale activation networks. PMID:27656202
The coupled cluster method and entanglement in three fermion systems
Lévay, Péter; Nagy, Szilvia; Pipek, János; Sárosi, Gábor
2017-01-01
The Coupled Cluster (CC) and full CI expansions are studied for three fermions with six and seven modes. Surprisingly the CC expansion is tailor made to characterize the usual stochastic local operations and classical communication (SLOCC) entanglement classes. It means that the notion of a SLOCC transformation shows up quite naturally as a one relating the CC and CI expansions, and going from the CI expansion to the CC one is equivalent to obtaining a form for the state where the structure of the entanglement classes is transparent. In this picture, entanglement is characterized by the parameters of the cluster operators describing transitions from occupied states to singles, doubles, and triples of non-occupied ones. Using the CC parametrization of states in the seven-mode case, we give a simple formula for the unique SLOCC invariant J . Then we consider a perturbation problem featuring a state from the unique SLOCC class characterized by J ≠ 0 . For this state with entanglement generated by doubles, we investigate the phenomenon of changing the entanglement type due to the perturbing effect of triples. We show that there are states with real amplitudes such that their entanglement encoded into configurations of clusters of doubles is protected from errors generated by triples. Finally we put forward a proposal to use the parameters of the cluster operator describing transitions to doubles for entanglement characterization. Compared to the usual SLOCC classes, this provides a coarse grained approach to fermionic entanglement.
Quantum Monte Carlo methods and lithium cluster properties
Owen, R.K.
1990-12-01
Properties of small lithium clusters with sizes ranging from n = 1 to 5 atoms were investigated using quantum Monte Carlo (QMC) methods. Cluster geometries were found from complete active space self consistent field (CASSCF) calculations. A detailed development of the QMC method leading to the variational QMC (V-QMC) and diffusion QMC (D-QMC) methods is shown. The many-body aspect of electron correlation is introduced into the QMC importance sampling electron-electron correlation functions by using density dependent parameters, and are shown to increase the amount of correlation energy obtained in V-QMC calculations. A detailed analysis of D-QMC time-step bias is made and is found to be at least linear with respect to the time-step. The D-QMC calculations determined the lithium cluster ionization potentials to be 0.1982(14) [0.1981], 0.1895(9) [0.1874(4)], 0.1530(34) [0.1599(73)], 0.1664(37) [0.1724(110)], 0.1613(43) [0.1675(110)] Hartrees for lithium clusters n = 1 through 5, respectively; in good agreement with experimental results shown in the brackets. Also, the binding energies per atom was computed to be 0.0177(8) [0.0203(12)], 0.0188(10) [0.0220(21)], 0.0247(8) [0.0310(12)], 0.0253(8) [0.0351(8)] Hartrees for lithium clusters n = 2 through 5, respectively. The lithium cluster one-electron density is shown to have charge concentrations corresponding to nonnuclear attractors. The overall shape of the electronic charge density also bears a remarkable similarity with the anisotropic harmonic oscillator model shape for the given number of valence electrons.
Henry, David; Dymnicki, Allison B; Mohatt, Nathaniel; Allen, James; Kelly, James G
2015-10-01
Qualitative methods potentially add depth to prevention research but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed-methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed-methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-means clustering, and latent class analysis produced similar levels of accuracy with binary data and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a "real-world" example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities.
Henry, David; Dymnicki, Allison B.; Mohatt, Nathaniel; Allen, James; Kelly, James G.
2016-01-01
Qualitative methods potentially add depth to prevention research, but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data, but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-Means clustering, and latent class analysis produced similar levels of accuracy with binary data, and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a “real-world” example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities. PMID:25946969
On the nature of local instabilities in rotating galactic coronae and cool cores of galaxy clusters
Nipoti, Carlo
2014-01-01
A long-standing question is whether radiative cooling can lead to local condensations of cold gas in the hot atmospheres of galaxies and galaxy clusters. We address this problem by studying the nature of local instabilities in rotating, stratified, weakly magnetized, optically thin plasmas in the presence of radiative cooling and anisotropic thermal conduction. For both axisymmetric and non-axisymmetric linear perturbations we provide the general equations that can be applied locally to specific systems to establish whether they are unstable and, in case of instability, to determine the kind of evolution (monotonically growing or over-stable) and the growth rates of unstable modes. We present results for models of rotating plasmas representative of Milky Way-like galaxy coronae and cool-cores of galaxy clusters. It is shown that the unstable modes arise from a combination of thermal, magnetothermal, magnetorotational and heat-flux-driven buoyancy instabilities. Local condensation of cold clouds tends to be ha...
Teslic, Luka; Hartmann, Benjamin; Nelles, Oliver; Skrjanc, Igor
2011-12-01
This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.
Seeking the Local Convergence Depth. The Abell Cluster Dipole Flow to $200h^{-1}$ Mpc
Dale, D A; Haynes, M P; Campusano, L E; Hardy, E; Borgani, S; Dale, Daniel A.; Giovanelli, Riccardo; Haynes, Martha P.; Campusano, Luis E.; Hardy, Eduardo; Borgani, Stefano
1999-01-01
We have obtained new Tully-Fisher (TF) peculiar velocity measurements for 52 Abell galaxy clusters distributed throughout the sky between ~ 50 and 200 Mpc/h.The measurements are based on I band photometry and optical rotation curves for a sample of 522 spiral galaxies, from which an accurate TF template relation has been constructed. Individual cluster TF relations are referred to the template to compute cluster peculiar motions. The reflex motion of the Local Group of galaxies is measured with respect to the reference frame defined by our cluster sample and the distant portion of the Giovanelli et al. (1998) cluster set. We find the Local Group motion in this frame to be 565+/-113 km/s in the direction (l,b)=(267,26)+/-10 when peculiar velocities are weighted according to their errors. After optimizing the dipole calculation to sample equal volumes equally, the vector is 509+/-195 km/s towards (255,33)+/-22. Both solutions agree, to within 1-sigma or better, with the Local Group motion as inferred from the c...
Analysis of protein profiles using fuzzy clustering methods
Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya
clustering methods for their classification followed by various validation measures. The clustering algorithms used for the study were K- means, K- medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva. The results presented in this study conclude that the protein profiles of tissue...... samples recorded by using the HPLC- LIF system and the data analyzed by clustering algorithms quite successfully classifies them as belonging from normal and malignant conditions....
Yong-Ju Yang
2013-01-01
Full Text Available The local fractional variational iteration method for local fractional Laplace equation is investigated in this paper. The operators are described in the sense of local fractional operators. The obtained results reveal that the method is very effective.
Evolution of Local Microstructures: Spatial Instabilities in Coarsening Clusters
2003-01-01
Diffusion-limited capillarity-driven coarsening of precipitates is an important and intensively studied phenomenon. The classic coarsening theory developed by Lifshitz and Syozov and Wagner (LSW theory) is limited to infinitesimally small volume fractions, V(sub nu), therefore neglects all direct interparticle interactions. This work uses modeling and holographic imaging to compare coarsening rates in "high" volume fraction versus low volume fraction microstructures by observing mixed-dimensional droplets (spherical caps on a surface coarsening by two-dimensional diffusion) during ground-based investigations. The method involves filling a cell with selected homogeneous parent phase, and cooling below the consolute temperature to the isopycnic temperature in the two-phase region of a monotectic system. A microgravity holographic experiment is required for three-dimensional observations to minimize sedimentation during long-term coarsening. Determination of sizes and positions of the many droplets in the holographic images requires automation. We have developed software for automated data analysis, and demonstrated good agreement between regenerated maps and scaled photographs of the original holograms for mixed dimensional coarsening. The results of these experiments were presented in a formal microgravity Science Concept Review (SCR) on December 18, 2000.
A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION
X. Lin
2017-09-01
Full Text Available Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.
Dynamic screening of a localized hole during photoemission from a metal cluster
Koval, N E; Borisov, A G; Muiño, R Díez
2012-01-01
Recent advances in attosecond spectroscopy techniques have fueled the interest in the theoretical description of electronic processes taking place in the subfemtosecond time scale. We here study the coupled dynamic screening of a localized hole and a photoelectron emitted from a metal cluster using a semi-classical model. Electron density dynamics in the cluster is calculated with Time Dependent Density Functional Theory and the motion of the photoemitted electron is described classically. We show that the dynamic screening of the hole by the cluster electrons affects the motion of the photoemitted electron. At the very beginning of the photoemission process, the emitted electron is accelerated by the cluster electrons that pile up to screen the hole. This is a velocity dependent effect that needs to be accounted for when calculating the energy lost by the electron due to inelastic processes.
Histological image segmentation using fast mean shift clustering method
Wu, Geming; Zhao, Xinyan; Luo, Shuqian; Shi, Hongli
2015-01-01
Background Colour image segmentation is fundamental and critical for quantitative histological image analysis. The complexity of the microstructure and the approach to make histological images results in variable staining and illumination variations. And ultra-high resolution of histological images makes it is hard for image segmentation methods to achieve high-quality segmentation results and low computation cost at the same time. Methods Mean Shift clustering approach is employed for histol...
Clustering Method in Data Mining%数据挖掘中的聚类方法
王实; 高文
2000-01-01
In this paper we introduce clustering method at Data Mining.Clustering has been studied very deeply.In the field of Data Mining,clustering is facing the new situation.We summarize the major clustering methods and introduce four kinds of clustering method that have been used broadly in Data Mitring.Finally we draw a conclusion that the partitional clustering method based on distance in data mining is a typical two phase iteration process:1)appoint cluster;2)update the center of cluster.
Kim, Miju; Kim, Tae-Jin; Kim, Hye Mi; Doh, Junsang; Lee, Kyung-Mi
2017-01-01
Multi-cellular cluster formation of natural killer (NK) cells occurs during in vivo priming and potentiates their activation to IL-2. However, the precise mechanism underlying this synergy within NK cell clusters remains unclear. We employed lymphocyte-laden microwell technologies to modulate contact-mediated multi-cellular interactions among activating NK cells and to quantitatively assess the molecular events occurring in multi-cellular clusters of NK cells. NK cells in social microwells, which allow cell-to-cell contact, exhibited significantly higher levels of IL-2 receptor (IL-2R) signaling compared with those in lonesome microwells, which prevent intercellular contact. Further, CD25, an IL-2R α chain, and lytic granules of NK cells in social microwells were polarized toward MTOC. Live cell imaging of lytic granules revealed their dynamic and prolonged polarization toward neighboring NK cells without degranulation. These results suggest that IL-2 bound on CD25 of one NK cells triggered IL-2 signaling of neighboring NK cells. These results were further corroborated by findings that CD25-KO NK cells exhibited lower proliferation than WT NK cells, and when mixed with WT NK cells, underwent significantly higher level of proliferation. These data highlights the existence of IL-2 trans-presentation between NK cells in the local microenvironment where the availability of IL-2 is limited.
Natural triple excitations in local coupled cluster calculations with pair natural orbitals
Riplinger, Christoph; Sandhoefer, Barbara; Hansen, Andreas; Neese, Frank
2013-10-01
In this work, the extension of the previously developed domain based local pair-natural orbital (DLPNO) based singles- and doubles coupled cluster (DLPNO-CCSD) method to perturbatively include connected triple excitations is reported. The development is based on the concept of triples-natural orbitals that span the joint space of the three pair natural orbital (PNO) spaces of the three electron pairs that are involved in the calculation of a given triple-excitation contribution. The truncation error is very smooth and can be significantly reduced through extrapolation to the zero threshold. However, the extrapolation procedure does not improve relative energies. The overall computational effort of the method is asymptotically linear with the system size O(N). Actual linear scaling has been confirmed in test calculations on alkane chains. The accuracy of the DLPNO-CCSD(T) approximation relative to semicanonical CCSD(T0) is comparable to the previously developed DLPNO-CCSD method relative to canonical CCSD. Relative energies are predicted with an average error of approximately 0.5 kcal/mol for a challenging test set of medium sized organic molecules. The triples correction typically adds 30%-50% to the overall computation time. Thus, very large systems can be treated on the basis of the current implementation. In addition to the linear C150H302 (452 atoms, >8800 basis functions) we demonstrate the first CCSD(T) level calculation on an entire protein, Crambin with 644 atoms, and more than 6400 basis functions.
Wang, Qiaoyun; Dierkes, Rüdiger; Kaufmann, Rainer; Cremer, Christoph
2014-04-01
In this report, we applied a special localization microscopy technique (Spectral Precision Distance/Spatial Position Determination Microscopy/SPDM) to quantitatively analyze the effect of influenza A virus (IAV) infection on the spatial distribution of individual HGFR (Hepatocyte Growth Factor Receptor) proteins on the membrane of human epithelial cells at the single molecule resolution level. We applied this SPDM method to Alexa 488 labeled HGFR proteins with two different ligands. The ligands were either HGF (Hepatocyte Growth Factor), or IAV. In addition, the HGFR distribution in a control group of mock-incubated cells without any ligands was investigated. The spatial distribution of 1×10(6) individual HGFR proteins localized in large regions of interest on membranes of 240 cells was quantitatively analyzed and found to be highly non-random. Between 21% and 24% of the HGFR molecules were located in 44,304 small clusters with an average diameter of 54nm. The mean density of HGFR molecule signals per individual cluster was very similar in control cells, in cells with ligand only, and in IAV infected cells, independent of the incubation time. From the density of HGFR molecule signals in the clusters and the diameter of the clusters, the number of HGFR molecule signals per cluster was estimated to be in the range between 4 and 11 (means 5-6). This suggests that the membrane bound HGFR clusters form small molecular complexes with a maximum diameter of few tens of nm, composed of a relatively low number of HGFR molecules. This article is part of a Special Issue entitled: Viral Membrane Proteins - Channels for Cellular Networking. Copyright © 2013 Elsevier B.V. All rights reserved.
Distinguishing Functional DNA Words; A Method for Measuring Clustering Levels
Moghaddasi, Hanieh; Khalifeh, Khosrow; Darooneh, Amir Hossein
2017-01-01
Functional DNA sub-sequences and genome elements are spatially clustered through the genome just as keywords in literary texts. Therefore, some of the methods for ranking words in texts can also be used to compare different DNA sub-sequences. In analogy with the literary texts, here we claim that the distribution of distances between the successive sub-sequences (words) is q-exponential which is the distribution function in non-extensive statistical mechanics. Thus the q-parameter can be used as a measure of words clustering levels. Here, we analyzed the distribution of distances between consecutive occurrences of 16 possible dinucleotides in human chromosomes to obtain their corresponding q-parameters. We found that CG as a biologically important two-letter word concerning its methylation, has the highest clustering level. This finding shows the predicting ability of the method in biology. We also proposed that chromosome 18 with the largest value of q-parameter for promoters of genes is more sensitive to dietary and lifestyle. We extended our study to compare the genome of some selected organisms and concluded that the clustering level of CGs increases in higher evolutionary organisms compared to lower ones. PMID:28128320
Distinguishing Functional DNA Words; A Method for Measuring Clustering Levels
Moghaddasi, Hanieh; Khalifeh, Khosrow; Darooneh, Amir Hossein
2017-01-01
Functional DNA sub-sequences and genome elements are spatially clustered through the genome just as keywords in literary texts. Therefore, some of the methods for ranking words in texts can also be used to compare different DNA sub-sequences. In analogy with the literary texts, here we claim that the distribution of distances between the successive sub-sequences (words) is q-exponential which is the distribution function in non-extensive statistical mechanics. Thus the q-parameter can be used as a measure of words clustering levels. Here, we analyzed the distribution of distances between consecutive occurrences of 16 possible dinucleotides in human chromosomes to obtain their corresponding q-parameters. We found that CG as a biologically important two-letter word concerning its methylation, has the highest clustering level. This finding shows the predicting ability of the method in biology. We also proposed that chromosome 18 with the largest value of q-parameter for promoters of genes is more sensitive to dietary and lifestyle. We extended our study to compare the genome of some selected organisms and concluded that the clustering level of CGs increases in higher evolutionary organisms compared to lower ones.
An improved unsupervised clustering-based intrusion detection method
Hai, Yong J.; Wu, Yu; Wang, Guo Y.
2005-03-01
Practical Intrusion Detection Systems (IDSs) based on data mining are facing two key problems, discovering intrusion knowledge from real-time network data, and automatically updating them when new intrusions appear. Most data mining algorithms work on labeled data. In order to set up basic data set for mining, huge volumes of network data need to be collected and labeled manually. In fact, it is rather difficult and impractical to label intrusions, which has been a big restrict for current IDSs and has led to limited ability of identifying all kinds of intrusion types. An improved unsupervised clustering-based intrusion model working on unlabeled training data is introduced. In this model, center of a cluster is defined and used as substitution of this cluster. Then all cluster centers are adopted to detect intrusions. Testing on data sets of KDDCUP"99, experimental results demonstrate that our method has good performance in detection rate. Furthermore, the incremental-learning method is adopted to detect those unknown-type intrusions and it decreases false positive rate.
Unbiased methods for removing systematics from galaxy clustering measurements
Elsner, Franz; Peiris, Hiranya V
2015-01-01
Measuring the angular clustering of galaxies as a function of redshift is a powerful method for tracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics - template subtraction, basic mode projection, and extended mode projection - all of which make use of externally supplied template maps, designed to characterise and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to th...
Sweeney, Timothy E; Chen, Albert C; Gevaert, Olivier
2015-11-19
In order to discover new subsets (clusters) of a data set, researchers often use algorithms that perform unsupervised clustering, namely, the algorithmic separation of a dataset into some number of distinct clusters. Deciding whether a particular separation (or number of clusters, K) is correct is a sort of 'dark art', with multiple techniques available for assessing the validity of unsupervised clustering algorithms. Here, we present a new technique for unsupervised clustering that uses multiple clustering algorithms, multiple validity metrics, and progressively bigger subsets of the data to produce an intuitive 3D map of cluster stability that can help determine the optimal number of clusters in a data set, a technique we call COmbined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL). COMMUNAL locally optimizes algorithms and validity measures for the data being used. We show its application to simulated data with a known K, and then apply this technique to several well-known cancer gene expression datasets, showing that COMMUNAL provides new insights into clustering behavior and stability in all tested cases. COMMUNAL is shown to be a useful tool for determining K in complex biological datasets, and is freely available as a package for R.
Nonlinear modal methods for crack localization
Sutin, Alexander; Ostrovsky, Lev; Lebedev, Andrey
2003-10-01
A nonlinear method for locating defects in solid materials is discussed that is relevant to nonlinear modal tomography based on the signal cross-modulation. The scheme is illustrated by a theoretical model in which a thin plate or bar with a single crack is excited by a strong low-frequency wave and a high-frequency probing wave (ultrasound). A crack is considered as a small contact-type defect which does not perturb the modal structure of sound in linear approximation but creates combinational-frequency components whose amplitudes depend on their closeness to a resonance and crack position. Using different crack models, including the hysteretic ones, the nonlinear part of its volume variations under the given stress and then the combinational wave components in the bar can be determined. Evidently, their amplitude depends strongly on the crack position with respect to the peaks or nodes of the corresponding linear signals which can be used for localization of the crack position. Exciting the sample by sweeping ultrasound frequencies through several resonances (modes) reduces the ambiguity in the localization. Some aspects of inverse problem solution are also discussed, and preliminary experimental results are presented.
A Novel Partial Discharge Localization Method in Substation Based on a Wireless UHF Sensor Array.
Li, Zhen; Luo, Lingen; Zhou, Nan; Sheng, Gehao; Jiang, Xiuchen
2017-08-18
Effective Partial Discharge (PD) localization can detect the insulation problems of the power equipment in a substation and improve the reliability of power systems. Typical Ultra-High Frequency (UHF) PD localization methods are mainly based on time difference information, which need a high sampling rate system. This paper proposes a novel PD localization method based on a received signal strength indicator (RSSI) fingerprint to quickly locate the power equipment with potential insulation defects. The proposed method consists of two stages. In the offline stage, the RSSI fingerprint data of the detection area is measured by a wireless UHF sensor array and processed by a clustering algorithm to reduce the PD interference and abnormal RSSI values. In the online stage, when PD happens, the RSSI fingerprint of PD is measured via the input of pattern recognition for PD localization. To achieve an accurate localization, the pattern recognition process is divided into two steps: a preliminary localization is implemented by cluster recognition to reduce the localization region, and the compressed sensing algorithm is used for accurate PD localization. A field test in a substation indicates that the mean localization error of the proposed method is 1.25 m, and 89.6% localization errors are less than 3 m.
Dark matter searches with Cherenkov telescopes: nearby dwarf galaxies or local galaxy clusters?
Sanchez-Conde, Miguel A; Zandanel, F; Gomez, Mario E; Prada, F
2011-01-01
In the last few years, most of the attention in gamma-ray dark matter (DM) searches has been devoted to neutralino annihilations in nearby dwarf galaxies. However, massive galaxy clusters in the local Universe may constitute very good targets as well. The main aim of this work is to compare both dwarf galaxies and local galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies and galaxy clusters, and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman~1 appears as the best candidate in the sample and, given the morphology of its annihilation signal, it is also one of the objects more readily observable by IACTs. As for galaxy clusters, Virgo represents the one with the hi...
Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models
Elsheikh, Ahmed H.
2013-05-01
A novel multi-modal parameter estimation algorithm is introduced. Parameter estimation is an ill-posed inverse problem that might admit many different solutions. This is attributed to the limited amount of measured data used to constrain the inverse problem. The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation. ISEM employs an ensemble of directional derivatives within a Gauss-Newton iteration for nonlinear parameter estimation. ISEM is augmented with a clustering step based on k-means algorithm to form sub-ensembles. These sub-ensembles are used to explore different parts of the search space. Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima. Numerical testing demonstrates the potential of the proposed algorithm in dealing with multi-modal nonlinear parameter estimation for subsurface flow models. © 2013 Elsevier B.V.
Seeking the Local Convergence Depth; 4, Tully-Fisher Observations of 35 Abell Clusters
Dale, D A; Haynes, M P; Hardy, E; Campusano, L E; Dale, Daniel A.; Giovanelli, Riccardo; Haynes, Martha P.; Hardy, Eduardo; Campusano, Luis E.
1999-01-01
We present Tully-Fisher observations for 35 rich Abell clusters of galaxies. Results from I band photometry and optical rotation curve work comprise the bulk of this paper. This is the third such data installment of an all-sky survey of 52 clusters in the distance range 50 to 200\\h Mpc. The complete data set provides the basis for determining an accurate Tully-Fisher template relation and for estimating the amplitude and direction of the local bulk flow on a 100\\h Mpc scale.
Quark-gluon plasma phase transition using cluster expansion method
Syam Kumar, A. M.; Prasanth, J. P.; Bannur, Vishnu M.
2015-08-01
This study investigates the phase transitions in QCD using Mayer's cluster expansion method. The inter quark potential is modified Cornell potential. The equation of state (EoS) is evaluated for a homogeneous system. The behaviour is studied by varying the temperature as well as the number of Charm Quarks. The results clearly show signs of phase transition from Hadrons to Quark-Gluon Plasma (QGP).
Translationally-invariant coupled-cluster method for finite systems
Guardiola, R; Navarro, J; Portesi, M
1998-01-01
The translational invariant formulation of the coupled-cluster method is presented here at the complete SUB(2) level for a system of nucleons treated as bosons. The correlation amplitudes are solution of a non-linear coupled system of equations. These equations have been solved for light and medium systems, considering the central but still semi-realistic nucleon-nucleon S3 interaction.
Chen, Lin-Yuan; Tang, Ping-Han; Wu, Ten-Ming
2016-07-14
In terms of the local bond-orientational order (LBOO) parameters, a cluster approach to analyze local structures of simple liquids was developed. In this approach, a cluster is defined as a combination of neighboring seeds having at least nb local-orientational bonds and their nearest neighbors, and a cluster ensemble is a collection of clusters with a specified nb and number of seeds ns. This cluster analysis was applied to investigate the microscopic structures of liquid Ga at ambient pressure (AP). The liquid structures studied were generated through ab initio molecular dynamics simulations. By scrutinizing the static structure factors (SSFs) of cluster ensembles with different combinations of nb and ns, we found that liquid Ga at AP contained two types of cluster structures, one characterized by sixfold orientational symmetry and the other showing fourfold orientational symmetry. The SSFs of cluster structures with sixfold orientational symmetry were akin to the SSF of a hard-sphere fluid. On the contrary, the SSFs of cluster structures showing fourfold orientational symmetry behaved similarly as the anomalous SSF of liquid Ga at AP, which is well known for exhibiting a high-q shoulder. The local structures of a highly LBOO cluster whose SSF displayed a high-q shoulder were found to be more similar to the structure of β-Ga than those of other solid phases of Ga. More generally, the cluster structures showing fourfold orientational symmetry have an inclination to resemble more to β-Ga.
A novel PPGA-based clustering analysis method for business cycle indicator selection
Dabin ZHANG; Lean YU; Shouyang WANG; Yingwen SONG
2009-01-01
A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator selection. In the proposed method,the category of each indicator is coded by real numbers,and some illegal chromosomes are repaired by the identi-fication arid restoration of empty class. Two mutation op-erators, namely the discrete random mutation operator andthe optimal direction mutation operator, are designed to bal-ance the local convergence speed and the global convergence performance, which are then combined with migration strat-egy and insertion strategy. For the purpose of verification and illustration, the proposed method is compared with the K-means clustering algorithm and the standard genetic algo-rithms via a numerical simulation experiment. The experi-mental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis algorithm. Meanwhile,the proposed clustering analysis algorithm is also applied to select the business cycle indicators to examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly select some leading indicators, coincident indicators, and lagging indi-cators to reflect the business cycle, which is extremely op-erational for some macro economy administrative managers and business decision-makers.
Adapted G-mode Clustering Method applied to Asteroid Taxonomy
Hasselmann, Pedro H.; Carvano, Jorge M.; Lazzaro, D.
2013-11-01
The original G-mode was a clustering method developed by A. I. Gavrishin in the late 60's for geochemical classification of rocks, but was also applied to asteroid photometry, cosmic rays, lunar sample and planetary science spectroscopy data. In this work, we used an adapted version to classify the asteroid photometry from SDSS Moving Objects Catalog. The method works by identifying normal distributions in a multidimensional space of variables. The identification starts by locating a set of points with smallest mutual distance in the sample, which is a problem when data is not planar. Here we present a modified version of the G-mode algorithm, which was previously written in FORTRAN 77, in Python 2.7 and using NumPy, SciPy and Matplotlib packages. The NumPy was used for array and matrix manipulation and Matplotlib for plot control. The Scipy had a import role in speeding up G-mode, Scipy.spatial.distance.mahalanobis was chosen as distance estimator and Numpy.histogramdd was applied to find the initial seeds from which clusters are going to evolve. Scipy was also used to quickly produce dendrograms showing the distances among clusters. Finally, results for Asteroids Taxonomy and tests for different sample sizes and implementations are presented.
Super pixel density based clustering automatic image classification method
Xu, Mingxing; Zhang, Chuan; Zhang, Tianxu
2015-12-01
The image classification is an important means of image segmentation and data mining, how to achieve rapid automated image classification has been the focus of research. In this paper, based on the super pixel density of cluster centers algorithm for automatic image classification and identify outlier. The use of the image pixel location coordinates and gray value computing density and distance, to achieve automatic image classification and outlier extraction. Due to the increased pixel dramatically increase the computational complexity, consider the method of ultra-pixel image preprocessing, divided into a small number of super-pixel sub-blocks after the density and distance calculations, while the design of a normalized density and distance discrimination law, to achieve automatic classification and clustering center selection, whereby the image automatically classify and identify outlier. After a lot of experiments, our method does not require human intervention, can automatically categorize images computing speed than the density clustering algorithm, the image can be effectively automated classification and outlier extraction.
Path-integral Monte Carlo method for the local Z2 Berry phase.
Motoyama, Yuichi; Todo, Synge
2013-02-01
We present a loop cluster algorithm Monte Carlo method for calculating the local Z(2) Berry phase of the quantum spin models. The Berry connection, which is given as the inner product of two ground states with different local twist angles, is expressed as a Monte Carlo average on the worldlines with fixed spin configurations at the imaginary-time boundaries. The "complex weight problem" caused by the local twist is solved by adopting the meron cluster algorithm. We present the results of simulation on the antiferromagnetic Heisenberg model on an out-of-phase bond-alternating ladder to demonstrate that our method successfully detects the change in the valence bond pattern at the quantum phase transition point. We also propose that the gauge-fixed local Berry connection can be an effective tool to estimate precisely the quantum critical point.
Dark Matter Searches with Cherenkov Telescopes: Nearby Dwarf Galaxies or Local Galaxy Clusters?
Sanchez-Conde, Miguel A.; /KIPAC, Menlo Park /SLAC /IAC, La Laguna /Laguna U., Tenerife; Cannoni, Mirco; /Huelva U.; Zandanel, Fabio; /IAA, Granada; Gomez, Mario E.; /Huelva U.; Prada, Francisco; /IAA, Granada
2012-06-06
In this paper, we compare dwarf galaxies and galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies (Draco, Ursa Minor, Wilman 1 and Segue 1) and local galaxy clusters (Perseus, Coma, Ophiuchus, Virgo, Fornax, NGC 5813 and NGC 5846), and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman 1 appears as the best candidate in the sample. However, its mass modeling is still rather uncertain, so probably other candidates with less uncertainties and quite similar fluxes, namely Ursa Minor and Segue 1, might be better options. As for galaxy clusters, Virgo represents the one with the highest flux. However, its large spatial extension can be a serious handicap for IACT observations and posterior data analysis. Yet, other local galaxy cluster candidates with more moderate emission regions, such as Perseus, may represent good alternatives. After comparing dwarfs and clusters, we found that the former exhibit annihilation flux profiles that, at the center, are roughly one order of magnitude higher than those of clusters, although galaxy clusters can yield similar, or even higher, integrated fluxes for the whole object once substructure is taken into account. Even when any of these objects are strictly point-like according to the properties of their annihilation signals, we conclude that dwarf galaxies are best suited for observational strategies based on the search of point-like sources, while galaxy clusters represent best targets for analyses that can deal with rather extended emissions. Finally, we study the detection prospects for present and future IACTs in the framework of the constrained minimal supersymmetric standard model. We
Dark matter searches with Cherenkov telescopes: nearby dwarf galaxies or local galaxy clusters?
Sánchez-Conde, Miguel A. [SLAC National Laboratory and Kavli Institute for Particle Astrophysics and Cosmology, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Cannoni, Mirco; Gómez, Mario E. [Dpto. Física Aplicada, Facultad de Ciencias Experimentales, Universidad de Huelva, 21071 Huelva (Spain); Zandanel, Fabio; Prada, Francisco, E-mail: masc@stanford.edu, E-mail: mirco.cannoni@dfa.uhu.es, E-mail: fabio@iaa.es, E-mail: mario.gomez@dfa.uhu.es, E-mail: fprada@iaa.es [Instituto de Astrofísica de Andalucía (CSIC), E-18008, Granada (Spain)
2011-12-01
In this paper, we compare dwarf galaxies and galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies (Draco, Ursa Minor, Wilman 1 and Segue 1) and local galaxy clusters (Perseus, Coma, Ophiuchus, Virgo, Fornax, NGC 5813 and NGC 5846), and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman 1 appears as the best candidate in the sample. However, its mass modeling is still rather uncertain, so probably other candidates with less uncertainties and quite similar fluxes, namely Ursa Minor and Segue 1, might be better options. As for galaxy clusters, Virgo represents the one with the highest flux. However, its large spatial extension can be a serious handicap for IACT observations and posterior data analysis. Yet, other local galaxy cluster candidates with more moderate emission regions, such as Perseus, may represent good alternatives. After comparing dwarfs and clusters, we found that the former exhibit annihilation flux profiles that, at the center, are roughly one order of magnitude higher than those of clusters, although galaxy clusters can yield similar, or even higher, integrated fluxes for the whole object once substructure is taken into account. Even when any of these objects are strictly point-like according to the properties of their annihilation signals, we conclude that dwarf galaxies are best suited for observational strategies based on the search of point-like sources, while galaxy clusters represent best targets for analyses that can deal with rather extended emissions. Finally, we study the detection prospects for present and future IACTs in the framework of the constrained minimal supersymmetric standard model. We
Time-dependent coupled-cluster method for atomic nuclei
Pigg, D A; Nam, H; Papenbrock, T
2012-01-01
We study time-dependent coupled-cluster theory in the framework of nuclear physics. Based on Kvaal's bi-variational formulation of this method [S. Kvaal, arXiv:1201.5548], we explicitly demonstrate that observables that commute with the Hamiltonian are conserved under time evolution. We explore the role of the energy and of the similarity-transformed Hamiltonian under real and imaginary time evolution and relate the latter to similarity renormalization group transformations. Proof-of-principle computations of He-4 and O-16 in small model spaces, and computations of the Lipkin model illustrate the capabilities of the method.
Segmentation of MRI Volume Data Based on Clustering Method
Ji Dongsheng
2016-01-01
Full Text Available Here we analyze the difficulties of segmentation without tag line of left ventricle MR images, and propose an algorithm for automatic segmentation of left ventricle (LV internal and external profiles. Herein, we propose an Incomplete K-means and Category Optimization (IKCO method. Initially, using Hough transformation to automatically locate initial contour of the LV, the algorithm uses a simple approach to complete data subsampling and initial center determination. Next, according to the clustering rules, the proposed algorithm finishes MR image segmentation. Finally, the algorithm uses a category optimization method to improve segmentation results. Experiments show that the algorithm provides good segmentation results.
A Comparison of Methods for Player Clustering via Behavioral Telemetry
Drachen, Anders; Thurau, Christian; Sifa, Rafet
2013-01-01
can be exceptionally complex, with features recorded for a varying population of users over a temporal segment that can reach years in duration. Categorization of behaviors, whether through descriptive methods (e.g. segmentation) or unsupervised/supervised learning techniques, is valuable for finding...... patterns in the behavioral data, and developing profiles that are actionable to game developers. There are numerous methods for unsupervised clustering of user behavior, e.g. k-means/c-means, Nonnegative Matrix Factorization, or Principal Component Analysis. Although all yield behavior categorizations...
Xiang, D.; Ni, W.; Zhang, H.; Wu, J.; Yan, W.; Su, Y.
2017-09-01
Superpixel segmentation has an advantage that can well preserve the target shape and details. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel segmentation method is proposed. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel segmentation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed segmentation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.
Controlling optical responses through local dielectric resonance in nanometre metallic clusters
Chen Liang-Liang; Gu Ying; Wang Li-Jin; Gong Qi-Huang
2007-01-01
Optical responses in dilute composites are controlled through the local dielectric resonance of metallic clusters. We consider two located metallic clusters close to each other with admittances ε1 and ε2. Through varying the difference admittance ratio η[= (ε2 - ε0)/(ε1 - ε0)], we find that their optical responses are determined by the local resonance.There is a blueshift of absorption peaks with the increase of η. Simultaneously, it is known that the absorption peaks will be redshifted by enlarging the cluster size. By adjusting the nano-metallic cluster geometry, size and admittances,we can control the positions and intensities of absorption peaks effectively. We have also deduced the effective linear optical responses of three-comPonent composites εe = ε0 (1 + ∑nsn=1 [(γn1 + ηγn2 )/(ε0 (s - sn))]), and the sum rule of cross sections: ∑nsn=1 (γn1 + ηγn2) = Nh1 + Nh2, where Nh1and Nh2 are the numbers of ε1 and ε2 bonds along the electric field, respectively. These results may be beneficial to the study of surface plasmon resonances on a nanometre scale.
Sheng-Ping Yan
2014-01-01
Full Text Available We perform a comparison between the local fractional Adomian decomposition and local fractional function decomposition methods applied to the Laplace equation. The operators are taken in the local sense. The results illustrate the significant features of the two methods which are both very effective and straightforward for solving the differential equations with local fractional derivative.
ALMA Reveals Potential Localized Dust Enrichment from Massive Star Clusters in II Zw 40
Consiglio, S. Michelle; Turner, Jean L.; Beck, Sara; Meier, David S.
2016-12-01
We present subarcsecond images of submillimeter CO and continuum emission from a local galaxy forming massive star clusters: the blue compact dwarf galaxy II Zw 40. At ˜0.″4 resolution (20 pc), the CO(3-2), CO(1-0), 3 mm, and 870 μm continuum maps illustrate star formation on the scales of individual molecular clouds. Dust contributes about one-third of the 870 μm continuum emission, with free-free accounting for the rest. On these scales, there is not a good correspondence between gas, dust, and free-free emission. Dust continuum is enhanced toward the star-forming region as compared to the CO emission. We suggest that an unexpectedly low and spatially variable gas-to-dust ratio is the result of rapid and localized dust enrichment of clouds by the massive clusters of the starburst.
Gao, Jing; Chen, Junling; Cai, Mingjun; Xu, Haijiao; Jiang, Junguang; Tong, Ti; Wang, Hongda
2017-06-01
Signal transducer and activator of transcription 3 (STAT3) plays a key role in various cellular processes such as cell proliferation, differentiation, apoptosis and immune responses. In particular, STAT3 has emerged as a potential molecular target for cancer therapy. The functional role and standard activation mechanism of STAT3 have been well studied, however, the spatial distribution of STAT3 during the cell cycle is poorly known. Therefore, it is indispensable to study STAT3 spatial arrangement and nuclear-cytoplasimic localization at the different phase of cell cycle in cancer cells. By direct stochastic optical reconstruction microscopy imaging, we find that STAT3 forms various number and size of clusters at the different cell-cycle stage, which could not be clearly observed by conventional fluorescent microscopy. STAT3 clusters get more and larger gradually from G1 to G2 phase, during which time transcription and other related activities goes on consistently. The results suggest that there is an intimate relationship between the clustered characteristic of STAT3 and the cell-cycle behavior. Meanwhile, clustering would facilitate STAT3 rapid response to activating signals due to short distances between molecules. Our data might open a new door to develop an antitumor drug for inhibiting STAT3 signaling pathway by destroying its clusters.
Eldridge, Sandra M; Ashby, Deborah; Kerry, Sally
2006-10-01
Cluster randomized trials are increasingly popular. In many of these trials, cluster sizes are unequal. This can affect trial power, but standard sample size formulae for these trials ignore this. Previous studies addressing this issue have mostly focused on continuous outcomes or methods that are sometimes difficult to use in practice. We show how a simple formula can be used to judge the possible effect of unequal cluster sizes for various types of analyses and both continuous and binary outcomes. We explore the practical estimation of the coefficient of variation of cluster size required in this formula and demonstrate the formula's performance for a hypothetical but typical trial randomizing UK general practices. The simple formula provides a good estimate of sample size requirements for trials analysed using cluster-level analyses weighting by cluster size and a conservative estimate for other types of analyses. For trials randomizing UK general practices the coefficient of variation of cluster size depends on variation in practice list size, variation in incidence or prevalence of the medical condition under examination, and practice and patient recruitment strategies, and for many trials is expected to be approximately 0.65. Individual-level analyses can be noticeably more efficient than some cluster-level analyses in this context. When the coefficient of variation is <0.23, the effect of adjustment for variable cluster size on sample size is negligible. Most trials randomizing UK general practices and many other cluster randomized trials should account for variable cluster size in their sample size calculations.
Local and cluster critical dynamics of the 3d random-site Ising model
Ivaneyko, D.; Ilnytskyi, J.; Berche, B.; Holovatch, Yu.
2006-10-01
We present the results of Monte Carlo simulations for the critical dynamics of the three-dimensional site-diluted quenched Ising model. Three different dynamics are considered, these correspond to the local update Metropolis scheme as well as to the Swendsen-Wang and Wolff cluster algorithms. The lattice sizes of L=10-96 are analysed by a finite-size-scaling technique. The site dilution concentration p=0.85 was chosen to minimize the correction-to-scaling effects. We calculate numerical values of the dynamical critical exponents for the integrated and exponential autocorrelation times for energy and magnetization. As expected, cluster algorithms are characterized by lower values of dynamical critical exponent than the local one: also in the case of dilution critical slowing down is more pronounced for the Metropolis algorithm. However, the striking feature of our estimates is that they suggest that dilution leads to decrease of the dynamical critical exponent for the cluster algorithms. This phenomenon is quite opposite to the local dynamics, where dilution enhances critical slowing down.
CluLoR: Clustered Localized Routing for FiWi Networks
Yousef Dashti
2014-04-01
Full Text Available The integration of passive optical networks (PONs and wireless mesh networks (WMNs into Fiber- Wireless (FiWi networks can lead to effective access networks. Existing routing schemes for FiWi networks consider mainly hop-count and delay metrics over a flat WMN node topology and do not specifically prioritize the local network structure, i.e., the local wireless-optical network gateway. In this study, we explore a simple, yet effective routing algorithm for FiWi networks with a WMN organized into zones operating on different radio channels. We examine the effects of routing the traffic into and out of a zone through one or more cluster heads. We investigate the effectiveness of localized routing that prioritizes transmissions over the local gateway to the optical network and avoids wireless packet transmissions in zones that do not contain the packet source or destination. We find that this combination of clustered and localized routing (CluLoR gives good throughput-delay performance compared to routing schemes that transmit packets wirelessly through “transit zones” (that do not contain the packet source or destination following minimum hop-count routing.
Efficient Cluster Head Selection Methods for Wireless Sensor Networks
Jong-Shin Chen
2010-08-01
Full Text Available The past few years have witnessed increased in the potential use of wireless sensor network (WSN such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. Since a WSN is composed of nodes with nonreplenishable energy resource, elongating the network lifetime is the main concern. To support scalability, nodes are often grouped into disjoint clusters. Each cluster would have a leader, often referred as cluster head (CH. A CH is responsible for not only the general request but also assisting the general nodes to route the sensed data to the target nodes. The power-consumption of a CH is higher then of a general (non-CH node. Therefore, the CH selection will affect the lifetime of a WSN. However, the application scenario contexts of WSNs that determine the definitions of lifetime will impact to achieve the objective of elongating lifetime. In this study, we classify the lifetime into different types and give the corresponding CH selection method to achieve the life-time extension objective. Simulation results demonstrate our study can enlarge the life-time for different requests of the sensor networks.
The Integral- and Intermediate-Screened Coupled-Cluster Method
Sørensen, L K
2016-01-01
We present the formulation and implementation of the integral- and intermediate-screened coupled-cluster method (ISSCC). The IISCC method gives a simple and rigorous integral and intermediate screening (IIS) of the coupled-cluster method and will significantly reduces the scaling for all orders of the CC hierarchy exactly like seen for the integral-screened configuration-interaction method (ISCI). The rigorous IIS in the IISCC gives a robust and adjustable error control which should allow for the possibility of converging the energy without any loss of accuracy while retaining low or linear scaling at the same time. The derivation of the IISCC is performed in a similar fashion as in the ISCI where we show that the tensor contractions for the nested commutators are separable up to an overall sign and that this separability can lead to a rigorous IIS. In the nested commutators the integrals are screened in the first tensor contraction and the intermediates are screened in all successive tensor contractions. The...
Optimal sensor placement using FRFs-based clustering method
Li, Shiqi; Zhang, Heng; Liu, Shiping; Zhang, Zhe
2016-12-01
The purpose of this work is to develop an optimal sensor placement method by selecting the most relevant degrees of freedom as actual measure position. Based on observation matrix of a structure's frequency response, two optimal criteria are used to avoid the information redundancy of the candidate degrees of freedom. By using principal component analysis, the frequency response matrix can be decomposed into principal directions and their corresponding singular. A relatively small number of principal directions will maintain a system's dominant response information. According to the dynamic similarity of each degree of freedom, the k-means clustering algorithm is designed to classify the degrees of freedom, and effective independence method deletes the sensors which are redundant of each cluster. Finally, two numerical examples and a modal test are included to demonstrate the efficient of the derived method. It is shown that the proposed method provides a way to extract sub-optimal sets and the selected sensors are well distributed on the whole structure.
Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms
Moradmand, Hajar; Targhi, Hossein Khazaei
2012-01-01
The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer method has been proposed to support radiologists in detection MCCs in digital mammography. First, in order to facilitate and improve the detection step, mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and watershed segmentation. Finally, the detected MCCs areas in different algorithms will be compared to find out which segmentation method is more appropriate for extracting MCCs in mammograms.
The Sorting Methods of Support Vector Clustering Based on Boundary Extraction and Category Utility
Chen Weigao
2016-01-01
Full Text Available According to the problems of low accuracy and high computational complexity in the classification of unknown radar signals, a method of unsupervised Support Vector Clustering (SVC based on boundary extraction and Category Utility (CU of unknown radar signals is studied. By analyzing the principle of SVC, only the boundary data of data sets contribute to the support vector extracted. Thus firstly, for reducing the data set, at the same time reducing the computational complexity, the algorithm is designed to extract the boundary data through local normal vector. Then using CU select the optimal parameters. At last distinguish different categories and get the sorting results by Cone Cluster Labelling (CCL and Depth-First Search (DFS. Through comparing the simulation results, the proposed method which is based on boundary extraction and CU is proved to have turned out quite good time effectiveness, which not only improves the accuracy of classification, but also reduces the computational complexity greatly.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-05-08
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-12-03
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
S. Roh
2015-05-01
Full Text Available In ensemble Kalman filtering (EnKF, the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.
2015-12-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Hooshyar, Milad; Wang, Dingbao; Kim, Seoyoung; Medeiros, Stephen C.; Hagen, Scott C.
2016-10-01
A method for automatic extraction of valley and channel networks from high-resolution digital elevation models (DEMs) is presented. This method utilizes both positive (i.e., convergent topography) and negative (i.e., divergent topography) curvature to delineate the valley network. The valley and ridge skeletons are extracted using the pixels' curvature and the local terrain conditions. The valley network is generated by checking the terrain for the existence of at least one ridge between two intersecting valleys. The transition from unchannelized to channelized sections (i.e., channel head) in each first-order valley tributary is identified independently by categorizing the corresponding contours using an unsupervised approach based on k-means clustering. The method does not require a spatially constant channel initiation threshold (e.g., curvature or contributing area). Moreover, instead of a point attribute (e.g., curvature), the proposed clustering method utilizes the shape of contours, which reflects the entire cross-sectional profile including possible banks. The method was applied to three catchments: Indian Creek and Mid Bailey Run in Ohio and Feather River in California. The accuracy of channel head extraction from the proposed method is comparable to state-of-the-art channel extraction methods.
Clustering and interpretation of local earthquake tomography models in the southern Dead Sea basin
Bauer, Klaus; Braeuer, Benjamin
2016-04-01
The Dead Sea transform (DST) marks the boundary between the Arabian and the African plates. Ongoing left-lateral relative plate motion and strike-slip deformation started in the Early Miocene (20 MA) and produced a total shift of 107 km until presence. The Dead Sea basin (DSB) located in the central part of the DST is one of the largest pull-apart basins in the world. It was formed from step-over of different fault strands at a major segment boundary of the transform fault system. The basin development was accompanied by deposition of clastics and evaporites and subsequent salt diapirism. Ongoing deformation within the basin and activity of the boundary faults are indicated by increased seismicity. The internal architecture of the DSB and the crustal structure around the DST were subject of several large scientific projects carried out since 2000. Here we report on a local earthquake tomography study from the southern DSB. In 2006-2008, a dense seismic network consisting of 65 stations was operated for 18 months in the southern part of the DSB and surrounding regions. Altogether 530 well-constrained seismic events with 13,970 P- and 12,760 S-wave arrival times were used for a travel time inversion for Vp, Vp/Vs velocity structure and seismicity distribution. The work flow included 1D inversion, 2.5D and 3D tomography, and resolution analysis. We demonstrate a possible strategy how several tomographic models such as Vp, Vs and Vp/Vs can be integrated for a combined lithological interpretation. We analyzed the tomographic models derived by 2.5D inversion using neural network clustering techniques. The method allows us to identify major lithologies by their petrophysical signatures. Remapping the clusters into the subsurface reveals the distribution of basin sediments, prebasin sedimentary rocks, and crystalline basement. The DSB shows an asymmetric structure with thickness variation from 5 km in the west to 13 km in the east. Most importantly, a well-defined body
No sign (yet) of intergalactic globular clusters in the Local Group
Mackey, Dougal; Leaman, Ryan
2016-01-01
We present Gemini/GMOS imaging of twelve candidate intergalactic globular clusters (IGCs) in the Local Group, identified in a recent survey of the SDSS footprint by di Tullio Zinn & Zinn (2015). Our image quality is sufficiently high, at $\\sim 0.4^{\\prime\\prime} - 0.7^{\\prime\\prime}$, that we are able to unambiguously classify all twelve targets as distant galaxies. To reinforce this conclusion we use GMOS images of globular clusters in the M31 halo, taken under very similar conditions, to show that any genuine clusters in the putative IGC sample would be straightforward to distinguish. Based on the stated sensitivity of the di Tullio Zinn & Zinn (2015) search algorithm, we conclude that there cannot be a significant number of IGCs with $M_V \\le -6$ lying unseen in the SDSS area if their properties mirror those of globular clusters in the outskirts of M31 -- even a population of $4$ would have only a $\\approx 1\\%$ chance of non-detection.
A new Self-Adaptive disPatching System for local clusters
Kan, Bowen; Shi, Jingyan; Lei, Xiaofeng
2015-12-01
The scheduler is one of the most important components of a high performance cluster. This paper introduces a self-adaptive dispatching system (SAPS) based on Torque[1] and Maui[2]. It promotes cluster resource utilization and improves the overall speed of tasks. It provides some extra functions for administrators and users. First of all, in order to allow the scheduling of GPUs, a GPU scheduling module based on Torque and Maui has been developed. Second, SAPS analyses the relationship between the number of queueing jobs and the idle job slots, and then tunes the priority of users’ jobs dynamically. This means more jobs run and fewer job slots are idle. Third, integrating with the monitoring function, SAPS excludes nodes in error states as detected by the monitor, and returns them to the cluster after the nodes have recovered. In addition, SAPS provides a series of function modules including a batch monitoring management module, a comprehensive scheduling accounting module and a real-time alarm module. The aim of SAPS is to enhance the reliability and stability of Torque and Maui. Currently, SAPS has been running stably on a local cluster at IHEP (Institute of High Energy Physics, Chinese Academy of Sciences), with more than 12,000 cpu cores and 50,000 jobs running each day. Monitoring has shown that resource utilization has been improved by more than 26%, and the management work for both administrator and users has been reduced greatly.
No sign (yet) of intergalactic globular clusters in the Local Group
Mackey, A. D.; Beasley, M. A.; Leaman, R.
2016-07-01
We present Gemini Multi-Object Spectrograph (GMOS) imaging of 12 candidate intergalactic globular clusters (IGCs) in the Local Group, identified in a recent survey of the Sloan Digital Sky Survey (SDSS) footprint by di Tullio Zinn & Zinn. Our image quality is sufficiently high, at ˜0.4-0.7 arcsec, that we are able to unambiguously classify all 12 targets as distant galaxies. To reinforce this conclusion we use GMOS images of globular clusters in the M31 halo, taken under very similar conditions, to show that any genuine clusters in the putative IGC sample would be straightforward to distinguish. Based on the stated sensitivity of the di Tullio Zinn & Zinn search algorithm, we conclude that there cannot be a significant number of IGCs with MV ≤ -6 lying unseen in the SDSS area if their properties mirror those of globular clusters in the outskirts of M31 - even a population of 4 would have only a ≈1 per cent chance of non-detection.
On the relationship between the non-local clustering mechanism and preferential concentration
Bragg, Andrew D; Collins, Lance R
2015-01-01
`Preferential concentration' (\\emph{Phys. Fluids} \\textbf{A3}:1169--78, 1991) refers to the clustering of inertial particles in the high-strain, low-rotation regions of turbulence. The `centrifuge mechanism' of Maxey (\\emph{J. Fluid Mech.} \\textbf{174}:441--65, 1987) appears to explain this phenomenon. In a recent paper, Bragg \\& Collins (\\emph{New J. Phys.} \\textbf{16}:055013, 2014) showed that the centrifuge mechanism is dominant only in the regime ${St\\ll1}$, where $St$ is the Stokes number based on the Kolmogorov time scale. Outside this regime, the centrifuge mechanism gives way to a non-local, path-history symmetry breaking mechanism. However, despite the change in the clustering mechanism, the instantaneous particle positions continue to correlate with high-strain, low-rotation regions of the turbulence. In this paper, we analyze the exact equation governing the radial distribution function and show how the non-local clustering mechanism is influenced by, but not dependent upon, the preferential sa...
Correlation between local clusters and structure of Al71Cu29 melt
陈莹; 边秀房; 孙民华; 王丽
2003-01-01
The structures of Al1-4Cu1-2 clusters were optimized by B3LYP method and the six geometries ground states were obtained. Al71Cu29 alloy melt has been investigated using X-ray diffractometry at 700℃. The experimental data were compared with calculated results to find the relation between the structures of Al-Cu clusters and melt structure. It is shown that there exists a strong interaction between Al and Cu atoms. The bond length in some geometries is very close to the experimental atomic distance. Such optimized geometries have close correlation with the liquid structure of Al-Cu alloy.
Cristina Martins
2016-03-01
Full Text Available This paper intends to investigate the scientific literature on the relation between tourism and technology clusters (TourTech in promoting local development on the databases Business Source Complete of the Online Research Databases (EBSCO and Leisure Tourism Database (CABI until the year 2014. With a mixed approach (qualitative and quantitative, the research is classified as descriptive and bibliographic. The strategy adopted for data collection used bibliometric criteria and the data analysis applied was content analysis. The results showed that there are some possible theoretical gaps to be developed: not only about the conection between tourism clusters and technology clusters for local development, but also the relation between tourism and technology clusters and their impact to promote innovation that can improve the local development and finally, how the investments to develop a cluster individually can impact on the development of the other.
A Comparison of Methods for Player Clustering via Behavioral Telemetry
Drachen, Anders; Thurau, Christian; Sifa, Rafet;
2013-01-01
The analysis of user behavior in digital games has been aided by the introduction of user telemetry in game development, which provides unprecedented access to quantitative data on user behavior from the installed game clients of the entire population of players. Player behavior telemetry datasets...... can be exceptionally complex, with features recorded for a varying population of users over a temporal segment that can reach years in duration. Categorization of behaviors, whether through descriptive methods (e.g. segmentation) or unsupervised/supervised learning techniques, is valuable for finding...... patterns in the behavioral data, and developing profiles that are actionable to game developers. There are numerous methods for unsupervised clustering of user behavior, e.g. k-means/c-means, Nonnegative Matrix Factorization, or Principal Component Analysis. Although all yield behavior categorizations...
Relativistic extended coupled cluster method for magnetic hyperfine structure constant
Sasmal, Sudip; Nayak, Malaya K; Vaval, Nayana; Pal, Sourav
2015-01-01
This article deals with the general implementation of 4-component spinor relativistic extended coupled cluster (ECC) method to calculate first order property of atoms and molecules in their open-shell ground state configuration. The implemented relativistic ECC is employed to calculate hyperfine structure (HFS) constant of alkali metals (Li, Na, K, Rb and Cs), singly charged alkaline earth metal atoms (Be+, Mg+, Ca+ and Sr+) and molecules (BeH, MgF and CaH). We have compared our ECC results with the calculations based on restricted active space configuration interaction (RAS-CI) method. Our results are in better agreement with the available experimental values than those of the RAS-CI values.
Anda, E.; Chiappe, G.; Busser, C.; Davidovich, M.; Martins, G.; H-Meisner, F.; Dagotto, E.
2008-03-01
A numerical algorithm to study transport properties of highly correlated local structures is proposed. The method, dubbed the Logarithmic Discretization Embedded Cluster Approximation (LDECA), consists of diagonalizing a finite cluster containing the many-body terms of the Hamiltonian and embedding it into the rest of the system, combined with Wilson's ideas of a logarithmic discretization of the representation of the Hamiltonian. LDECA's rapid convergence eliminates finite-size effects commonly present in the embedding cluster approximation (ECA) method. The physics associated with both one embedded dot and a string of two dots side-coupled to leads is discussed. In the former case, our results accurately agree with Bethe ansatz (BA) data, while in the latter, the results are framed in the conceptual background of a two-stage Kondo problem. A diagrammatic expansion provides the theoretical foundation for the method. It is argued that LDECA allows for the study of complex problems that are beyond the reach of currently available numerical methods.
A compute unified system architecture for graphics clusters incorporating data locality.
Müller, Christoph; Frey, Steffen; Strengert, Magnus; Dachsbacher, Carsten; Ertl, Thomas
2009-01-01
We present a development environment for distributed GPU computing targeted for multi-GPU systems, as well as graphics clusters. Our system is based on CUDA and logically extends its parallel programming model for graphics processors to higher levels of parallelism, namely, the PCI bus and network interconnects. While the extended API mimics the full function set of current graphics hardware-including the concept of global memory-on all distribution layers, the underlying communication mechanisms are handled transparently for the application developer. To allow for high scalability, in particular for network-interconnected environments, we introduce an automatic GPU-accelerated scheduling mechanism that is aware of data locality. This way, the overall amount of transmitted data can be heavily reduced, which leads to better GPU utilization and faster execution. We evaluate the performance and scalability of our system for bus and especially network-level parallelism on typical multi-GPU systems and graphics clusters.
ALMA Reveals Potential Localized Dust Enrichment from Massive Star Clusters in II Zw 40
Consiglio, S Michelle; Beck, Sara; Meier, David S
2016-01-01
We present subarcsecond images of submillimeter CO and continuum emission from a local galaxy forming massive star clusters: the blue compact dwarf galaxy II Zw 40. At $\\sim$0.4" resolution (20 pc), the CO(3-2), CO(1-0), 3mm and 870${\\mu}$m continuum maps illustrate star formation on the scales of individual molecular clouds. Dust contributes about a third of the 870${\\mu}$m continuum emission, with free-free accounting for the rest. On these scales, there is not a good correspondence between gas, dust, and free-free emission. Dust continuum is enhanced toward the star-forming region as compared to the CO emission. We suggest that an unexpectedly low and spatially variable gas-to-dust ratio is the result of massive clusters of the starburst.
Star formation and black hole accretion activity in rich local clusters of galaxies
Bianconi, Matteo; Fadda, Dario
2016-01-01
We present a study of the star formation and central black hole accretion activity of the galaxies hosted in the two nearby (z$\\sim$0.2) rich galaxy clusters Abell 983 and 1731. Aims: We are able to quantify both the obscured and unobscured star formation rates, as well as the presence of active galactic nuclei (AGN) as a function of the environment in which the galaxy is located. Methods: We targeted the clusters with unprecedented deep infrared Spitzer observations (0.2 mJy @ 24 micron), near-IR Palomar imaging and optical WIYN spectroscopy. The extent of our observations ($\\sim$ 3 virial radii) covers the vast range of possible environments, from the very dense cluster centre to the very rarefied cluster outskirts and accretion regions. Results: The star forming members of the two clusters present star formation rates comparable with those measured in coeval field galaxies. The analysis of the spatial arrangement of the spectroscopically confirmed members reveals an elongated distribution for A1731 with re...
Fu Yuhua
2016-08-01
Full Text Available By using Neutrosophy and Quad-stage Method, the expansions of comparative literature include: comparative social sciences clusters, comparative natural sciences clusters, comparative interdisciplinary sciences clusters, and so on. Among them, comparative social sciences clusters include: comparative literature, comparative history, comparative philosophy, and so on; comparative natural sciences clusters include: comparative mathematics, comparative physics, comparative chemistry, comparative medicine, comparative biology, and so on.
Dogulu, Nilay; Solomatine, Dimitri; Lal Shrestha, Durga
2014-05-01
Within the context of flood forecasting, assessment of predictive uncertainty has become a necessity for most of the modelling studies in operational hydrology. There are several uncertainty analysis and/or prediction methods available in the literature; however, most of them rely on normality and homoscedasticity assumptions for model residuals occurring in reproducing the observed data. This study focuses on a statistical method analyzing model residuals without having any assumptions and based on a clustering approach: Uncertainty Estimation based on local Errors and Clustering (UNEEC). The aim of this work is to provide a comprehensive evaluation of the UNEEC method's performance in view of clustering approach employed within its methodology. This is done by analyzing normality of model residuals and comparing uncertainty analysis results (for 50% and 90% confidence level) with those obtained from uniform interval and quantile regression methods. An important part of the basis by which the methods are compared is analysis of data clusters representing different hydrometeorological conditions. The validation measures used are PICP, MPI, ARIL and NUE where necessary. A new validation measure linking prediction interval to the (hydrological) model quality - weighted mean prediction interval (WMPI) - is also proposed for comparing the methods more effectively. The case study is Brue catchment, located in the South West of England. A different parametrization of the method than its previous application in Shrestha and Solomatine (2008) is used, i.e. past error values in addition to discharge and effective rainfall is considered. The results show that UNEEC's notable characteristic in its methodology, i.e. applying clustering to data of predictors upon which catchment behaviour information is encapsulated, contributes increased accuracy of the method's results for varying flow conditions. Besides, classifying data so that extreme flow events are individually
A Method for Clustering Web Attacks Using Edit Distance
Petrovic, Slobodan; Alvarez, Gonzalo
2003-01-01
Cluster analysis often serves as the initial step in the process of data classification. In this paper, the problem of clustering different length input data is considered. The edit distance as the minimum number of elementary edit operations needed to transform one vector into another is used. A heuristic for clustering unequal length vectors, analogue to the well known k-means algorithm is described and analyzed. This heuristic determines cluster centroids expanding shorter vectors to the l...
Clusters and local development: the case of the textile district of Atuntaqui
César Paredes
2013-09-01
Full Text Available Atuntaqui is heralded as a local economic development success story. The author scrutinizes the experience of the textile industrial district in Atuntaqui in the province of Imbabura, and concludes that the district actually represents a case of overspecialization, given a lack of economic diversification. Moreover the author notes that the municipality has an urban bias, pointing out the need for a broader ¨territorial¨ approach to local and regional development planning that factors in issues like water scarcity, rural poverty and exploitation of female labour, as opposed to the current myopic view that ignores rural urban linkages. In the article the success story of Atuntaqui is downplayed, stating that donors exaggerated the economic impact of the textile cluster.Atuntaqui is viewed as a model by neighboring cities as a result of its recent economic dynamism. Local policy makers need to look deeper into these efforts, and also take into account negative externalities, concluding that clusters are not a panacea for quick industrial development.
Methods of regional innovative clusters forming and development programs elaboration
Marchuk, Olha
2013-01-01
The aim of the article is to select programmes for the formation and development of innovative cluster structures. The analysis of the backgrounds of formation of innovative clusters was made in the regions of Ukraine. Two types of programmes were suggested for the implamentation of cluster policy at the regional level.
Scalable fault tolerant algorithms for linear-scaling coupled-cluster electronic structure methods.
Leininger, Matthew L.; Nielsen, Ida Marie B.; Janssen, Curtis L.
2004-10-01
By means of coupled-cluster theory, molecular properties can be computed with an accuracy often exceeding that of experiment. The high-degree polynomial scaling of the coupled-cluster method, however, remains a major obstacle in the accurate theoretical treatment of mainstream chemical problems, despite tremendous progress in computer architectures. Although it has long been recognized that this super-linear scaling is non-physical, the development of efficient reduced-scaling algorithms for massively parallel computers has not been realized. We here present a locally correlated, reduced-scaling, massively parallel coupled-cluster algorithm. A sparse data representation for handling distributed, sparse multidimensional arrays has been implemented along with a set of generalized contraction routines capable of handling such arrays. The parallel implementation entails a coarse-grained parallelization, reducing interprocessor communication and distributing the largest data arrays but replicating as many arrays as possible without introducing memory bottlenecks. The performance of the algorithm is illustrated by several series of runs for glycine chains using a Linux cluster with an InfiniBand interconnect.
Lestari, D.; Raharjo, D.; Bustamam, A.; Abdillah, B.; Widhianto, W.
2017-07-01
Dengue virus consists of 10 different constituent proteins and are classified into 4 major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and then we analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. Protein interactions that are connected in a tissue, form a complex protein that serves as a specific biological process unit. The analysis of result shows the R-MCL clustering produces clusters of dengue virus family based on the similarity role of their constituent protein, regardless of serotypes.
Composite likelihood method for inferring local pedigrees
Nielsen, Rasmus
2017-01-01
Pedigrees contain information about the genealogical relationships among individuals and are of fundamental importance in many areas of genetic studies. However, pedigrees are often unknown and must be inferred from genetic data. Despite the importance of pedigree inference, existing methods are limited to inferring only close relationships or analyzing a small number of individuals or loci. We present a simulated annealing method for estimating pedigrees in large samples of otherwise seemingly unrelated individuals using genome-wide SNP data. The method supports complex pedigree structures such as polygamous families, multi-generational families, and pedigrees in which many of the member individuals are missing. Computational speed is greatly enhanced by the use of a composite likelihood function which approximates the full likelihood. We validate our method on simulated data and show that it can infer distant relatives more accurately than existing methods. Furthermore, we illustrate the utility of the method on a sample of Greenlandic Inuit. PMID:28827797
A Data Cleansing Method for Clustering Large-scale Transaction Databases
Loh, Woong-Kee; Kang, Jun-Gyu
2010-01-01
In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.
A dynamic hierarchical clustering method for trajectory-based unusual video event detection.
Jiang, Fan; Wu, Ying; Katsaggelos, Aggelos K
2009-04-01
The proposed unusual video event detection method is based on unsupervised clustering of object trajectories, which are modeled by hidden Markov models (HMM). The novelty of the method includes a dynamic hierarchical process incorporated in the trajectory clustering algorithm to prevent model overfitting and a 2-depth greedy search strategy for efficient clustering.
Image Clustering Method Based on Density Maps Derived from Self-Organizing Mapping: SOM
Kohei Arai
2012-07-01
Full Text Available A new method for image clustering with density maps derived from Self-Organizing Maps (SOM is proposed together with a clarification of learning processes during a construction of clusters. It is found that the proposed SOM based image clustering method shows much better clustered result for both simulation and real satellite imagery data. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. In accordance with the experimental results with Landsat-5 TM image, it takes more than 20000 of iteration for convergence of the SOM learning processes.
Mapping the Generator Coordinate Method to the Coupled Cluster Approach
Stuber, Jason L
2015-01-01
The generator coordinate method (GCM) casts the wavefunction as an integral over a weighted set of non-orthogonal single determinantal states. In principle this representation can be used like the configuration interaction (CI) or shell model to systematically improve the approximate wavefunction towards an exact solution. In practice applications have generally been limited to systems with less than three degrees of freedom. This bottleneck is directly linked to the exponential computational expense associated with the numerical projection of broken symmetry Hartree-Fock (HF) or Hartree-Fock-Bogoliubov (HFB) wavefunctions and to the use of a variational rather than a bi-variational expression for the energy. We circumvent these issues by choosing a hole-particle representation for the generator and applying algebraic symmetry projection, via the use of tensor operators and the invariant mean (operator average). The resulting GCM formulation can be mapped directly to the coupled cluster (CC) approach, leading...
Displacement of Building Cluster Using Field Analysis Method
Al Tinghua
2003-01-01
This paper presents a field based method to deal with the displacement of building cluster,which is driven by the street widening. The compress of street boundary results in the force to push the building moving inside and the force propagation is a decay process. To describe the phenomenon above, the field theory is introduced with the representation model of isoline. On the basis of the skeleton of Delaunay triangulation,the displacement field is built in which the propagation force is related to the adjacency degree with respect to the street boundary. The study offers the computation of displacement direction and offset distance for the building displacement. The vector operation is performed on the basis of grade and other field concepts.
Local renormalization method for random systems
Gittsovich O.; Hubener R.; Rico E.; Briegel H.J.
2010-01-01
In this paper, we introduce a real-space renormalization transformation for random spin systems on 2D lattices. The general method is formulated for random systems and results from merging two well known real space renormalization techniques, namely the strong disorder renormalization technique (SDRT) and the contractor renormalization (CORE). We analyze the performance of the method on the 2D random transverse field Ising model (RTFIM).
A New Method for Local Dependence Map and Its Applications
Burcu H. ÜÇER
2009-01-01
Full Text Available Objective: This work introduces a new method to construct local dependence map based on the estimate for the linear local dependence function H(x,y, which is generalization of Pearson correlation coefficient. The new local dependence map demonstrates a practical tool for local dependence structure between two random variables. The analysis of theoretical concepts is verified by an application based on real datasets in endocrinology. Material and Methods: The method, local dependence map, requires the estimation new local dependence function which is based on regression concepts. After this local dependence function must be converted with local permutation tests in local dependence map which make the local dependence function more interpretable by identifying the regions of positive, negative and zero local dependence. Results: Based on the proposed method and we give two examples based on the real data C-peptide, insulin and TSH, FT3, FT4 from endocrinology in order to show the advantageous of the current dependence maps. They show interesting local dependence features on the other hand overall correlation coefficient is not much informative. Conclusion: Scalar dependence measures such as correlation coefficient are often used as a measure of dependence for data in medical and biological science. However, they cannot reflect the complex dependence structure of two variables. Hence we are now concerned exclusively with the statistical aspects of the dependence structure in dependence maps that will be constructed for the dataset. In this work a new method to construct local dependence map based on the regression concept for the linear local dependence function H(x,y, which is generalization of Pearson correlation coefficient, is established. The proposed new local dependence map is devoted to two examples based on the real data C-peptide, insulin and TSH, FT3, FT4 from endocrinology in order to illustrate the usefulness of the current dependence
The UV-optical colour dependence of galaxy clustering in the local universe
Loh, Yeong-Shang; Rich, R. Michael; Heinis, Sébastien; Scranton, Ryan; Mallery, Ryan P.; Salim, Samir; Martin, D. Christopher; Wyder, Ted; Arnouts, Stéphane; Barlow, Tom A.; Forster, Karl; Friedman, Peter G.; Morrissey, Patrick; Neff, Susan G.; Schiminovich, David; Seibert, Mark; Bianchi, Luciana; Donas, Jose; Heckman, Timothy M.; Lee, Young-Wook; Madore, Barry F.; Milliard, Bruno; Szalay, Alex S.; Welsh, Barry Y.
2010-09-01
We measure the UV-optical colour dependence of galaxy clustering in the local Universe. Using the clean separation of the red and blue sequences made possible by the NUV - r colour-magnitude diagram, we segregate the galaxies into red, blue and intermediate `green' classes. We explore the clustering as a function of this segregation by removing the dependence on luminosity and by excluding edge-on galaxies as a means of a non-model dependent veto of highly extincted galaxies. We find that ξ(rp, π) for both red and green galaxies shows strong redshift-space distortion on small scales - the `finger-of-God' effect, with green galaxies having a lower amplitude than is seen for the red sequence, and the blue sequence showing almost no distortion. On large scales, ξ(rp, π) for all three samples show the effect of large-scale streaming from coherent infall. On scales of 1h-1Mpc power law with slope γ ~ 1.93 and amplitude r0 ~ 7.5 and 5.3, compared with γ ~ 1.75 and r0 ~ 3.9 h-1 Mpc for blue sequence galaxies. Compared to the clustering of a fiducial L* galaxy, the red, green and blue have a relative bias of 1.5, 1.1 and 0.9, respectively. The wp(rp) for blue galaxies display an increase in convexity at ~ 1 h-1 Mpc, with an excess of large-scale clustering. Our results suggest that the majority of blue galaxies are likely central galaxies in less massive haloes, while red and green galaxies have larger satellite fractions, and preferentially reside in virialized structures. If blue sequence galaxies migrate to the red sequence via processes like mergers or quenching that take them through the green valley, such a transformation may be accompanied by a change in environment in addition to any change in luminosity and colour.
Research in clustering algorithm based on local agglomerative characteristics%利用局部集聚特性的聚类算法的研究
牛习现; 赵立川
2011-01-01
基于SNN相似性和密度的聚类算法是当前主要的无监督聚类方法之一,该类算法在发现不同大小形状簇的聚类过程中都取得了较好的结果.但是该类算法也存在局限性,如Jarvis-Patrick算法通过单连结的方式发现簇,可能分割真正的簇或者合并应该保持分离的簇,而SNN密度类算法的Eps,MinPts参数的确定对用户来说是比较困难的.针对该类问题,本文对聚类过程中的局部集聚特征进行了分析和定义,提出了利用数据的局部集聚特征来控制聚类过程的的聚类算法.通过验证,该算法对发现不同密度以及任意形状的数据集合的聚类分析问题是有效的,突出了数据分析的局部集聚特征,改进了数据聚类的质量.%The SNN similarity and density based clustering, as one of the most important unsupervised clustering method, has been proved to produce good results in finding clusters of various sizes and shapes. But these algorithms still have some limitations. For example, Jarvis-Patrick scheme of finding clusters by single link, may separate real clusters or merge clusters which should be kept separated in certain situations, and the determination of Eps and MinPts, the parameters of SNN density method, is hard for users. To deal with these problems, the paper gives analysis and definition of local agglomerative characteristics presented in clustering procedure; then proposes a new clustering algorithm which use local gathering features to control clustering progress. The algorithm can work well in finding different size and density clusters, highlighting the local features of data analysis and improving the quality of data clusters.
Coupled-cluster methods for core-hole dynamics
Picon, Antonio; Cheng, Lan; Hammond, Jeff R.; Stanton, John F.; Southworth, Stephen H.
2014-05-01
Coupled cluster (CC) is a powerful numerical method used in quantum chemistry in order to take into account electron correlation with high accuracy and size consistency. In the CC framework, excited, ionized, and electron-attached states can be described by the equation of motion (EOM) CC technique. However, bringing CC methods to describe molecular dynamics induced by x rays is challenging. X rays have the special feature of interacting with core-shell electrons that are close to the nucleus. Core-shell electrons can be ionized or excited to a valence shell, leaving a core-hole that will decay very fast (e.g. 2.4 fs for K-shell of Ne) by emitting photons (fluorescence process) or electrons (Auger process). Both processes are a clear manifestation of a many-body effect, involving electrons in the continuum in the case of Auger processes. We review our progress of developing EOM-CC methods for core-hole dynamics. Results of the calculations will be compared with measurements on core-hole decays in atomic Xe and molecular XeF2. This work is funded by the Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy, under Contract No. DE-AC02-06CH11357.
Nonlinear modal method of crack localization
Ostrovsky, Lev; Sutin, Alexander; Lebedev, Andrey
2004-05-01
A simple scheme for crack localization is discussed that is relevant to nonlinear modal tomography based on the cross-modulation of two signals at different frequencies. The scheme is illustrated by a theoretical model, in which a thin plate or bar with a single crack is excited by a strong low-frequency wave and a high-frequency probing wave (ultrasound). The crack is assumed to be small relative to all wavelengths. Nonlinear scattering from the crack is studied using a general matrix approach as well as simplified models allowing one to find the nonlinear part of crack volume variations under the given stress and then the combinational wave components in the tested material. The nonlinear response strongly depends on the crack position with respect to the peaks or nodes of the corresponding interacting signals which can be used for determination of the crack position. Juxtaposing various resonant modes interacting at the crack it is possible to retrieve both crack location and orientation. Some aspects of inverse problem solutions are also discussed, and preliminary experimental results are presented.
Multi-Scale Jacobi Method for Anderson Localization
Imbrie, John Z.
2014-01-01
A new KAM-style proof of Anderson localization is obtained. A sequence of local rotations is defined, such that off-diagonal matrix elements of the Hamiltonian are driven rapidly to zero. This leads to the first proof via multi-scale analysis of exponential decay of the eigenfunction correlator (this implies strong dynamical localization). The method has been used in recent work on many-body localization [arXiv:1403.7837].
Motion estimation using point cluster method and Kalman filter.
Senesh, M; Wolf, A
2009-05-01
The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures--PCT, Kalman filter followed by PCT, and low pass filter followed by PCT--enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal
Karnbach, R.; Castex, M. C.; Keto, J. W.; Joppien, M.; Wörmer, J.; Zimmerer, G.; Möller, T.
1993-02-01
Excitation and decay processes in Kr N clusters ( N=2-10 4) were investigated via time- and energy-resolved fluorescence methods with synchrotron radiation excitation. In small clusters ( N<50) in addition to the well-known emission bands of condensed Kr another broad continuous emission is observed. It is assigned to a radiative decay of Kr excimers desorbing from the cluster surface. There are indications that the cluster size where the desorption rate becomes slow is related to a change in sign of the electron affinity of the cluster. Changes of spectral distribution of the fluorescence light with cluster size are interpreted as variations of the vibrational energy flow.
Cooper James B
2010-03-01
Full Text Available Abstract Background Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. Results We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. Conclusions By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.
Efficiency of a Multi-Reference Coupled Cluster method
Giner, Emmanuel; Scemama, Anthony; Malrieu, Jean Paul
2015-01-01
The multi-reference Coupled Cluster method first proposed by Meller et al (J. Chem. Phys. 1996) has been implemented and tested. Guess values of the amplitudes of the single and double excitations (the ${\\hat T}$ operator) on the top of the references are extracted from the knowledge of the coefficients of the Multi Reference Singles and Doubles Configuration Interaction (MRSDCI) matrix. The multiple parentage problem is solved by scaling these amplitudes on the interaction between the references and the Singles and Doubles. Then one proceeds to a dressing of the MRSDCI matrix under the effect of the Triples and Quadruples, the coefficients of which are estimated from the action of ${\\hat T}^2$. This dressing follows the logics of the intermediate effective Hamiltonian formalism. The dressed MRSDCI matrix is diagonalized and the process is iterated to convergence. The method is tested on a series of benchmark systems from Complete Active Spaces (CAS) involving 2 or 4 active electrons up to bond breakings. The...
A newself-localization method for wireless sensor networks
无
2008-01-01
Many applications of wireless sensor networks can benefit from fine-grained localization. In this paper, we proposed an accurate, distributed localization method based on the time difference between radio signal and sound wave. In a trilateration, each node adaptively chooses a neighborhood of sensors and updates its position estimate with trilateration, and then passes this update to neighboring sensors. Application examples demonstrate that the proposed method is more robust and accurate in localizing nod...
Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials
Sanders, Elizabeth A.
2011-01-01
This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…
Global/local methods research using the CSM testbed
Knight, Norman F., Jr.; Ransom, Jonathan B.; Griffin, O. Hayden, Jr.; Thompson, Danniella M.
1990-01-01
Research activities in global/local stress analysis are described including both two- and three-dimensional analysis methods. These methods are being developed within a common structural analysis framework. Representative structural analysis problems are presented to demonstrate the global/local methodologies being developed.
Hudjimartsu, S. A.; Djatna, T.; Ambarwari, A.; Apriliantono
2017-01-01
The forest fires in Indonesia occurs frequently in the dry season. Almost all the causes of forest fires are caused by the human activity itself. The impact of forest fires is the loss of biodiversity, pollution hazard and harm the economy of surrounding communities. To prevent fires required the method, one of them with spatial temporal clustering. Spatial temporal clustering formed grouping data so that the results of these groupings can be used as initial information on fire prevention. To analyze the fires, used hotspot data as early indicator of fire spot. Hotspot data consists of spatial and temporal dimensions can be processed using the Spatial Temporal Clustering with Kulldorff Scan Statistic (KSS). The result of this research is to the effectiveness of KSS method to cluster spatial hotspot in a case within Riau Province and produces two types of clusters, most cluster and secondary cluster. This cluster can be used as an early fire warning information.
Modified Burgers' equation by the local discontinuous Galerkin method
Zhang Rong-Pei; Yu Xi-Jun; Zhao Guo-Zhong
2013-01-01
In this paper,we present the local discontinuous Galerkin method for solving Burgers' equation and the modified Burgers' equation.We describe the algorithm formulation and practical implementation of the local discontinuous Galerkin method in detail.The method is applied to the solution of the one-dimensional viscous Burgers' equation and two forms of the modified Burgers' equation.The numerical results indicate that the method is very accurate and efficient.
Susan Worner
2013-09-01
Full Text Available For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the co-occurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM, a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k
A Localization Method for Multistatic SAR Based on Convex Optimization.
Xuqi Zhong
Full Text Available In traditional localization methods for Synthetic Aperture Radar (SAR, the bistatic range sum (BRS estimation and Doppler centroid estimation (DCE are needed for the calculation of target localization. However, the DCE error greatly influences the localization accuracy. In this paper, a localization method for multistatic SAR based on convex optimization without DCE is investigated and the influence of BRS estimation error on localization accuracy is analysed. Firstly, by using the information of each transmitter and receiver (T/R pair and the target in SAR image, the model functions of T/R pairs are constructed. Each model function's maximum is on the circumference of the ellipse which is the iso-range for its model function's T/R pair. Secondly, the target function whose maximum is located at the position of the target is obtained by adding all model functions. Thirdly, the target function is optimized based on gradient descent method to obtain the position of the target. During the iteration process, principal component analysis is implemented to guarantee the accuracy of the method and improve the computational efficiency. The proposed method only utilizes BRSs of a target in several focused images from multistatic SAR. Therefore, compared with traditional localization methods for SAR, the proposed method greatly improves the localization accuracy. The effectivity of the localization approach is validated by simulation experiment.
A Localization Method for Multistatic SAR Based on Convex Optimization.
Zhong, Xuqi; Wu, Junjie; Yang, Jianyu; Sun, Zhichao; Huang, Yuling; Li, Zhongyu
2015-01-01
In traditional localization methods for Synthetic Aperture Radar (SAR), the bistatic range sum (BRS) estimation and Doppler centroid estimation (DCE) are needed for the calculation of target localization. However, the DCE error greatly influences the localization accuracy. In this paper, a localization method for multistatic SAR based on convex optimization without DCE is investigated and the influence of BRS estimation error on localization accuracy is analysed. Firstly, by using the information of each transmitter and receiver (T/R) pair and the target in SAR image, the model functions of T/R pairs are constructed. Each model function's maximum is on the circumference of the ellipse which is the iso-range for its model function's T/R pair. Secondly, the target function whose maximum is located at the position of the target is obtained by adding all model functions. Thirdly, the target function is optimized based on gradient descent method to obtain the position of the target. During the iteration process, principal component analysis is implemented to guarantee the accuracy of the method and improve the computational efficiency. The proposed method only utilizes BRSs of a target in several focused images from multistatic SAR. Therefore, compared with traditional localization methods for SAR, the proposed method greatly improves the localization accuracy. The effectivity of the localization approach is validated by simulation experiment.
Multi-Scale Jacobi Method for Anderson Localization
Imbrie, John Z.
2015-11-01
A new KAM-style proof of Anderson localization is obtained. A sequence of local rotations is defined, such that off-diagonal matrix elements of the Hamiltonian are driven rapidly to zero. This leads to the first proof via multi-scale analysis of exponential decay of the eigenfunction correlator (this implies strong dynamical localization). The method has been used in recent work on many-body localization (Imbrie in On many-body localization for quantum spin chains, arXiv:1403.7837 , 2014).
Bento, G C; Oliveira, P R
2008-01-01
Local convergence analysis of the proximal point method for special class of nonconvex function on Hadamard manifold is presented in this paper. The well definedness of the sequence generated by the proximal point method is guaranteed. Moreover, is proved that each cluster point of this sequence satisfies the necessary optimality conditions and, under additional assumptions, its convergence for a minimizer is obtained.
The UV-Optical Color Dependence of Galaxy Clustering in the Local Universe
Loh, Yeong-Shang; Heinis, Sébastien; Scranton, Ryan; Mallery, Ryan P; Salim, Samir; Martin, D Christopher; Wyder, Ted; Arnouts, Stéphane; Barlow, Tom A; Forster, Karl; Friedman, Peter G; Morrissey, Patrick; Neff, Susan G; Schiminovich, David; Seibert, Mark; Bianchi, Luciana; Donas, Jose; Heckman, Timothy M; Lee, Young-Wook; Madore, Barry F; Milliard, Bruno; Szalay, Alex S; Welsh, Barry Y; Yi, Suk Young
2010-01-01
We measure the UV-optical color dependence of galaxy clustering in the local universe. Using the clean separation of the red and blue sequences made possible by the NUV - r color-magnitude diagram, we segregate the galaxies into red, blue and intermediate "green" classes. We explore the clustering as a function of this segregation by removing the dependence on luminosity and by excluding edge-on galaxies as a means of a non-model dependent veto of highly extincted galaxies. We find that \\xi (r_p, \\pi) for both red and green galaxies shows strong redshift space distortion on small scales -- the "finger-of-God" effect, with green galaxies having a lower amplitude than is seen for the red sequence, and the blue sequence showing almost no distortion. On large scales, \\xi (r_p, \\pi) for all three samples show the effect of large-scale streaming from coherent infall. On scales 1 Mpc/h < r_p < 10 Mpc/h, the projected auto-correlation function w_p(r_p) for red and green galaxies fits a power-law with slope \\gam...
Effects of Λ hyperon on localization and clustering in p- and sd-shell nuclei
Xu, Renli, E-mail: xurenli.phy@gmail.com [Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing 210093 (China); Wu, Chen, E-mail: wuchenoffd@gmail.com [Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800 (China); Ren, Zhongzhou, E-mail: zren@nju.edu.cn [Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing 210093 (China); Center of Theoretical Nuclear Physics, National Laboratory of Heavy-Ion Accelerator, Lanzhou 730000 (China); Joint Center of Nuclear Science and Technology, Nanjing University, Nanjing 210093 (China)
2015-01-15
In the present work, the properties of several deformed p- and sd-shell Λ-hypernuclei have been investigated using the self-consistent relativistic mean field theory. The calculated ground state energies are consistent with the experimental values. In particular, the possibility of localization and clustering in deformed nuclei is discussed, concentrating on experimentally accessible light nuclei. Utilizing the framework of relativistic mean field model, our calculation predicts formation of clusterlike structure in the ground state of {sup 28}Si and {sup 32}S, while the core nuclei in {sup 29}{sub Λ}Si and {sup 33}{sub Λ}S show a delocalized formation in the presence hyperon depending on the nuclear effective interaction.
de Grijs, Richard
2015-01-01
Aiming at providing a firm mean distance estimate to the Small Magellanic Cloud (SMC), and thus to place it within the internally consistent Local Group distance framework we recently established, we compiled the current-largest database of published distance estimates to the galaxy. Based on careful statistical analysis, we derive mean distance estimates to the SMC using eclipsing binary systems, variable stars, stellar population tracers, and star cluster properties. Their weighted mean leads to a final recommendation for the mean SMC distance of $(m-M)_0^{\\rm SMC} = 18.96 \\pm 0.02$ mag, where the uncertainty represents the formal error. Systematic effects related to lingering uncertainties in extinction corrections, our physical understanding of the stellar tracers used, and the SMC's complex geometry---including its significant line-of-sight depth, its irregular appearance which renders definition of the galaxy's center uncertain, as well as its high inclination and possibly warped disk---may contribute a...
NUV-IR colours of red sequence galaxies in local clusters
Rawle, Timothy D; Lucey, John R; Hudson, Michael J; Wegner, Gary A
2008-01-01
We present GALEX near-UV (NUV) and 2MASS J band photometry for red sequence galaxies in local clusters. We define quiescent samples according to a strict emission threshold, removing galaxies with very recent star formation. We analyse the NUV-J colour-magnitude relation (CMR) and find that the intrinsic scatter is an order of magnitude larger than for the analogous optical CMR (~0.35 rather than 0.05 mag), in agreement with previous studies. Comparing the NUV-J colours with spectroscopically-derived stellar population parameters, we find a strong (> 5.5sigma) correlation with metallicity, only a marginal trend with age, and no correlation with the alpha/Fe ratio. We explore the origin of the large scatter and conclude that neither aperture effects nor the UV upturn phenomenon contribute significantly. We show that the scatter could be attributed to simple `frosting' by either a young or a low metallicity subpopulation.
Kuptsov, Pavel V; Kuptsova, Anna V
2014-09-01
Covariant Lyapunov vectors for scale-free networks of Hénon maps are highly localized. We revealed two mechanisms of the localization related to full and phase cluster synchronization of network nodes. In both cases the localization nodes remain unaltered in the course of the dynamics, i.e., the localization is nonwandering. Moreover, this is predictable: The localization nodes are found to have specific dynamical and topological properties and they can be found without computing of the covariant vectors. This is an example of explicit relations between the system topology, its phase-space dynamics, and the associated tangent-space dynamics of covariant Lyapunov vectors.
A People-Localization Method for Multi-Robot Systems First Approach for Guiding-Tours
Edgar Martinez-Garcia
2008-11-01
Full Text Available Throughout this article we present a methodology to localize multiple people in a group by a multi-robot system (MRS. The aim of the MRS is to conduct people through hallways in indoors as a guided-tour service task. However, further than guidance process, we detail a method for humans' localization by sharing distributed sensor data arising from the team of robots instrumented with stereo vision. The robustness of the method is presented, and by matching the real environment against the computed results, error in human localization is showed as well. As a first approach of the entire MRS goal, this paper explains from a task approach the way for environment ranging, spatial noise filtering, distributed sensor data fusion and clustering based segmentation. Likewise, through the paper experimental results are shown to verify the feasibility of the method.
Kohei Arai
2013-07-01
Full Text Available Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in the data sets. In this paper, we propose to provide a consistent partitioning of a dataset which allows identifying any shape of cluster patterns in case of numerical clustering, convex or non-convex. The method is based on layered structure representation that be obtained from measurement distance and angle of numerical data to the centroid data and based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge. Encourage result show the effectiveness of the proposed technique.
Comparison of Localization Methods for a Robot Soccer Team
H. Levent Akın
2008-11-01
Full Text Available In this work, several localization algorithms that are designed and implemented for Cerberus'05 Robot Soccer Team are analyzed and compared. These algorithms are used for global localization of autonomous mobile agents in the robotic soccer domain, to overcome the uncertainty in the sensors, environment and the motion model. The algorithms are Reverse Monte Carlo Localization (R-MCL, Simple Localization (S-Loc and Sensor Resetting Localization (SRL. R-MCL is a hybrid method based on both Markov Localization (ML and Monte Carlo Localization (MCL where the ML module finds the region where the robot should be and MCL predicts the geometrical location with high precision by selecting samples in this region. S-Loc is another localization method where just one sample per percept is drawn, for global localization. Within this method another novel method My Environment (ME is designed to hold the history and overcome the lack of information due to the drastically decrease in the number of samples in S-Loc. ME together with S-Loc is used in the Technical Challenges in Robocup 2005 and play an important role in ranking the First Place in the Challenges. In this work, these methods together with SRL, which is a widely used successful localization algorithm, are tested with both offline and real-time tests. First they are tested on a challenging data set that is used by many researches and compared in terms of error rate against different levels of noise, and sparsity. Besides time required recovering from kidnapping and speed of the methods are tested and compared. Then their performances are tested with real-time tests with scenarios like the ones in the Technical Challenges in ROBOCUP. The main aim is to find the best method which is very robust and fast and requires less computational power and memory compared to similar approaches and is accurate enough for high level decision making which is vital for robot soccer.
Ing, Alex; Schwarzbauer, Christian
2014-01-01
Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods – the cluster size statistic (CSS) and cluster mass statistic (CMS) – are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity. PMID:24906136
Ing, Alex; Schwarzbauer, Christian
2014-01-01
Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.
Sieglinde Kindl da Cunha; João Carlos da Cunha
2005-01-01
This article proposes a model to measure impacts of tourism cluster in local development with a view to assess tourism cluster interaction, competitiveness and sustainability, and its impact on the...
Sun, Xu; Yang, Lina; Gao, Lianru; Zhang, Bing; Li, Shanshan; Li, Jun
2015-01-01
Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC-MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC-MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC-MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC-MRF-cluster showed good stability.
Tu, Xiaoguang; Gao, Jingjing; Zhu, Chongjing; Cheng, Jie-Zhi; Ma, Zheng; Dai, Xin; Xie, Mei
2016-12-01
Though numerous segmentation algorithms have been proposed to segment brain tissue from magnetic resonance (MR) images, few of them consider combining the tissue segmentation and bias field correction into a unified framework while simultaneously removing the noise. In this paper, we present a new unified MR image segmentation algorithm whereby tissue segmentation, bias correction and noise reduction are integrated within the same energy model. Our method is presented by a total variation term introduced to the coherent local intensity clustering criterion function. To solve the nonconvex problem with respect to membership functions, we add auxiliary variables in the energy function such as Chambolle's fast dual projection method can be used and the optimal segmentation and bias field estimation can be achieved simultaneously throughout the reciprocal iteration. Experimental results show that the proposed method has a salient advantage over the other three baseline methods on either tissue segmentation or bias correction, and the noise is significantly reduced via its applications on highly noise-corrupted images. Moreover, benefiting from the fast convergence of the proposed solution, our method is less time-consuming and robust to parameter setting.
Application of the Clustering Method in Molecular Dynamics Simulation of the Diffusion Coefficient
无
2008-01-01
Using molecular dynamics (MD) simulation, the diffusion of oxygen, methane, ammonia and carbon dioxide in water was simulated in the canonical NVT ensemble, and the diffusion coefficient was analyzed by the clustering method. By comparing to the conventional method (using the Einstein model) and the differentiation-interval variation method, we found that the results obtained by the clustering method used in this study are more close to the experimental values. This method proved to be more reasonable than the other two methods.
New Iris Localization Method Based on Chaos Genetic Algorithm
Jia Dongli; Muhammad Khurram Khan; Zhang Jiashu
2005-01-01
This paper present a new method based on Chaos Genetic Algorithm (CGA) to localize the human iris in a given image. First, the iris image is preprocessed to estimate the range of the iris localization, and then CGA is used to extract the boundary of the iris. Simulation results show that the proposed algorithms is efficient and robust, and can achieve sub pixel precision. Because Genetic Algorithms (GAs) can search in a large space, the algorithm does not need accurate estimation of iris center for subsequent localization, and hence can lower the requirement for original iris image processing. On this point, the present localization algirithm is superior to Daugmans algorithm.
Classification of excessive domestic water consumption using Fuzzy Clustering Method
Zairi Zaidi, A.; Rasmani, Khairul A.
2016-08-01
Demand for clean and treated water is increasing all over the world. Therefore it is crucial to conserve water for better use and to avoid unnecessary, excessive consumption or wastage of this natural resource. Classification of excessive domestic water consumption is a difficult task due to the complexity in determining the amount of water usage per activity, especially as the data is known to vary between individuals. In this study, classification of excessive domestic water consumption is carried out using a well-known Fuzzy C-Means (FCM) clustering algorithm. Consumer data containing information on daily, weekly and monthly domestic water usage was employed for the purpose of classification. Using the same dataset, the result produced by the FCM clustering algorithm is compared with the result obtained from a statistical control chart. The finding of this study demonstrates the potential use of the FCM clustering algorithm for the classification of domestic consumer water consumption data.
The Local Dimension: a method to quantify the Cosmic Web
Sarkar, Prakash
2008-01-01
It is now well accepted that the galaxies are distributed in filaments, sheets and clusters all of which form an interconnected network known as the Cosmic Web. It is a big challenge to quantify the shapes of the interconnected structural elements that form this network. Tools like the Minkowski functionals which use global properties, though well suited for an isolated object like a single sheet or filament, are not suited for an interconnected network of such objects. We consider the Local Dimension $D$, defined through $N(R)=A R^D$, where $N(R)$ is the galaxy number count within a sphere of comoving radius $R$ centered on a particular galaxy, as a tool to locally quantify the shape in the neigbourhood of different galaxies along the Cosmic Web. We expect $D \\sim 1,2$ and 3 for a galaxy located in a filament, sheet and cluster respectively. Using LCDM N-body simulations we find that it is possible to determine $D$ through a power law fit to $N(R)$ across the length-scales 2 to $10 {\\rm Mpc}$ for $\\sim 33 %$...
Šubelj, Lovro; Waltman, Ludo
2015-01-01
Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between di...
Method for discovering relationships in data by dynamic quantum clustering
Weinstein, Marvin; Horn, David
2014-10-28
Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.
Method for discovering relationships in data by dynamic quantum clustering
Weinstein, Marvin; Horn, David
2014-10-28
Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.
Method for discovering relationships in data by dynamic quantum clustering
Weinstein, Marvin; Horn, David
2017-05-09
Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.
Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid
Kiyun Yu
2009-04-01
Full Text Available In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS data. The method creates a raster digital surface model (DSM by interpolating point data with inverse distance weighting (IDW, and produces a digital terrain model (DTM by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.
An Empirical Comparison of Variable Standardization Methods in Cluster Analysis.
Schaffer, Catherine M.; Green, Paul E.
1996-01-01
The common marketing research practice of standardizing the columns of a persons-by-variables data matrix prior to clustering the entities corresponding to the rows was evaluated with 10 large-scale data sets. Results indicate that the column standardization practice may be problematic for some kinds of data that marketing researchers used for…
Galhenage, Randima P; Xie, Kangmin; Diao, Weijian; Tengco, John Meynard M; Seuser, Grant S; Monnier, John R; Chen, Donna A
2015-11-14
Bimetallic Pt-Ru clusters have been grown on highly ordered pyrolytic graphite (HOPG) surfaces by vapor deposition and by electroless deposition. These studies help to bridge the material gap between well-characterized vapor deposited clusters and electrolessly deposited clusters, which are better suited for industrial catalyst preparation. In the vapor deposition experiments, bimetallic clusters were formed by the sequential deposition of Pt on Ru or Ru on Pt. Seed clusters of the first metal were grown on HOPG surfaces that were sputtered with Ar(+) to introduce defects, which act as nucleation sites for Pt or Ru. On the unmodified HOPG surface, both Pt and Ru clusters preferentially nucleated at the step edges, whereas on the sputtered surface, clusters with relatively uniform sizes and spatial distributions were formed. Low energy ion scattering experiments showed that the surface compositions of the bimetallic clusters are Pt-rich, regardless of the order of deposition, indicating that the interdiffusion of metals within the clusters is facile at room temperature. Bimetallic clusters on sputtered HOPG were prepared by the electroless deposition of Pt on Ru seed clusters from a Pt(+2) solution using dimethylamine borane as the reducing agent at pH 11 and 40 °C. After exposure to the electroless deposition bath, Pt was selectively deposited on Ru, as demonstrated by the detection of Pt on the surface by XPS, and the increase in the average cluster height without an increase in the number of clusters, indicating that Pt atoms are incorporated into the Ru seed clusters. Electroless deposition of Ru on Pt seed clusters was also achieved, but it should be noted that this deposition method is extremely sensitive to the presence of other metal ions in solution that have a higher reduction potential than the metal ion targeted for deposition.
A nodal discontinuous Galerkin method for reverse-time migration on GPU clusters
Modave, A.; St-Cyr, A.; Mulder, W. A.; Warburton, T.
2015-11-01
Improving both accuracy and computational performance of numerical tools is a major challenge for seismic imaging and generally requires specialized implementations to make full use of modern parallel architectures. We present a computational strategy for reverse-time migration (RTM) with accelerator-aided clusters. A new imaging condition computed from the pressure and velocity fields is introduced. The model solver is based on a high-order discontinuous Galerkin time-domain (DGTD) method for the pressure-velocity system with unstructured meshes and multirate local time stepping. We adopted the MPI+X approach for distributed programming where X is a threaded programming model. In this work we chose OCCA, a unified framework that makes use of major multithreading languages (e.g. CUDA and OpenCL) and offers the flexibility to run on several hardware architectures. DGTD schemes are suitable for efficient computations with accelerators thanks to localized element-to-element coupling and the dense algebraic operations required for each element. Moreover, compared to high-order finite-difference schemes, the thin halo inherent to DGTD method reduces the amount of data to be exchanged between MPI processes and storage requirements for RTM procedures. The amount of data to be recorded during simulation is reduced by storing only boundary values in memory rather than on disk and recreating the forward wavefields. Computational results are presented that indicate that these methods are strong scalable up to at least 32 GPUs for a three-dimensional RTM case.
Sieglinde Kindl da Cunha
2005-07-01
Full Text Available This article proposes a model to measure tourism cluster impact on local development with a view to assessing tourism cluster interaction, competitiveness and sustainability impacts on the economy, society and the environment. The theoretical basis for this model is founded on cluster concept and typology adapting and integrating the systemic competitiveness and sustainability concepts within economic, social, cultural, environmental and political dimensions. The proposed model shows a holistic, multidisciplinary and multi-sector view of local development brought back through a systemic approach to the concepts of competitiveness, social equity and sustainability. Its results make possible strategic guidance to agents responsible for public sector tourism policies, as well as the strategies for competitiveness, competition, cooperation and sustainability in private companies and institutions.
Identification of rural landscape classes through a GIS clustering method
Irene Diti
2013-09-01
Full Text Available The paper presents a methodology aimed at supporting the rural planning process. The analysis of the state of the art of local and regional policies focused on rural and suburban areas, and the study of the scientific literature in the field of spatial analysis methodologies, have allowed the definition of the basic concept of the research. The proposed method, developed in a GIS, is based on spatial metrics selected and defined to cover various agricultural, environmental, and socio-economic components. The specific goal of the proposed methodology is to identify homogeneous extra-urban areas through their objective characterization at different scales. Once areas with intermediate urban-rural characters have been identified, the analysis is then focused on the more detailed definition of periurban agricultural areas. The synthesis of the results of the analysis of the various landscape components is achieved through an original interpretative key which aims to quantify the potential impacts of rural areas on the urban system. This paper presents the general framework of the methodology and some of the main results of its first implementation through an Italian case study.
The initial conditions of observed star clusters - I. Method description and validation
Pijloo, J T; Alexander, P E R; Gieles, M; Larsen, S S; Groot, P J; Devecchi, B
2015-01-01
We have coupled a fast, parametrized star cluster evolution code to a Markov Chain Monte Carlo code to determine the distribution of probable initial conditions of observed star clusters, which may serve as a starting point for future $N$-body calculations. In this paper we validate our method by applying it to a set of star clusters which have been studied in detail numerically with $N$-body simulations and Monte Carlo methods: the Galactic globular clusters M4, 47 Tucanae, NGC 6397, M22, $\\omega$ Centauri, Palomar 14 and Palomar 4, the Galactic open cluster M67, and the M31 globular cluster G1. For each cluster we derive a distribution of initial conditions that, after evolution up to the cluster's current age, evolves to the currently observed conditions. We find that there is a connection between the morphology of the distribution of initial conditions and the dynamical age of a cluster and that a degeneracy in the initial half-mass radius towards small radii is present for clusters which have undergone a...
Oleg A. Donichev
2013-01-01
Full Text Available The article describes the main problems of formation of innovation clusters in the regions, the role and the importance of government in these issues. The characteristics of the main socio-economic and innovative performances of the region are analyzed to determine its potential for creating innovative economic cluster. The methods for detecting possible potential areas of formation of such cluster are developed.
Ahmad, Munir; Shahzad, Tasawar; Masood, Khalid; Rashid, Khalid; Tanveer, Muhammad; Iqbal, Rabail; Hussain, Nasir; Shahid, Abubakar; Fazal-E-Aleem
2016-06-01
Emission tomographic image reconstruction is an ill-posed problem due to limited and noisy data and various image-degrading effects affecting the data and leads to noisy reconstructions. Explicit regularization, through iterative reconstruction methods, is considered better to compensate for reconstruction-based noise. Local smoothing and edge-preserving regularization methods can reduce reconstruction-based noise. However, these methods produce overly smoothed images or blocky artefacts in the final image because they can only exploit local image properties. Recently, non-local regularization techniques have been introduced, to overcome these problems, by incorporating geometrical global continuity and connectivity present in the objective image. These techniques can overcome drawbacks of local regularization methods; however, they also have certain limitations, such as choice of the regularization function, neighbourhood size or calibration of several empirical parameters involved. This work compares different local and non-local regularization techniques used in emission tomographic imaging in general and emission computed tomography in specific for improved quality of the resultant images.
An introduction to the locally-corrected Nystrom method
Peterson, Andrew; Balanis, Constantine
2010-01-01
This lecture provides a tutorial introduction to the Nyström and locally-corrected Nyström methods when used for the numerical solutions of the common integral equations of two-dimensional electromagnetic fields. These equations exhibit kernel singularities that complicate their numerical solution. Classical and generalized Gaussian quadrature rules are reviewed. The traditional Nyström method is summarized, and applied to the magnetic field equation for illustration. To obtain high order accuracy in the numerical results, the locally-corrected Nyström method is developed and applied to both t
Localization of metastases from medullary thyroid carcinoma using different methods
Cabezas, R.C.; Berna, L.; Estorch, M.; Carrio, I.; Garcia-Ameijeiras, A.
1989-01-01
We analyzed the efficiency of three different noninvasive methods in the localization of recurrent medullary thyroid carcinoma (MTC). Nine patients (six females and three males) with biochemical evidence of disease after primary surgery were subjected to {sup 131}I anti-carcinoembryonic antigen (anti-CEA) antibody, {sup 131}I meta-iodo-benzylguanidine (MIBG), and computed tomography. Another female patient, in biochemical remission for six years after initial surgery, was also studied using the same methods. Three of the ten patients had negative results with all three methods (including the patient in remission). The other seven patients showed abnormal uptake of labeled anti-CEA antibody in various localizations; only two of these patients had a corresponding pathological image by computed tomography and only one by {sup 131}I MIBG. These preliminary results suggest that {sup 131}I anti-CEA scanning may be the most sensitive noninvasive method for the localization of MTC recurrences.
Construction of Lyapunov functions by the localization method
Krishchenko, A. P.; Kanatnikov, A. N.
2017-07-01
In this paper, we examine the problem of construction of Lyapunov functions for asymptotically stable equilibrium points. We exploit conditions of asymptotic stability in terms of compact invariant sets and positively invariant sets. Our results are methods of verification of these conditions and construction of Lyapunov functions by the localization method of compact invariant sets. These results are illustrated by an example.
A cluster merging method for time series microarray with production values.
Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio
2014-09-01
A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.
Alignment of Red-Sequence Cluster Dwarf Galaxies: From the Frontier Fields to the Local Universe
Barkhouse, Wayne Alan; Archer, Haylee; Burgad, Jaford; Foote, Gregory; Rude, Cody; Lopez-Cruz, Omar
2015-08-01
Galaxy clusters are the largest virialized structures in the universe. Due to their high density and mass, they are an excellent laboratory for studying the environmental effects on galaxy evolution. Numerical simulations have predicted that tidal torques acting on dwarf galaxies as they fall into the cluster environment will cause the major axis of the galaxies to align with their radial position vector (a line that extends from the cluster center to the galaxy's center). We have undertaken a study to measure the redshift evolution of the alignment of red-sequence cluster dwarf galaxies based on a sample of 57 low-redshift Abell clusters imaged at KPNO using the 0.9-meter telescope, and 64 clusters from the WINGS dataset. To supplement our low-redshift sample, we have included galaxies selected from the Hubble Space Telescope Frontier fields. Leveraging the HST data allows us to look for evolutionary changes in the alignment of red-sequence cluster dwarf galaxies over a redshift range of 0 < z < 0.35. The alignment of the major axis of the dwarf galaxies is measured by fitting a Sersic function to each red-sequence galaxy using GALFIT. The quality of each model is checked visually after subtracting the model from the galaxy. The cluster sample is then combined by scaling each cluster by r200. We present our preliminary results based on the alignment of the red-sequence dwarf galaxies with: 1) the major axis of the brightest cluster galaxy, 2) the major axis of the cluster defined by the position of cluster members, and 3) a radius vector pointing from the cluster center to individual dwarf galaxies. Our combined cluster sample is sub-divided into different radial regions and redshift bins.
Estimation of the FRF Through the Improved Local Bandwidth Selection in the Local Polynomial Method
Thummala, Prasanth; Schoukens, Johan
2012-01-01
This paper presents a nonparametric method to measure an improved frequency response function (FRF) of a linear dynamic system excited by a random input. Recently, the local polynomial method (LPM) has been proposed as a technique to reduce the leakage errors on FRF measurements. The noise...
Local traps as nanoscale reaction-diffusion probes: B clustering in c-Si
Pawlak, B. J., E-mail: bartekpawlak72@gmail.com [Globalfoundries, Kapeldreef 75, B-3001 Leuven (Belgium); Cowern, N. E. B.; Ahn, C. [School of Electrical and Electronic Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne NE1 7RU (United Kingdom); Vandervorst, W. [IMEC, Kapeldreef 75, B-3001 Leuven, Belgium and IKS, Department of Physics, KU Leuven, Leuven (Belgium); Gwilliam, R. [Surrey Ion Beam Centre, Nodus Laboratory, University of Surrey, Guildford, Surrey GU2 7XH (United Kingdom); Berkum, J. G. M. van [Philips CFT, Prof. Holstlaan 4, 5656 AA Eindhoven (Netherlands)
2014-12-01
A series of B implantation experiments into initially amorphized and not fully recrystallized Si, i.e., into an existing a/c-Si bi-layer material, have been conducted. We varied B dose, energy, and temperature during implantation process itself. Significant B migration has been observed within c-Si part near the a/c-interface and near the end-of-range region before any activation annealing. We propose a general concept of local trapping sites as experimental probes of nanoscale reaction-diffusion processes. Here, the a/c-Si interface acts as a trap, and the process itself is explored as the migration and clustering of mobile BI point defects in nearby c-Si during implantation at temperatures from 77 to 573 K. We find that at room temperature—even at B concentrations as high as 1.6 atomic %, the key B-B pairing step requires diffusion lengths of several nm owing to a small, ∼0.1 eV, pairing energy barrier. Thus, in nanostructures doped by ion implantation, the implant distribution can be strongly influenced by thermal migration to nearby impurities, defects, and interfaces.
de Grijs, Richard; Bono, Giuseppe
2014-01-01
The distance to the Large Magellanic Cloud (LMC) represents a key local rung of the extragalactic distance ladder. Yet, the galaxy's distance modulus has long been an issue of contention, in particular in view of claims that most newly determined distance moduli cluster tightly - and with a small spread - around the "canonical" distance modulus, (m-M)_0 = 18.50 mag. We compiled 233 separate LMC distance determinations published between 1990 and 2013. Our analysis of the individual distance moduli, as well as of their two-year means and standard deviations resulting from this largest data set of LMC distance moduli available to date, focuses specifically on Cepheid and RR Lyrae variable-star tracer populations, as well as on distance estimates based on features in the observational Hertzsprung-Russell diagram. We conclude that strong publication bias is unlikely to have been the main driver of the majority of published LMC distance moduli. However, for a given distance tracer, the body of publications leading ...
Planck Early Results: Calibration of the local galaxy cluster Sunyaev-Zeldovich scaling relations
Ade, P A R; Arnaud, M; Ashdown, M; Aumont, J; Baccigalupi, C; Balbi, A; Banday, A J; Barreiro, R B; Bartelmann, M; Bartlett, J G; Battaner, E; Benabed, K; Benoît, A; Bernard, J -P; Bersanelli, M; Bhatia, R; Bock, J J; Bonaldi, A; Bond, J R; Borrill, J; Bouchet, F R; Bourdin, H; Brown, M L; Bucher, M; Burigana, C; Cabella, P; Cardoso, J -F; Catalano, A; Cayón, L; Challinor, A; Chamballu, A; Chiang, L -Y; Chiang, C; Chon, G; Christensen, P R; Churazov, E; Clements, D L; Colafrancesco, S; Colombi, S; Couchot, F; Coulais, A; Crill, B P; Cuttaia, F; Da Silva, A; Dahle, H; Danese, L; de Bernardis, P; de Gasperis, G; de Rosa, A; de Zotti, G; Delabrouille, J; Delouis, J -M; Désert, F -X; Diego, J M; Dolag, K; Donzelli, S; Doré, O; Dörl, U; Douspis, M; Dupac, X; Efstathiou, G; En\\sslin, T A; Finelli, F; Flores, I; Forni, O; Frailis, M; Franceschi, E; Fromenteau, S; Galeotta, S; Ganga, K; Génova-Santos, R T; Giard, M; Giardino, G; Giraud-Héraud, Y; González-Nuevo, J; Górski, K M; Gratton, S; Gregorio, A; Gruppuso, A; Harrison, D; Henrot-Versillé, S; Hernández-Monteagudo, C; Herranz, D; Hildebrandt, S R; Hivon, E; Hobson, M; Holmes, W A; Hovest, W; Hoyland, R J; Huffenberger, K M; Jaffe, A H; Jones, W C; Juvela, M; Keihänen, E; Keskitalo, R; Kisner, T S; Kneissl, R; Knox, L; Kurki-Suonio, H; Lagache, G; Lamarre, J -M; Lanoux, J; Lasenby, A; Laureijs, R J; Lawrence, C R; Leach, S; Leonardi, R; Liddle, A; Lilje, P B; Linden-V\\ornle, M; López-Caniego, M; Lubin, P M; Macías-Pérez, J F; MacTavish, C J; Maffei, B; Maino, D; Mandolesi, N; Mann, R; Maris, M; Marleau, F; Martínez-González, E; Masi, S; Matarrese, S; Matthai, F; Mazzotta, P; Melchiorri, A; Melin, J -B; Mendes, L; Mennella, A; Mitra, S; Miville-Deschênes, M -A; Moneti, A; Montier, L; Morgante, G; Mortlock, D; Munshi, D; Murphy, A; Naselsky, P; Natoli, P; Netterfield, C B; N\\orgaard-Nielsen, H U; Noviello, F; Novikov, D; Novikov, I; Osborne, S; Pajot, F; Pasian, F; Patanchon, G; Perdereau, O; Perotto, L; Perrotta, F; Piacentini, F; Piat, M; Pierpaoli, E; Piffaretti, R; Plaszczynski, S; Pointecouteau, E; Polenta, G; Ponthieu, N; Poutanen, T; Pratt, G W; Prézeau, G; Prunet, S; Puget, J -L; Rachen, J P; Rebolo, R; Reinecke, M; Renault, C; Ricciardi, S; Riller, T; Ristorcelli, I; Rocha, G; Rosset, C; Rubiño-Martín, J A; Rusholme, B; Sandri, M; Santos, D; Savini, G; Schaefer, B M; Scott, D; Seiffert, M D; Shellard, P; Smoot, G F; Starck, J -L; Stivoli, F; Stolyarov, V; Sudiwala, R; Sunyaev, R; Sygnet, J -F; Tauber, J A; Terenzi, L; Toffolatti, L; Tomasi, M; Torre, J -P; Tristram, M; Tuovinen, J; Valenziano, L; Vibert, L; Vielva, P; Villa, F; Vittorio, N; Wade, L A; Wandelt, B D; White, S D M; White, M; Yvon, D; Zacchei, A; Zonca, A
2011-01-01
We present precise Sunyaev-Zeldovich (SZ) effect measurements in the direction of 62 nearby galaxy clusters (z <0.5) detected at high signal-to-noise in the first Planck all-sky dataset. The sample spans approximately a decade in total mass, 10^14 < M_500 < 10^15, where M_500 is the mass corresponding to a total density contrast of 500. Combining these high quality Planck measurements with deep XMM-Newton X-ray data, we investigate the relations between D_A^2 Y_500, the integrated Compton parameter due to the SZ effect, and the X-ray-derived gas mass M_g,500, temperature T_X, luminosity L_X, SZ signal analogue Y_X,500 = M_g,500 * T_X, and total mass M_500. After correction for the effect of selection bias on the scaling relations, we find results that are in excellent agreement with both X-ray predictions and recently-published ground-based data derived from smaller samples. The present data yield an exceptionally robust, high-quality local reference, and illustrate Planck's unique capabilities for a...
Multi-Wavelength Properties of Barred Galaxies in the Local Universe. I: Virgo Cluster
Giordano, Lea; Moore, Ben; Saintonge, Amelie
2010-01-01
We study in detail how the barred galaxy fraction varies as a function of luminosity, HI gas mass, morphology and color in the Virgo cluster in order to provide a well defined, statistically robust measurement of the bar fraction in the local universe spanning a wide range in luminosity (factor of ~100) and HI gas mass. We combine multiple public data-sets (UKIDSS near-infrared imaging, ALFALFA HI gas masses, GOLDMine photometry). After excluding highly inclined systems, we define three samples where galaxies are selected by their B-band luminosity, H-band luminosity, and HI gas mass. We visually assign bars using the high resolution H-band imaging from UKIDSS. When all morphologies are included, the barred fraction is ~17-24% while for morphologically selected discs, we find that the barred fraction in Virgo is ~29-34%: it does not depend strongly on how the sample is defined and does not show variations with luminosity or HI gas mass. The barred fraction depends most strongly on the morphological compositio...
Bogolyubov, D S; Batalova, F M; Ogorzałek, A
2007-10-01
An oocyte nucleus contains different extrachromosomal nuclear domains collectively called nuclear bodies (NBs). In the present work we revealed, using immunogold labeling electron microscopy, some marker components of interchromatin granule clusters (IGCs) and Cajal bodies (CBs) in morphologically heterogeneous oocyte NBs studied in three hemipteran species: Notostira elongata, Capsodes gothicus (Miridae) and Velia caprai (Veliidae). Both IGC and CB counterparts were revealed in oocyte nuclei of the studied species but morphological and biochemical criteria were found to be not sufficient to determine carefully the define type of oocyte NBs. We found that the molecular markers of the CBs (coilin and non-phosphorylated RNA polymerase II) and IGCs (SC35 protein) may be localized in the same NB. Anti-SC35 antibody may decorate not only a granular material representing "true" interchromatin granules but also masks some fibrillar parts of complex NBs. Our first observations on the hemipteran oocyte NBs confirm the high complexity and heterogeneity of insect oocyte IGCs and CBs in comparison with those in mammalian somatic cells and amphibian oocytes.
An extended affinity propagation clustering method based on different data density types.
Zhao, XiuLi; Xu, WeiXiang
2015-01-01
Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.
Improving Energy Efficient Clustering Method for Wireless Sensor Network
Md. Imran Hossain
2013-08-01
Full Text Available Wireless sensor networks have recently emerged as important computing platform. These sensors are power-limited and have limited computing resources. Therefore the sensor energy has to be managed wisely in order to maximize the lifetime of the network. Simply speaking, LEACH requires the knowledge of energy for every node in the network topology used. In LEACHs threshold which selects the cluster head is fixed so this protocol does not consider network topology environments. We proposed IELP algorithm, which selects cluster heads using different thresholds. New cluster head selection probability consists of the initial energy and the number of neighbor nodes. On rotation basis, a head-set member receives data from the neighboring nodes and transmits the aggregated results to the distant base station. For a given number of data collecting sensor nodes, the number of control and management nodes can be systematically adjusted to reduce the energy consumption, which increases the network life.The simulation results show that the performance of IELP has an improvement of 39% over LEACH and 20% over SEP in the area of 100m*100m for m=0.1, α =2 where advanced nodes (m and the additional energy factor between advanced and normal nodes (α.
Spectral methods and cluster structure in correlation-based networks
Heimo, Tapio; Tibély, Gergely; Saramäki, Jari; Kaski, Kimmo; Kertész, János
2008-10-01
We investigate how in complex systems the eigenpairs of the matrices derived from the correlations of multichannel observations reflect the cluster structure of the underlying networks. For this we use daily return data from the NYSE and focus specifically on the spectral properties of weight W=|-δ and diffusion matrices D=W/sj-δ, where C is the correlation matrix and si=∑jW the strength of node j. The eigenvalues (and corresponding eigenvectors) of the weight matrix are ranked in descending order. As in the earlier observations, the first eigenvector stands for a measure of the market correlations. Its components are, to first approximation, equal to the strengths of the nodes and there is a second order, roughly linear, correction. The high ranking eigenvectors, excluding the highest ranking one, are usually assigned to market sectors and industrial branches. Our study shows that both for weight and diffusion matrices the eigenpair analysis is not capable of easily deducing the cluster structure of the network without a priori knowledge. In addition we have studied the clustering of stocks using the asset graph approach with and without spectrum based noise filtering. It turns out that asset graphs are quite insensitive to noise and there is no sharp percolation transition as a function of the ratio of bonds included, thus no natural threshold value for that ratio seems to exist. We suggest that these observations can be of use for other correlation based networks as well.
Chen Xiangli
2012-01-01
Industry cluster is an economic phenomenon; that sees regionalized concentration of production here and there in the country. Currently, there are 14 pilot units of knitting industry clusters of which the production output exceeded 100 billion yuan in 2011. The industry cluster is divided into two categories;
Ammar Ali Neamah
2014-01-01
Full Text Available The paper uses the Local fractional variational Iteration Method for solving the second kind Volterra integro-differential equations within the local fractional integral operators. The analytical solutions within the non-differential terms are discussed. Some illustrative examples will be discussed. The obtained results show the simplicity and efficiency of the present technique with application to the problems for the integral equations.
A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES
Andrade-Santos, Felipe; Lima Neto, Gastao B.; Lagana, Tatiana F. [Departamento de Astronomia, Instituto de Astronomia, Geofisica e Ciencias Atmosfericas, Universidade de Sao Paulo, Geofisica e Ciencias Atmosfericas, Rua do Matao 1226, Cidade Universitaria, 05508-090 Sao Paulo, SP (Brazil)
2012-02-20
We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model ({beta}-model or Sersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range z in [0.02, 0.2] and have a signal-to-noise ratio (S/N) greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high- and low-substructure levels. We conclude, using Monte Carlo simulations, that the method recuperates very well the true amount of substructure for small angular core radii clusters (with respect to the whole image size) and good S/N observations. We find no evidence of correlation between the substructure level and physical properties of the clusters such as gas temperature, X-ray luminosity, and redshift; however, analysis suggest a trend between the substructure level and cluster mass. The scaling relations for the two sub-samples (high- and low-substructure level clusters) are different (they present an offset, i.e., given a fixed mass or temperature, low-substructure clusters tend to be more X-ray luminous), which is an important result for cosmological tests using the mass-luminosity relation to obtain the cluster mass function, since they rely on the assumption that clusters do not present different scaling relations according to their dynamical state.
Banik, Subrata; Pal, Sourav; Prasad, M Durga
2010-10-12
An effective operator approach based on the coupled cluster method is described and applied to calculate vibrational expectation values and absolute transition matrix elements. Coupled cluster linear response theory (CCLRT) is used to calculate excited states. The convergence pattern of these properties with the rank of the excitation operator is studied. The method is applied to a water molecule. Arponen-type double similarity transformation in extended coupled cluster (ECCM) framework is also used to generate an effective operator, and the convergence pattern of these properties is compared to the normal coupled cluster (NCCM) approach. It is found that the coupled cluster method provides an accurate description of these quantities for low lying vibrational excited states. The ECCM provides a significant improvement for the calculation of the transition matrix elements.
Clustering Methods; Part IV of Scientific Report No. ISR-18, Information Storage and Retrieval...
Cornell Univ., Ithaca, NY. Dept. of Computer Science.
Two papers are included as Part Four of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Controlled Single Pass Classification Algorithm with Application to Multilevel Clustering" by D. B. Johnson and J. M. Laferente presents a single pass clustering method which compares favorably…
Dumenci, Levent; Windle, Michael
2001-01-01
Used Monte Carlo methods to evaluate the adequacy of cluster analysis to recover group membership based on simulated latent growth curve (LCG) models. Cluster analysis failed to recover growth subtypes adequately when the difference between growth curves was shape only. Discusses circumstances under which it was more successful. (SLD)
A method of using cluster analysis to study statistical dependence in multivariate data
Borucki, W. J.; Card, D. H.; Lyle, G. C.
1975-01-01
A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.
Clustering of hydrological data: a review of methods for runoff predictions in ungauged basins
Dogulu, Nilay; Kentel, Elcin
2017-04-01
There is a great body of research that has looked into the challenge of hydrological predictions in ungauged basins as driven by the Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS). Transfer of hydrological information (e.g. model parameters, flow signatures) from gauged to ungauged catchment, often referred as "regionalization", is the main objective and benefits from identification of hydrologically homogenous regions. Within this context, indirect representation of hydrologic similarity for ungauged catchments, which is not a straightforward task due to absence of streamflow measurements and insufficient knowledge of hydrologic behavior, has been explored in the literature. To this aim, clustering methods have been widely adopted. While most of the studies employ hard clustering techniques such as hierarchical (divisive or agglomerative) clustering, there have been more recent attempts taking advantage of fuzzy set theory (fuzzy clustering) and nonlinear methods (e.g. self-organizing maps). The relevant research findings from this fundamental task of hydrologic sciences have revealed the value of different clustering methods for improved understanding of catchment hydrology. However, despite advancements there still remains challenges and yet opportunities for research on clustering for regionalization purposes. The present work provides an overview of clustering techniques and their applications in hydrology with focus on regionalization for the PUB problem. Identifying their advantages and disadvantages, we discuss the potential of innovative clustering methods and reflect on future challenges in view of the research objectives of the PUB initiative.
K2: A new method for the detection of galaxy clusters based on CFHTLS multicolor images
Thanjavur, Karun; Crampton, David
2009-01-01
We have developed a new method, K2, optimized for the detection of galaxy clusters in multicolor images. Based on the Red Sequence approach, K2 detects clusters using simultaneous enhancements in both colors and position. The detection significance is robustly determined through extensive Monte-Carlo simulations and through comparison with available cluster catalogs based on two different optical methods, and also on X-ray data. K2 also provides quantitative estimates of the candidate clusters' richness and photometric redshifts. Initially K2 was applied to 161 sq deg of two color gri images of the CFHTLS-Wide data. Our simulations show that the false detection rate, at our selected threshold, is only ~1%, and that the cluster catalogs are ~80% complete up to a redshift of 0.6 for Fornax-like and richer clusters and to z ~0.3 for poorer clusters. Based on Terapix T05 release gri photometric catalogs, 35 clusters/sq deg are detected, with 1-2 Fornax-like or richer clusters every two square degrees. Catalogs co...
Evaluation and comparison of mammalian subcellular localization prediction methods
Fink J Lynn
2006-12-01
Full Text Available Abstract Background Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER, peroxisome, and lysosome. The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE
A method of periodic pattern localization on document images
Chernov, Timofey S.; Nikolaev, Dmitry P.; Kliatskine, Vitali M.
2015-12-01
Periodic patterns often present on document images as holograms, watermarks or guilloche elements which are mostly used for fraud protection. Localization of such patterns lets an embedded OCR system to vary its settings depending on pattern presence in particular image regions and improves the precision of pattern removal to preserve as much useful data as possible. Many document images' noise detection and removal methods deal with unstructured noise or clutter on documents with simple background. In this paper we propose a method of periodic pattern localization on document images which uses discrete Fourier transform that works well on documents with complex background.
Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.
Williams, N J; Nasuto, S J; Saddy, J D
2015-07-30
The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.
D. A. Viattchenin
2009-01-01
Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible clusterization with partial training is proposed in the paper. The method is based on data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.
Ichiki, Kiyotomo; Oguri, Masamune
2015-01-01
The discrepancy between the amplitudes of matter fluctuations inferred from Sunyaev-Zel'dovich (SZ) cluster number counts, the primary temperature, and the polarization anisotropies of the cosmic microwave background (CMB) measured by the Planck satellite can be reconciled if the local universe is embedded in an under-dense region as shown by Lee, 2014. Here using a simple void model assuming the open Friedmann-Robertson-Walker geometry and a Markov Chain Monte Carlo technique, we investigate how deep the local under-dense region needs to be to resolve this discrepancy. Such local void, if exists, predicts the local Hubble parameter value that is different from the global Hubble constant. We derive the posterior distribution of the local Hubble parameter from a joint fitting of the Planck CMB data and SZ cluster number counts assuming the simple void model. We show that the predicted local Hubble parameter value of $H_{\\rm loc}=70.1\\pm0.34~{\\rm km\\,s^{-1}Mpc^{-1}}$ is in better agreement with direct local Hub...
A two-stage method for microcalcification cluster segmentation in mammography by deformable models
Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.; Karahaliou, A.; Costaridou, L., E-mail: costarid@upatras.gr [Department of Medical Physics, School of Medicine, University of Patras, Patras 26504 (Greece); Vassiou, K. [Department of Anatomy, School of Medicine, University of Thessaly, Larissa 41500 (Greece)
2015-10-15
Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods are applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST{sub cluster}, average of minimum distance—AMINDIST{sub cluster}) and the area overlap measure (AOM{sub cluster}). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing
Local coding based matching kernel method for image classification.
Yan Song
Full Text Available This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.
The Swift UVOT Stars Survey: I. Methods and Test Clusters
Siegel, Michael H; Linevsky, Jacquelyn S; Bond, Howard E; Holland, Stephen T; Hoversten, Erik A; Berrier, Joshua L; Breeveld, Alice A; Brown, Peter J; Gronwall, Caryl A
2014-01-01
We describe the motivations and background of a large survey of nearby stel- lar populations using the Ultraviolet Optical Telescope (UVOT) aboard the Swift Gamma-Ray Burst Mission. UVOT, with its wide field, NUV sensitivity, and 2.3 spatial resolution, is uniquely suited to studying nearby stellar populations and providing insight into the NUV properties of hot stars and the contribution of those stars to the integrated light of more distant stellar populations. We review the state of UV stellar photometry, outline the survey, and address problems spe- cific to wide- and crowded-field UVOT photometry. We present color-magnitude diagrams of the nearby open clusters M 67, NGC 188, and NGC 2539, and the globular cluster M 79. We demonstrate that UVOT can easily discern the young- and intermediate-age main sequences, blue stragglers, and hot white dwarfs, pro- ducing results consistent with previous studies. We also find that it characterizes the blue horizontal branch of M 79 and easily identifies a known post-...
The swift UVOT stars survey. I. Methods and test clusters
Siegel, Michael H.; Porterfield, Blair L.; Linevsky, Jacquelyn S.; Bond, Howard E.; Hoversten, Erik A.; Berrier, Joshua L.; Gronwall, Caryl A. [Department of Astronomy and Astrophysics, The Pennsylvania State University, 525 Davey Laboratory, University Park, PA 16802 (United States); Holland, Stephen T. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Breeveld, Alice A. [Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, Surrey RH5 6NT (United Kingdom); Brown, Peter J., E-mail: siegel@astro.psu.edu, E-mail: blp14@psu.edu, E-mail: heb11@psu.edu, E-mail: caryl@astro.psu.edu, E-mail: sholland@stsci.edu, E-mail: aab@mssl.ucl.ac.uk, E-mail: grbpeter@yahoo.com [George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, Texas A. and M. University, Department of Physics and Astronomy, 4242 TAMU, College Station, TX 77843 (United States)
2014-12-01
We describe the motivations and background of a large survey of nearby stellar populations using the Ultraviolet Optical Telescope (UVOT) on board the Swift Gamma-Ray Burst Mission. UVOT, with its wide field, near-UV sensitivity, and 2.″3 spatial resolution, is uniquely suited to studying nearby stellar populations and providing insight into the near-UV properties of hot stars and the contribution of those stars to the integrated light of more distant stellar populations. We review the state of UV stellar photometry, outline the survey, and address problems specific to wide- and crowded-field UVOT photometry. We present color–magnitude diagrams of the nearby open clusters M67, NGC 188, and NGC 2539, and the globular cluster M79. We demonstrate that UVOT can easily discern the young- and intermediate-age main sequences, blue stragglers, and hot white dwarfs, producing results consistent with previous studies. We also find that it characterizes the blue horizontal branch of M79 and easily identifies a known post-asymptotic giant branch star.
An empirical method to cluster objective nebulizer adherence data among adults with cystic fibrosis
Hoo ZH
2017-03-01
Full Text Available Zhe H Hoo,1,2 Michael J Campbell,1 Rachael Curley,1,2 Martin J Wildman1,2 1School of Health and Related Research (ScHARR, University of Sheffield, 2Sheffield Adult Cystic Fibrosis Centre, Northern General Hospital, Sheffield, UK Background: The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions.Methods to cluster adherence data: This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster (“cluster 1” = very low adherence; “cluster 2” = low adherence; “cluster 3” = moderate adherence; “cluster 4” = high adherence, and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data.Three illustrative cases: We present three cases to illustrate the use of the proposed clustering method.Conclusion: This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months’ worth of data are unavailable and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested
An online substructure identification method for local structural health monitoring
Hou, Jilin; Jankowski, Łukasz; Ou, Jinping
2013-09-01
This paper proposes a substructure isolation method, which uses time series of measured local response for online monitoring of substructures. The proposed monitoring process consists of two key steps: construction of the isolated substructure, and its identification. The isolated substructure is an independent virtual structure, which is numerically isolated from the global structure by placing virtual supports on the interface. First, the isolated substructure is constructed by a specific linear combination of time series of its measured local responses. Then, the isolated substructure is identified using its local natural frequencies extracted from the combined responses. The substructure is assumed to be linear; the outside part of the global structure can have any characteristics. The method has no requirements on the initial state of the structure, and so the process can be carried out repetitively for online monitoring. Online isolation and monitoring is illustrated in a numerical example with a frame model, and then verified in a cantilever beam experiment.
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
clues: An R Package for Nonparametric Clustering Based on Local Shrinking
Fang Chang
2010-02-01
Full Text Available Determining the optimal number of clusters appears to be a persistent and controversial issue in cluster analysis. Most existing R packages targeting clustering require the user to specify the number of clusters in advance. However, if this subjectively chosen number is far from optimal, clustering may produce seriously misleading results. In order to address this vexing problem, we develop the R package clues to automate and evaluate the selection of an optimal number of clusters, which is widely applicable in the field of clustering analysis. Package clues uses two main procedures, shrinking and partitioning, to estimate an optimal number of clusters by maximizing an index function, either the CH index or the Silhouette index, rather than relying on guessing a pre-specified number. Five agreement indices (Rand index, Hubert and Arabie’s adjusted Rand index, Morey and Agresti’s adjusted Rand index, Fowlkes and Mallows index and Jaccard index, which measure the degree of agreement between any two partitions, are also provided in clues. In addition to numerical evidence, clues also supplies a deeper insight into the partitioning process with trajectory plots.
Sascha Fuerst
2010-01-01
...” to describe local cluster development in the 3D -animation industry in Colombia. It is argued that the participation in global value chains can have a positive impact on cluster growth and innovation, and the individual firm as well...
Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo
2016-01-01
Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.
Local discontinuous Galerkin methods for phase transition problems
Tian, Lulu
2015-01-01
In this thesis we develop a local discontinuous Galerkin (LDG) finite element method to solve mathematical models for phase transitions in solids and fluids. The first model we study is called a viscosity-capillarity (VC) system associated with phase transitions in elastic bars and Van der Waals
Collaborative Methods for Real-time Localization in Urban Centers
Sébastien Peyraud
2015-11-01
Full Text Available This article presents an effective solution for the localization of a vehicle in dense urban areas where GNSS-based methods fail because of poor satellite visibility. It advocates the use of a visual-based method processing georeferenced landmarks obtained after a learning path and stored in a new layer of the geographical information system (GIS used for navigation. Real-time localization gives, with few failures, accurate results in the areas covered by the GIS. The integrity of the localization is obtained by running another algorithm in parallel, processing odometric data combined with the geometric model of the drivable area and, when available, GNSS data in tight coupling. An ellipsoidal confidence domain is updated by using both extended Kalman filtering (EKF and set-membership estimation. Although less accurate, this estimation is reliable and, when the visual method fails, the availability of a confidence domain enables us to speed up the restart of the visual method while navigating cautiously. A large-scale experiment (>4 km was conducted in the centre of Paris. We compare the absolute localization results with the ground truth obtained by combining RTK-GPS and a high-end inertial measurement unit (IMU.
A special purpose knowledge-based face localization method
Hassanat, Ahmad; Jassim, Sabah
2008-04-01
This paper is concerned with face localization for visual speech recognition (VSR) system. Face detection and localization have got a great deal of attention in the last few years, because it is an essential pre-processing step in many techniques that handle or deal with faces, (e.g. age, face, gender, race and visual speech recognition). We shall present an efficient method for localization human's faces in video images captured on mobile constrained devices, under a wide variation in lighting conditions. We use a multiphase method that may include all or some of the following steps starting with image pre-processing, followed by a special purpose edge detection, then an image refinement step. The output image will be passed through a discrete wavelet decomposition procedure, and the computed LL sub-band at a certain level will be transformed into a binary image that will be scanned by using a special template to select a number of possible candidate locations. Finally, we fuse the scores from the wavelet step with scores determined by color information for the candidate location and employ a form of fuzzy logic to distinguish face from non-face locations. We shall present results of large number of experiments to demonstrate that the proposed face localization method is efficient and achieve high level of accuracy that outperforms existing general-purpose face detection methods.
Seeking the Local Convergence Depth; 5, Tully-Fisher Peculiar Velocities for 52 Abell Clusters
Dale, D A; Haynes, M P; Campusano, L E; Hardy, E; Dale, Daniel A.; Giovanelli, Riccardo; Haynes, Martha P.; Campusano, Luis E.; Hardy, Eduardo
1999-01-01
We have obtained I band Tully-Fisher (TF) measurements for 522 late-type galaxies in the fields of 52 rich Abell clusters distributed throughout the sky between 50 and 200\\h Mpc. Here we estimate corrections to the data for various forms of observational bias, most notably Malmquist and cluster population incompleteness bias. The bias-corrected data are applied to the construction of an I band TF template, resulting in a relation with a dispersion of 0.38 magnitudes and a kinematical zero-point accurate to 0.02 magnitudes. This represents the most accurate TF template relation currently available. Individual cluster TF relations are referred to the average template relation to compute cluster peculiar motions. The line-of-sight dispersion in the peculiar motions is 341+/-93 km/s, in general agreement with that found for the cluster sample of Giovanelli and coworkers.
Morgan, Katy E; Forbes, Andrew B; Keogh, Ruth H; Jairath, Vipul; Kahan, Brennan C
2017-01-30
In cluster randomised cross-over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two-period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period-within-cluster, which do not account for any extra within-period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within-period correlation was present, a hierarchical model with random effects for cluster and period-within-cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster-level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within-period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within-period correlation should be accounted for. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Optoelectronic scanning system upgrade by energy center localization methods
Flores-Fuentes, W.; Sergiyenko, O.; Rodriguez-Quiñonez, J. C.; Rivas-López, M.; Hernández-Balbuena, D.; Básaca-Preciado, L. C.; Lindner, L.; González-Navarro, F. F.
2016-11-01
A problem of upgrading an optoelectronic scanning system with digital post-processing of the signal based on adequate methods of energy center localization is considered. An improved dynamic triangulation analysis technique is proposed by an example of industrial infrastructure damage detection. A modification of our previously published method aimed at searching for the energy center of an optoelectronic signal is described. Application of the artificial intelligence algorithm of compensation for the error of determining the angular coordinate in calculating the spatial coordinate through dynamic triangulation is demonstrated. Five energy center localization methods are developed and tested to select the best method. After implementation of these methods, digital compensation for the measurement error, and statistical data analysis, a non-parametric behavior of the data is identified. The Wilcoxon signed rank test is applied to improve the result further. For optical scanning systems, it is necessary to detect a light emitter mounted on the infrastructure being investigated to calculate its spatial coordinate by the energy center localization method.
Gianturco, F.A.; De Lara-Castells, M.P. [Univ. of Rome (Italy)
1996-10-05
Several modelings of exchange and correlation forces which can be carried out using density functional theory (DFT) methods have been analyzed to study their efficiency and reliability when evaluating possible competing structures of helium ionic clusters of increasing size. This study examines He{sub n}{sup +} systems with n from 1 to 7 and compares the present calculations with earlier evaluations that used more conventional, and more computationally intensive, methods with configuration interaction (CI) approaches. The present results indicate that it is indeed possible to strike a fruitful balance between reduction of computational times and quality of the ensuing structural information. 62 refs., 1 fig., 8 tabs.
Proposing Cluster_Similarity Method in Order to Find as Much Better Similarities in Databases
Feizi-Derakhshi, Mohammad-Reza
2011-01-01
Different ways of entering data into databases result in duplicate records that cause increasing of databases' size. This is a fact that we cannot ignore it easily. There are several methods that are used for this purpose. In this paper, we have tried to increase the accuracy of operations by using cluster similarity instead of direct similarity of fields. So that clustering is done on fields of database and according to accomplished clustering on fields, similarity degree of records is obtained. In this method by using present information in database, more logical similarity is obtained for deficient information that in general, the method of cluster similarity could improve operations 24% compared with previous methods.
K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data
Kai Wang
2015-01-01
Full Text Available With modern technologies such as microarray, deep sequencing, and liquid chromatography-mass spectrometry (LC-MS, it is possible to measure the expression levels of thousands of genes/proteins simultaneously to unravel important biological processes. A very first step towards elucidating hidden patterns and understanding the massive data is the application of clustering techniques. Nonlinear relations, which were mostly unutilized in contrast to linear correlations, are prevalent in high-throughput data. In many cases, nonlinear relations can model the biological relationship more precisely and reflect critical patterns in the biological systems. Using the general dependency measure, Distance Based on Conditional Ordered List (DCOL that we introduced before, we designed the nonlinear K-profiles clustering method, which can be seen as the nonlinear counterpart of the K-means clustering algorithm. The method has a built-in statistical testing procedure that ensures genes not belonging to any cluster do not impact the estimation of cluster profiles. Results from extensive simulation studies showed that K-profiles clustering not only outperformed traditional linear K-means algorithm, but also presented significantly better performance over our previous General Dependency Hierarchical Clustering (GDHC algorithm. We further analyzed a gene expression dataset, on which K-profile clustering generated biologically meaningful results.
Nicoló Musmeci
Full Text Available We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].
Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T
2015-01-01
We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].
Okunev, V. D.; Samoilenko, Z. A.; Szymczak, H.; Szewczyk, A.; Szymczak, R.; Lewandowski, S. J.; Aleshkevych, P.; Malinowski, A.; Gierłowski, P.; Więckowski, J.; Wolny-Marszałek, M.; Jeżabek, M.; Varyukhin, V. N.; Antoshina, I. A.
2016-02-01
We show that сluster magnetism in ferromagnetic amorphous Fe67Cr18B15 alloy is related to the presence of large, D=150-250 Å, α-(Fe Cr) clusters responsible for basic changes in cluster magnetism, small, D=30-100 Å, α-(Fe, Cr) and Fe3B clusters and subcluster atomic α-(Fe, Cr, B) groupings, D=10-20 Å, in disordered intercluster medium. For initial sample and irradiated one (Φ=1.5×1018 ions/cm2) superconductivity exists in the cluster shells of metallic α-(Fe, Cr) phase where ferromagnetism of iron is counterbalanced by antiferromagnetism of chromium. At Φ=3×1018 ions/cm2, the internal stresses intensify and the process of iron and chromium phase separation, favorable for mesoscopic superconductivity, changes for inverse one promoting more homogeneous distribution of iron and chromium in the clusters as well as gigantic (twice as much) increase in density of the samples. As a result, in the cluster shells ferromagnetism is restored leading to the increase in magnetization of the sample and suppression of local superconductivity. For initial samples, the temperature dependence of resistivity ρ(T) T2 is determined by the electron scattering on quantum defects. In strongly inhomogeneous samples, after irradiation by fluence Φ=1.5×1018 ions/cm2, the transition to a dependence ρ(T) T1/2 is caused by the effects of weak localization. In more homogeneous samples, at Φ=3×1018 ions/cm2, a return to the dependence ρ(T) T2 is observed.
A comparison of four clustering methods for brain expression microarray data
Owen Michael J
2008-11-01
Full Text Available Abstract Background DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed. Results k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone, without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia. Conclusion Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced
Export Methods in Fault Detection and Localization Mechanisms
Aymen Belghith
2012-07-01
Full Text Available Monitoring the quality of service in a multi-domain network allows providers to ensure the control of multi-domain service performance. A multi-domain service is a service that crosses multiple domains. In this paper, we propose several mechanisms for fault detection and fault localization. A fault is detected when an end-to-end contract is not respected. Faulty domains are domains that do not fulfill their Quality of Service (QoS requirements. Our three proposed fault detection and localization mechanisms (FDLM depend on the export method used. These export methods define how the measurement results are exported for analysis. We consider the periodic export, the triggered export, and a combined method. For each FDLM, we propose two sub-schemes that use different fault detection strategies. In this paper, we describe these mechanisms and evaluate their performance using Network Simulator (NS-2.
Improved non-singular local boundary integral equation method
无
2007-01-01
When the source nodes are on the global boundary in the implementation of local boundary integral equation method (LBIEM), singularities in the local boundary integrals need to be treated specially. In the current paper, local integral equations are adopted for the nodes inside the domain and moving least square approximation (MLSA)for the nodes on the global boundary, thus singularities will not occur in the new algorithm. At the same time, approximation errors of boundary integrals are reduced significantly. As applications and numerical tests, Laplace equation and Helmholtz equation problems are considered and excellent numerical results are obtained. Furthermore,when solving the Helmholtz problems, the modified basis functions with wave solutions areadapted to replace the usually-used monomial basis functions. Numerical results show that this treatment is simple and effective and its application is promising in solutions for the wave propagation problem with high wave number.
A localized meshless method for diffusion on folded surfaces
Cheung, Ka Chun; Ling, Leevan; Ruuth, Steven J.
2015-09-01
Partial differential equations (PDEs) on surfaces arise in a variety of application areas including biological systems, medical imaging, fluid dynamics, mathematical physics, image processing and computer graphics. In this paper, we propose a radial basis function (RBF) discretization of the closest point method. The corresponding localized meshless method may be used to approximate diffusion on smooth or folded surfaces. Our method has the benefit of having an a priori error bound in terms of percentage of the norm of the solution. A stable solver is used to avoid the ill-conditioning that arises when the radial basis functions (RBFs) become flat.
A novel localization method for noninvasive monitoring capsule
He Wenhui; Yan Guozheng; Jiang Pingping; Guo Xudong
2006-01-01
Noninvasive monitoring capsule for gastrointestinal tract can be swallowed by patient. It is of great importance for the physician to monitor the precise position of capsule in gastrointestinal tract. The authors investigated a novel method for it. Using three coils with DC current to excite magnetic field and one triaxial magnetoresistive sensor to measure the excited magnetic vectors, they tried to solve the problem.The authors provided the localization principle of the method and analyzed it by an experiment, too. The method may be applied in practice in the future though it is still immature now.
Steenbergen, K G; Gaston, N
2014-02-14
Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.
Dongliang Guo
2014-01-01
Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.
A method for context-based adaptive QRS clustering in real-time
Castro, Daniel; Presedo, Jesús
2014-01-01
Continuous follow-up of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This work presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially, grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, Derivative Dynamic Time Warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and...
Alexandre Luiz Schlemper
2016-05-01
Full Text Available Discussions of literature point to a change in the capitalist system in the last decades of the twentieth century, mainly due to the passage of the mode mass production for a system of flexible specialization, leading to clusters as a strategy for regional development. In this scenario, this research aimed to conduct a sector analysis of the clusters from southwestern of Paraná, order to guide the elaboration of development strategies. The guiding methodological occurred with the application of the SWOT methodology for the compilation of the results of field research, generating a set of notes regarding the strengths, weaknesses, opportunities and threats in the clusters analyzed. The results demonstrated the relevance of this research for the development of strategies and policies related to the development of these clusters and their region.
Source clustering in the Hi-GAL survey determined using a minimum spanning tree method
Beuret, M.; Billot, N.; Cambrésy, L.; Eden, D. J.; Elia, D.; Molinari, S.; Pezzuto, S.; Schisano, E.
2017-01-01
Aims: The aims are to investigate the clustering of the far-infrared sources from the Herschel infrared Galactic Plane Survey (Hi-GAL) in the Galactic longitude range of -71 to 67 deg. These clumps, and their spatial distribution, are an imprint of the original conditions within a molecular cloud. This will produce a catalogue of over-densities. Methods: The minimum spanning tree (MST) method was used to identify the over-densities in two dimensions. The catalogue was further refined by folding in heliocentric distances, resulting in more reliable over-densities, which are cluster candidates. Results: We found 1633 over-densities with more than ten members. Of these, 496 are defined as cluster candidates because of the reliability of the distances, with a further 1137 potential cluster candidates. The spatial distributions of the cluster candidates are different in the first and fourth quadrants, with all clusters following the spiral structure of the Milky Way. The cluster candidates are fractal. The clump mass functions of the clustered and isolated are statistically indistinguishable from each other and are consistent with Kroupa's initial mass function. Hi-GAL is a key-project of the Herschel Space Observatory survey (Pilbratt et al. 2010) and uses the PACS (Poglitsch et al. 2010) and SPIRE (Griffin et al. 2010) cameras in parallel mode.The catalogues of cluster candidates and potential clusters are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/597/A114
Wild; Blankley
2000-01-01
Four different two-dimensional fingerprint types (MACCS, Unity, BCI, and Daylight) and nine methods of selecting optimal cluster levels from the output of a hierarchical clustering algorithm were evaluated for their ability to select clusters that represent chemical series present in some typical examples of chemical compound data sets. The methods were evaluated using a Ward's clustering algorithm on subsets of the publicly available National Cancer Institute HIV data set, as well as with compounds from our corporate data set. We make a number of observations and recommendations about the choice of fingerprint type and cluster level selection methods for use in this type of clustering
Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.
2011-01-01
Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.
A New Edge-directed Subpixel Edge Localization Method
于新瑞; 徐威; 王石刚; 李倩
2004-01-01
Localization of the inspected chip image is one of the key problems with machine vision aided surface mount devices (SMD) and other micro-electronic equipments. This paper presents a new edge-directed subpixel edge localization method. The image is divided into two regions, edge and non-edge, using edge detection to emphasize the edge feature. Since the edges of the chip image are straight, they have straight-line characteristics locally and globally. First,the line segments of the straight edge are located to subpixel precision, according to their local straight properties, in a 3 × 3 neighborhood of the edge region. Second, the subpixel midpoints of the line segments are computed. Finally, the straight edge is fitted using the midpoints and the least square method, according to its global straight property in the entire edge region. In this way, the edge is located to subpixel precision. While fitting the edge, the irregular points are eliminated by the angles of the line segments to improve the precision. We can also distinguish different edges and their intersections using the angles of the line segments and distances between the edge points, then give the vectorial result of the image edge with high precision.
Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y
2014-05-01
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.
Orbital spaces in the divide-expand-consolidate coupled cluster method
Ettenhuber, Patrick; Baudin, Pablo; Kjærgaard, Thomas; Jørgensen, Poul; Kristensen, Kasper
2016-04-01
The theoretical foundation for solving coupled cluster singles and doubles (CCSD) amplitude equations to a desired precision in terms of independent fragment calculations using restricted local orbital spaces is reinvestigated with focus on the individual error sources. Four different error sources are identified theoretically and numerically and it is demonstrated that, for practical purposes, local orbital spaces for CCSD calculations can be identified from calculations at the MP2 level. The development establishes a solid theoretical foundation for local CCSD calculations for the independent fragments, and thus for divide-expand-consolidate coupled cluster calculations for large molecular systems with rigorous error control. Based on this theoretical foundation, we have developed an algorithm for determining the orbital spaces needed for obtaining the single fragment energies to a requested precision and numerically demonstrated the robustness and precision of this algorithm.
Local Approximation and Hierarchical Methods for Stochastic Optimization
Cheng, Bolong
In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the optimal learning problem class, we introduce a locally linear model with radial basis function for estimating the posterior mean of the unknown objective function. The method uses a compact representation of the function which avoids storing the entire history, as is typically required by nonparametric methods. We derive a knowledge gradient policy with the locally parametric model, which maximizes the expected value of information. We show the policy is asymptotically optimal in theory, and experimental works suggests that the method can reliably find the optimal solution on a range of test functions. For the Markov decision processes problem class, we are motivated by an application where we want to co-optimize a battery for multiple revenue, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Computing the exact optimal policy becomes intractable due to the large state space and the number of time steps. We propose two methods to circumvent the computation bottleneck. First, we propose a nested MDP model that structure the co-optimization problem into smaller sub-problems with reduced state space. This new model allows us to understand how the battery behaves down to the two-second dynamics (that of the frequency regulation market). Second, we introduce a low-rank value function approximation for backward dynamic programming. This new method only requires computing the exact value function for a small subset of the state space and approximate the entire value function via low-rank matrix completion. We test these methods on historical price data from the
DEMON: a Local-First Discovery Method for Overlapping Communities
Coscia, Michele; Giannotti, Fosca; Pedreschi, Dino
2012-01-01
Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly ou...
Stenning, D. C.; Wagner-Kaiser, R.; Robinson, E.; van Dyk, D. A.; von Hippel, T.; Sarajedini, A.; Stein, N.
2016-07-01
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations. Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties—age, metallicity, helium abundance, distance, absorption, and initial mass—are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and also show how model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github.com/argiopetech/base/releases).
A new method to search for high-redshift clusters using photometric redshifts
Castignani, G.; Celotti, A. [SISSA, Via Bonomea 265, I-34136 Trieste (Italy); Chiaberge, M.; Norman, C., E-mail: castigna@sissa.it [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States)
2014-09-10
We describe a new method (Poisson probability method, PPM) to search for high-redshift galaxy clusters and groups by using photometric redshift information and galaxy number counts. The method relies on Poisson statistics and is primarily introduced to search for megaparsec-scale environments around a specific beacon. The PPM is tailored to both the properties of the FR I radio galaxies in the Chiaberge et al. sample, which are selected within the COSMOS survey, and to the specific data set used. We test the efficiency of our method of searching for cluster candidates against simulations. Two different approaches are adopted. (1) We use two z ∼ 1 X-ray detected cluster candidates found in the COSMOS survey and we shift them to higher redshift up to z = 2. We find that the PPM detects the cluster candidates up to z = 1.5, and it correctly estimates both the redshift and size of the two clusters. (2) We simulate spherically symmetric clusters of different size and richness, and we locate them at different redshifts (i.e., z = 1.0, 1.5, and 2.0) in the COSMOS field. We find that the PPM detects the simulated clusters within the considered redshift range with a statistical 1σ redshift accuracy of ∼0.05. The PPM is an efficient alternative method for high-redshift cluster searches that may also be applied to both present and future wide field surveys such as SDSS Stripe 82, LSST, and Euclid. Accurate photometric redshifts and a survey depth similar or better than that of COSMOS (e.g., I < 25) are required.
Novel Local Search Method for the Traveling Salesman Problem
Huang Wenqi; Wang Lei
2005-01-01
A new local search method for the traveling salesman problem based on an original greedy representation of solution space and neighborhood structure is proposed. First, a partial closed route that only consists of three cities is given; then other cities are added to this route by a greedy procedure successively. Implemented on a personal computer, this algorithm finds optimal solutions for 24 out of 27 standard benchmarks, and outperforms the Full Subpath Ejection Algorithm (F-SEC) proposed by Rego in 1998.
A local level set method based on a finite element method for unstructured meshes
Ngo, Long Cu; Choi, Hyoung Gwon [School of Mechanical Engineering, Seoul National University of Science and Technology, Seoul (Korea, Republic of)
2016-12-15
A local level set method for unstructured meshes has been implemented by using a finite element method. A least-square weighted residual method was employed for implicit discretization to solve the level set advection equation. By contrast, a direct re-initialization method, which is directly applicable to the local level set method for unstructured meshes, was adopted to re-correct the level set function to become a signed distance function after advection. The proposed algorithm was constructed such that the advection and direct reinitialization steps were conducted only for nodes inside the narrow band around the interface. Therefore, in the advection step, the Gauss–Seidel method was used to update the level set function using a node-by-node solution method. Some benchmark problems were solved by using the present local level set method. Numerical results have shown that the proposed algorithm is accurate and efficient in terms of computational time.
Duanmu, Kaining; Truhlar, Donald G.
2015-04-30
We report a systematic study of small silver clusters, Agn, Agn+, and Agn–, n = 1–7. We studied all possible isomers of clusters with n = 5–7. We tested 42 exchange–correlation functionals, and we assess these functionals for their accuracy in three respects: geometries (quantitative prediction of internuclear distances), structures (the nature of the lowest-energy structure, for example, whether it is planar or nonplanar), and energies. We find that the ingredients of exchange–correlation functionals are indicators of their success in predicting geometries and structures: local exchange–correlation functionals are generally better than hybrid functionals for geometries; functionals depending on kinetic energy density are the best for predicting the lowest-energy isomer correctly, especially for predicting two-dimensional to three-dimenstional transitions correctly. The accuracy for energies is less sensitive to the ingredient list. Our findings could be useful for guiding the selection of methods for computational catalyst design.
A new method to search for high redshift clusters using photometric redshifts
Castignani, Gianluca; Celotti, Annalisa; Norman, Colin
2014-01-01
We describe a new method (Poisson Probability Method, PPM) to search for high redshift galaxy clusters and groups by using photometric redshift information and galaxy number counts. The method relies on Poisson statistics and is primarily introduced to search for Mpc-scale environments around a specific beacon. The PPM is tailored to both the properties of the FR I radio galaxies in the Chiaberge et al. (2009) sample, that are selected within the COSMOS survey, and on the specific dataset used. We test the efficiency of our method of searching for cluster candidates against simulations. Two different approaches are adopted. i) We use two z~1 X-ray detected cluster candidates found in the COSMOS survey and we shift them to higher redshift up to z=2. We find that the PPM detects the cluster candidates up to z=1.5, and it correctly estimates both the redshift and size of the two clusters. ii) We simulate spherically symmetric clusters of different size and richness, and we locate them at different redshifts (i.e...
An efficient method of key-frame extraction based on a cluster algorithm.
Zhang, Qiang; Yu, Shao-Pei; Zhou, Dong-Sheng; Wei, Xiao-Peng
2013-12-18
This paper proposes a novel method of key-frame extraction for use with motion capture data. This method is based on an unsupervised cluster algorithm. First, the motion sequence is clustered into two classes by the similarity distance of the adjacent frames so that the thresholds needed in the next step can be determined adaptively. Second, a dynamic cluster algorithm called ISODATA is used to cluster all the frames and the frames nearest to the center of each class are automatically extracted as key-frames of the sequence. Unlike many other clustering techniques, the present improved cluster algorithm can automatically address different motion types without any need for specified parameters from users. The proposed method is capable of summarizing motion capture data reliably and efficiently. The present work also provides a meaningful comparison between the results of the proposed key-frame extraction technique and other previous methods. These results are evaluated in terms of metrics that measure reconstructed motion and the mean absolute error value, which are derived from the reconstructed data and the original data.
A novel experimental method for the measurement of the caloric curves of clusters
Chirot, Fabien; Zamith, Sébastien; Labastie, Pierre; L'Hermite, Jean-Marc; 10.1063/1.3000628
2008-01-01
A novel experimental scheme has been developed in order to measure the heat capacity of mass selected clusters. It is based on controlled sticking of atoms on clusters. This allows one to construct the caloric curve, thus determining the melting temperature and the latent heat of fusion in the case of first-order phase transitions. This method is model-free. It is transferable to many systems since the energy is brought to clusters through sticking collisions. As an example, it has been applied to Na\\_90\\^+ and Na\\_140\\^+. Our results are in good agreement with previous measurements.
Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations
M. J. McGrath
2012-03-01
Full Text Available The Atmospheric Cluster Dynamics Code (ACDC is presented and explored. This program was created to study the first steps of atmospheric new particle formation by examining the formation of molecular clusters from atmospherically relevant molecules. The program models the cluster kinetics by explicit solution of the birth–death equations, using an efficient computer script for their generation and the MATLAB ode15s routine for their solution. Through the use of evaporation rate coefficients derived from formation free energies calculated by quantum chemical methods for clusters containing dimethylamine or ammonia and sulphuric acid, we have explored the effect of changing various parameters at atmospherically relevant monomer concentrations. We have included in our model clusters with 0–4 base molecules and 0–4 sulfuric acid molecules for which we have commensurable quantum chemical data. The tests demonstrate that large effects can be seen for even small changes in different parameters, due to the non-linearity of the system. In particular, changing the temperature had a significant impact on the steady-state concentrations of all clusters, while the boundary effects (allowing clusters to grow to sizes beyond the largest cluster that the code keeps track of, or forbidding such processes, coagulation sink terms, non-monomer collisions, sticking probabilities and monomer concentrations did not show as large effects under the conditions studied. Removal of coagulation sink terms prevented the system from reaching the steady state when all the initial cluster concentrations were set to the default value of 1 m^{−3}, which is probably an effect caused by studying only relatively small cluster sizes.
Grid-Search Location Methods for Ground-Truth Collection from Local and Regional Seismic Networks
Schultz, C A; Rodi, W; Myers, S C
2003-07-24
The objective of this project is to develop improved seismic event location techniques that can be used to generate more and better quality reference events using data from local and regional seismic networks. Their approach is to extend existing methods of multiple-event location with more general models of the errors affecting seismic arrival time data, including picking errors and errors in model-based travel-times (path corrections). Toward this end, they are integrating a grid-search based algorithm for multiple-event location (GMEL) with a new parameterization of travel-time corrections and new kriging method for estimating the correction parameters from observed travel-time residuals. Like several other multiple-event location algorithms, GMEL currently assumes event-independent path corrections and is thus restricted to small event clusters. The new parameterization assumes that travel-time corrections are a function of both the event and station location, and builds in source-receiver reciprocity and correlation between the corrections from proximate paths as constraints. The new kriging method simultaneously interpolates travel-time residuals from multiple stations and events to estimate the correction parameters as functions of position. They are currently developing the algorithmic extensions to GMEL needed to combine the new parameterization and kriging method with the simultaneous location of events. The result will be a multiple-event location method which is applicable to non-clustered, spatially well-distributed events. They are applying the existing components of the new multiple-event location method to a data set of regional and local arrival times from Nevada Test Site (NTS) explosions with known origin parameters. Preliminary results show the feasibility and potential benefits of combining the location and kriging techniques. They also show some preliminary work on generalizing of the error model used in GMEL with the use of mixture
Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method
Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels
2014-01-01
method can be used for cluster analysis to further validate the discrete Laplace method. A very important practical fact is that the calculations can be performed on a normal computer. We identified two sub-clusters of the Eastern and Western European Y-STR haplotypes similar to results of previous...... studies. We also compared pairwise distances (between geographically separated samples) with those obtained using the AMOVA method and found good agreement. Further analyses that are impossible with AMOVA were made using the discrete Laplace method: analysis of the homogeneity in two different ways......The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models...
Meshfree local radial basis function collocation method with image nodes
Baek, Seung Ki; Kim, Minjae
2017-07-01
We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem domain, similarly to the method of images in electrostatics, to perform collocation on the domain boundaries. We can thereby take into account the temperature profile as well as its gradients specified by boundary conditions at the same time, which holds true even for a node where two or more boundaries meet with different boundary conditions. We argue that the image method is computationally efficient when combined with the local RBF collocation method, whereas the addition of image nodes becomes very costly in case of the global collocation. We apply our modified method to a benchmark test of a boundary value problem, and find that this simple modification reduces the maximum error from the analytic solution significantly. The reduction is small for an initial value problem with simpler boundary conditions. We observe increased numerical instability, which has to be compensated for by a sufficient number of sample nodes and/or more careful parameter choices for time integration.
Biopsy Needle Localization and Tracking Using ROI-RK Method
Yue Zhao
2014-01-01
Full Text Available ROI-RK method is a biopsy needle localization and tracking method. Previous research work has proved that it has a robust performance on different series of simulated 3D US volumes. Unfortunately, in real situations, because of the strong speckle noise of the ultrasound image and the different echogenic properties of the tissues, the real 3D US volumes have more complex background than the simulated images used previously. In this paper, to adapt the ROI-RK method in real 3D US volumes, a line-filter enhancement calculation only in the ROI is added to increase the contrast between the needle and background tissue, decreasing the phenomenon of expansion of the biopsy needle due to reverberation of ultrasound in the needle. To make the ROI-RK method more stable, a self-correction system is also implemented. Real data have been acquired on an ex vivo heart of lamb. The result of the ROI-RK method shows that it is capable to localize and track the biopsy needle in real situations, and it satisfies the demand of real-time application.
Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling
Peterson, J. R.; Marshall, P. J.; Andersson, K.
2005-01-01
We propose an ambitious new method that models the intracluster medium in clusters of galaxies as a set of X-ray emitting smoothed particles of plasma. Each smoothed particle is described by a handful of parameters including temperature, location, size, and elemental abundances. Hundreds to thousands of these particles are used to construct a model cluster of galaxies, with the appropriate complexity estimated from the data quality. This model is then compared iteratively with X-ray data in t...
Issam SAHMOUDI
2013-12-01
Full Text Available Document Clustering is a branch of a larger area of scientific study kn own as data mining .which is an unsupervised classification using to find a structu re in a collection of unlabeled data. The useful information in the documents can be accompanied b y a large amount of noise words when using Full Tex t Representation, and therefore will affect negativel y the result of the clustering process. So it is w ith great need to eliminate the noise words and keeping just the useful information in order to enhance the qual ity of the clustering results. This problem occurs with di fferent degree for any language such as English, European, Hindi, Chinese, and Arabic Language. To o vercome this problem, in this paper, we propose a new and efficient Keyphrases extraction method base d on the Suffix Tree data structure (KpST, the extracted Keyphrases are then used in the clusterin g process instead of Full Text Representation. The proposed method for Keyphrases extraction is langua ge independent and therefore it may be applied to a ny language. In this investigation, we are interested to deal with the Arabic language which is one of th e most complex languages. To evaluate our method, we condu ct an experimental study on Arabic Documents using the most popular Clustering approach of Hiera rchical algorithms: Agglomerative Hierarchical algorithm with seven linkage techniques and a varie ty of distance functions and similarity measures to perform Arabic Document Clustering task. The obtain ed results show that our method for extracting Keyphrases increases the quality of the clustering results. We propose also to study the effect of using the stemming for the testing dataset to cluster it with the same documents clustering techniques and similarity/distance measures.
Moving sound source localization based on triangulation method
Miao, Feng; Yang, Diange; Wen, Junjie; Lian, Xiaomin
2016-12-01
This study develops a sound source localization method that extends traditional triangulation to moving sources. First, the possible sound source locating plane is scanned. Secondly, for each hypothetical source location in this possible plane, the Doppler effect is removed through the integration of sound pressure. Taking advantage of the de-Dopplerized signals, the moving time difference of arrival (MTDOA) is calculated, and the sound source is located based on triangulation. Thirdly, the estimated sound source location is compared to the original hypothetical location and the deviations are recorded. Because the real sound source location leads to zero deviation, the sound source can be finally located by minimizing the deviation matrix. Simulations have shown the superiority of MTDOA method over traditional triangulation in case of moving sound sources. The MTDOA method can be used to locate moving sound sources with as high resolution as DAMAS beamforming, as shown in the experiments, offering thus a new method for locating moving sound sources.
A semantics-based method for clustering of Chinese web search results
Zhang, Hui; Wang, Deqing; Wang, Li; Bi, Zhuming; Chen, Yong
2014-01-01
Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets' semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.
Non-parametric method for measuring gas inhomogeneities from X-ray observations of galaxy clusters
Morandi, Andrea; Cui, Wei
2013-01-01
We present a non-parametric method to measure inhomogeneities in the intracluster medium (ICM) from X-ray observations of galaxy clusters. Analyzing mock Chandra X-ray observations of simulated clusters, we show that our new method enables the accurate recovery of the 3D gas density and gas clumping factor profiles out to large radii of galaxy clusters. We then apply this method to Chandra X-ray observations of Abell 1835 and present the first determination of the gas clumping factor from the X-ray cluster data. We find that the gas clumping factor in Abell 1835 increases with radius and reaches ~2-3 at r=R_{200}. This is in good agreement with the predictions of hydrodynamical simulations, but it is significantly below the values inferred from recent Suzaku observations. We further show that the radially increasing gas clumping factor causes flattening of the derived entropy profile of the ICM and affects physical interpretation of the cluster gas structure, especially at the large cluster-centric radii. Our...
Jibing Wu
2017-01-01
Full Text Available Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.
MHCcluster, a method for functional clustering of MHC molecules
Thomsen, Martin Christen Frølund; Lundegaard, Claus; Buus, Søren;
2013-01-01
binding specificity. The method has a flexible web interface that allows the user to include any MHC of interest in the analysis. The output consists of a static heat map and graphical tree-based visualizations of the functional relationship between MHC variants and a dynamic TreeViewer interface where...
A comparison of clustering methods for writer identification and verification
Bulacu, M.L.; Schomaker, L.R.B.
2005-01-01
An effective method for writer identification and verification is based on assuming that each writer acts as a stochastic generator of ink-trace fragments, or graphemes. The probability distribution of these simple shapes in a given handwriting sample is characteristic for the writer and is computed
Adaptive cluster sampling: An efficient method for assessing inconspicuous species
Andrea M. Silletti; Joan Walker
2003-01-01
Restorationistis typically evaluate the success of a project by estimating the population sizes of species that have been planted or seeded. Because total census is raely feasible, they must rely on sampling methods for population estimates. However, traditional random sampling designs may be inefficient for species that, for one reason or another, are challenging to...
Multiple instance learning tracking method with local sparse representation
Xie, Chengjun
2013-10-01
When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.
Source clustering in the Hi-GAL survey determined using a minimum spanning tree method
Beuret, Maxime; Cambrésy, Laurent; Eden, David J; Elia, Davide; Molinari, Sergio; Pezzuto, Stefano; Schisano, Eugenio
2016-01-01
The aims are to investigate the clustering of the far-infrared sources from the Herschel infrared Galactic Plane Survey (Hi-GAL) in the Galactic longitude range of -71 to 67 deg. These clumps, and their spatial distribution, are an imprint of the original conditions within a molecular cloud. This will produce a catalogue of over-densities. The minimum spanning tree (MST) method was used to identify the over-densities in two dimensions. The catalogue was further refined by folding in heliocentric distances, resulting in more reliable over-densities, which are cluster candidates. We found 1,633 over-densities with more than ten members. Of these, 496 are defined as cluster candidates because of the reliability of the distances, with a further 1,137 potential cluster candidates. The spatial distributions of the cluster candidates are different in the first and fourth quadrants, with all clusters following the spiral structure of the Milky Way. The cluster candidates are fractal. The clump mass functions of the c...
Los cluster tecnológicos en México y Argentina: una estrategia para el desarrollo local
Prudencio Mochi Alemu00E1n
2009-01-01
Full Text Available El objetivo de este trabajo es, por una parte, darle continuidad a la línea de trabajo anterior sobre la industria de software y servicios informáticos, pero en esta oportunidad centrada en el estudio de la dinámica de los cluster tecnológicos en experiencias locales. Para ello se indagará esta dinámica en dos ciudades: Mérida (Yucatán-México y Rosario (Santa Fe-Argentina. El objetivo de enfocar estos dos casos de estudio se fundamenta en el interés por estas dos ciudades, ya que presentan un perfil productivo innovador, con tasas de crecimiento importante y que además esta estrategia se suma a otras actividades de alto valor agregado. En este sentido la producción de software y de nuevas tecnologías, están creando un clima propicio de desarrollo local. En este trabajo se analiza el contexto socio económico de cada ciudad, los antecedentes de la creación del cluster tecnológico, la cooperación inter empresarial e inter institucional, las políticas públicas territorializadas en el cluster, el perfil y las actividades de las empresas que conforman el mismo, así como las características de sus recursos humanos.
Robustness of serial clustering of extratropical cyclones to the choice of tracking method
Joaquim G. Pinto
2016-07-01
Full Text Available Cyclone clusters are a frequent synoptic feature in the Euro-Atlantic area. Recent studies have shown that serial clustering of cyclones generally occurs on both flanks and downstream regions of the North Atlantic storm track, while cyclones tend to occur more regulary on the western side of the North Atlantic basin near Newfoundland. This study explores the sensitivity of serial clustering to the choice of cyclone tracking method using cyclone track data from 15 methods derived from ERA-Interim data (1979–2010. Clustering is estimated by the dispersion (ratio of variance to mean of winter [December – February (DJF] cyclone passages near each grid point over the Euro-Atlantic area. The mean number of cyclone counts and their variance are compared between methods, revealing considerable differences, particularly for the latter. Results show that all different tracking methods qualitatively capture similar large-scale spatial patterns of underdispersion and overdispersion over the study region. The quantitative differences can primarily be attributed to the differences in the variance of cyclone counts between the methods. Nevertheless, overdispersion is statistically significant for almost all methods over parts of the eastern North Atlantic and Western Europe, and is therefore considered as a robust feature. The influence of the North Atlantic Oscillation (NAO on cyclone clustering displays a similar pattern for all tracking methods, with one maximum near Iceland and another between the Azores and Iberia. The differences in variance between methods are not related with different sensitivities to the NAO, which can account to over 50% of the clustering in some regions. We conclude that the general features of underdispersion and overdispersion of extratropical cyclones over the North Atlantic and Western Europe are robust to the choice of tracking method. The same is true for the influence of the NAO on cyclone dispersion.
Liakos, Dimitrios G; Neese, Frank
2015-09-08
The recently developed domain-based local pair natural orbital coupled cluster theory with single, double, and perturbative triple excitations (DLPNO-CCSD(T)) delivers results that are closely approaching those of the parent canonical coupled cluster method at a small fraction of the computational cost. A recent extended benchmark study established that, depending on the three main truncation thresholds, it is possible to approach the canonical CCSD(T) results within 1 kJ (default setting, TightPNO), 1 kcal/mol (default setting, NormalPNO), and 2-3 kcal (default setting, LoosePNO). Although thresholds for calculations with TightPNO are 2-4 times slower than those based on NormalPNO thresholds, they are still many orders of magnitude faster than canonical CCSD(T) calculations, even for small and medium sized molecules where there is little locality. The computational effort for the coupled cluster step scales nearly linearly with system size. Since, in many instances, the coupled cluster step in DLPNO-CCSD(T) is cheaper or at least not much more expensive than the preceding Hartree-Fock calculation, it is useful to compare the method against modern density functional theory (DFT), which requires an effort comparable to that of Hartree-Fock theory (at least if Hartree-Fock exchange is part of the functional definition). Double hybrid density functionals (DHDF's) even require a MP2-like step. The purpose of this article is to evaluate the cost vs accuracy ratio of DLPNO-CCSD(T) against modern DFT (including the PBE, B3LYP, M06-2X, B2PLYP, and B2GP-PLYP functionals and, where applicable, their van der Waals corrected counterparts). To eliminate any possible bias in favor of DLPNO-CCSD(T), we have chosen established benchmark sets that were specifically proposed for evaluating DFT functionals. It is demonstrated that DLPNO-CCSD(T) with any of the three default thresholds is more accurate than any of the DFT functionals. Furthermore, using the aug-cc-pVTZ basis set and
A Sensitive Attribute based Clustering Method for kanonymization
Bhaladhare, Pawan R
2012-01-01
In medical organizations large amount of personal data are collected and analyzed by the data miner or researcher, for further perusal. However, the data collected may contain sensitive information such as specific disease of a patient and should be kept confidential. Hence, the analysis of such data must ensure due checks that ensure protection against threats to the individual privacy. In this context, greater emphasis has now been given to the privacy preservation algorithms in data mining research. One of the approaches is anonymization approach that is able to protect private information; however, valuable information can be lost. Therefore, the main challenge is how to minimize the information loss during an anonymization process. The proposed method is grouping similar data together based on sensitive attribute and then anonymizes them. Our experimental results show the proposed method offers better outcomes with respect to information loss and execution time.
GMCL: a robust global localization method for mobile robot
Luo Ronghua; Hong Bingrong; Min Huaqing
2006-01-01
A large sample size is required for Monte Carlo localization (MCL) in multi-robot dynamic environment, because of the "kidnapped robot" phenomenon, which will locate most of the samples in the regions with small value of desired posterior density. For this problem the crossover and mutation operators in evolutionary computation are introduced into MCL to make samples move towards the regions where the desired posterior density is large, so that the sample set can represent the density better. The proposed method is termed genetic Monte Carlo localization (GMCL). Application in robot soccer system shows that GMCL can considerably reduce the required number of samples, and is more precise and robust in dynamic environment.
An effective trust-based recommendation method using a novel graph clustering algorithm
Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin
2015-10-01
Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.
A New Method to Quantify X-ray Substructures in Clusters of Galaxies
Andrade-Santos, Felipe; Laganá, Tatiana Ferraz
2011-01-01
We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model (beta-model or S\\'ersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range 0.02
Improved fuzzy identification method based on Hough transformation and fuzzy clustering
刘福才; 路平立; 潘江华; 裴润
2004-01-01
This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.
Burhan Ergen
2014-01-01
Full Text Available This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT and Magnetic Resonance Imaging (MRI devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Ergen, Burhan
2014-01-01
This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Characterising the local void with the X-ray cluster survey REFLEX II
Collins, Chris A.; Böhringer, Hans; Bristow, Martyn; Chon, Gayoung
2016-10-01
Claims of a significant underdensity or void in the density distribution on scales out to ~= 300 Mpc have recently been made using samples of galaxies. We present the results of an alternative test of the matter distribution on these scales using clusters of galaxies, which provide an independent and powerful probe of large-scale structure. We study the density distribution of X-ray clusters from the ROSAT-based REFLEX II catalogue, which covers a contiguous area of 4.24 steradians in the southern hempsphere (34% of the entire sky). Using the normalised comoving number density of clusters we find evidence for an underdensity (30-40%), out to z~ 0.04, equivalent to ~=170 Mpc and with a significance of 3.4σ. On scales between 300 Mpc and 1 Gpc the distribution of REFLEX II clusters is consistent with being uniform. We also confirm recent results that the underdensity has a large contribution from the direction of the South Galactic Cap region, but is not significant in the direction of the Northern Galactic Cap as viewed from the southern sky. Both the limited size of the detected underdensity and its lack of isotropy, argue against the idea that the Type Ia supernovae data can be explained without the need for dark energy.
Albrechtsen, Reidar; Hansen, Dorte Stautz; Sanjay, Archana;
2011-01-01
-Src interaction site in the ADAM12 cytoplasmic domain, but was independent of the catalytic activity of ADAM12. Caveolin-1 and transmembrane protease MMP14/MT1-MMP were both present in the ADAM12-induced clusters of invadopodia, and cholesterol depletion prevented their formation, suggesting that lipid-raft...
THE PUPPIS CLUSTER OF GALAXIES BEHING THE GALACTIC PLANE AND THE ORIGIN OF THE LOCAL ANOMALY
LAHAV, O; YAMADA, T; SCHARF, C; KRAANKORTEWEG, RC
1993-01-01
Recent surveys of galaxies behind the Galactic plane have revealed the Puppis cluster, centred at l approximately 240-degrees, b approximately 0-degrees and redshift cz approximately 1000-2000 km s-1. We supplement the recent 2-Jy IRAS redshift survey of Strauss et al. for absolute value of b >
Paccagnella, Angela; Poggianti, Bianca Maria; Moretti, Alessia; Fritz, Jacopo; Gullieuszik, Marco; Couch, Warrick; Bettoni, Daniela; Cava, Antonio; Fasano, Giovanni; D'Onofrio, Mauro
2015-01-01
The star formation quenching depends on environment, but a full understanding of what mechanisms drive it is still missing. Exploiting a sample of galaxies with masses $M_\\ast>10^{9.8}M_\\odot$, drawn from the WIde-field Nearby Galaxy-cluster Survey (WINGS) and its recent extension OMEGAWINGS, we investigate the star formation rate (SFR) as a function of stellar mass (M$_*$) in galaxy clusters at $0.04
Bucher, M.; Delabrouille, J.; Giraud-Héraud, Y.;
2011-01-01
We present precise Sunyaev-Zeldovich (SZ) effect measurements in the direction of 62 nearby galaxy clusters (z <0.5) detected at high signal-to-noise in the first Planck all-sky data set. The sample spans approximately a decade in total mass, 2 × 1014 M
Ocak, Mahir E
2012-01-01
Firstly, a sequential symmetry adaptation procedure is derived for semidirect product groups. Then, this sequential symmetry adaptation procedure is used in the development of new method named Monomer Basis Representation (MBR) for calculating the vibration-rotation-tunneling (VRT) spectra of molecular clusters. The method is based on generation of optimized bases for each monomer in the cluster as a linear combination of some primitive basis functions and then using the sequential symmetry adaptation procedure for generating a small symmetry adapted basis for the solution of the full problem. It is seen that given an optimized basis for each monomer the application of the sequential symmetry adaptation procedure leads to a generalized eigenvalue problem instead of a standard eigenvalue problem if the procedure is used as it is. In this paper, MBR method will be developed as a solution of that problem such that it leads to generation of an orthogonal optimized basis for the cluster being studied regardless of...
An image analysis method to quantify CFTR subcellular localization.
Pizzo, Lucilla; Fariello, María Inés; Lepanto, Paola; Aguilar, Pablo S; Kierbel, Arlinet
2014-08-01
Aberrant protein subcellular localization caused by mutation is a prominent feature of many human diseases. In Cystic Fibrosis (CF), a recessive lethal disorder that results from dysfunction of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), the most common mutation is a deletion of phenylalanine-508 (pF508del). Such mutation produces a misfolded protein that fails to reach the cell surface. To date, over 1900 mutations have been identified in CFTR gene, but only a minority has been analyzed at the protein level. To establish if a particular CFTR variant alters its subcellular distribution, it is necessary to quantitatively determine protein localization in the appropriate cellular context. To date, most quantitative studies on CFTR localization have been based on immunoprecipitation and western blot. In this work, we developed and validated a confocal microscopy-image analysis method to quantitatively examine CFTR at the apical membrane of epithelial cells. Polarized MDCK cells transiently transfected with EGFP-CFTR constructs and stained for an apical marker were used. EGFP-CFTR fluorescence intensity in a region defined by the apical marker was normalized to EGFP-CFTR whole cell fluorescence intensity, rendering "apical CFTR ratio". We obtained an apical CFTR ratio of 0.67 ± 0.05 for wtCFTR and 0.11 ± 0.02 for pF508del. In addition, this image analysis method was able to discriminate intermediate phenotypes: partial rescue of the pF508del by incubation at 27 °C rendered an apical CFTR ratio value of 0.23 ± 0.01. We concluded the method has a good sensitivity and accurately detects milder phenotypes. Improving axial resolution through deconvolution further increased the sensitivity of the system as rendered an apical CFTR ratio of 0.76 ± 0.03 for wild type and 0.05 ± 0.02 for pF508del. The presented procedure is faster and simpler when compared with other available methods and it is therefore suitable as a screening method to identify
The tidal tails of globular cluster Palomar 5 based on the neural networks method
Hu Zou; Zhen-Yu WU; Jun Ma; Xu Zhou
2009-01-01
The sixth Data Release (DR6) of the Sloan Digital Sky Survey (SDSS) provides more photometric regions,new features and more accurate data around globular cluster Palomar 5.A new method,Back Propagation Neural Network (BPNN),is used to estimate the cluster membership probability in order to detect its tidal tails.Cluster and field stars,used for training the networks,are extracted over a 40×20 deg~2 field by color-magnitude diagrams (CMDs).The best BPNNs with two hidden layers and a Levenberg-Marquardt(LM) training algorithm are determined by the chosen cluster and field samples.The membership probabilities of stars in the whole field are obtained with the BPNNs,and contour maps of the probability distribution show that a tail extends 5.42°to the north of the cluster and another tail extends 3.77°to the south.The tails are similar to those detected by Odenkirchen et al.,but no more debris from the cluster is found to the northeast in the sky.The radial density profiles are investigated both along the tails and near the cluster center.Quite a few substructures are discovered in the tails.The number density profile of the cluster is fitted with the King model and the tidal radius is determined as 14.28'.However,the King model cannot fit the observed profile at the outer regions (R ＞ 8') because of the tidal tails generated by the tidal force.Luminosity functions of the cluster and the tidal tails are calculated,which confirm that the tails originate from Palomar 5.
A new EEG measure using the 1D cluster variation method
Maren, Alianna J.; Szu, Harold H.
2015-05-01
A new information measure, drawing on the 1-D Cluster Variation Method (CVM), describes local pattern distributions (nearest-neighbor and next-nearest neighbor) in a binary 1-D vector in terms of a single interaction enthalpy parameter h for the specific case where the fractions of elements in each of two states are the same (x1=x2=0.5). An example application of this method would be for EEG interpretation in Brain-Computer Interfaces (BCIs), especially in the frontier of invariant biometrics based on distinctive and invariant individual responses to stimuli containing an image of a person with whom there is a strong affiliative response (e.g., to a person's grandmother). This measure is obtained by mapping EEG observed configuration variables (z1, z2, z3 for next-nearest neighbor triplets) to h using the analytic function giving h in terms of these variables at equilibrium. This mapping results in a small phase space region of resulting h values, which characterizes local pattern distributions in the source data. The 1-D vector with equal fractions of units in each of the two states can be obtained using the method for transforming natural images into a binarized equi-probability ensemble (Saremi & Sejnowski, 2014; Stephens et al., 2013). An intrinsically 2-D data configuration can be mapped to 1-D using the 1-D Peano-Hilbert space-filling curve, which has demonstrated a 20 dB lower baseline using the method compared with other approaches (cf. SPIE ICA etc. by Hsu & Szu, 2014). This CVM-based method has multiple potential applications; one near-term one is optimizing classification of the EEG signals from a COTS 1-D BCI baseball hat. This can result in a convenient 3-D lab-tethered EEG, configured in a 1-D CVM equiprobable binary vector, and potentially useful for Smartphone wireless display. Longer-range applications include interpreting neural assembly activations via high-density implanted soft, cellular-scale electrodes.
lane-Curvature Method:A New method for Local Obstacle Avoidance
Ko, Nak Yong; Lee, Sang Kee [Chosun University (Korea, Republic of)
1999-03-01
The lane-curvature method (LCM) presented in this paper is anew local obstacle avoidance method for indoor mobile robots. The method combines curvature-velocity method (CVM) with a new directional method called the lane method. The lane method divides the environment into lanes taking the information on obstacles and desired heading of the robot into account; then it chooses the best lane to follow to optimize travel along a desired heading. A local heading is then calculated for entering and following the best lane, and CVM uses this heading to determine the optimal translational and rotational velocities, considering the heading direction, physical limitations, and environmental constraints. By combining both the directional and velocity space methods, LCM yields safe collision-free motion as well as smooth motion taking the dynamics of the robot into account. Experiments using the mobile robot Xavier, show the efficiency of the proposed method. (author). 13 refs., 8 figs.
Balakrishnan, Bijinu; Karki, Suman; Chiu, Shih-Hau; Kim, Hyun-Ju; Suh, Jae-Won; Nam, Bora; Yoon, Yeo-Min; Chen, Chien-Chi; Kwon, Hyung-Jin
2013-07-01
Monascus spp. produce several well-known polyketides such as monacolin K, citrinin, and azaphilone pigments. In this study, the azaphilone pigment biosynthetic gene cluster was identified through T-DNA random mutagenesis in Monascus purpureus. The albino mutant W13 bears a T-DNA insertion upstream of a transcriptional regulator gene (mppR1). The transcription of mppR1 and the nearby polyketide synthase gene (MpPKS5) was significantly repressed in the W13 mutant. Targeted inactivation of MpPKS5 also gave rise to an albino mutant, confirming that mppR1 and MpPKS5 belong to an azaphilone pigment biosynthetic gene cluster. This M. purpureus sequence was used to identify the whole biosynthetic gene cluster in the Monascus pilosus genome. MpPKS5 contains SAT/KS/AT/PT/ACP/MT/R domains, and this domain organization is preserved in other azaphilone polyketide synthases. This biosynthetic gene cluster also encodes fatty acid synthase (FAS), which is predicted to assist the synthesis of 3-oxooactanoyl-CoA and 3-oxodecanoyl-CoA. These 3-oxoacyl compounds are proposed to be incorporated into the azaphilone backbone to complete the pigment biosynthesis. A monooxygenase gene (an azaH and tropB homolog) that is located far downstream of the FAS gene is proposed to be involved in pyrone ring formation. A homology search on other fungal genome sequences suggests that this azaphilone pigment gene cluster also exists in the Penicillium marneffei and Talaromyces stipitatus genomes.
New Clustering Method in High-Dimensional Space Based on Hypergraph-Models
CHEN Jian-bin; WANG Shu-jing; SONG Han-tao
2006-01-01
To overcome the limitation of the traditional clustering algorithms which fail to produce meanirigful clusters in high-dimensional, sparseness and binary value data sets, a new method based on hypergraph model is proposed. The hypergraph model maps the relationship present in the original data in high dimensional space into a hypergraph. A hyperedge represents the similarity of attribute-value distribution between two points. A hypergraph partitioning algorithm is used to find a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized. The quality of the clustering result can be evaluated by applying the intra-cluster singularity value.Analysis and experimental results have demonstrated that this approach is applicable and effective in wide ranging scheme.
The IMACS Cluster Building Survey. I. Description of the Survey and Analysis Methods
Oemler,, Augustus; Gladders, Michael G; Rigby, Jane R; Bai, Lei; Kelson, Daniel; Villanueva, Edward; Fritz, Jacopo; Rieke, George; Poggianti, Bianca M; Vulcani, Benedetta
2013-01-01
The IMACS Cluster Building Survey uses the wide field spectroscopic capabilities of the IMACS spectrograph on the 6.5m Baade Telescope to survey the large-scale environment surrounding rich intermediate-redshift clusters of galaxies. The goal is to understand the processes which may be transforming star-forming field galaxies into quiescent cluster members as groups and individual galaxies fall into the cluster from the surrounding supercluster. This first paper describes the survey: the data taking and reduction methods. We provide new calibrations of star formation rates derived from optical and infrared spectroscopy and photometry. We demonstrate that there is a tight relation between the observed star formation rate per unit B luminosity, and the ratio of the extinctions of the stellar continuum and the optical emission lines. With this, we can obtain accurate extinction-corrected colors of galaxies. Using these colors as well as other spectral measures, we determine new criteria for the existence of ongo...
A Load Balancing Algorithm Based on Maximum Entropy Methods in Homogeneous Clusters
Long Chen
2014-10-01
Full Text Available In order to solve the problems of ill-balanced task allocation, long response time, low throughput rate and poor performance when the cluster system is assigning tasks, we introduce the concept of entropy in thermodynamics into load balancing algorithms. This paper proposes a new load balancing algorithm for homogeneous clusters based on the Maximum Entropy Method (MEM. By calculating the entropy of the system and using the maximum entropy principle to ensure that each scheduling and migration is performed following the increasing tendency of the entropy, the system can achieve the load balancing status as soon as possible, shorten the task execution time and enable high performance. The result of simulation experiments show that this algorithm is more advanced when it comes to the time and extent of the load balance of the homogeneous cluster system compared with traditional algorithms. It also provides novel thoughts of solutions for the load balancing problem of the homogeneous cluster system.
Barker, Daniel; D'Este, Catherine; Campbell, Michael J; McElduff, Patrick
2017-03-09
Stepped wedge cluster randomised trials frequently involve a relatively small number of clusters. The most common frameworks used to analyse data from these types of trials are generalised estimating equations and generalised linear mixed models. A topic of much research into these methods has been their application to cluster randomised trial data and, in particular, the number of clusters required to make reasonable inferences about the intervention effect. However, for stepped wedge trials, which have been claimed by many researchers to have a statistical power advantage over the parallel cluster randomised trial, the minimum number of clusters required has not been investigated. We conducted a simulation study where we considered the most commonly used methods suggested in the literature to analyse cross-sectional stepped wedge cluster randomised trial data. We compared the per cent bias, the type I error rate and power of these methods in a stepped wedge trial setting with a binary outcome, where there are few clusters available and when the appropriate adjustment for a time trend is made, which by design may be confounding the intervention effect. We found that the generalised linear mixed modelling approach is the most consistent when few clusters are available. We also found that none of the common analysis methods for stepped wedge trials were both unbiased and maintained a 5% type I error rate when there were only three clusters. Of the commonly used analysis approaches, we recommend the generalised linear mixed model for small stepped wedge trials with binary outcomes. We also suggest that in a stepped wedge design with three steps, at least two clusters be randomised at each step, to ensure that the intervention effect estimator maintains the nominal 5% significance level and is also reasonably unbiased.
Cluster Analysis of the Newcastle Electronic Corpus of Tyneside English: A Comparison of Methods
Moisl, Hermann; Jones, Val
2005-01-01
This article examines the feasibility of an empirical approach to sociolinguistic analysis of the Newcastle Electronic Corpus of Tyneside English using exploratory multivariate methods. It addresses a known problem with one class of such methods, hierarchical cluster analysis¿that different clusteri
A Cluster-based Method to Map Urban Area from DMSP/OLS Nightlights
Zhou, Yuyu; Smith, Steven J.; Elvidge, Christopher; Zhao, Kaiguang; Thomson, Allison M.; Imhoff, Marc L.
2014-05-05
Accurate information of urban areas at regional and global scales is important for both the science and policy-making communities. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime stable light data (NTL) provide a potential way to map urban area and its dynamics economically and timely. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the DMSP/OLS NTL data in five major steps, including data preprocessing, urban cluster segmentation, logistic model development, threshold estimation, and urban extent delineation. Different from previous fixed threshold method with over- and under-estimation issues, in our method the optimal thresholds are estimated based on cluster size and overall nightlight magnitude in the cluster, and they vary with clusters. Two large countries of United States and China with different urbanization patterns were selected to map urban extents using the proposed method. The result indicates that the urbanized area occupies about 2% of total land area in the US ranging from lower than 0.5% to higher than 10% at the state level, and less than 1% in China, ranging from lower than 0.1% to about 5% at the province level with some municipalities as high as 10%. The derived thresholds and urban extents were evaluated using high-resolution land cover data at the cluster and regional levels. It was found that our method can map urban area in both countries efficiently and accurately. Compared to previous threshold techniques, our method reduces the over- and under-estimation issues, when mapping urban extent over a large area. More important, our method shows its potential to map global urban extents and temporal dynamics using the DMSP/OLS NTL data in a timely, cost-effective way.
Unified cluster expansion method applied to the configurational thermodynamics of cubic Ti1-xAlxN
Alling, Björn; Ruban, Andrei; Karimi, Ayat; Hultman, Lars; Abrikosov, Igor
2012-02-01
We study the thermodynamics of cubic Ti1-xAlxN using a unified cluster expansion approach for the alloy problem [1]. The purely configurational part of the alloy Hamiltonian is expanded in terms of concentration and volume-dependent effective cluster interactions. By separate expansions of the chemical fixed lattice, and local lattice relaxation terms of the ordering energies, we demonstrate how the screened generalized perturbation method can be fruitfully combined with a concentration-dependent Connolly-Williams cluster expansion method, getting the best out of both two schemes that are traditionally used separately. Utilizing the obtained Hamiltonian in Monte Carlo simulations we access the free energy of Ti1-xAlxN alloys and construct the isostructural phase diagram. The results show striking similarities with the previously obtained mean-field results: The metastable c-TiAlN is subject to coherent spinodal decomposition over a large part of the concentration range, e.g., from x 0.33 at 2000 K. [4pt] [1] B. Alling, A. V. Ruban, A. Karimi, L. Hultman, and I. A. Abrikosov, PHYSICAL REVIEW B 83, 104203 (2011)
I. Crawford
2015-07-01
Full Text Available In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution
I. Crawford
2015-11-01
Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the
An adaptive image denoising method based on local parameters optimization
Hari Om; Mantosh Biswas
2014-08-01
In image denoising algorithms, the noise is handled by either modifying term-by-term, i.e., individual pixels or block-by-block, i.e., group of pixels, using suitable shrinkage factor and threshold function. The shrinkage factor is generally a function of threshold and some other characteristics of the neighbouring pixels of the pixel to be thresholded (denoised). The threshold is determined in terms of the noise variance present in the image and its size. The VisuShrink, SureShrink, and NeighShrink methods are important denoising methods that provide good results. The first two, i.e., VisuShrink and SureShrink methods follow term-by-term approach, i.e., modify the individual pixel and the third one, i.e., NeighShrink and its variants: ModiNeighShrink, IIDMWD, and IAWDMBMC, follow block-by-block approach, i.e., modify the pixels in groups, in order to remove the noise. The VisuShrink, SureShrink, and NeighShrink methods however do not give very good visual quality because they remove too many coefficients due to their high threshold values. In this paper, we propose an image denoising method that uses the local parameters of the neighbouring coefficients of the pixel to be denoised in the noisy image. In our method, we propose two new shrinkage factors and the threshold at each decomposition level, which lead to better visual quality. We also establish the relationship between both the shrinkage factors. We compare the performance of our method with that of the VisuShrink and NeighShrink including various variants. Simulation results show that our proposed method has high peak signal-to-noise ratio and good visual quality of the image as compared to the traditional methods:Weiner filter, VisuShrink, SureShrink, NeighBlock, NeighShrink, ModiNeighShrink, LAWML, IIDMWT, and IAWDMBNC methods.
Orsi, Rebecca
2017-02-01
Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research.
Scattering cluster wave functions on the lattice using the adiabatic projection method
Rokash, Alexander; Elhatisari, Serdar; Lee, Dean; Epelbaum, Evgeny; Krebs, Hermann
2015-01-01
The adiabatic projection method is a general framework for studying scattering and reactions on the lattice. It provides a low-energy effective theory for clusters which becomes exact in the limit of large Euclidean projection time. Previous studies have used the adiabatic projection method to extract scattering phase shifts from finite periodic-box energy levels using L\\"uschers method. In this paper we demonstrate that scattering observables can be computed directly from asymptotic cluster wave functions. For a variety of examples in one and three spatial dimensions, we extract elastic phase shifts from asymptotic cluster standing waves corresponding to spherical wall boundary conditions. We find that this approach of extracting scattering wave functions from the adiabatic Hamiltonian to be less sensitive to small stochastic and systematic errors as compared with using periodic-box energy levels.
Omidvarnia, Amir; Pedersen, Mangor; Walz, Jennifer M; Vaughan, David N; Abbott, David F; Jackson, Graeme D
2016-05-01
Dynamic functional brain connectivity analysis is a fast expanding field in computational neuroscience research with the promise of elucidating brain network interactions. Sliding temporal window based approaches are commonly used in order to explore dynamic behavior of brain networks in task-free functional magnetic resonance imaging (fMRI) data. However, the low effective temporal resolution of sliding window methods fail to capture the full dynamics of brain activity at each time point. These also require subjective decisions regarding window size and window overlap. In this study, we introduce dynamic regional phase synchrony (DRePS), a novel analysis approach that measures mean local instantaneous phase coherence within adjacent fMRI voxels. We evaluate the DRePS framework on simulated data showing that the proposed measure is able to estimate synchrony at higher temporal resolution than sliding windows of local connectivity. We applied DRePS analysis to task-free fMRI data of 20 control subjects, revealing ultra-slow dynamics of local connectivity in different brain areas. Spatial clustering based on the DRePS feature time series reveals biologically congruent local phase synchrony networks (LPSNs). Taken together, our results demonstrate three main findings. Firstly, DRePS has increased temporal sensitivity compared to sliding window correlation analysis in capturing locally synchronous events. Secondly, DRePS of task-free fMRI reveals ultra-slow fluctuations of ∼0.002-0.02 Hz. Lastly, LPSNs provide plausible spatial information about time-varying brain local phase synchrony. With the DRePS method, we introduce a framework for interrogating brain local connectivity, which can potentially provide biomarkers of human brain function in health and disease. Hum Brain Mapp 37:1970-1985, 2016. © 2016 Wiley Periodicals, Inc.
An Efficient Initialization Method for K-Means Clustering of Hyperspectral Data
Alizade Naeini, A.; Jamshidzadeh, A.; Saadatseresht, M.; Homayouni, S.
2014-10-01
K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman-Pearson detection theory based eigen-thresholding method (i.e., the HFC method) has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES) algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF) and Random methods) are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods' performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping.
Darabant, András; Rai, Prem Bahadur; Staudhammer, Christina Lynn; Dorji, Tshewang
2016-08-01
Dendrocalamus hamiltonii, a large, clump-forming bamboo, has great potential to contribute towards poverty alleviation efforts across its distributional range. Harvesting methods that maximize yield while they fulfill local objectives and ensure sustainability are a research priority. Documenting local ecological knowledge on the species and identifying local users' goals for its production, we defined three harvesting treatments (selective cut, horseshoe cut, clear cut) and experimentally compared them with a no-intervention control treatment in an action research framework. We implemented harvesting over three seasons and monitored annually and two years post-treatment. Even though the total number of culms positively influenced the number of shoots regenerated, a much stronger relationship was detected between the number of culms harvested and the number of shoots regenerated, indicating compensatory growth mechanisms to guide shoot regeneration. Shoot recruitment declined over time in all treatments as well as the control; however, there was no difference among harvest treatments. Culm recruitment declined with an increase in harvesting intensity. When univariately assessing the number of harvested culms and shoots, there were no differences among treatments. However, multivariate analyses simultaneously considering both variables showed that harvested output of shoots and culms was higher with clear cut and horseshoe cut as compared to selective cut. Given the ease of implementation and issues of work safety, users preferred the horseshoe cut, but the lack of sustainability of shoot production calls for investigating longer cutting cycles.
Paccagnella, A.; Vulcani, B.; Poggianti, B. M.; Moretti, A.; Fritz, J.; Gullieuszik, M.; Couch, W.; Bettoni, D.; Cava, A.; D'Onofrio, M.; Fasano, G.
2016-01-01
The star formation quenching depends on environment, but a full understanding of what mechanisms drive it is still missing. Exploiting a sample of galaxies with masses {M}*\\gt {10}9.8{M}⊙ , drawn from the WIde-field Nearby Galaxy-cluster Survey (WINGS) and its recent extension OMEGAWINGS, we investigate the star formation rate (SFR) as a function of stellar mass (M{}*) in galaxy clusters at 0.04\\lt z\\lt 0.07. We use non-member galaxies at 0.02 relation in the two environments, but detect a population of cluster galaxies with reduced SFRs, which is rare in the field. These transition galaxies are mainly found within the cluster virial radius (R200), but they impact on the SFR-M{}* relation only within 0.6R200. The ratio of transition to pure star-forming galaxies strongly depends on environment, being larger than 0.6 within 0.3R200 and rapidly decreasing with distance, while it is almost flat with M*. As galaxies move downward from the SFR-M{}* main sequence, they become redder and present older luminosity- and mass-weighted ages. These trends, together with the analysis of the star formation histories, suggest that transition galaxies have had a reduced SFR for the past 2-5 Gyr. Our results are consistent with the hypothesis that the interaction of galaxies with the intracluster medium via strangulation causes a gradual shut down of star formation, giving birth to an evolved population of galaxies in transition from being star forming to becoming passive.
Clustering of attitudes towards obesity: a mixed methods study of Australian parents and children
2013-01-01
Background Current population-based anti-obesity campaigns often target individuals based on either weight or socio-demographic characteristics, and give a ‘mass’ message about personal responsibility. There is a recognition that attempts to influence attitudes and opinions may be more effective if they resonate with the beliefs that different groups have about the causes of, and solutions for, obesity. Limited research has explored how attitudinal factors may inform the development of both upstream and downstream social marketing initiatives. Methods Computer-assisted face-to-face interviews were conducted with 159 parents and 184 of their children (aged 9–18 years old) in two Australian states. A mixed methods approach was used to assess attitudes towards obesity, and elucidate why different groups held various attitudes towards obesity. Participants were quantitatively assessed on eight dimensions relating to the severity and extent, causes and responsibility, possible remedies, and messaging strategies. Cluster analysis was used to determine attitudinal clusters. Participants were also able to qualify each answer. Qualitative responses were analysed both within and across attitudinal clusters using a constant comparative method. Results Three clusters were identified. Concerned Internalisers (27% of the sample) judged that obesity was a serious health problem, that Australia had among the highest levels of obesity in the world and that prevalence was rapidly increasing. They situated the causes and remedies for the obesity crisis in individual choices. Concerned Externalisers (38% of the sample) held similar views about the severity and extent of the obesity crisis. However, they saw responsibility and remedies as a societal rather than an individual issue. The final cluster, the Moderates, which contained significantly more children and males, believed that obesity was not such an important public health issue, and judged the extent of obesity to be
Localization and cooperative communication methods for cognitive radio
Duval, Olivier
) condition, increasing the localization error, even when the AOA estimate is accurate. We present a real-time localization solver (RTLS) for time-of-arrival (TOA) estimates using ray-tracing methods on the map of the geometry of walls and compare its performance with classical TOA trilateration localization methods. Extensive simulations and field trials for indoor environments show that our method increases the coverage area from 1.9% of the floor to 82.3 % and the accuracy by a 10-fold factor when compared with trilateration. We implemented our ray tracing model in C++ using the CGAL computational geometry algorithm library. We illustrate the real-time property of our RTLS that performs most ray tracing tasks in a preprocessing phase with time and space complexity analyses and profiling of our software.
Galaxy cluster X-ray luminosity scaling relations from a representative local sample (REXCESS)
Pratt, G W; Arnaud, M; Böhringer, H
2008-01-01
(Abridged) We examine the X-ray luminosity scaling relations of 31 nearby galaxy clusters from the Representative XMM-Newton Cluster Structure Survey (REXCESS). The objects are selected in X-ray luminosity only, optimally sampling the cluster luminosity function; temperatures range from 2 to 9 keV and there is no bias toward any particular morphological type. Pertinent values are extracted in an aperture corresponding to R_500, estimated using the tight correlation between Y_X and total mass. The data exhibit power law relations between bolometric X-ray luminosity and temperature, Y_X and total mass, all with slopes that are significantly steeper than self-similar expectations. We examine the causes for the steepening, finding that the primary driver appears to be a systematic variation of the gas content with mass. Scatter about the relations is dominated in all cases by the presence of cool cores. The logarithmic scatter about the raw X-ray luminosity-temperature relation is approximately 30%, and that abou...
Color image segmentation using watershed and Nyström method based spectral clustering
Bai, Xiaodong; Cao, Zhiguo; Yu, Zhenghong; Zhu, Hu
2011-11-01
Color image segmentation draws a lot of attention recently. In order to improve efficiency of spectral clustering in color image segmentation, a novel two-stage color image segmentation method is proposed. In the first stage, we use vector gradient approach to detect color image gradient information, and watershed transformation to get the pre-segmentation result. In the second stage, Nyström extension based spectral clustering is used to get the final result. To verify the proposed algorithm, it is applied to color images from the Berkeley Segmentation Dataset. Experiments show our method can bring promising results and reduce the runtime significantly.
Brabec, Jiri; Banik, Subrata; Kowalski, Karol; Pittner, Jiří
2016-10-28
The implementation details of the universal state-selective (USS) multi-reference coupled cluster (MRCC) formalism with singles and doubles (USS(2)) are discussed on the example of several benchmark systems. We demonstrate that the USS(2) formalism is capable of improving accuracies of state specific multi-reference coupled-cluster (MRCC) methods based on the Brillouin-Wigner and Mukherjee’s sufficiency conditions. Additionally, it is shown that the USS(2) approach significantly alleviates problems associated with the lack of invariance of MRCC theories upon the rotation of active orbitals. We also discuss the perturbative USS(2) formulations that significantly reduce numerical overhead of the full USS(2) method.
Clustering method and representative feeder selection for the California solar initiative
Broderick, Robert Joseph; Williams, Joseph R.; Munoz-Ramos, Karina
2014-02-01
The screening process for DG interconnection procedures needs to be improved in order to increase the PV deployment level on the distribution grid. A significant improvement in the current screening process could be achieved by finding a method to classify the feeders in a utility service territory and determine the sensitivity of particular groups of distribution feeders to the impacts of high PV deployment levels. This report describes the utility distribution feeder characteristics in California for a large dataset of 8,163 feeders and summarizes the California feeder population including the range of characteristics identified and most important to hosting capacity. The report describes the set of feeders that are identified for modeling and analysis as well as feeders identified for the control group. The report presents a method for separating a utilitys distribution feeders into unique clusters using the k-means clustering algorithm. An approach for determining the feeder variables of interest for use in a clustering algorithm is also described. The report presents an approach for choosing the feeder variables to be utilized in the clustering process and a method is identified for determining the optimal number of representative clusters.
New orbit correction method uniting global and local orbit corrections
Nakamura, N.; Takaki, H.; Sakai, H.; Satoh, M.; Harada, K.; Kamiya, Y.
2006-01-01
A new orbit correction method, called the eigenvector method with constraints (EVC), is proposed and formulated to unite global and local orbit corrections for ring accelerators, especially synchrotron radiation(SR) sources. The EVC can exactly correct the beam positions at arbitrarily selected ring positions such as light source points, simultaneously reducing closed orbit distortion (COD) around the whole ring. Computer simulations clearly demonstrate these features of the EVC for both cases of the Super-SOR light source and the Advanced Light Source (ALS) that have typical structures of high-brilliance SR sources. In addition, the effects of errors in beam position monitor (BPM) reading and steering magnet setting on the orbit correction are analytically expressed and also compared with the computer simulations. Simulation results show that the EVC is very effective and useful for orbit correction and beam position stabilization in SR sources.
A novel method for mobile robot simultaneous localization and mapping
LI Mao-hai; HONG Bing-rong; LUO Rong-hua; WEI Zhen-hua
2006-01-01
A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the RaoBlackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.
The Tidal Tails of Globular Cluster Palomar 5 Based on Neural Networks Method
Zou, H; Ma, J; Zhou, X
2009-01-01
The Sixth Data Release (DR6) in the Sloan Digital Sky Survey (SDSS) provides more photometric regions, new features and more accurate data around globular cluster Palomar 5. A new method, Back Propagation Neural Network (BPNN), is used to estimate the probability of cluster member to detect its tidal tails. Cluster and field stars, used for training the networks, are extracted over a $40\\times20$ deg$^2$ field by color-magnitude diagrams (CMDs). The best BPNNs with two hidden layers and Levenberg-Marquardt (LM) training algorithm are determined by the chosen cluster and field samples. The membership probabilities of stars in the whole field are obtained with the BPNNs, and contour maps of the probability distribution show that a tail extends $5.42\\dg$ to the north of the cluster and a tail extends $3.77\\dg$ to the south. The whole tails are similar to those detected by \\citet{od03}, but no longer debris of the cluster is found to the northeast of the sky. The radial density profiles are investigated both alon...
AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
A. Alizade Naeini
2014-10-01
Full Text Available K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-thresholding method (i.e., the HFC method has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF and Random methods are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods’ performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping.
Suvorova L.A.
2017-01-01
Full Text Available The cluster approach is considered by the authors as the tool to ensure the accelerated development of the country’s industrial complex. In the article the authors examine the problem of forming the model of the cluster development in high-tech sectors of industry and the methods for evaluating its economic effectiveness. Unlike traditional approaches, the authors of the article identify a cluster unit as the main structural element of the development model of the innovative industrial cluster. They think that a cluster unit is one member of the cluster (i.e.one enterprise. This point of view is differed from modern scientists` opinion, who view a cluster unit as the complex of enterprises operating within cluster units. The purpose of the study was the development and the economic evaluation of the model of the cluster development. In this research the authors examined the cluster of industrial biotechnologies. They have developed and proposed the model of the development of the cluster of industrial biotechnologies: the Non-commercial partnership (NP “The biotechnology cluster of the Kirov region”. This model takes into account the peculiarities of the innovative production. The authors have calculated the absolute and relative effect from clustering taking into account the effectiveness and profitability indicators of the cluster units activities within the cluster in question and the evaluation of the project activity of the cluster. Thus the authors have proved the economic effectiveness of the proposed model of the cluster development. The received research results allow us to conclude that the designed model of the development of the NP “The biotechnology cluster of the Kirov region” provides a steady growth trend of positive economic effect from the corporate activities of the enterprises within the cluster and increase in the region’s competitiveness in the production of high-tech industrial products.
Open-Source Sequence Clustering Methods Improve the State Of the Art.
Kopylova, Evguenia; Navas-Molina, Jose A; Mercier, Céline; Xu, Zhenjiang Zech; Mahé, Frédéric; He, Yan; Zhou, Hong-Wei; Rognes, Torbjørn; Caporaso, J Gregory; Knight, Rob
2016-01-01
Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http
The Local Variational Multiscale Method for Turbulence Simulation.
Collis, Samuel Scott; Ramakrishnan, Srinivas
2005-05-01
Accurate and efficient turbulence simulation in complex geometries is a formidable chal-lenge. Traditional methods are often limited by low accuracy and/or restrictions to simplegeometries. We explore the merger of Discontinuous Galerkin (DG) spatial discretizationswith Variational Multi-Scale (VMS) modeling, termed Local VMS (LVMS), to overcomethese limitations. DG spatial discretizations support arbitrarily high-order accuracy on un-structured grids amenable for complex geometries. Furthermore, high-order, hierarchicalrepresentation within DG provides a natural framework fora prioriscale separation crucialfor VMS implementation. We show that the combined benefits of DG and VMS within theLVMS method leads to promising new approach to LES for use in complex geometries.The efficacy of LVMS for turbulence simulation is assessed by application to fully-developed turbulent channelflow. First, a detailed spatial resolution study is undertakento record the effects of the DG discretization on turbulence statistics. Here, the localhp[?]refinement capabilites of DG are exploited to obtain reliable low-order statistics effi-ciently. Likewise, resolution guidelines for simulating wall-bounded turbulence using DGare established. We also explore the influence of enforcing Dirichlet boundary conditionsindirectly through numericalfluxes in DG which allows the solution to jump (slip) at thechannel walls. These jumps are effective in simulating the influence of the wall commen-surate with the local resolution and this feature of DG is effective in mitigating near-wallresolution requirements. In particular, we show that by locally modifying the numericalviscousflux used at the wall, we are able to regulate the near-wall slip through a penaltythat leads to improved shear-stress predictions. This work, demonstrates the potential ofthe numerical viscousflux to act as a numerically consistent wall-model and this successwarrents future research.As in any high-order numerical method some
A quaternion-based spectral clustering method for color image segmentation
Li, Xiang; Jin, Lianghai; Liu, Hong; He, Zeng
2011-11-01
Spectral clustering method has been widely used in image segmentation. A key issue in spectral clustering is how to build the affinity matrix. When it is applied to color image segmentation, most of the existing methods either use Euclidean metric to define the affinity matrix, or first converting color-images into gray-level images and then use the gray-level images to construct the affinity matrix (component-wise method). However, it is known that Euclidean distances can not represent the color differences well and the component-wise method does not consider the correlation between color channels. In this paper, we propose a new method to produce the affinity matrix, in which the color images are first represented in quaternion form and then the similarities between color pixels are measured by quaternion rotation (QR) mechanism. The experimental results show the superiority of the new method.
Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods
Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne
studies have used non-valid analysis of skewed data. We propose two different methods to compare mean cost in two groups. Firstly, we use a non-parametric bootstrap method where the re-sampling takes place on two levels in order to take into account the cluster effect. Secondly, we proceed with a log...... We consider health care data from a cluster-randomized intervention study in primary care to test whether the average health care costs among study patients differ between the two groups. The problems of analysing cost data are that most data are severely skewed. Median instead of mean...... is commonly used for skewed distributions. For health care data, however, we need to recover the total cost in a given patient population. Thus, we focus, on making inferences on population means. Furthermore, a problem of clustered data is added as data related to patients in primary care are organized...
Form gene clustering method about pan-ethnic-group products based on emotional semantic
Chen, Dengkai; Ding, Jingjing; Gao, Minzhuo; Ma, Danping; Liu, Donghui
2016-09-01
The use of pan-ethnic-group products form knowledge primarily depends on a designer's subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers' perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.
Felfer, P; Ceguerra, A V; Ringer, S P; Cairney, J M
2015-03-01
The analysis of the formation of clusters in solid solutions is one of the most common uses of atom probe tomography. Here, we present a method where we use the Voronoi tessellation of the solute atoms and its geometric dual, the Delaunay triangulation to test for spatial/chemical randomness of the solid solution as well as extracting the clusters themselves. We show how the parameters necessary for cluster extraction can be determined automatically, i.e. without user interaction, making it an ideal tool for the screening of datasets and the pre-filtering of structures for other spatial analysis techniques. Since the Voronoi volumes are closely related to atomic concentrations, the parameters resulting from this analysis can also be used for other concentration based methods such as iso-surfaces. Copyright © 2014 Elsevier B.V. All rights reserved.
Intraoperative methods to stage and localize pancreatic and duodenal tumors.
Norton, J A
1999-01-01
Intraoperative methods to stage and localize tumors have dramatically improved. Advances include less invasive methods to obtain comparable results and precise localization of previously occult tumors. The use of new technology including laparoscopy and ultrasound has provided some of these advances, while improved operative techniques have provided others. Laparoscopy with ultrasound has allowed for improved staging of patients with pancreatic cancer and exclusion of patients who are not resectable for cure. We performed laparoscopy with ultrasound on 50 consecutive patients with adenocarcinoma of the pancreas or liver who appeared to have resectable tumors based on preoperative computed tomography. 22 patients (44%) were found to be unresectable because of tumor nodules on the liver and/or peritoneal surfaces or unsuspected distant nodal or liver metastases. The site of disease making the patient unresectable was confirmed by biopsy in each case. Of the 28 remaining patients in whom laparoscopic ultrasound predicted to be resectable for cure, 26 (93%) had all tumor removed. Thus laparoscopy with ultrasound was the best method to select patients for curative surgery. Intraoperative ultrasound (IOUS) has been a critical method to identify insulinomas that are not palpable. Nonpalpable tumors are most commonly in the pancreatic head. Because the pancreatic head is thick and insulinomas are small, of 9 pancreatic head insulinomas only 3 (33%) were palpable. However, IOUS precisely identified each (100%). Others have recommended blind distal pancreatectomy for individuals with insulinoma in whom no tumor can be identified. However, our data suggest that this procedure is contraindicated as these occult tumors are usually within the pancreatic head. Recent series suggest that previously missed gastrinomas are commonly in the duodenum. IOUS is not able to identify these tumors, but other methods can. Of 27 patients with 31 duodenal gastrinomas, palpation identified 19
Detecting and extracting clusters in atom probe data: A simple, automated method using Voronoi cells
Felfer, P., E-mail: peter.felfer@sydney.edu.au [Australian Centre for Microscopy and Microanalysis, The University of Sydney, NSW 2006 (Australia); School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006 (Australia); Ceguerra, A.V., E-mail: anna.ceguerra@sydney.edu.au [Australian Centre for Microscopy and Microanalysis, The University of Sydney, NSW 2006 (Australia); School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006 (Australia); Ringer, S.P., E-mail: simon.ringer@sydney.edu.au [Australian Centre for Microscopy and Microanalysis, The University of Sydney, NSW 2006 (Australia); School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006 (Australia); Cairney, J.M., E-mail: julie.cairney@sydney.edu.au [Australian Centre for Microscopy and Microanalysis, The University of Sydney, NSW 2006 (Australia); School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006 (Australia)
2015-03-15
The analysis of the formation of clusters in solid solutions is one of the most common uses of atom probe tomography. Here, we present a method where we use the Voronoi tessellation of the solute atoms and its geometric dual, the Delaunay triangulation to test for spatial/chemical randomness of the solid solution as well as extracting the clusters themselves. We show how the parameters necessary for cluster extraction can be determined automatically, i.e. without user interaction, making it an ideal tool for the screening of datasets and the pre-filtering of structures for other spatial analysis techniques. Since the Voronoi volumes are closely related to atomic concentrations, the parameters resulting from this analysis can also be used for other concentration based methods such as iso-surfaces. - Highlights: • Cluster analysis of atom probe data can be significantly simplified by using the Voronoi cell volumes of the atomic distribution. • Concentration fields are defined on a single atomic basis using Voronoi cells. • All parameters for the analysis are determined by optimizing the separation probability of bulk atoms vs clustered atoms.
Simultaneous localization and mapping using single cluster probability hypothesis density filters
Lee, Chee Sing
2015-01-01
The majority of research in feature-based SLAM builds on the legacy of foundational work using the EKF, a single-object estimation technique. Because feature-based SLAM is an inherently multi-object problem, this has led to a number of suboptimalities in popular solutions. We develop an algorithm using the SC-PHD filter, a multi-object estimator modeled on cluster processes. This algorithm hosts capabilities not typically seen with feature-base SLAM solutions such as principled handling of cl...
Differential localization of LGR5 and Nanog in clusters of colon cancer stem cells.
Amsterdam, Abraham; Raanan, Calanit; Schreiber, Letizia; Freyhan, Ora; Fabrikant, Yakov; Melzer, Ehud; Givol, David
2013-05-01
One paradigm of cancer development claims that cancer emerges at the niche of tissue stem cells and these cells continue to proliferate in the tumor as cancer stem cells. LGR5, a membrane receptor, was recently found to be a marker of normal colon stem cells in colon polyps and is also expressed in colon cancer stem cells. Nanog, an embryonic stem cell nuclear factor, is expressed in several embryonic tissues, but Nanog expression is not well documented in cancerous stem cells. Our aim was to examine whether both LGR5 and Nanog are expressed in the same clusters of colon stem cells or cancer stem cells, using immunocytochemistry with specific antibodies to each antigen. We analyzed this aspect using paraffin embedded tumor tissue sections obtained from 18 polyps and 36 colon cancer specimens at stages I-IV. Antibodies to LGR5 revealed membrane and cytoplasm immunostaining of scattered labeled cells in normal crypts, with no labeling of Nanog. However, in close proximity to the tumors, staining to LGR5 was much more intensive in the crypts, including that of the epithelial cells. In cancer tissue, positive LGR5 clusters of stem cells were observed mainly in poorly differentiated tumors and in only a few scattered cells in the highly differentiated tumors. In contrast, antibodies to Nanog mainly stained the growing edges of carcinoma cells, leaving the poorly differentiated tumor cells unlabeled, including the clustered stem cells that could be detected even by direct morphological examination. In polyp tissues, scattered labeled cells were immunostained with antibodies to Nanog and to a much lesser extent with antibodies to LGR5. We conclude that expression of LGR5 is probably specific to stem cells of poorly differentiated tumors, whereas Nanog is mainly expressed at the edges of highly differentiated tumors. However, some of the cell layers adjacent to the carcinoma cell layers that still remained undifferentiated, expressed mainly Nanog with only a few cells
A downscaling method for the assessment of local climate change
Bruno, E.; Portoghese, I.; Vurro, M.
2009-04-01
The use of complimentary models is necessary to study the impact of climate change scenarios on the hydrological response at different space-time scales. However, the structure of GCMs is such that their space resolution (hundreds of kilometres) is too coarse and not adequate to describe the variability of extreme events at basin scale (Burlando and Rosso, 2002). To bridge the space-time gap between the climate scenarios and the usual scale of the inputs for hydrological prediction models is a fundamental requisite for the evaluation of climate change impacts on water resources. Since models operate a simplification of a complex reality, their results cannot be expected to fit with climate observations. Identifying local climate scenarios for impact analysis implies the definition of more detailed local scenario by downscaling GCMs or RCMs results. Among the output correction methods we consider the statistical approach by Déqué (2007) reported as a ‘Variable correction method' in which the correction of model outputs is obtained by a function build with the observation dataset and operating a quantile-quantile transformation (Q-Q transform). However, in the case of daily precipitation fields the Q-Q transform is not able to correct the temporal property of the model output concerning the dry-wet lacunarity process. An alternative correction method is proposed based on a stochastic description of the arrival-duration-intensity processes in coherence with the Poissonian Rectangular Pulse scheme (PRP) (Eagleson, 1972). In this proposed approach, the Q-Q transform is applied to the PRP variables derived from the daily rainfall datasets. Consequently the corrected PRP parameters are used for the synthetic generation of statistically homogeneous rainfall time series that mimic the persistency of daily observations for the reference period. Then the PRP parameters are forced through the GCM scenarios to generate local scale rainfall records for the 21st century. The
Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.
2017-07-01
One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.
von der Linden, Anja; Applegate, Douglas E; Kelly, Patrick L; Allen, Steven W; Ebeling, Harald; Burchat, Patricia R; Burke, David L; Donovan, David; Morris, R Glenn; Blandford, Roger; Erben, Thomas; Mantz, Adam
2012-01-01
This is the first in a series of papers in which we measure accurate weak-lensing masses for 51 of the most X-ray luminous galaxy clusters known at redshifts 0.15
Leuze Michael
2009-07-01
Full Text Available Abstract Background The Centers for Disease Control and Prevention's (CDC's BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Our study focused on finding useful anomalies at manageable alert rates according to available BioSense data history. Methods The study dataset included more than 3 years of daily counts of military outpatient clinic visits for respiratory and rash syndrome groupings. We applied four spatial estimation methods in implementations of space-time scan statistics cross-checked in Matlab and C. We compared the utility of these methods according to the resultant background cluster rate (a false alarm surrogate and sensitivity to injected cluster signals. The comparison runs used a spatial resolution based on the facility zip code in the patient record and a finer resolution based on the residence zip code. Results Simple estimation methods that account for day-of-week (DOW data patterns yielded a clear advantage both in background cluster rate and in signal sensitivity. A 28-day baseline gave the most robust results for this estimation; the preferred baseline is long enough to remove daily fluctuations but short enough to reflect recent disease trends and data representation. Background cluster rates were lower for the rash syndrome counts than for the respiratory counts, likely because of seasonality and the large scale of the respiratory counts. Conclusion The spatial estimation method should be chosen according to characteristics of the selected data streams. In this dataset with strong day-of-week effects, the overall best detection performance was achieved using subregion averages over a 28-day baseline stratified by weekday or weekend/holiday behavior. Changing the estimation method for particular scenarios involving
Constraining ultra-compact dwarf galaxy formation with galaxy clusters in the local universe
Pfeffer, Joel; Baumgardt, Holger; Griffen, Brendan F
2016-01-01
We compare the predictions of a semi-analytic model for ultra-compact dwarf galaxy (UCD) formation by tidal stripping to the observed properties of globular clusters (GCs) and UCDs in the Fornax and Virgo clusters. For Fornax we find the predicted number of stripped nuclei agrees very well with the excess number of GCs$+$UCDs above the GC luminosity function. GCs$+$UCDs with masses $>10^{7.3}$ M$_\\odot$ are consistent with being entirely formed by tidal stripping. Stripped nuclei can also account for Virgo UCDs with masses $>10^{7.3}$ M$_\\odot$ where numbers are complete by mass. For both Fornax and Virgo, the predicted velocity dispersions and radial distributions of stripped nuclei are consistent with that of UCDs within $\\sim$50-100 kpc but disagree at larger distances where dispersions are too high and radial distributions too extended. Stripped nuclei are predicted to have radially biased anisotropies at all radii, agreeing with Virgo UCDs at clustercentric distances larger than 50 kpc. However, ongoing ...
Application of Grey Relational Cluster Method in Muon Tomography for Materials Detection
无
2011-01-01
When the number of particles is small, We try to use grey system theory better in dealing the work which has little sample and incomplete information. Grey relational cluster method is applied for materials detection of the research of Muon tomography
Non-Hierarchical Clustering as a method to analyse an open-ended ...
Apple
tests, provide instructors with tools to probe students' conceptual knowledge of various fields of science and ... quantitative non-hierarchical clustering analysis method known as k-means (Everitt, Landau, Leese & Stahl, ...... undergraduate engineering students in creating ... mathematics-formal reasoning and the contextual.
Spatial and Spectral Methods for Weed Detection and Localization
Truchetet Frédéric
2002-01-01
Full Text Available This study concerns the detection and localization of weed patches in order to improve the knowledge on weed-crop competition. A remote control aircraft provided with a camera allowed to obtain low cost and repetitive information. Different processings were involved to detect weed patches using spatial then spectral methods. First, a shift of colorimetric base allowed to separate the soil and plant pixels. Then, a specific algorithm including Gabor filter was applied to detect crop rows on the vegetation image. Weed patches were then deduced from the comparison of vegetation and crop images. Finally, the development of a multispectral acquisition device is introduced. First results for the discrimination of weeds and crops using the spectral properties are shown from laboratory tests. Application of neural networks were mostly studied.
Lange, Oliver; Meyer-Baese, Anke; Wismuller, Axel; Hurdal, Monica
2005-03-01
We employ unsupervised clustering techniques for the analysis of dynamic contrast-enhanced perfusion MRI time-series in patients with and without stroke. "Neural gas" network, fuzzy clustering based on deterministic annealing, self-organizing maps, and fuzzy c-means clustering enable self-organized data-driven segmentation w.r.t.fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.
An ASIFT-Based Local Registration Method for Satellite Imagery
Xiangjun Wang
2015-05-01
Full Text Available Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion. Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE and better distribution of feature points.
1987-06-26
BUREAU OF STANDAR-S1963-A Nw BOM -ILE COPY -. 4eo .?3sa.9"-,,A WIN* MAT HEMATICAL SCIENCES _*INSTITUTE AD-A184 687 DTICS!ELECTE ANNOTATED COMPUTER OUTPUT...intoduction to the use of mixture models in clustering. Cornell University Biometrics Unit Technical Report BU-920-M and Mathematical Sciences Institute...mixture method and two comparable methods from SAS. Cornell University Biometrics Unit Technical Report BU-921-M and Mathematical Sciences Institute
Zheng, Ying; Yeh, Chen-Wei; Yang, Chi-Da; Jang, Shi-Shang; Chu, I-Ming
2007-08-31
Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach-information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.
The Application of High-Level Iterative Coupled-Cluster Methods to the Cytosine Molecule
Kowalski, Karol; Valiev, Marat
2008-06-19
The need for inclusion higher-order correlation effects for adequate description of the excitation energies of the DNA bases became clear in the last few years. In particular, we demonstrated that there is a sizable effect of triply excited configurations estimated in a non-iterative manner on the coupled-cluster excitation energies of the cytosine molecule in DNA environment. In this paper we discuss the accuracies of the non-iterative methods for biologically relevant systems in realistic environment in comparison with interative formulations that explicitly include the effect of triply excited clusters.
A simple and fast method to determine the parameters for fuzzy c-means cluster analysis
Schwämmle, Veit; Jensen, Ole Nørregaard
2010-01-01
MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values...... on the main properties of the dataset. Taking the dimension of the set and the number of objects as input values instead of evaluating the entire dataset allows us to propose a functional relationship determining the fuzzifier directly. This result speaks strongly against using a predefined fuzzifier...
Alvarez Eugenia
2005-06-01
Full Text Available Abstract Background There is a low incidence of malaria in Iquitos, Peru, suburbs detected by passive case-detection. This low incidence might be attributable to infections clustered in some households/regions and/or undetected asymptomatic infections. Methods Passive case-detection (PCD during the malaria season (February-July and an active case-detection (ACD community-wide survey (March surveyed 1,907 persons. Each month, April-July, 100-metre at-risk zones were defined by location of Plasmodium falciparum infections in the previous month. Longitudinal ACD and PCD (ACP+PCD occurred within at-risk zones, where 137 houses (573 persons were randomly selected as sentinels, each with one month of weekly active sampling. Entomological captures were conducted in the sentinel houses. Results The PCD incidence was 0.03 P. falciparum and 0.22 Plasmodium vivax infections/person/malaria-season. However, the ACD+PCD prevalence was 0.13 and 0.39, respectively. One explanation for this 4.33 and 1.77-fold increase, respectively, was infection clustering within at-risk zones and contiguous households. Clustering makes PCD, generalized to the entire population, artificially low. Another attributable-factor was that only 41% and 24% of the P. falciparum and P. vivax infections were associated with fever and 80% of the asymptomatic infections had low-density or absent parasitaemias the following week. After accounting for asymptomatic infections, a 2.6-fold increase in ACD+PCD versus PCD was attributable to clustered transmission in at-risk zones. Conclusion Even in low transmission, there are frequent highly-clustered asymptomatic infections, making PCD an inadequate measure of incidence. These findings support a strategy of concentrating ACD and insecticide campaigns in houses adjacent to houses were malaria was detected one month prior.
A method for clustering of miRNA sequences using fragmented programming
Ivashchenko, Anatoly; Pyrkova, Anna; Niyazova, Raigul
2016-01-01
Clustering of miRNA sequences is an important problem in molecular genetics associated cellular biology. Thousands of such sequences are known today through advancement in sophisticated molecular tools, sequencing techniques, computational resources and rule based mathematical models. Analysis of such large-scale miRNA sequences for inferring patterns towards deducing cellular function is a great challenge in modern molecular biology. Therefore, it is of interest to develop mathematical models specific for miRNA sequences. The process is to group (cluster) such miRNA sequences using well-defined known features. We describe a method for clustering of miRNA sequences using fragmented programming. Subsequently, we illustrated the utility of the model using a dendrogram (a tree diagram) for publically known A.thaliana miRNA nucleotide sequences towards the inference of observed conserved patterns PMID:27212839
Parallel recovery method in shared-nothing spatial database cluster system
YOU Byeong-seob; KIM Myung-keun; ZOU Yong-gui; BAE Hae-young
2004-01-01
Shared-nothing spatial database cluster system provides high availability since a replicated node can continue service even if any node in cluster system was crashed.However if the failed node wouldn't be recovered quickly, whole system performance will decrease since the other nodes must process the queries which the failed node may be processed. Therefore the recovery of cluster system is very important to provide the stable service. In most previous proposed techniques, external logs should be recorded in all nodes even if the failed node does not exist. So update transactions are processed slowly.Also recovery time of the failed node increases since a single storage for all database is used to record external logs in each node. Therefore we propose a parallel recovery method for recovering the failed node quickly.
Li, Bing Nan; Chui, Chee Kong; Chang, Stephen; Ong, S H
2011-01-01
The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
Kafieh, Rahele; Mehridehnavi, Alireza
2013-01-01
In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. In order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. Therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. The destination is finding the most powerful method according to a pre-specified criterion determined with numerical methods and matrix similarity measures. Furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. Therefore, we proposed Hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. The results show the remarkable ability of Rayleigh mixture model in comparison with other methods in reliability analysis task. PMID:24083134
Galaxy Cluster Mass Reconstruction Project: I. Methods and first results on galaxy-based techniques
Old, L; Pearce, F R; Croton, D; Muldrew, S I; Muñoz-Cuartas, J C; Gifford, D; Gray, M E; von der Linden, A; Mamon, G A; Merrifield, M R; Müller, V; Pearson, R J; Ponman, T J; Saro, A; Sepp, T; Sifón, C; Tempel, E; Tundo, E; Wang, Y O; Wojtak, R
2014-01-01
This paper is the first in a series in which we perform an extensive comparison of various galaxy-based cluster mass estimation techniques that utilise the positions, velocities and colours of galaxies. Our primary aim is to test the performance of these cluster mass estimation techniques on a diverse set of models that will increase in complexity. We begin by providing participating methods with data from a simple model that delivers idealised clusters, enabling us to quantify the underlying scatter intrinsic to these mass estimation techniques. The mock catalogue is based on a Halo Occupation Distribution (HOD) model that assumes spherical Navarro, Frenk and White (NFW) haloes truncated at R_200, with no substructure nor colour segregation, and with isotropic, isothermal Maxwellian velocities. We find that, above 10^14 M_solar, recovered cluster masses are correlated with the true underlying cluster mass with an intrinsic scatter of typically a factor of two. Below 10^14 M_solar, the scatter rises as the nu...
Assessing the Eutrophication of Shengzhong Reservoir Based on Grey Clustering Method
Pan An; Hu Lihui; Li Tesong; Li Chengzhu
2009-01-01
Reservoir water environment is a grey system.The grey clustering method is applied to assessing the reservoir water envi-ronment to establish a relatively complete model suitable for the reservoir eutrophication evaluation and appropriately evaluate the quality of reservoir water, providing evidence for reservoir man-agement.According to Chiua's lakes and reservoir eutrophication criteria and the characteristics of China's entrophication, as well as certain evaluation indices, the degree of eutrophication is classified into six categories with the utilization of grey classified whitening weight function to represent the boundaries of classification, to determine the clustering weight and clustering coefficient of each index in grey classifications, and the classification of each cluster-lag object.The comprehensive evaluation of reservoir eutrophica-tion is established on such a foundation, with Sichuan Shengzhong Reservoir as the survey object and the analysis of the data attained by several typical monitoring points there in 2006.It is found that eutrophication of Tiebian Power Generation Station, Guoyu-anchang and Dashiqiao Bridge is the heaviest, Tielusi and Qing-gangya the second, and Lijiaba the least.The eutrophication of this reservoir is closely relevant to the irrational exploitation in its surrounding areas, especially to the aggravation of the non-point source pollution and the increase of net-culture fishing.Therefore, it is feasible to use grey clustering in environment quality evalu-ation, and the point lies in the correct division of grey whitening function
IP2P K-means: an efficient method for data clustering on sensor networks
Peyman Mirhadi
2013-03-01
Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.
Bucher, M.; Delabrouille, J.; Giraud-Héraud, Y.;
2011-01-01
corresponding to a total density contrast of 500. Combining these high quality Planck measurements with deep XMM-Newton X-ray data, we investigate the relations between DA2 Y500, the integrated Compton parameter due to the SZ effect, and the X-ray-derived gas mass M g,500, temperature TX, luminosity LX,500, SZ...... signal analogue YX,500 = Mg,500 × TX, and total mass M500. After correction for the effect of selection bias on the scaling relations, we find results that are in excellent agreement with both X-ray predictions and recently-published ground-based data derived from smaller samples. The present data yield......We present precise Sunyaev-Zeldovich (SZ) effect measurements in the direction of 62 nearby galaxy clusters (z mass, 2 × 1014 M mass...
Clustering of local group distances: Publication bias or correlated measurements? II. M31 and beyond
De Grijs, Richard [Kavli Institute for Astronomy and Astrophysics, Peking University, Yi He Yuan Lu 5, Hai Dian District, Beijing 100871 (China); Bono, Giuseppe [Dipartimento di Fisica, Università di Roma Tor Vergata, via Della Ricerca Scientifica 1, I-00133, Roma (Italy)
2014-07-01
The accuracy of extragalactic distance measurements ultimately depends on robust, high-precision determinations of the distances to the galaxies in the local volume. Following our detailed study addressing possible publication bias in the published distance determinations to the Large Magellanic Cloud (LMC), here we extend our distance range of interest to include published distance moduli to M31 and M33, as well as to a number of their well-known dwarf galaxy companions. We aim at reaching consensus on the best, most homogeneous, and internally most consistent set of Local Group distance moduli to adopt for future, more general use based on the largest set of distance determinations to individual Local Group galaxies available to date. Based on a careful, statistically weighted combination of the main stellar population tracers (Cepheids, RR Lyrae variables, and the magnitude of the tip of the red-giant branch), we derive a recommended distance modulus to M31 of (m−M){sub 0}{sup M31}=24.46±0.10 mag—adopting as our calibration an LMC distance modulus of (m−M){sub 0}{sup LMC}=18.50 mag—and a fully internally consistent set of benchmark distances to key galaxies in the local volume, enabling us to establish a robust and unbiased, near-field extragalactic distance ladder.
Qunyi Xie
2016-01-01
Full Text Available Content-based image retrieval has recently become an important research topic and has been widely used for managing images from repertories. In this article, we address an efficient technique, called MNGS, which integrates multiview constrained nonnegative matrix factorization (NMF and Gaussian mixture model- (GMM- based spectral clustering for image retrieval. In the proposed methodology, the multiview NMF scheme provides competitive sparse representations of underlying images through decomposition of a similarity-preserving matrix that is formed by fusing multiple features from different visual aspects. In particular, the proposed method merges manifold constraints into the standard NMF objective function to impose an orthogonality constraint on the basis matrix and satisfy the structure preservation requirement of the coefficient matrix. To manipulate the clustering method on sparse representations, this paper has developed a GMM-based spectral clustering method in which the Gaussian components are regrouped in spectral space, which significantly improves the retrieval effectiveness. In this way, image retrieval of the whole database translates to a nearest-neighbour search in the cluster containing the query image. Simultaneously, this study investigates the proof of convergence of the objective function and the analysis of the computational complexity. Experimental results on three standard image datasets reveal the advantages that can be achieved with the proposed retrieval scheme.
Methods for accurate analysis of galaxy clustering on non-linear scales
Vakili, Mohammadjavad
2017-01-01
Measurements of galaxy clustering with the low-redshift galaxy surveys provide sensitive probe of cosmology and growth of structure. Parameter inference with galaxy clustering relies on computation of likelihood functions which requires estimation of the covariance matrix of the observables used in our analyses. Therefore, accurate estimation of the covariance matrices serves as one of the key ingredients in precise cosmological parameter inference. This requires generation of a large number of independent galaxy mock catalogs that accurately describe the statistical distribution of galaxies in a wide range of physical scales. We present a fast method based on low-resolution N-body simulations and approximate galaxy biasing technique for generating mock catalogs. Using a reference catalog that was created using the high resolution Big-MultiDark N-body simulation, we show that our method is able to produce catalogs that describe galaxy clustering at a percentage-level accuracy down to highly non-linear scales in both real-space and redshift-space.In most large-scale structure analyses, modeling of galaxy bias on non-linear scales is performed assuming a halo model. Clustering of dark matter halos has been shown to depend on halo properties beyond mass such as halo concentration, a phenomenon referred to as assembly bias. Standard large-scale structure studies assume that halo mass alone is sufficient in characterizing the connection between galaxies and halos. However, modeling of galaxy bias can face systematic effects if the number of galaxies are correlated with other halo properties. Using the Small MultiDark-Planck high resolution N-body simulation and the clustering measurements of Sloan Digital Sky Survey DR7 main galaxy sample, we investigate the extent to which the dependence of galaxy bias on halo concentration can improve our modeling of galaxy clustering.
Javad Aramideh
2014-11-01
Full Text Available Wireless sensor networks have attracted attention of researchers considering their abundant applications. One of the important issues in this network is limitation of energy consumption which is directly related to life of the network. One of the main works which have been done recently to confront with this problem is clustering. In this paper, an attempt has been made to present clustering method which performs clustering in two stages. In the first stage, it specifies candidate nodes for being head cluster with fuzzy method and in the next stage, the node of the head cluster is determined among the candidate nodes with cellular learning automata. Advantage of the clustering method is that clustering has been done based on three main parameters of the number of neighbors, energy level of nodes and distance between each node and sink node which results in selection of the best nodes as a candidate head of cluster nodes. Connectivity of network is also evaluated in the second part of head cluster determination. Therefore, more energy will be stored by determining suitable head clusters and creating balanced clusters in the network and consequently, life of the network increases.
Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.
Weber, Lukas M; Robinson, Mark D
2016-12-01
Recent technological developments in high-dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by "manual gating" can be inefficient and unreliable in these high-dimensional settings, which has led to the development of a large number of automated analysis methods. Methods designed for unsupervised analysis use specialized clustering algorithms to detect and define cell populations for further downstream analysis. Here, we have performed an up-to-date, extensible performance comparison of clustering methods for high-dimensional flow and mass cytometry data. We evaluated methods using several publicly available data sets from experiments in immunology, containing both major and rare cell populations, with cell population identities from expert manual gating as the reference standard. Several methods performed well, including FlowSOM, X-shift, PhenoGraph, Rclusterpp, and flowMeans. Among these, FlowSOM had extremely fast runtimes, making this method well-suited for interactive, exploratory analysis of large, high-dimensional data sets on a standard laptop or desktop computer. These results extend previously published comparisons by focusing on high-dimensional data and including new methods developed for CyTOF data. R scripts to reproduce all analyses are available from GitHub (https://github.com/lmweber/cytometry-clustering-comparison), and pre-processed data files are available from FlowRepository (FR-FCM-ZZPH), allowing our comparisons to be extended to include new clustering methods and reference data sets. © 2016 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.
Periodic local MP2 method employing orbital specific virtuals
Usvyat, Denis, E-mail: denis.usvyat@chemie.uni-regensburg.de; Schütz, Martin, E-mail: martin.schuetz@chemie.uni-regensburg.de [Institute for Physical and Theoretical Chemistry, Universität Regensburg, Universitätsstraße 31, D-93040 Regensburg (Germany); Maschio, Lorenzo, E-mail: lorenzo.maschio@unito.it [Dipartimento di Chimica, and Centre of Excellence NIS (Nanostructured Interfaces and Surfaces), Università di Torino, via Giuria 5, I-10125 Torino (Italy)
2015-09-14
We introduce orbital specific virtuals (OSVs) to represent the truncated pair-specific virtual space in periodic local Møller-Plesset perturbation theory of second order (LMP2). The OSVs are constructed by diagonalization of the LMP2 amplitude matrices which correspond to diagonal Wannier-function (WF) pairs. Only a subset of these OSVs is adopted for the subsequent OSV-LMP2 calculation, namely, those with largest contribution to the diagonal pair correlation energy and with the accumulated value of these contributions reaching a certain accuracy. The virtual space for a general (non diagonal) pair is spanned by the union of the two OSV sets related to the individual WFs of the pair. In the periodic LMP2 method, the diagonal LMP2 amplitude matrices needed for the construction of the OSVs are calculated in the basis of projected atomic orbitals (PAOs), employing very large PAO domains. It turns out that the OSVs are excellent to describe short range correlation, yet less appropriate for long range van der Waals correlation. In order to compensate for this bias towards short range correlation, we augment the virtual space spanned by the OSVs by the most diffuse PAOs of the corresponding minimal PAO domain. The Fock and overlap matrices in OSV basis are constructed in the reciprocal space. The 4-index electron repulsion integrals are calculated by local density fitting and, for distant pairs, via multipole approximation. New procedures for determining the fit-domains and the distant-pair lists, leading to higher efficiency in the 4-index integral evaluation, have been implemented. Generally, and in contrast to our previous PAO based periodic LMP2 method, the OSV-LMP2 method does not require anymore great care in the specification of the individual domains (to get a balanced description when calculating energy differences) and is in that sense a black box procedure. Discontinuities in potential energy surfaces, which may occur for PAO-based calculations if one is not
Discrete Spectral Local Measurement Method for Testing Solar Concentrators
Huifu Zhao
2012-01-01
Full Text Available In order to compensate for the inconvenience and instability of outdoor photovoltaic concentration test system which are caused by the weather changes, we design an indoor concentration test system with a large caliber and a high parallelism, and then verify its feasibility and scientificity. Furthermore, we propose a new concentration test method: the discrete spectral local measurement method. A two-stage Fresnel concentration system is selected as the test object. The indoor and the outdoor concentration experiments are compared. The results show that the outdoor concentration efficiency of the two-stage Fresnel concentration system is 85.56%, while the indoor is 85.45%. The two experimental results are so close that we can verify the scientificity and feasibility of the indoor concentration test system. The light divergence angle of the indoor concentration test system is 0.267° which also matches with sunlight divergence angle. The indoor concentration test system with large diameter (145 mm, simple structure, and low cost will have broad applications in solar concentration field.
William E Stutz
Full Text Available Genes of the vertebrate major histocompatibility complex (MHC are of great interest to biologists because of their important role in immunity and disease, and their extremely high levels of genetic diversity. Next generation sequencing (NGS technologies are quickly becoming the method of choice for high-throughput genotyping of multi-locus templates like MHC in non-model organisms. Previous approaches to genotyping MHC genes using NGS technologies suffer from two problems:1 a "gray zone" where low frequency alleles and high frequency artifacts can be difficult to disentangle and 2 a similar sequence problem, where very similar alleles can be difficult to distinguish as two distinct alleles. Here were present a new method for genotyping MHC loci--Stepwise Threshold Clustering (STC--that addresses these problems by taking full advantage of the increase in sequence data provided by NGS technologies. Unlike previous approaches for genotyping MHC with NGS data that attempt to classify individual sequences as alleles or artifacts, STC uses a quasi-Dirichlet clustering algorithm to cluster similar sequences at increasing levels of sequence similarity. By applying frequency and similarity based criteria to clusters rather than individual sequences, STC is able to successfully identify clusters of sequences that correspond to individual or similar alleles present in the genomes of individual samples. Furthermore, STC does not require duplicate runs of all samples, increasing the number of samples that can be genotyped in a given project. We show how the STC method works using a single sample library. We then apply STC to 295 threespine stickleback (Gasterosteus aculeatus samples from four populations and show that neighboring populations differ significantly in MHC allele pools. We show that STC is a reliable, accurate, efficient, and flexible method for genotyping MHC that will be of use to biologists interested in a variety of downstream applications.
The morphing method as a flexible tool for adaptive local/non-local simulation of static fracture
Azdoud, Yan
2014-04-19
We introduce a framework that adapts local and non-local continuum models to simulate static fracture problems. Non-local models based on the peridynamic theory are promising for the simulation of fracture, as they allow discontinuities in the displacement field. However, they remain computationally expensive. As an alternative, we develop an adaptive coupling technique based on the morphing method to restrict the non-local model adaptively during the evolution of the fracture. The rest of the structure is described by local continuum mechanics. We conduct all simulations in three dimensions, using the relevant discretization scheme in each domain, i.e., the discontinuous Galerkin finite element method in the peridynamic domain and the continuous finite element method in the local continuum mechanics domain. © 2014 Springer-Verlag Berlin Heidelberg.
A Localization Method for the Internet of Things
Chen, Zhikui; Huang, Tao; Bu, Fanyu; Wang, Haozhe; 10.1007/s11227-011-0693-2
2012-01-01
Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization solutions, which increase the cost and considerably limit the location-based applications. The Internet of Things (IOT) integrates many technologies, such as Internet, Zigbee, Bluetooth, infrared, WiFi, GPRS, 3G, etc, which can enable different ways to obtain the location information of various objects. Location-based service is a primary service of the IOT, while localization accuracy is a key issue. In this paper, a higher accuracy localization scheme is proposed which can effectively satisfy diverse requirements for many indoor and outdoor location services. The proposed scheme composes of two phases: 1) partition phase, in which the target region is split into small grids; 2) localization refinement phase, in which a higher accuracy of localization can ...
Satoh, Kazuhiro; Okabe, Yutaka
1993-01-01
Numerical study is done on a critical phenomenon in a neural network model of the McCulloch-Pitts type. Such a net, one of excitable media, consists of “neurons” (binary decision elements) each of which randomly sits on a square lattice and is connected to its four neighbors. When the net is activated locally, the “fire” spreads over from the origin according to the deterministic rule. After transient, a self-sustained mode of excitation (time-periodic firing pattern) is established. It is found that a size of the largest excitation tends to diverge as the excitability of the net is increased (a localization-delocalization transition). Numerically evaluated power-law exponents suggest that the criticality of such transition belongs to the same universality class of the percolation transition.
Clustering of Local Group distances: publication bias or correlated measurements? II. M31 and beyond
de Grijs, Richard
2014-01-01
The accuracy of extragalactic distance measurements ultimately depends on robust, high-precision determinations of the distances to the galaxies in the local volume. Following our detailed study addressing possible publication bias in the published distance determinations to the Large Magellanic Cloud (LMC), here we extend our distance range of interest to include published distance moduli to M31 and M33, as well as to a number of their well-known dwarf galaxy companions. We aim at reaching consensus on the best, most homogeneous, and internally most consistent set of Local Group distance moduli to adopt for future, more general use based on the largest set of distance determinations to individual Local Group galaxies available to date. Based on a careful, statistically weighted combination of the main stellar population tracers (Cepheids, RR Lyrae variables, and the magnitude of the tip of the red-giant branch), we derive a recommended distance modulus to M31 of $(m-M)_0^{\\rm M31} = 24.46 \\pm 0.10$ mag---ado...
Are fragment-based quantum chemistry methods applicable to medium-sized water clusters?
Yuan, Dandan; Shen, Xiaoling; Li, Wei; Li, Shuhua
2016-06-28
Fragment-based quantum chemistry methods are either based on the many-body expansion or the inclusion-exclusion principle. To compare the applicability of these two categories of methods, we have systematically evaluated the performance of the generalized energy based fragmentation (GEBF) method (J. Phys. Chem. A, 2007, 111, 2193) and the electrostatically embedded many-body (EE-MB) method (J. Chem. Theory Comput., 2007, 3, 46) for medium-sized water clusters (H2O)n (n = 10, 20, 30). Our calculations demonstrate that the GEBF method provides uniformly accurate ground-state energies for 10 low-energy isomers of three water clusters under study at a series of theory levels, while the EE-MB method (with one water molecule as a fragment and without using the cutoff distance) shows a poor convergence for (H2O)20 and (H2O)30 when the basis set contains diffuse functions. Our analysis shows that the neglect of the basis set superposition error for each subsystem has little effect on the accuracy of the GEBF method, but leads to much less accurate results for the EE-MB method. The accuracy of the EE-MB method can be dramatically improved by using an appropriate cutoff distance and using two water molecules as a fragment. For (H2O)30, the average deviation of the EE-MB method truncated up to the three-body level calculated using this strategy (relative to the conventional energies) is about 0.003 hartree at the M06-2X/6-311++G** level, while the deviation of the GEBF method with a similar computational cost is less than 0.001 hartree. The GEBF method is demonstrated to be applicable for electronic structure calculations of water clusters at any basis set.
a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis
Huang, W.; Li, S.; Xu, S.
2016-06-01
How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the
A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS
W. Huang
2016-06-01
Full Text Available How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time to four dimensions (space, time and semantics. More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The
AptaCluster - A Method to Cluster HT-SELEX Aptamer Pools and Lessons from its Application.
Hoinka, Jan; Berezhnoy, Alexey; Sauna, Zuben E; Gilboa, Eli; Przytycka, Teresa M
2014-01-01
Systematic Evolution of Ligands by EXponential Enrichment (SELEX) is a well established experimental procedure to identify aptamers - synthetic single-stranded (ribo)nucleic molecules that bind to a given molecular target. Recently, new sequencing technologies have revolutionized the SELEX protocol by allowing for deep sequencing of the selection pools after each cycle. The emergence of High Throughput SELEX (HT-SELEX) has opened the field to new computational opportunities and challenges that are yet to be addressed. To aid the analysis of the results of HT-SELEX and to advance the understanding of the selection process itself, we developed AptaCluster. This algorithm allows for an efficient clustering of whole HT-SELEX aptamer pools; a task that could not be accomplished with traditional clustering algorithms due to the enormous size of such datasets. We performed HT-SELEX with Interleukin 10 receptor alpha chain (IL-10RA) as the target molecule and used AptaCluster to analyze the resulting sequences. AptaCluster allowed for the first survey of the relationships between sequences in different selection rounds and revealed previously not appreciated properties of the SELEX protocol. As the first tool of this kind, AptaCluster enables novel ways to analyze and to optimize the HT-SELEX procedure. Our AptaCluster algorithm is available as a very fast multiprocessor implementation upon request.
Korkusuz Öztürk, Yasemin; Meral Özel, Nurcan
2016-04-01
Extensional focal mechanism solutions are mostly observed even in the Central Marmara by this comprehensive research although the main Marmara Fault that is the western branch of the NAF, is dominated by a right lateral strike-slip regime. Marmara Region, a seismically very active area, is located at the western section of the North Anatolian Fault Zone (NAFZ). The 1912 Mürefte and 1999 Izmit earthquakes are the last devastating events of the western and eastern sections of this region, respectively. The region between the locations of these earthquakes, is prone to a large earthquake. Therefore, the analysis of the Sea of Marmara is significant. The main objective of this research is to determine earthquake hypocenters and focal mechanism solutions accurately, hence we obtain recent states of stresses for this region. Accordingly, this research aims to define branches of fault structures and its geometrical orientations in the Sea of Marmara. In this study, a cluster of events in the Central Marmara is analyzed using hypocenter program as a usual location technique. In addition, these events and other clustered events (Korkusuz Öztürk et al., 2015) are relocated using HYPODD relocation procedure. Even though NAF is mostly dominated by a right lateral strike slip fault, we found out many extensional source mechanisms. Also, from the comparison of relocation results of hypocenter and HYPODD programs, it is found out that most of the relocations have the same orientations and dipping angles of the segments of the main Marmara Fault are not clear. As a result, since we observe many normal faulting mechanisms in the Sea of Marmara, we expect to observe some deviations in orientations of vertical orientations of the fault segments comparing a dip-slip model. Therefore, this research will continue to clearly identify fault dip angles of main fault segments in Marmara Sea. Further, our sensitive relocation and stress analyses will make an important contribution to a
Stroobant, M.; Locritani, M.; Marini, D.; Sabbadini, L.; Carmisciano, C.; Manzella, G.; Magaldi, M.; Aliani, S.
2012-04-01
DLTM is the Ligurian Region (north Italy) cluster of Centre of Excellence (CoE) in waterborne technologies, that involves about 120 enterprises - of which, more than 100 SMEs -, the University of Genoa, all the main National Research Centres dealing with maritime and marine technologies established in Liguria (CNR, INGV, ENEA-UTMAR), the NATO Undersea Research Centre (NURC) and the Experimental Centre of the Italian Navy (CSSN), the Bank, the Port Authority and the Chamber of Commerce of the city of La Spezia. Following its mission, DLTM has recently established three Collaborative Research Laboratories focused on: 1. Computational Fluid dynamics (CFD_Lab) 2. High Performance Computing (HPC_Lab) 3. Monitoring and Analysis of Marine Ecosystems (MARE_Lab). The main role of them is to improve the relationships among the research centres and the enterprises, encouraging a systematic networking approach and sharing of knowledge, data, services, tools and human resources. Two of the key objectives of Lab_MARE are the establishment of: - an integrated system of observation and sea forecasting; - a Regional Marine Instrument Centre (RMIC) for oceanographic and metereological instruments (assembled using 'shared' tools and facilities). Besides, an important and innovative research project has been recently submitted to the Italian Ministry for Education, University and Research (MIUR). This project, in agreement with the European Directives (COM2009 (544)), is aimed to develop a Management Information System (MIS) for oceanographic and meteorological data in the Mediterranean Sea. The availability of adequate HPC inside DLTM is, of course, an important asset for achieving useful results; for example, the Regional Ocean Modeling System (ROMS) model is currently running on a high-resolution mesh on the cluster to simulate and reproduce the circulation within the Ligurian Sea. ROMS outputs will have broad and multidisciplinary impacts because ocean circulation affects the
A new method to assign galaxy cluster membership using photometric redshifts
Castignani, Gianluca
2016-01-01
We introduce a new effective strategy to assign group and cluster membership probabilities $P_{mem}$ to galaxies using photometric redshift information. Large dynamical ranges both in halo mass and cosmic time are considered. The method takes the magnitude distribution of both cluster and field galaxies as well as the radial distribution of galaxies in clusters into account using a non-parametric formalism and relies on Bayesian inference to take photometric redshift uncertainties into account. We successfully test the method against 1,208 galaxy clusters within redshifts $z=0.05-2.55$ and masses $10^{13.29-14.80}~M_\\odot$ drawn from wide field simulated galaxy mock catalogs developed for the Euclid mission. Median purity $(55^{+17}_{-15})\\%$ and completeness $(95^{+5}_{-10})\\%$ are reached for galaxies brighter than 0.25$L_\\ast$ within $r_{200}$ of each simulated halo and for a statistical photometric redshift accuracy $\\sigma((z_s-z_p)/(1+z_s))=0.03$. The mean values $\\overline{\\mathsf{p}}=56\\%$ and $\\overl...
Deepa Devasenapathy
2015-01-01
Full Text Available The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.
Pre-crash scenarios at road junctions: A clustering method for car crash data.
Nitsche, Philippe; Thomas, Pete; Stuetz, Rainer; Welsh, Ruth
2017-08-22
Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth "On-the-Spot" database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Cui Jia
2017-05-01
Full Text Available With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.
Cui, Jia; Hong, Bei; Jiang, Xuepeng; Chen, Qinghua
2017-05-01
With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.
Threshold selection for classification of MR brain images by clustering method
Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)
2015-12-07
Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.
Health state evaluation of shield tunnel SHM using fuzzy cluster method
Zhou, Fa; Zhang, Wei; Sun, Ke; Shi, Bin
2015-04-01
Shield tunnel SHM is in the path of rapid development currently while massive monitoring data processing and quantitative health grading remain a real challenge, since multiple sensors belonging to different types are employed in SHM system. This paper addressed the fuzzy cluster method based on fuzzy equivalence relationship for the health evaluation of shield tunnel SHM. The method was optimized by exporting the FSV map to automatically generate the threshold value. A new holistic health score(HHS) was proposed and its effectiveness was validated by conducting a pilot test. A case study on Nanjing Yangtze River Tunnel was presented to apply this method. Three types of indicators, namely soil pressure, pore pressure and steel strain, were used to develop the evaluation set U. The clustering results were verified by analyzing the engineering geological conditions; the applicability and validity of the proposed method was also demonstrated. Besides, the advantage of multi-factor evaluation over single-factor model was discussed by using the proposed HHS. This investigation indicated the fuzzy cluster method and HHS is capable of characterizing the fuzziness of tunnel health, and it is beneficial to clarify the tunnel health evaluation uncertainties.
On the local radio luminosity function of galaxies; 1, the Virgo cluster
Gavazzi, G
1999-01-01
We cross-correlate the galaxies brighter than mB=18 in the Virgo cluster with the radio sources in the NVSS survey (1.4 GHz), resulting in 180 radio-optical identifications. We determine the radio luminosity function of the Virgo galaxies, separately for the early- and late-types. Late-type galaxies develop radio sources with a probability proportional to their optical luminosity. In fact their radio/optical (RB) distribution is gaussian, centered at log RB=-0.5, i.e. the radio luminosity is 0.3 of the optical one. The probability of late-type galaxies to develop radio sources is almost independent of their detailed Hubble type, except for Sa (and S0+S0a) which are a factor of 5 less frequent than later types at any RB. Giant elliptical galaxies feed "monster" radio sources with a probability strongly increasing with mass. However the frequency of fainter radio sources is progressively less sensitive on the system mass. The faintest giant E galaxies (MB=-17) have a probability of feeding low power radio sourc...
Liu, Yi-Rong; Wen, Hui; Huang, Teng; Lin, Xiao-Xiao; Gai, Yan-Bo; Hu, Chang-Jin; Zhang, Wei-Jun; Huang, Wei
2014-01-16
Exploration of the low-lying structures of atomic or molecular clusters remains a fundamental problem in nanocluster science. Basin hopping is typically employed in conjunction with random motion, which is a perturbation of a local minimum structure. We have combined two different sampling technologies, "random sampling" and "compressed sampling", to explore the potential energy surface of molecular clusters. We used the method to study water, nitrate/water, and oxalate/water cluster systems at the MP2/aug-cc-pVDZ level of theory. An isomer of the NO3(-)(H2O)3 cluster molecule with a 3D structure was lower in energy than the planar structure, which had previously been reported by experimental study as the lowest-energy structure. The lowest-energy structures of the NO3(-)(H2O)5 and NO3(-)(H2O)7 clusters were found to have structures similar to pure (H2O)8 and (H2O)10 clusters, which contradicts previous experimental result by Wang et al.(J. Chem. Phys. 2002, 116, 561-570). The new minimum energy structures for C2O4(2-)(H2O)5 and C2O4(2-)(H2O)6 are found by our calculations.
An application of the KNND method for detecting nearby open clusters based on Gaia-DR1
Gao, Xin-Hua
2017-05-01
This paper presents a preliminary test of the k-th nearest neighbor distance (KNND) method for detecting nearby open clusters based on Gaia-DR1. We select 38 386 nearby stars (< 100 {pc}) from the Gaia-DR1 catalog, and then use the KNND method to detect overdense regions in three-dimensional space. We find two overdense regions (the Hyades and Coma Berenices (Coma Ber) open clusters), and obtain 57 reliable cluster members. Based on these cluster members, the distances to the Hyades and Coma Ber clusters are determined to be 46.0±0.2 and 83.5±0.3 pc, respectively. Our results demonstrate that the KNND method can be used to detect open clusters based on a large volume of astrometry data.
Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.
1985-01-01
The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.
Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.
1985-08-01
The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references.
Virtual local target method for avoiding local minimum in potential field based robot navigation.
Zou, Xi-Yong; Zhu, Jing
2003-01-01
A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation. Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.
Virtual local target method for avoiding local minimum in potential field based robot navigation
邹细勇; 诸静
2003-01-01
A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation. Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.