Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan
2018-02-01
Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.
The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
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.
Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis
Directory of Open Access Journals (Sweden)
Sen Zhang
2015-01-01
Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.
Electron localization in water clusters
International Nuclear Information System (INIS)
Landman, U.; Barnett, R.N.; Cleveland, C.L.; Jortner, J.
1987-01-01
Electron attachment to water clusters was explored by the quantum path integral molecular dynamics method, demonstrating that the energetically favored localization mode involves a surface state of the excess electron, rather than the precursor of the hydrated electron. The cluster size dependence, the energetics and the charge distribution of these novel electron-cluster surface states are explored. 20 refs., 2 figs., 1 tab
LENUS (Irish Health Repository)
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.
Guo, Yang; Becker, Ute; Neese, Frank
2018-03-01
Local correlation theories have been developed in two main flavors: (1) "direct" local correlation methods apply local approximation to the canonical equations and (2) fragment based methods reconstruct the correlation energy from a series of smaller calculations on subsystems. The present work serves two purposes. First, we investigate the relative efficiencies of the two approaches using the domain-based local pair natural orbital (DLPNO) approach as the "direct" method and the cluster in molecule (CIM) approach as the fragment based approach. Both approaches are applied in conjunction with second-order many-body perturbation theory (MP2) as well as coupled-cluster theory with single-, double- and perturbative triple excitations [CCSD(T)]. Second, we have investigated the possible merits of combining the two approaches by performing CIM calculations with DLPNO methods serving as the method of choice for performing the subsystem calculations. Our cluster-in-molecule approach is closely related to but slightly deviates from approaches in the literature since we have avoided real space cutoffs. Moreover, the neglected distant pair correlations in the previous CIM approach are considered approximately. Six very large molecules (503-2380 atoms) were studied. At both MP2 and CCSD(T) levels of theory, the CIM and DLPNO methods show similar efficiency. However, DLPNO methods are more accurate for 3-dimensional systems. While we have found only little incentive for the combination of CIM with DLPNO-MP2, the situation is different for CIM-DLPNO-CCSD(T). This combination is attractive because (1) the better parallelization opportunities offered by CIM; (2) the methodology is less memory intensive than the genuine DLPNO-CCSD(T) method and, hence, allows for large calculations on more modest hardware; and (3) the methodology is applicable and efficient in the frequently met cases, where the largest subsystem calculation is too large for the canonical CCSD(T) method.
Directory of Open Access Journals (Sweden)
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.
A Local Pair Natural Orbital-Based Multireference Mukherjee’s Coupled Cluster Method
Czech Academy of Sciences Publication Activity Database
Demel, Ondřej; Pittner, Jiří
2015-01-01
Roč. 11, č. 7 (2015), s. 3104-3114 ISSN 1549-9618 R&D Projects: GA ČR GAP208/11/2222; GA ČR(CZ) GJ15-00058Y Institutional support: RVO:61388955 Keywords : ELECTRON CORRELATION METHODS * BRILLOUIN-WIGNER * CONFIGURATION-INTERACTION Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 5.301, year: 2015
International Nuclear Information System (INIS)
Liska, J.; Svetlik, M.; Slama, K.
1982-01-01
The acoustic emission method is a promising tool for checking reactor pressure vessel integrity. Localization of emission sources is the first and the most important step in processing emission signals. The paper describes the emission sources localization method which is based on cluster analysis of a set of points depicting the emission events in the plane of coordinates of their occurrence. The method is based on using this set of points for constructing the minimum spanning tree and its partition into fragments corresponding to point clusters. Furthermore, the laws are considered of probability distribution of the minimum spanning tree edge length for one and several clusters with the aim of finding the optimum length of the critical edge for the partition of the tree. Practical application of the method is demonstrated on localizing the emission sources detected during a hydrotest of a pressure vessel used for testing the reactor pressure vessel covers. (author)
Clustering methods for the optimization of atomic cluster structure
Bagattini, Francesco; Schoen, Fabio; Tigli, Luca
2018-04-01
In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.
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.
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.
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
Information and Communication Technology Clusters, Local Firm ...
International Development Research Centre (IDRC) Digital Library (Canada)
Information and Communication Technology Clusters, Local Firm Performance, and Employment Generation. As countries steadily increase the share and value of knowledge, information, and services in their economies, governments have been crafting policies to attract foreign investment and establish large ...
Domain-Based Local Pair Natural Orbital Version of Mukherjee’s State-Specific Coupled Cluster Method
Czech Academy of Sciences Publication Activity Database
Brabec, Jiří; Lang, Jakub; Saitow, M.; Pittner, Jiří; Neese, F.; Demel, Ondřej
2018-01-01
Roč. 14, č. 3 (2018), s. 1370-1382 ISSN 1549-9618 R&D Projects: GA ČR GJ15-00058Y Institutional support: RVO:61388955 Keywords : MULTIREFERENCE PERTURBATION-THEORY * SINGLE-REFERENCE FORMALISM * ELECTRON CORRELATION METHODS Subject RIV: CF - Physical ; Theoretical Chemistry OBOR OECD: Physical chemistry Impact factor: 5.245, year: 2016
SUPERDENSE MASSIVE GALAXIES IN WINGS LOCAL CLUSTERS
International Nuclear Information System (INIS)
Valentinuzzi, T.; D'Onofrio, M.; Fritz, J.; Poggianti, B. M.; Bettoni, D.; Fasano, G.; Moretti, A.; Omizzolo, A.; Varela, J.; Cava, A.; Couch, W. J.; Dressler, A.; Moles, M.; Kjaergaard, P.; Vanzella, E.
2010-01-01
Massive quiescent galaxies at z > 1 have been found to have small physical sizes, and hence to be superdense. Several mechanisms, including minor mergers, have been proposed for increasing galaxy sizes from high- to low-z. We search for superdense massive galaxies in the WIde-field Nearby Galaxy-cluster Survey (WINGS) of X-ray selected galaxy clusters at 0.04 10 M sun , are mostly S0 galaxies, have a median effective radius (R e ) = 1.61 ± 0.29 kpc, a median Sersic index (n) = 3.0 ± 0.6, and very old stellar populations with a median mass-weighted age of 12.1 ± 1.3 Gyr. We calculate a number density of 2.9 x 10 -2 Mpc -3 for superdense galaxies in local clusters, and a hard lower limit of 1.3 x 10 -5 Mpc -3 in the whole comoving volume between z = 0.04 and z = 0.07. We find a relation between mass, effective radius, and luminosity-weighted age in our cluster galaxies, which can mimic the claimed evolution of the radius with redshift, if not properly taken into account. We compare our data with spectroscopic high-z surveys and find that-when stellar masses are considered-there is consistency with the local WINGS galaxy sizes out to z ∼ 2, while a discrepancy of a factor of 3 exists with the only spectroscopic z > 2 study. In contrast, there is strong evidence for a large evolution in radius for the most massive galaxies with M * > 4 x 10 11 M sun compared to similarly massive galaxies in WINGS, i.e., the brightest cluster galaxies.
Batista-Romero, Fidel A; Pajón-Suárez, Pedro; Bernal-Uruchurtu, Margarita I; Hernández-Lamoneda, Ramón
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(12)6(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.
Guo, Yang
2018-01-04
In this communication, an improved perturbative triples correction (T) algorithm for domain based local pair-natural orbital singles and doubles coupled cluster (DLPNO-CCSD) theory is reported. In our previous implementation, the semi-canonical approximation was used and linear scaling was achieved for both the DLPNO-CCSD and (T) parts of the calculation. In this work, we refer to this previous method as DLPNO-CCSD(T0) to emphasize the semi-canonical approximation. It is well-established that the DLPNO-CCSD method can predict very accurate absolute and relative energies with respect to the parent canonical CCSD method. However, the (T0) approximation may introduce significant errors in absolute energies as the triples correction grows up in magnitude. In the majority of cases, the relative energies from (T0) are as accurate as the canonical (T) results of themselves. Unfortunately, in rare cases and in particular for small gap systems, the (T0) approximation breaks down and relative energies show large deviations from the parent canonical CCSD(T) results. To address this problem, an iterative (T) algorithm based on the previous DLPNO-CCSD(T0) algorithm has been implemented [abbreviated here as DLPNO-CCSD(T)]. Using triples natural orbitals to represent the virtual spaces for triples amplitudes, storage bottlenecks are avoided. Various carefully designed approximations ease the computational burden such that overall, the increase in the DLPNO-(T) calculation time over DLPNO-(T0) only amounts to a factor of about two (depending on the basis set). Benchmark calculations for the GMTKN30 database show that compared to DLPNO-CCSD(T0), the errors in absolute energies are greatly reduced and relative energies are moderately improved. The particularly problematic case of cumulene chains of increasing lengths is also successfully addressed by DLPNO-CCSD(T).
Guo, Yang; Riplinger, Christoph; Becker, Ute; Liakos, Dimitrios G.; Minenkov, Yury; Cavallo, Luigi; Neese, Frank
2018-01-01
In this communication, an improved perturbative triples correction (T) algorithm for domain based local pair-natural orbital singles and doubles coupled cluster (DLPNO-CCSD) theory is reported. In our previous implementation, the semi-canonical approximation was used and linear scaling was achieved for both the DLPNO-CCSD and (T) parts of the calculation. In this work, we refer to this previous method as DLPNO-CCSD(T0) to emphasize the semi-canonical approximation. It is well-established that the DLPNO-CCSD method can predict very accurate absolute and relative energies with respect to the parent canonical CCSD method. However, the (T0) approximation may introduce significant errors in absolute energies as the triples correction grows up in magnitude. In the majority of cases, the relative energies from (T0) are as accurate as the canonical (T) results of themselves. Unfortunately, in rare cases and in particular for small gap systems, the (T0) approximation breaks down and relative energies show large deviations from the parent canonical CCSD(T) results. To address this problem, an iterative (T) algorithm based on the previous DLPNO-CCSD(T0) algorithm has been implemented [abbreviated here as DLPNO-CCSD(T)]. Using triples natural orbitals to represent the virtual spaces for triples amplitudes, storage bottlenecks are avoided. Various carefully designed approximations ease the computational burden such that overall, the increase in the DLPNO-(T) calculation time over DLPNO-(T0) only amounts to a factor of about two (depending on the basis set). Benchmark calculations for the GMTKN30 database show that compared to DLPNO-CCSD(T0), the errors in absolute energies are greatly reduced and relative energies are moderately improved. The particularly problematic case of cumulene chains of increasing lengths is also successfully addressed by DLPNO-CCSD(T).
Cluster-based global firms' use of local capabilities
DEFF Research Database (Denmark)
Andersen, Poul Houman; Bøllingtoft, Anne
2011-01-01
Purpose – Despite growing interest in clusters role for the global competitiveness of firms, there has been little research into how globalization affects cluster-based firms’ (CBFs) use of local knowledge resources and the combination of local and global knowledge used. Using the cluster......’s 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...... in three clusters strongly affected by increasing global division of labour. Findings – The paper suggests that globalization has affected how firms use local resources and combine local and global knowledge. Unexpectedly, clustered firms with explicit procedures and established global fora for exchanging...
Local Clusters in a Globalized World
DEFF Research Database (Denmark)
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. Thi...
Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.
2018-04-01
Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.
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.
Local Community Detection Algorithm Based on Minimal Cluster
Directory of Open Access Journals (Sweden)
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 expansion for ground states of local Hamiltonians
Directory of Open Access Journals (Sweden)
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-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.
Joint local and global consistency on interdocument and interword relationships for co-clustering.
Bao, Bing-Kun; Min, Weiqing; Li, Teng; Xu, Changsheng
2015-01-01
Co-clustering has recently received a lot of attention due to its effectiveness in simultaneously partitioning words and documents by exploiting the relationships between them. However, most of the existing co-clustering methods neglect or only partially reveal the interword and interdocument relationships. To fully utilize those relationships, the local and global consistencies on both word and document spaces need to be considered, respectively. Local consistency indicates that the label of a word/document can be predicted from its neighbors, while global consistency enforces a smoothness constraint on words/documents labels over the whole data manifold. In this paper, we propose a novel co-clustering method, called co-clustering via local and global consistency, to not only make use of the relationship between word and document, but also jointly explore the local and global consistency on both word and document spaces, respectively. The proposed method has the following characteristics: 1) the word-document relationships is modeled by following information-theoretic co-clustering (ITCC); 2) the local consistency on both interword and interdocument relationships is revealed by a local predictor; and 3) the global consistency on both interword and interdocument relationships is explored by a global smoothness regularization. All the fitting errors from these three-folds are finally integrated together to formulate an objective function, which is iteratively optimized by a convergence provable updating procedure. The extensive experiments on two benchmark document datasets validate the effectiveness of the proposed co-clustering method.
Locality-Aware CTA Clustering For Modern GPUs
Energy Technology Data Exchange (ETDEWEB)
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.
Directory of Open Access Journals (Sweden)
Ichiro IWASAKI
2010-06-01
Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.
Comparing the performance of biomedical clustering methods
DEFF Research Database (Denmark)
Wiwie, Christian; Baumbach, Jan; Röttger, Richard
2015-01-01
expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future......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....... This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide...
The polarizable embedding coupled cluster method
DEFF Research Database (Denmark)
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...
Directory of Open Access Journals (Sweden)
Xiangbing Zhou
2018-04-01
Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.
METHOD OF CONSTRUCTION OF GENETIC DATA CLUSTERS
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N. A. Novoselova
2016-01-01
Full Text Available The paper presents a method of construction of genetic data clusters (functional modules using the randomized matrices. To build the functional modules the selection and analysis of the eigenvalues of the gene profiles correlation matrix is performed. The principal components, corresponding to the eigenvalues, which are significantly different from those obtained for the randomly generated correlation matrix, are used for the analysis. Each selected principal component forms gene cluster. In a comparative experiment with the analogs the proposed method shows the advantage in allocating statistically significant different-sized clusters, the ability to filter non- informative genes and to extract the biologically interpretable functional modules matching the real data structure.
Locally adaptive decision in detection of clustered microcalcifications in mammograms
Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi
2018-02-01
In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.
Radionuclide identification using subtractive clustering method
International Nuclear Information System (INIS)
Farias, Marcos Santana; Mourelle, Luiza de Macedo
2011-01-01
Radionuclide identification is crucial to planning protective measures in emergency situations. This paper presents the application of a method for a classification system of radioactive elements with a fast and efficient response. To achieve this goal is proposed the application of subtractive clustering algorithm. The proposed application can be implemented in reconfigurable hardware, a flexible medium to implement digital hardware circuits. (author)
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Minetti Andrea
2012-10-01
Full Text Available Abstract Background Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS approach has been proposed as an alternative, as smaller sample sizes are required. Methods We explored (i the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. Results VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i health areas not requiring supplemental activities; ii health areas requiring additional vaccination; iii health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3, standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Conclusions Small sample cluster surveys (10 × 15 are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.
International Nuclear Information System (INIS)
Lau, Erwin T.; Nagai, Daisuke; Kravtsov, Andrey V.; Vikhlinin, Alexey; Zentner, Andrew R.
2012-01-01
Recent simulations of cluster formation have demonstrated that condensation of baryons into central galaxies during cluster formation can drive the shape of the gas distribution in galaxy clusters significantly rounder out to their virial radius. These simulations generally predict stellar fractions within cluster virial radii that are ∼2-3 times larger than the stellar masses deduced from observations. In this paper, we compare ellipticity profiles of simulated clusters performed with varying input physics (radiative cooling, star formation, and supernova feedback) to the cluster ellipticity profiles derived from Chandra and ROSAT observations, in an effort to constrain the fraction of gas that cools and condenses into the central galaxies within clusters. We find that local relaxed clusters have an average ellipticity of ε = 0.18 ± 0.05 in the radial range of 0.04 ≤ r/r 500 ≤ 1. At larger radii r > 0.1r 500 , the observed ellipticity profiles agree well with the predictions of non-radiative simulations. In contrast, the ellipticity profiles of simulated clusters that include dissipative gas physics deviate significantly from the observed ellipticity profiles at all radii. The dissipative simulations overpredict (underpredict) ellipticity in the inner (outer) regions of galaxy clusters. By comparing simulations with and without dissipative gas physics, we show that gas cooling causes the gas distribution to be more oblate in the central regions, but makes the outer gas distribution more spherical. We find that late-time gas cooling and star formation are responsible for the significantly oblate gas distributions in cluster cores, but the gas shapes outside of cluster cores are set primarily by baryon dissipation at high redshift (z ≥ 2). Our results indicate that the shapes of X-ray emitting gas in galaxy clusters, especially at large radii, can be used to place constraints on cluster gas physics, making it potential probes of the history of baryonic
Minetti, Andrea; Riera-Montes, Margarita; Nackers, Fabienne; Roederer, Thomas; Koudika, Marie Hortense; Sekkenes, Johanne; Taconet, Aurore; Fermon, Florence; Touré, Albouhary; Grais, Rebecca F; Checchi, Francesco
2012-10-12
Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required. We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.
The clustering of local maxima in random noise
International Nuclear Information System (INIS)
Coles, P.
1989-01-01
A mixture of analytic and numerical techniques is used to study the clustering properties of local maxima of random noise. Technical complexities restrict us to the case of 1D noise, but the results obtained should give a reasonably accurate picture of the behaviour of cosmological density peaks in noise defined on a 3D domain. We give estimates of the two-point correlation function of local maxima, for both Gaussian and non-Gaussian noise and show that previous approximations are not accurate. (author)
Nugroho, P.
2018-02-01
Creative industries existence is inseparable from the underlying social construct which provides sources for creativity and innovation. The working of social capital in a society facilitates information exchange, knowledge transfer and technology acquisition within the industry through social networks. As a result, a socio-spatial divide exists in directing the growth of the creative industries. This paper aims to examine how such a socio-spatial divide contributes to the local creative industry development in Semarang and Kudus batik clusters. Explanatory sequential mixed methods approach covering a quantitative approach followed by a qualitative approach is chosen to understand better the interplay between tangible and intangible variables in the local batik clusters. Surveys on secondary data taken from the government statistics and reports, previous studies, and media exposures are completed in the former approach to identify clustering pattern of the local batik industry and the local embeddedness factors which have shaped the existing business environment. In-depth interviews, content analysis, and field observations are engaged in the latter approach to explore reciprocal relationships between the elements of social capital and the local batik cluster development. The result demonstrates that particular social ties have determined the forms of spatial proximity manifested in forward and backward business linkages. Trust, shared norms, and inherited traditions are the key social capital attributes that lead to such a socio-spatial divide. Therefore, the intermediating roles of the bridging actors are necessary to encouraging cooperation among the participating stakeholders for a better cluster development.
Swarm: robust and fast clustering method for amplicon-based studies
Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah
2014-01-01
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. PMID:25276506
Swarm: robust and fast clustering method for amplicon-based studies
Directory of Open Access Journals (Sweden)
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.
[Classification of local anesthesia methods].
Petricas, A Zh; Medvedev, D V; Olkhovskaya, E B
The traditional classification methods of dental local anesthesia must be modified. In this paper we proved that the vascular mechanism is leading component of spongy injection. It is necessary to take into account the high effectiveness and relative safety of spongy anesthesia, as well as versatility, ease of implementation and the growing prevalence in the world. The essence of the proposed modification is to distinguish the methods in diffusive (including surface anesthesia, infiltration and conductive anesthesia) and vascular-diffusive (including intraosseous, intraligamentary, intraseptal and intrapulpal anesthesia). For the last four methods the common term «spongy (intraosseous) anesthesia» may be used.
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
Energy Technology Data Exchange (ETDEWEB)
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.
RRW: repeated random walks on genome-scale protein networks for local cluster discovery
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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.
Clustering of 18 Local Black Rice Base on Total Anthocyanin
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Kristamtini Kristamtini
2017-10-01
Full Text Available Black rice has a high anthocyanin content in the pericarp layer, which provides a dark purple color. Anthocyanin serve as an antioxidant that control cholesterol level in the blood, prevent anemia, potentially improve the body's resistance to disease, improve damage to liver cells (hepatitis and chirrosis, prevent impaired kidney function, prevent cancer/tumors, slows down antiaging, and prevent atherosclerosis and cardiovascular disease. Exploration results at AIAT Yogyakarta, Indonesia from 2011 to 2014 obtained 18 cultivar of local black rice Indonesia. The names of the rice are related to the color (black, red or purple formed by anthocyanin deposits in the pericarp layer, seed coat or aleuron. The objective of the study was to classify several types of local black rice from explorations based on the total anthocyanin content. The study was conducted by clustering analyzing the total anthocyanin content of 18 local black rice cultivars in Indonesia. Cluster analysis of total anthocyanin content were done using SAS ver. 9.2. Clustering dendogram shows that there were 4 groups of black rice cultivars based on the total anthocyanin content. Group I consists of Melik black rice, Patalan black rice, Yunianto black rice, Muharjo black rice, Ngatijo black rice, short life of Tugiyo black rice, Andel hitam 1, Jlitheng, and Sragen black rice. Group II consists of Pari ireng, Magelang black hairy rice, Banjarnegara-Wonosobo black rice, and Banjarnegara black rice. Group III consists of NTT black rice, Magelang non hairy black rice, Sembada hitam, and longevity Tugiyo black rice. Group IV consist only one type of black rice namely Cempo ireng. The grouping result indicate the existence of duplicate names among the black rice namely Patalan with Yunianto black rice, and short life Tugiyo with Andel hitam 1 black rice.
Membership determination of open clusters based on a spectral clustering method
Gao, Xin-Hua
2018-06-01
We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.
Integration of cloud, grid and local cluster resources with DIRAC
International Nuclear Information System (INIS)
Fifield, Tom; Sevior, Martin; Carmona, Ana; Casajús, Adrián; Graciani, Ricardo
2011-01-01
Grid computing was developed to provide users with uniform access to large-scale distributed resources. This has worked well, however there are significant resources available to the scientific community that do not follow this paradigm - those on cloud infrastructure providers, HPC supercomputers or local clusters. DIRAC (Distributed Infrastructure with Remote Agent Control) was originally designed to support direct submission to the Local Resource Management Systems (LRMS) of such clusters for LHCb, matured to support grid workflows and has recently been updated to support Amazon's Elastic Compute Cloud. This raises a number of new possibilities - by opening avenues to new resources, virtual organisations can change their resources with usage patterns and use these dedicated facilities for a given time. For example, user communities such as High Energy Physics experiments, have computing tasks with a wide variety of requirements in terms of CPU, data access or memory consumption, and their usage profile is never constant throughout the year. Having the possibility to transparently absorb peaks on the demand for these kinds of tasks using Cloud resources could allow a reduction in the overall cost of the system. This paper investigates interoperability by following a recent large-scale production exercise utilising resources from these three different paradigms, during the 2010 Belle Monte Carlo run. Through this, it discusses the challenges and opportunities of such a model.
Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.
1982-02-26
UPGMA ), and Ward’s method. Ling’s papers describe a (k,r) clustering method. Each of these methods have individual characteristics which make them...Reference 7), UPGMA is probably the most frequently used clustering strategy. UPGMA tries to group new points into an existing cluster by using an
Directory of Open Access Journals (Sweden)
Fang Fang
2018-05-01
Full Text Available In geometrically frustrated clusters of polyhedra, gaps between faces can be closed without distorting the polyhedra by the long established method of discrete curvature, which consists of curving the space into a fourth dimension, resulting in a dihedral angle at the joint between polyhedra in 4D. An alternative method—the twist method—has been recently suggested for a particular case, whereby the gaps are closed by twisting the cluster in 3D, resulting in an angular offset of the faces at the joint between adjacent polyhedral. In this paper, we show the general applicability of the twist method, for local clusters, and present the surprising result that both the required angle of the twist transformation and the consequent angle at the joint are the same, respectively, as the angle of bending to 4D in the discrete curvature and its resulting dihedral angle. The twist is therefore not only isomorphic, but isogonic (in terms of the rotation angles to discrete curvature. Our results apply to local clusters, but in the discussion we offer some justification for the conjecture that the isomorphism between twist and discrete curvature can be extended globally. Furthermore, we present examples for tetrahedral clusters with three-, four-, and fivefold symmetry.
MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS
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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.
Cluster temperature. Methods for its measurement and stabilization
International Nuclear Information System (INIS)
Makarov, G N
2008-01-01
Cluster temperature is an important material parameter essential to many physical and chemical processes involving clusters and cluster beams. Because of the diverse methods by which clusters can be produced, excited, and stabilized, and also because of the widely ranging values of atomic and molecular binding energies (approximately from 10 -5 to 10 eV) and numerous energy relaxation channels in clusters, cluster temperature (internal energy) ranges from 10 -3 to about 10 8 K. This paper reviews research on cluster temperature and describes methods for its measurement and stabilization. The role of cluster temperature in and its influence on physical and chemical processes is discussed. Results on the temperature dependence of cluster properties are presented. The way in which cluster temperature relates to cluster structure and to atomic and molecular interaction potentials in clusters is addressed. Methods for strong excitation of clusters and channels for their energy relaxation are discussed. Some applications of clusters and cluster beams are considered. (reviews of topical problems)
Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid
2014-12-30
Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.
Clustering Methods Application for Customer Segmentation to Manage Advertisement Campaign
Maciej Kutera; Mirosława Lasek
2010-01-01
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 sel...
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.
Spatial clustering and local risk of leprosy in São Paulo, Brazil.
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Antônio Carlos Vieira Ramos
2017-02-01
Full Text Available 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.
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
Energy Technology Data Exchange (ETDEWEB)
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
Electron localization, polarons and clustered states in manganites
International Nuclear Information System (INIS)
Mannella, N.
2004-01-01
Full text: A recent multi-spectroscopic study of prototypical colossal magnetoresistance (CMR) compounds La 1-x Sr x MnO 3 (LSMO, x = 0.3, 0.4) using photoemission (PE), x-ray absorption (XAS), x-ray emission (XES) and extended x-ray absorption e structure (EXAFS) has exposed a dramatic change in the electronic structure on crossing the ferromagnetic-to-paramagnetic transition temperature (T C ). In particular, this investigation revealed an increase of the Mn magnetic moment by ca. 1 Bohr magneton and charge transfer to the Mn atom on crossing T C concomitant with the presence of Jahn-Teller distortions, thus providing direct evidence of lattice polaron formation. These results thus challenge the belief of some authors that the LSMO compounds are canonical double-exchange (DE) systems in which polaron formation is unimportant, and thus help to unify the theoretical description of the CMR oxides. The relationship of these data to other recent work suggesting electron localization, polarons and phase separation, along with additional measurements of magnetic susceptibility indicating the formation of ferromagnetic clusters in the metallic paramagnetic state above T C will be discussed
Feature Selection and Kernel Learning for Local Learning-Based Clustering.
Zeng, Hong; Cheung, Yiu-ming
2011-08-01
The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.
Lee, Jennifer E.; Watson, David; Frey-Law, Laura A.
2012-01-01
Background Recent studies suggest an underlying three- or four-factor structure explains the conceptual overlap and distinctiveness of several negative emotionality and pain-related constructs. However, the validity of these latent factors for predicting pain has not been examined. Methods A cohort of 189 (99F; 90M) healthy volunteers completed eight self-report negative emotionality and pain-related measures (Eysenck Personality Questionnaire-Revised; Positive and Negative Affect Schedule; State-Trait Anxiety Inventory; Pain Catastrophizing Scale; Fear of Pain Questionnaire; Somatosensory Amplification Scale; Anxiety Sensitivity Index; Whiteley Index). Using principal axis factoring, three primary latent factors were extracted: General Distress; Catastrophic Thinking; and Pain-Related Fear. Using these factors, individuals clustered into three subgroups of high, moderate, and low negative emotionality responses. Experimental pain was induced via intramuscular acidic infusion into the anterior tibialis muscle, producing local (infusion site) and/or referred (anterior ankle) pain and hyperalgesia. Results Pain outcomes differed between clusters (multivariate analysis of variance and multinomial regression), with individuals in the highest negative emotionality cluster reporting the greatest local pain (p = 0.05), mechanical hyperalgesia (pressure pain thresholds; p = 0.009) and greater odds (2.21 OR) of experiencing referred pain compared to the lowest negative emotionality cluster. Conclusion Our results provide support for three latent psychological factors explaining the majority of the variance between several pain-related psychological measures, and that individuals in the high negative emotionality subgroup are at increased risk for (1) acute local muscle pain; (2) local hyperalgesia; and (3) referred pain using a standardized nociceptive input. PMID:23165778
Homological methods, representation theory, and cluster algebras
Trepode, Sonia
2018-01-01
This text presents six mini-courses, all devoted to interactions between representation theory of algebras, homological algebra, and the new ever-expanding theory of cluster algebras. The interplay between the topics discussed in this text will continue to grow and this collection of courses stands as a partial testimony to this new development. The courses are useful for any mathematician who would like to learn more about this rapidly developing field; the primary aim is to engage graduate students and young researchers. Prerequisites include knowledge of some noncommutative algebra or homological algebra. Homological algebra has always been considered as one of the main tools in the study of finite-dimensional algebras. The strong relationship with cluster algebras is more recent and has quickly established itself as one of the important highlights of today’s mathematical landscape. This connection has been fruitful to both areas—representation theory provides a categorification of cluster algebras, wh...
CCM: A Text Classification Method by Clustering
DEFF Research Database (Denmark)
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...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
In unsupervised classiﬁcation, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...
Applying local binary patterns in image clustering problems
Skorokhod, Nikolai N.; Elizarov, Alexey I.
2017-11-01
Due to the fact that the cloudiness plays a critical role in the Earth radiative balance, the study of the distribution of different types of clouds and their movements is relevant. The main sources of such information are artificial satellites that provide data in the form of images. The most commonly used method of solving tasks of processing and classification of images of clouds is based on the description of texture features. The use of a set of local binary patterns is proposed to describe the texture image.
Prediction of Solvent Physical Properties using the Hierarchical Clustering Method
Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...
A Web service substitution method based on service cluster nets
Du, YuYue; Gai, JunJing; Zhou, MengChu
2017-11-01
Service substitution is an important research topic in the fields of Web services and service-oriented computing. This work presents a novel method to analyse and substitute Web services. A new concept, called a Service Cluster Net Unit, is proposed based on Web service clusters. A service cluster is converted into a Service Cluster Net Unit. Then it is used to analyse whether the services in the cluster can satisfy some service requests. Meanwhile, the substitution methods of an atomic service and a composite service are proposed. The correctness of the proposed method is proved, and the effectiveness is shown and compared with the state-of-the-art method via an experiment. It can be readily applied to e-commerce service substitution to meet the business automation needs.
The Cluster Variation Method: A Primer for Neuroscientists.
Maren, Alianna J
2016-09-30
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.
The Cluster Variation Method: A Primer for Neuroscientists
Directory of Open Access Journals (Sweden)
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.
Fuzzy C-means method for clustering microarray data.
Dembélé, Doulaye; Kastner, Philippe
2003-05-22
Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/
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
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.
A Latent Variable Clustering Method for Wireless Sensor Networks
DEFF Research Database (Denmark)
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 and...
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.
Clustering Methods Application for Customer Segmentation to Manage Advertisement Campaign
Directory of Open Access Journals (Sweden)
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.
Guo, Lei; Abbosh, Amin
2018-05-01
For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed. It is based on microwave tomography, k-means clustering, and a support vector machine (SVM) method. The dielectric profile of the brain is first calculated using the Born iterative method, whereas the amplitude of the dielectric profile is then taken as the input to k-means clustering. The cluster is selected as the feature vector for constructing and testing the SVM. A database of MRI-derived realistic head phantoms at different signal-to-noise ratios is used in the classification procedure. The performance of the proposed framework is evaluated using the receiver operating characteristic (ROC) curve. The results based on a two-dimensional framework show that 88% classification accuracy, with a sensitivity of 91% and a specificity of 87%, can be achieved. Bioelectromagnetics. 39:312-324, 2018. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.
Fast optimization of binary clusters using a novel dynamic lattice searching method
International Nuclear Information System (INIS)
Wu, Xia; Cheng, Wen
2014-01-01
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
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
An Examination of Three Spatial Event Cluster Detection Methods
Directory of Open Access Journals (Sweden)
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.
Brain vascular image segmentation based on fuzzy local information C-means clustering
Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie
2017-02-01
Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.
Energy Technology Data Exchange (ETDEWEB)
Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng [Jiangnan University, Wuxi (China)
2014-11-15
The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy.
International Nuclear Information System (INIS)
Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng
2014-01-01
The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy
The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies
Energy Technology Data Exchange (ETDEWEB)
Grasha, K.; Calzetti, D. [Astronomy Department, University of Massachusetts, Amherst, MA 01003 (United States); Adamo, A.; Messa, M. [Dept. of Astronomy, The Oskar Klein Centre, Stockholm University, Stockholm (Sweden); Kim, H. [Gemini Observatory, La Serena (Chile); Elmegreen, B. G. [IBM Research Division, T.J. Watson Research Center, Yorktown Hts., NY (United States); Gouliermis, D. A. [Zentrum für Astronomie der Universität Heidelberg, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2, D-69120 Heidelberg (Germany); Dale, D. A. [Dept. of Physics and Astronomy, University of Wyoming, Laramie, WY (United States); Fumagalli, M. [Institute for Computational Cosmology and Centre for Extragalactic Astronomy, Durham University, Durham (United Kingdom); Grebel, E. K.; Shabani, F. [Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr. 12-14, D-69120 Heidelberg (Germany); Johnson, K. E. [Dept. of Astronomy, University of Virginia, Charlottesville, VA (United States); Kahre, L. [Dept. of Astronomy, New Mexico State University, Las Cruces, NM (United States); Kennicutt, R. C. [Institute of Astronomy, University of Cambridge, Cambridge (United Kingdom); Pellerin, A. [Dept. of Physics and Astronomy, State University of New York at Geneseo, Geneseo NY (United States); Ryon, J. E.; Ubeda, L. [Space Telescope Science Institute, Baltimore, MD (United States); Smith, L. J. [European Space Agency/Space Telescope Science Institute, Baltimore, MD (United States); Thilker, D., E-mail: kgrasha@astro.umass.edu [Dept. of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD (United States)
2017-05-10
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.
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.
The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies
International Nuclear Information System (INIS)
Grasha, K.; Calzetti, D.; Adamo, A.; Messa, M.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Shabani, F.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Pellerin, A.; Ryon, J. E.; Ubeda, L.; Smith, L. J.; Thilker, D.
2017-01-01
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.
The Hyades cluster-supercluster connection - Evidence for a local concentration of dark matter
Casertano, Stefano; Iben, Icko, Jr.; Shiels, Aaron
1993-01-01
Stars that evaporate from the Hyades cluster will remain within a few hundred parsecs of the cluster only if they are dynamically bound to a much more massive entity containing the cluster. A local mass enhancement of at least (5-10) x 10 exp 5 solar masses, with a radius of about 100 pc, can trap stars with an origin related to that of the Hyades cluster and explains the excess of stars with velocities near the Hyades velocity that constitutes the Hyades supercluster. Part of this mass enhancement can be in visible stars, but a substantial fraction is likely to be in the form of dark matter.
Removal of impulse noise clusters from color images with local order statistics
Ruchay, Alexey; Kober, Vitaly
2017-09-01
This paper proposes a novel algorithm for restoring images corrupted with clusters of impulse noise. The noise clusters often occur when the probability of impulse noise is very high. The proposed noise removal algorithm consists of detection of bulky impulse noise in three color channels with local order statistics followed by removal of the detected clusters by means of vector median filtering. With the help of computer simulation we show that the proposed algorithm is able to effectively remove clustered impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.
Sensitivity evaluation of dynamic speckle activity measurements using clustering methods
International Nuclear Information System (INIS)
Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H.
2010-01-01
We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.
Trend analysis using non-stationary time series clustering based on the finite element method
Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.
2014-05-01
In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.
Local wavelet correlation: applicationto timing analysis of multi-satellite CLUSTER data
Directory of Open Access Journals (Sweden)
J. Soucek
2004-12-01
Full Text Available Multi-spacecraft space observations, such as those of CLUSTER, can be used to infer information about local plasma structures by exploiting the timing differences between subsequent encounters of these structures by individual satellites. We introduce a novel wavelet-based technique, the Local Wavelet Correlation (LWC, which allows one to match the corresponding signatures of large-scale structures in the data from multiple spacecraft and determine the relative time shifts between the crossings. The LWC is especially suitable for analysis of strongly non-stationary time series, where it enables one to estimate the time lags in a more robust and systematic way than ordinary cross-correlation techniques. The technique, together with its properties and some examples of its application to timing analysis of bow shock and magnetopause crossing observed by CLUSTER, are presented. We also compare the performance and reliability of the technique with classical discontinuity analysis methods. Key words. Radio science (signal processing – Space plasma physics (discontinuities; instruments and techniques
International Nuclear Information System (INIS)
Avdonin, S.A.
1985-01-01
General characteristic and bases of X-ray spectrum local method used for qualitative and quantitative analyses of the mineral chemical composition with volumetric locality of several cubic micrometers. The method is based on the excitation in a sample of characteristic and bremsstrahlung spectra by means of a narrow electron beam at 5-50 keV accelerating voltage. Application of the method when studying uranium minerals and ores is considered. The method allows to determine the uranium presence forms in the ores, morphological features of the minerals, mineral microstructure, UO 2 and UO 3 ratios for unhydrous uraninites and pitchblendes and also to determine mineralization age
Leimar, Olof; Doebeli, Michael; Dieckmann, Ulf
2008-04-01
We have analyzed the evolution of a quantitative trait in populations that are spatially extended along an environmental gradient, with gene flow between nearby locations. In the absence of competition, there is stabilizing selection toward a locally best-adapted trait that changes gradually along the gradient. According to traditional ideas, gradual spatial variation in environmental conditions is expected to lead to gradual variation in the evolved trait. A contrasting possibility is that the trait distribution instead breaks up into discrete clusters. Doebeli and Dieckmann (2003) argued that competition acting locally in trait space and geographical space can promote such clustering. We have investigated this possibility using deterministic population dynamics for asexual populations, analyzing our model numerically and through an analytical approximation. We examined how the evolution of clusters is affected by the shape of competition kernels, by the presence of Allee effects, and by the strength of gene flow along the gradient. For certain parameter ranges clustering was a robust outcome, and for other ranges there was no clustering. Our analysis shows that the shape of competition kernels is important for clustering: the sign structure of the Fourier transform of a competition kernel determines whether the kernel promotes clustering. Also, we found that Allee effects promote clustering, whereas gene flow can have a counteracting influence. In line with earlier findings, we could demonstrate that phenotypic clustering was favored by gradients of intermediate slope.
Dynamic integration of remote cloud resources into local computing clusters
Energy Technology Data Exchange (ETDEWEB)
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).
Momentum-space cluster dual-fermion method
Iskakov, Sergei; Terletska, Hanna; Gull, Emanuel
2018-03-01
Recent years have seen the development of two types of nonlocal extensions to the single-site dynamical mean field theory. On one hand, cluster approximations, such as the dynamical cluster approximation, recover short-range momentum-dependent correlations nonperturbatively. On the other hand, diagrammatic extensions, such as the dual-fermion theory, recover long-ranged corrections perturbatively. The correct treatment of both strong short-ranged and weak long-ranged correlations within the same framework is therefore expected to lead to a quick convergence of results, and offers the potential of obtaining smooth self-energies in nonperturbative regimes of phase space. In this paper, we present an exact cluster dual-fermion method based on an expansion around the dynamical cluster approximation. Unlike previous formulations, our method does not employ a coarse-graining approximation to the interaction, which we show to be the leading source of error at high temperature, and converges to the exact result independently of the size of the underlying cluster. We illustrate the power of the method with results for the second-order cluster dual-fermion approximation to the single-particle self-energies and double occupancies.
Alignments of the galaxies in and around the Virgo cluster with the local velocity shear
International Nuclear Information System (INIS)
Lee, Jounghun; Rey, Soo Chang; Kim, Suk
2014-01-01
Observational evidence is presented for the alignment between the cosmic sheet and the principal axis of the velocity shear field at the position of the Virgo cluster. The galaxies in and around the Virgo cluster from the Extended Virgo Cluster Catalog that was recently constructed by Kim et al. are used to determine the direction of the local sheet. The peculiar velocity field reconstructed from the Sloan Digital Sky Survey Data Release 7 is analyzed to estimate the local velocity shear tensor at the Virgo center. Showing first that the minor principal axis of the local velocity shear tensor is almost parallel to the direction of the line of sight, we detect a clear signal of alignment between the positions of the Virgo satellites and the intermediate principal axis of the local velocity shear projected onto the plane of the sky. Furthermore, the dwarf satellites are found to appear more strongly aligned than their normal counterparts, which is interpreted as an indication of the following. (1) The normal satellites and the dwarf satellites fall in the Virgo cluster preferentially along the local filament and the local sheet, respectively. (2) The local filament is aligned with the minor principal axis of the local velocity shear while the local sheet is parallel to the plane spanned by the minor and intermediate principal axes. Our result is consistent with the recent numerical claim that the velocity shear is a good tracer of the cosmic web.
Polarizable Density Embedding Coupled Cluster Method
DEFF Research Database (Denmark)
Hršak, Dalibor; Olsen, Jógvan Magnus Haugaard; Kongsted, Jacob
2018-01-01
by an embedding potential consisting of a set of fragment densities obtained from calculations on isolated fragments with a quantum-chemistry method such as Hartree-Fock (HF) or Kohn-Sham density functional theory (KS-DFT) and dressed with a set of atom-centered anisotropic dipole-dipole polarizabilities...
Method for detecting clusters of possible uranium deposits
International Nuclear Information System (INIS)
Conover, W.J.; Bement, T.R.; Iman, R.L.
1978-01-01
When a two-dimensional map contains points that appear to be scattered somewhat at random, a question that often arises is whether groups of points that appear to cluster are merely exhibiting ordinary behavior, which one can expect with any random distribution of points, or whether the clusters are too pronounced to be attributable to chance alone. A method for detecting clusters along a straight line is applied to the two-dimensional map of 214 Bi anomalies observed as part of the National Uranium Resource Evaluation Program in the Lubbock, Texas, region. Some exact probabilities associated with this method are computed and compared with two approximate methods. The two methods for approximating probabilities work well in the cases examined and can be used when it is not feasible to obtain the exact probabilities
Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation
Directory of Open Access Journals (Sweden)
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.
Analysis of Non Local Image Denoising Methods
Pardo, Álvaro
Image denoising is probably one of the most studied problems in the image processing community. Recently a new paradigm on non local denoising was introduced. The Non Local Means method proposed by Buades, Morel and Coll attracted the attention of other researches who proposed improvements and modifications to their proposal. In this work we analyze those methods trying to understand their properties while connecting them to segmentation based on spectral graph properties. We also propose some improvements to automatically estimate the parameters used on these methods.
A novel clustering and supervising users' profiles method
Institute of Scientific and Technical Information of China (English)
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.
Galaxy clusters in simulations of the local Universe: a matter of constraints
Sorce, Jenny G.; Tempel, Elmo
2018-06-01
To study the full formation and evolution history of galaxy clusters and their population, high-resolution simulations of the latter are flourishing. However, comparing observed clusters to the simulated ones on a one-to-one basis to refine the models and theories down to the details is non-trivial. The large variety of clusters limits the comparisons between observed and numerical clusters. Simulations resembling the local Universe down to the cluster scales permit pushing the limit. Simulated and observed clusters can be matched on a one-to-one basis for direct comparisons provided that clusters are well reproduced besides being in the proper large-scale environment. Comparing random and local Universe-like simulations obtained with differently grouped observational catalogues of peculiar velocities, this paper shows that the grouping scheme used to remove non-linear motions in the catalogues that constrain the simulations affects the quality of the numerical clusters. With a less aggressive grouping scheme - galaxies still falling on to clusters are preserved - combined with a bias minimization scheme, the mass of the dark matter haloes, simulacra for five local clusters - Virgo, Centaurus, Coma, Hydra, and Perseus - is increased by 39 per cent closing the gap with observational mass estimates. Simulacra are found on average in 89 per cent of the simulations, an increase of 5 per cent with respect to the previous grouping scheme. The only exception is Perseus. Since the Perseus-Pisces region is not well covered by the used peculiar velocity catalogue, the latest release lets us foresee a better simulacrum for Perseus in a near future.
Image Registration Using Single Cluster PHD Methods
Campbell, M.; Schlangen, I.; Delande, E.; Clark, D.
Cadets in the Department of Physics at the United States Air Force Academy are using the technique of slitless spectroscopy to analyze the spectra from geostationary satellites during glint season. The equinox periods of the year are particularly favorable for earth-based observers to detect specular reflections off satellites (glints), which have been observed in the past using broadband photometry techniques. Three seasons of glints were observed and analyzed for multiple satellites, as measured across the visible spectrum using a diffraction grating on the Academy’s 16-inch, f/8.2 telescope. It is clear from the results that the glint maximum wavelength decreases relative to the time periods before and after the glint, and that the spectral reflectance during the glint is less like a blackbody. These results are consistent with the presumption that solar panels are the predominant source of specular reflection. The glint spectra are also quantitatively compared to different blackbody curves and the solar spectrum by means of absolute differences and standard deviations. Our initial analysis appears to indicate a potential method of determining relative power capacity.
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...
Directory of Open Access Journals (Sweden)
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.
Kernel method for clustering based on optimal target vector
International Nuclear Information System (INIS)
Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano
2006-01-01
We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering
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.
Ultra-Wideband Geo-Regioning: A Novel Clustering and Localization Technique
Directory of Open Access Journals (Sweden)
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.
Multishell method: Exact treatment of a cluster in an effective medium
International Nuclear Information System (INIS)
Gonis, A.; Garland, J.W.
1977-01-01
A method is presented for the exact determination of the Green's function of a cluster embedded in a given effective medium. This method, the multishell method, is applicable even to systems with off-diagonal disorder, extended-range hopping, multiple bands, and/or hybridization, and is computationally practicable for any system described by a tight-binding or interpolation-scheme Hamiltonian. It allows one to examine the effects of local environment on the densities of states and site spectral weight functions of disordered systems. For any given analytic effective medium characterized by a non-negative density of states the method yields analytic cluster Green's functions and non-negative site spectral weight functions. Previous methods used for the calculation of the Green's function of a cluster embedded in a given effective medium have not been exact. The results of numerical calculations for model systems show that even the best of these previous methods can lead to substantial errors, at least for small clusters in two- and three-dimensional lattices. These results also show that fluctuations in local environment have large effects on site spectral weight functions, even in cases in which the single-site coherent-potential approximation yields an accurate overall density of states
A cluster approximation for the transfer-matrix method
International Nuclear Information System (INIS)
Surda, A.
1990-08-01
A cluster approximation for the transfer-method is formulated. The calculation of the partition function of lattice models is transformed to a nonlinear mapping problem. The method yields the free energy, correlation functions and the phase diagrams for a large class of lattice models. The high accuracy of the method is exemplified by the calculation of the critical temperature of the Ising model. (author). 14 refs, 2 figs, 1 tab
Directory of Open Access Journals (Sweden)
Geoffrey M Jacquez
Full Text Available In case control studies disease risk not explained by the significant risk factors is the unexplained risk. Considering unexplained risk for specific populations, places and times can reveal the signature of unidentified risk factors and risk factors not fully accounted for in the case-control study. This potentially can lead to new hypotheses regarding disease causation.Global, local and focused Q-statistics are applied to data from a population-based case-control study of 11 southeast Michigan counties. Analyses were conducted using both year- and age-based measures of time. The analyses were adjusted for arsenic exposure, education, smoking, family history of bladder cancer, occupational exposure to bladder cancer carcinogens, age, gender, and race.Significant global clustering of cases was not found. Such a finding would indicate large-scale clustering of cases relative to controls through time. However, highly significant local clusters were found in Ingham County near Lansing, in Oakland County, and in the City of Jackson, Michigan. The Jackson City cluster was observed in working-ages and is thus consistent with occupational causes. The Ingham County cluster persists over time, suggesting a broad-based geographically defined exposure. Focused clusters were found for 20 industrial sites engaged in manufacturing activities associated with known or suspected bladder cancer carcinogens. Set-based tests that adjusted for multiple testing were not significant, although local clusters persisted through time and temporal trends in probability of local tests were observed.Q analyses provide a powerful tool for unpacking unexplained disease risk from case-control studies. This is particularly useful when the effect of risk factors varies spatially, through time, or through both space and time. For bladder cancer in Michigan, the next step is to investigate causal hypotheses that may explain the excess bladder cancer risk localized to areas of
Fuzzy Clustering Methods and their Application to Fuzzy Modeling
DEFF Research Database (Denmark)
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....... An illustrative synthetic example is analyzed, and prediction accuracy measures are compared between the different variants...
Dahms, Sven O; Kuester, Miriam; Streb, Carsten; Roth, Christian; Sträter, Norbert; Than, Manuel E
2013-02-01
Heavy-atom clusters (HA clusters) containing a large number of specifically arranged electron-dense scatterers are especially useful for experimental phase determination of large complex structures, weakly diffracting crystals or structures with large unit cells. Often, the determination of the exact orientation of the HA cluster and hence of the individual heavy-atom positions proves to be the critical step in successful phasing and subsequent structure solution. Here, it is demonstrated that molecular replacement (MR) with either anomalous or isomorphous differences is a useful strategy for the correct placement of HA cluster compounds. The polyoxometallate cluster hexasodium α-metatungstate (HMT) was applied in phasing the structure of death receptor 6. Even though the HA cluster is bound in alternate partially occupied orientations and is located at a special position, its correct localization and orientation could be determined at resolutions as low as 4.9 Å. The broad applicability of this approach was demonstrated for five different derivative crystals that included the compounds tantalum tetradecabromide and trisodium phosphotungstate in addition to HMT. The correct placement of the HA cluster depends on the length of the intramolecular vectors chosen for MR, such that both a larger cluster size and the optimal choice of the wavelength used for anomalous data collection strongly affect the outcome.
Dynamic analysis of clustered building structures using substructures methods
International Nuclear Information System (INIS)
Leimbach, K.R.; Krutzik, N.J.
1989-01-01
The dynamic substructure approach to the building cluster on a common base mat starts with the generation of Ritz-vectors for each building on a rigid foundation. The base mat plus the foundation soil is subjected to kinematic constraint modes, for example constant, linear, quadratic or cubic constraints. These constraint modes are also imposed on the buildings. By enforcing kinematic compatibility of the complete structural system on the basis of the constraint modes a reduced Ritz model of the complete cluster is obtained. This reduced model can now be analyzed by modal time history or response spectrum methods
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
International Nuclear Information System (INIS)
Dahms, Sven O.; Kuester, Miriam; Streb, Carsten; Roth, Christian; Sträter, Norbert; Than, Manuel E.
2013-01-01
A new approach is presented that allows the efficient localization and orientation of heavy-atom cluster compounds used in experimental phasing by a molecular replacement procedure. This permits the calculation of meaningful phases up to the highest resolution of the diffraction data. Heavy-atom clusters (HA clusters) containing a large number of specifically arranged electron-dense scatterers are especially useful for experimental phase determination of large complex structures, weakly diffracting crystals or structures with large unit cells. Often, the determination of the exact orientation of the HA cluster and hence of the individual heavy-atom positions proves to be the critical step in successful phasing and subsequent structure solution. Here, it is demonstrated that molecular replacement (MR) with either anomalous or isomorphous differences is a useful strategy for the correct placement of HA cluster compounds. The polyoxometallate cluster hexasodium α-metatungstate (HMT) was applied in phasing the structure of death receptor 6. Even though the HA cluster is bound in alternate partially occupied orientations and is located at a special position, its correct localization and orientation could be determined at resolutions as low as 4.9 Å. The broad applicability of this approach was demonstrated for five different derivative crystals that included the compounds tantalum tetradecabromide and trisodium phosphotungstate in addition to HMT. The correct placement of the HA cluster depends on the length of the intramolecular vectors chosen for MR, such that both a larger cluster size and the optimal choice of the wavelength used for anomalous data collection strongly affect the outcome
a Probabilistic Embedding Clustering Method for Urban Structure Detection
Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.
2017-09-01
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.
A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION
Directory of Open Access Journals (Sweden)
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.
Application of a Light-Front Coupled Cluster Method
International Nuclear Information System (INIS)
Chabysheva, S.S.; Hiller, J.R.
2012-01-01
As a test of the new light-front coupled-cluster method in a gauge theory, we apply it to the nonperturbative construction of the dressed-electron state in QED, for an arbitrary covariant gauge, and compute the electron's anomalous magnetic moment. The construction illustrates the spectator and Fock-sector independence of vertex and self-energy contributions and indicates resolution of the difficulties with uncanceled divergences that plague methods based on Fock-space truncation. (author)
A Clustering Method for Data in Cylindrical Coordinates
Directory of Open Access Journals (Sweden)
Kazuhisa Fujita
2017-01-01
Full Text Available We propose a new clustering method for data in cylindrical coordinates based on the k-means. The goal of the k-means family is to maximize an optimization function, which requires a similarity. Thus, we need a new similarity to obtain the new clustering method for data in cylindrical coordinates. In this study, we first derive a new similarity for the new clustering method by assuming a particular probabilistic model. A data point in cylindrical coordinates has radius, azimuth, and height. We assume that the azimuth is sampled from a von Mises distribution and the radius and the height are independently generated from isotropic Gaussian distributions. We derive the new similarity from the log likelihood of the assumed probability distribution. Our experiments demonstrate that the proposed method using the new similarity can appropriately partition synthetic data defined in cylindrical coordinates. Furthermore, we apply the proposed method to color image quantization and show that the methods successfully quantize a color image with respect to the hue element.
A method of clustering observers with different visual characteristics
Energy Technology Data Exchange (ETDEWEB)
Niimi, Takanaga [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Imai, Kuniharu [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Ikeda, Mitsuru [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Maeda, Hisatoshi [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan)
2006-01-15
Evaluation of observer's image perception in medical images is important, and yet has not been performed because it is difficult to quantify visual characteristics. In the present study, we investigated the observer's image perception by clustering a group of 20 observers. Images of a contrast-detail (C-D) phantom, which had cylinders of 10 rows and 10 columns with different diameters and lengths, were acquired with an X-ray screen-film system with fixed exposure conditions. A group of 10 films were prepared for visual evaluations. Sixteen radiological technicians, three radiologists and one medical physicist participated in the observation test. All observers read the phantom radiographs on a transillumination image viewer with room lights off. The detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using the number of columns. The detectability was calculated as the average of 10 readings for each observer, and plotted for different phantom diameter. The unweighted pair-group method using arithmetic averages (UPGMA) was adopted for clustering. The observers were clustered into two groups: one group selected objects with a demarcation from the vicinity, and the other group searched for the objects with their eyes constrained. This study showed a usefulness of the cluster method to select personnel with the similar perceptual predisposition when a C-D phantom was used in image quality control.
A method of clustering observers with different visual characteristics
International Nuclear Information System (INIS)
Niimi, Takanaga; Imai, Kuniharu; Ikeda, Mitsuru; Maeda, Hisatoshi
2006-01-01
Evaluation of observer's image perception in medical images is important, and yet has not been performed because it is difficult to quantify visual characteristics. In the present study, we investigated the observer's image perception by clustering a group of 20 observers. Images of a contrast-detail (C-D) phantom, which had cylinders of 10 rows and 10 columns with different diameters and lengths, were acquired with an X-ray screen-film system with fixed exposure conditions. A group of 10 films were prepared for visual evaluations. Sixteen radiological technicians, three radiologists and one medical physicist participated in the observation test. All observers read the phantom radiographs on a transillumination image viewer with room lights off. The detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using the number of columns. The detectability was calculated as the average of 10 readings for each observer, and plotted for different phantom diameter. The unweighted pair-group method using arithmetic averages (UPGMA) was adopted for clustering. The observers were clustered into two groups: one group selected objects with a demarcation from the vicinity, and the other group searched for the objects with their eyes constrained. This study showed a usefulness of the cluster method to select personnel with the similar perceptual predisposition when a C-D phantom was used in image quality control
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.
Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization
Liu, Zexi
2018-01-01
Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.
Weighted tunable clustering in local-world networks with increment behavior
International Nuclear Information System (INIS)
Ma, Ying-Hong; Li, Huijia; Zhang, Xiao-Dong
2010-01-01
Since some realistic networks are influenced not only by increment behavior but also by the tunable clustering mechanism with new nodes to be added to networks, it is interesting to characterize the model for those actual networks. In this paper, a weighted local-world model, which incorporates increment behavior and the tunable clustering mechanism, is proposed and its properties are investigated, such as degree distribution and clustering coefficient. Numerical simulations are fitted to the model and also display good right-skewed scale-free properties. Furthermore, the correlation of vertices in our model is studied which shows the assortative property. The epidemic spreading process by weighted transmission rate on the model shows that the tunable clustering behavior has a great impact on the epidemic dynamic
Generating clustered scale-free networks using Poisson based localization of edges
Türker, İlker
2018-05-01
We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.
Unbiased methods for removing systematics from galaxy clustering measurements
Elsner, Franz; Leistedt, Boris; Peiris, Hiranya V.
2016-02-01
Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting 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 characterize 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 the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.
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.
Active learning for semi-supervised clustering based on locally linear propagation reconstruction.
Chang, Chin-Chun; Lin, Po-Yi
2015-03-01
The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.
A Comparison of Methods for Player Clustering via Behavioral Telemetry
DEFF Research Database (Denmark)
Drachen, Anders; Thurau, C.; Sifa, R.
2013-01-01
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......, interpretation of the resulting categories in terms of actual play behavior can be difficult if not impossible. In this paper, a range of unsupervised techniques are applied together with Archetypal Analysis to develop behavioral clusters from playtime data of 70,014 World of Warcraft players, covering a five......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...
Cluster monte carlo method for nuclear criticality safety calculation
International Nuclear Information System (INIS)
Pei Lucheng
1984-01-01
One of the most important applications of the Monte Carlo method is the calculation of the nuclear criticality safety. The fair source game problem was presented at almost the same time as the Monte Carlo method was applied to calculating the nuclear criticality safety. The source iteration cost may be reduced as much as possible or no need for any source iteration. This kind of problems all belongs to the fair source game prolems, among which, the optimal source game is without any source iteration. Although the single neutron Monte Carlo method solved the problem without the source iteration, there is still quite an apparent shortcoming in it, that is, it solves the problem without the source iteration only in the asymptotic sense. In this work, a new Monte Carlo method called the cluster Monte Carlo method is given to solve the problem further
Closser, Kristina Danielle
superpositions of atomic states with surface states appearing close to the atomic excitation energies and interior states being blue shifted by up to ≈2 eV. The dynamics resulting from excitation of He_7 were subsequently explored using ab initio molecular dynamics (AIMD). These simulations were performed with classical adiabatic dynamics coupled to a new state-following algorithm on CIS potential energy surfaces. Most clusters were found to completely dissociate and resulted in a single excited atomic state (90%), however, some trajectories formed bound, He*2 (3%), and a few yielded excited trimers (<0.5%). Comparisons were made with available experimental information on much larger clusters. Various applications of this state following algorithm are also presented. In addition to AIMD, these include excited-state geometry optimization and minimal energy path finding via the growing string method. When using state following we demonstrate that more physical results can be obtained with AIMD calculations. Also, the optimized geometries of three excited states of cytosine, two of which were not found without state following, and the minimal energy path between the lowest two singlet excited states of protonated formaldimine are offered as example applications. Finally, to address large clusters, a local variation of CIS was developed. This method exploits the properties of absolutely localized molecular orbitals (ALMOs) to limit the total number of excitations to scaling only linearly with cluster size, which results in formal scaling with the third power of the system size. The derivation of the equations and design of the algorithm are discussed in detail, and computational timings as well as a pilot application to the size dependence of the helium cluster spectrum are presented.
Role of the Tourism Cluster of Porto de Galinhas in the Local Development
Directory of Open Access Journals (Sweden)
Natália Pelinca Braga
2013-04-01
Full Text Available This paper proposes to prepare a case study on the role of the tourism cluster of Porto de Galinhas in local development. For such, the analysis on the indicators listed in the database of the Annual Report of Social Information (RAIS was performed, whose time interval was 15 years comprised between 1994 and 2008, the pair years in the aforementioned period being analyzed. The results show that the impact of tourism cluster is positive, both from economic, as the social point of views, i.e. it entails development both in physical infrastructure and in terms of socio-economic relationships of the resident population. This study reinforces therefore, the argument of the positive influence of a tourism cluster in the region, specifically in case of Porto de Galinhas.
Dark matter searches with Cherenkov telescopes: nearby dwarf galaxies or local galaxy clusters?
Energy Technology Data Exchange (ETDEWEB)
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
Dark Matter Searches with Cherenkov Telescopes: Nearby Dwarf Galaxies or Local Galaxy Clusters?
Energy Technology Data Exchange (ETDEWEB)
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
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.
Directory of Open Access Journals (Sweden)
D. Xiang
2017-09-01
Full Text Available 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.
Method of removing crud deposited on fuel element clusters
International Nuclear Information System (INIS)
Yokota, Tokunobu; Yashima, Akira; Tajima, Jun-ichiro.
1982-01-01
Purpose: To enable easy elimination of claddings deposited on the surface of fuel element. Method: An operator manipulates a pole from above a platform, engages the longitudinal flange of the cover to the opening at the upper end of a channel box and starts up a suction pump. The suction amount of the pump is set such that water flow becomes within the channel box at greater flow rate than the operational flow rate in the channel box of the fuel element clusters during reactor operation. This enables to remove crud deposited on the surface of individual fuel elements with ease and rapidly without detaching the channel box. (Moriyama, K.)
Determining wood chip size: image analysis and clustering methods
Directory of Open Access Journals (Sweden)
Paolo Febbi
2013-09-01
Full Text Available One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149- 1:2010. Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm; the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang’s theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors and size descriptors (area, perimeter, Feret diameters, eccentricity was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3- dimensional acquisition process.
Atomistic spectrometrics of local bond-electron-energy pertaining to Na and K clusters
Energy Technology Data Exchange (ETDEWEB)
Bo, Maolin [Key Laboratory of Low-Dimensional Materials and Application Technologies, Ministry of Education, Xiangtan University, Hunan 411105 (China); Wang, Yan, E-mail: YWang8@hnust.edu.cn [School of Information and Electronic Engineering, Hunan University of Science and Technology, Hunan 411201 (China); Huang, Yongli; Liu, Yonghui [Key Laboratory of Low-Dimensional Materials and Application Technologies, Ministry of Education, Xiangtan University, Hunan 411105 (China); Li, Can [Center for Coordination Bond Engineering, School of Materials Science and Engineering, China Jiliang University, Hangzhou 330018 (China); Sun, Chang Q., E-mail: ecqsun@ntu.edu.sg [NOVITAS, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (Singapore)
2015-01-15
Graphical abstract: - Highlights: • Coordination environment resolves electron binding-energy shift of Na and K clusters. • Cohesive energy of the representative bond determines the core-level shift. • XPS derives the energy level of an isolated atom and its bulk shift. • XPS derives the local bond length, bond energy, binding energy density. - Abstract: Consistency between density functional theory calculations and photoelectron spectroscopy measurements confirmed our predications on the undercoordination-induced local bond relaxation and core level shift of Na and K clusters. It is clarified that the shorter and stronger bonds between under-coordinated atoms cause local densification and local potential well depression and shift the electron binding-energy accordingly. Numerical consistency turns out the energy levels for an isolated Na (E{sub 2p} = 31.167 eV) and K (E{sub 3p} = 18.034 eV) atoms and their respective bulk shifts of 2.401 eV and 2.754 eV, which is beyond the scope of conventional approaches. This strategy has also resulted in quantification of the local bond length, bond energy, binding energy density, and atomic cohesive energy associated with the undercoordinated atoms.
Bomont, Jean-Marc; Costa, Dino; Bretonnet, Jean-Louis
2017-06-14
We use Monte Carlo simulations to carry out a thorough analysis of structural correlations arising in a relatively dense fluid of rigid spherical particles with prototype competing interactions (short-range attractive and long-range repulsive two-Yukawa model). As the attraction strength increases, we show that the local density of the fluid displays a tiny reversal of trend within specific ranges of interparticle distances, whereupon it decreases first and increases afterwards, passing through a local minimum. Particles involved in this trend display, accordingly, distinct behaviours: for a sufficiently weak attraction, they seem to contribute to the long-wave oscillations typically heralding the formation of patterns in such fluids; for a stronger attraction, after the reversal of the local density has occurred, they form an outer shell of neighbours stabilizing the existing aggregation seeds. Following the increment of attraction, precisely in correspondence of the local density reversal, the local peak developed in the structure factor at small wavevectors markedly rises, signalling-in agreement with recent structural criteria-the onset of a clustered state. A detailed cluster analysis of microscopic configurations fully validates this picture.
Directory of Open Access Journals (Sweden)
Ai-Min Yang
2014-01-01
Full Text Available The local fractional Laplace variational iteration method was applied to solve the linear local fractional partial differential equations. The local fractional Laplace variational iteration method is coupled by the local fractional variational iteration method and Laplace transform. The nondifferentiable approximate solutions are obtained and their graphs are also shown.
International Nuclear Information System (INIS)
Zhu Yun; Zheng Zhi-Gang; Yang Jun-Zhong
2013-01-01
Dynamics of a one-dimensional array of non-locally coupled Kuramoto phase oscillators with an external potential is studied. A four-cluster chimera state is observed for the moderate strength of the external potential. Different from the clustered chimera states studied before, the instantaneous frequencies of the oscillators in a synchronized cluster are different in the presence of the external potential. As the strength of the external potential increases, a bifurcation from the two-cluster chimera state to the four-cluster chimera states can be found. These phenomena are well predicted analytically with the help of the Ott—Antonsen ansatz. (general)
Simple method to calculate percolation, Ising and Potts clusters
International Nuclear Information System (INIS)
Tsallis, C.
1981-01-01
A procedure ('break-collapse method') is introduced which considerably simplifies the calculation of two - or multirooted clusters like those commonly appearing in real space renormalization group (RG) treatments of bond-percolation, and pure and random Ising and Potts problems. The method is illustrated through two applications for the q-state Potts ferromagnet. The first of them concerns a RG calculation of the critical exponent ν for the isotropic square lattice: numerical consistence is obtained (particularly for q→0) with den Nijs conjecture. The second application is a compact reformulation of the standard star-triangle and duality transformations which provide the exact critical temperature for the anisotropic triangular and honeycomb lattices. (Author) [pt
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
Directory of Open Access Journals (Sweden)
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.
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.
Alexander, Nathan; Woetzel, Nils; Meiler, Jens
2011-02-01
Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.
Directory of Open Access Journals (Sweden)
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.
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.
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Rosemary M McCloskey
2017-11-01
Full Text Available Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP, which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85% and specificity (91% than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46% as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where
Clusters and local development: the case of the textile district of Atuntaqui
Directory of Open Access Journals (Sweden)
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.
A comparison of heuristic and model-based clustering methods for dietary pattern analysis.
Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia
2016-02-01
Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.; Jun, M.; Szunyogh, I.; Genton, Marc G.
2015-01-01
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
Kitis, Emine; Türkel, Ali
2017-01-01
The aim of this study is to find out Turkish pre-service teachers' views on effectiveness of cluster method as a writing teaching method. The Cluster Method can be defined as a connotative creative writing method. The way the method works is that the person who brainstorms on connotations of a word or a concept in abscence of any kind of…
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
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.; 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.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.; Jun, M.; Szunyogh, I.; Genton, Marc G.
2015-01-01
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 (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.
Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method
Directory of Open Access Journals (Sweden)
Yimei Wang
2018-04-01
Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.
An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm
Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin
2018-04-01
Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.
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
Directory of Open Access Journals (Sweden)
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.
Willem van Winden; Paulus Woets
2004-01-01
Regional interfirm networks are believed to be a vehicle for innovation and regional economic growth. From this perspective, local and regional governments are increasingly trying to promote these types of networks. This article discusses the relation between strategic networks and local development. It focuses on the role of local institutions that support strategic networking in ICT clusters in a number of European cities. It also discusses and analyses the way local and national government...
Experience of BESIII data production with local cluster and distributed computing model
International Nuclear Information System (INIS)
Deng, Z Y; Li, W D; Liu, H M; Sun, Y Z; Zhang, X M; Lin, L; Nicholson, C; Zhemchugov, A
2012-01-01
The BES III detector is a new spectrometer which works on the upgraded high-luminosity collider, BEPCII. The BES III experiment studies physics in the tau-charm energy region from 2 GeV to 4.6 GeV . From 2009 to 2011, BEPCII has produced 106M ψ(2S) events, 225M J/ψ events, 2.8 fb −1 ψ(3770) data, and 500 pb −1 data at 4.01 GeV. All the data samples were processed successfully and many important physics results have been achieved based on these samples. Doing data production correctly and efficiently with limited CPU and storage resources is a big challenge. This paper will describe the implementation of the experiment-specific data production for BESIII in detail, including data calibration with event-level parallel computing model, data reconstruction, inclusive Monte Carlo generation, random trigger background mixing and multi-stream data skimming. Now, with the data sample increasing rapidly, there is a growing demand to move from solely using a local cluster to a more distributed computing model. A distributed computing environment is being set up and expected to go into production use in 2012. The experience of BESIII data production, both with a local cluster and with a distributed computing model, is presented here.
HUBBLE SPACE TELESCOPE SNAPSHOT SEARCH FOR PLANETARY NEBULAE IN GLOBULAR CLUSTERS OF THE LOCAL GROUP
Energy Technology Data Exchange (ETDEWEB)
Bond, Howard E., E-mail: heb11@psu.edu [Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802 (United States)
2015-04-15
Single stars in ancient globular clusters (GCs) are believed incapable of producing planetary nebulae (PNs), because their post-asymptotic-giant-branch evolutionary timescales are slower than the dissipation timescales for PNs. Nevertheless, four PNs are known in Galactic GCs. Their existence likely requires more exotic evolutionary channels, including stellar mergers and common-envelope binary interactions. I carried out a snapshot imaging search with the Hubble Space Telescope (HST) for PNs in bright Local Group GCs outside the Milky Way. I used a filter covering the 5007 Å nebular emission line of [O iii], and another one in the nearby continuum, to image 66 GCs. Inclusion of archival HST frames brought the total number of extragalactic GCs imaged at 5007 Å to 75, whose total luminosity slightly exceeds that of the entire Galactic GC system. I found no convincing PNs in these clusters, aside from one PN in a young M31 cluster misclassified as a GC, and two PNs at such large angular separations from an M31 GC that membership is doubtful. In a ground-based spectroscopic survey of 274 old GCs in M31, Jacoby et al. found three candidate PNs. My HST images of one of them suggest that the [O iii] emission actually arises from ambient interstellar medium rather than a PN; for the other two candidates, there are broadband archival UV HST images that show bright, blue point sources that are probably the PNs. In a literature search, I also identified five further PN candidates lying near old GCs in M31, for which follow-up observations are necessary to confirm their membership. The rates of incidence of PNs are similar, and small but nonzero, throughout the GCs of the Local Group.
Profiling Local Optima in K-Means Clustering: Developing a Diagnostic Technique
Steinley, Douglas
2006-01-01
Using the cluster generation procedure proposed by D. Steinley and R. Henson (2005), the author investigated the performance of K-means clustering under the following scenarios: (a) different probabilities of cluster overlap; (b) different types of cluster overlap; (c) varying samples sizes, clusters, and dimensions; (d) different multivariate…
Directory of Open Access Journals (Sweden)
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
The cosmological analysis of X-ray cluster surveys - I. A new method for interpreting number counts
Clerc, N.; Pierre, M.; Pacaud, F.; Sadibekova, T.
2012-07-01
We present a new method aimed at simplifying the cosmological analysis of X-ray cluster surveys. It is based on purely instrumental observable quantities considered in a two-dimensional X-ray colour-magnitude diagram (hardness ratio versus count rate). The basic principle is that even in rather shallow surveys, substantial information on cluster redshift and temperature is present in the raw X-ray data and can be statistically extracted; in parallel, such diagrams can be readily predicted from an ab initio cosmological modelling. We illustrate the methodology for the case of a 100-deg2XMM survey having a sensitivity of ˜10-14 erg s-1 cm-2 and fit at the same time, the survey selection function, the cluster evolutionary scaling relations and the cosmology; our sole assumption - driven by the limited size of the sample considered in the case study - is that the local cluster scaling relations are known. We devote special attention to the realistic modelling of the count-rate measurement uncertainties and evaluate the potential of the method via a Fisher analysis. In the absence of individual cluster redshifts, the count rate and hardness ratio (CR-HR) method appears to be much more efficient than the traditional approach based on cluster counts (i.e. dn/dz, requiring redshifts). In the case where redshifts are available, our method performs similar to the traditional mass function (dn/dM/dz) for the purely cosmological parameters, but constrains better parameters defining the cluster scaling relations and their evolution. A further practical advantage of the CR-HR method is its simplicity: this fully top-down approach totally bypasses the tedious steps consisting in deriving cluster masses from X-ray temperature measurements.
Indian Academy of Sciences (India)
2017-09-27
Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...
Indian Academy of Sciences (India)
environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.
Hale, Robert L.; Dougherty, Donna
1988-01-01
Compared the efficacy of two methods of cluster analysis, the unweighted pair-groups method using arithmetic averages (UPGMA) and Ward's method, for students grouped on intelligence, achievement, and social adjustment by both clustering methods. Found UPGMA more efficacious based on output, on cophenetic correlation coefficients generated by each…
Mustaniroh, S. A.; Effendi, U.; Silalahi, R. L. R.; Sari, T.; Ala, M.
2018-03-01
The purposes of this research were to determine the grouping of apples dodol small and medium enterprises (SMEs) in Batu City and to determine an appropriate development strategy for each cluster. The methods used for clustering SMEs was k-means. The Analytical Hierarchy Process (AHP) approach was then applied to determine the development strategy priority for each cluster. The variables used in grouping include production capacity per month, length of operation, investment value, average sales revenue per month, amount of SMEs assets, and the number of workers. Several factors were considered in AHP include industry cluster, government, as well as related and supporting industries. Data was collected using the methods of questionaire and interviews. SMEs respondents were selected among SMEs appels dodol in Batu City using purposive sampling. The result showed that two clusters were formed from five apples dodol SMEs. The 1stcluster of apples dodol SMEs, classified as small enterprises, included SME A, SME C, and SME D. The 2ndcluster of SMEs apples dodol, classified as medium enterprises, consisted of SME B and SME E. The AHP results indicated that the priority development strategy for the 1stcluster of apples dodol SMEs was improving quality and the product standardisation, while for the 2nd cluster was increasing the marketing access.
Multivariate localization methods for ensemble Kalman filtering
S. Roh; M. Jun; I. Szunyogh; M. G. Genton
2015-01-01
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 ...
Cluster-cell calculation using the method of generalized homogenization
International Nuclear Information System (INIS)
Laletin, N.I.; Boyarinov, V.F.
1988-01-01
The generalized-homogenization method (GHM), used for solving the neutron transfer equation, was applied to calculating the neutron distribution in the cluster cell with a series of cylindrical cells with cylindrically coaxial zones. Single-group calculations of the technological channel of the cell of an RBMK reactor were performed using GHM. The technological channel was understood to be the reactor channel, comprised of the zirconium rod, the water or steam-water mixture, the uranium dioxide fuel element, and the zirconium tube, together with the adjacent graphite layer. Calculations were performed for channels with no internal sources and with unit incoming current at the external boundary as well as for channels with internal sources and zero current at the external boundary. The PRAKTINETs program was used to calculate the symmetric neutron distributions in the microcell and in channels with homogenized annular zones. The ORAR-TsM program was used to calculate the antisymmetric distribution in the microcell. The accuracy of the calculations were compared for the two channel versions
Comparative analysis of clustering methods for gene expression time course data
Directory of Open Access Journals (Sweden)
Ivan G. Costa
2004-01-01
Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.
Directory of Open Access Journals (Sweden)
V.Ya. Nusinov
2017-08-01
Full Text Available The research determines that the current existing methods of enterprise’s economic potential estimation are based on the use of additive, multiplicative and rating models. It is determined that the existing methods have a row of defects. For example, not all the methods take into account the branch features of the analysis, and also the level of development of the enterprise comparatively with other enterprises. It is suggested to level such defects by an account at the estimation of potential integral level not only by branch features of enterprises activity but also by the intra-account economic clusterization of such enterprises. Scientific works which are connected with the using of clusters for the estimation of economic potential are generalized. According to the results of generalization it is determined that it is possible to distinguish 9 scientific approaches in this direction: the use of natural clusterization of enterprises with the purpose of estimation and increase of region potential; the use of natural clusterization of enterprises with the purpose of estimation and increase of industry potential; use of artificial clusterization of enterprises with the purpose of estimation and increase of region potential; use of artificial clusterization of enterprises with the purpose of estimation and increase of industry potential; the use of artificial clusterization of enterprises with the purpose of clustering potential estimation; the use of artificial clusterization of enterprises with the purpose of estimation of clustering competitiveness potential; the use of natural (artificial clusterization for the estimation of clustering efficiency; the use of natural (artificial clusterization for the increase of level at region (industries development; the use of methods of economic potential of region (industries estimation or its constituents for the construction of the clusters. It is determined that the use of clusterization method in
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.
Local defect correction for boundary integral equation methods
Kakuba, G.; Anthonissen, M.J.H.
2014-01-01
The aim in this paper is to develop a new local defect correction approach to gridding for problems with localised regions of high activity in the boundary element method. The technique of local defect correction has been studied for other methods as finite difference methods and finite volume
Atomic and electronic structure of clusters from car-Parrinello method
International Nuclear Information System (INIS)
Kumar, V.
1994-06-01
With the development of ab-initio molecular dynamics method, it has now become possible to study the static and dynamical properties of clusters containing up to a few tens of atoms. Here I present a review of the method within the framework of the density functional theory and pseudopotential approach to represent the electron-ion interaction and discuss some of its applications to clusters. Particular attention is focussed on the structure and bonding properties of clusters as a function of their size. Applications to clusters of alkali metals and Al, non-metal - metal transition in divalent metal clusters, molecular clusters of carbon and Sb are discussed in detail. Some results are also presented on mixed clusters. (author). 121 refs, 24 ifigs
Globular clusters in high-redshift dwarf galaxies: a case study from the Local Group
Zick, Tom O.; Weisz, Daniel R.; Boylan-Kolchin, Michael
2018-06-01
We present the reconstructed evolution of rest-frame ultraviolet (UV) luminosities of the most massive Milky Way dwarf spheroidal satellite galaxy, Fornax, and its five globular clusters (GCs) across redshift, based on analysis of the stellar fossil record and stellar population synthesis modelling. We find that (1) Fornax's (proto-)GCs can generate 10-100 times more UV flux than the field population, despite comprising 3. (3) GC formation can introduce order-of-magnitude errors in abundance matching. We also find that some compact HFF objects are consistent with the reconstructed properties of Fornax's GCs at the same redshifts (e.g. surface brightness, star formation rate), suggesting we may have already detected proto-GCs in the early Universe. Finally, we discuss the prospects for improving the connections between local GCs and proto-GCs detected in the early Universe.
Matsumoto, Akihiro; Tachibana, Masao
2017-01-01
Even when the body is stationary, the whole retinal image is always in motion by fixational eye movements and saccades that move the eye between fixation points. Accumulating evidence indicates that the brain is equipped with specific mechanisms for compensating for the global motion induced by these eye movements. However, it is not yet fully understood how the retina processes global motion images during eye movements. Here we show that global motion images evoke novel coordinated firing in retinal ganglion cells (GCs). We simultaneously recorded the firing of GCs in the goldfish isolated retina using a multi-electrode array, and classified each GC based on the temporal profile of its receptive field (RF). A moving target that accompanied the global motion (simulating a saccade following a period of fixational eye movements) modulated the RF properties and evoked synchronized and correlated firing among local clusters of the specific GCs. Our findings provide a novel concept for retinal information processing during eye movements.
Directory of Open Access Journals (Sweden)
Peixin Zhao
2013-01-01
Full Text Available Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.
Trend analysis using non-stationary time series clustering based on the finite element method
Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.
2014-01-01
In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods ...
Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang
2017-01-01
Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.
Application of hierarchical clustering method to classify of space-time rainfall patterns
Yu, Hwa-Lung; Chang, Tu-Je
2010-05-01
Understanding the local precipitation patterns is essential to the water resources management and flooding mitigation. The precipitation patterns can vary in space and time depending upon the factors from different spatial scales such as local topological changes and macroscopic atmospheric circulation. The spatiotemporal variation of precipitation in Taiwan is significant due to its complex terrain and its location at west pacific and subtropical area, where is the boundary between the pacific ocean and Asia continent with the complex interactions among the climatic processes. This study characterizes local-scale precipitation patterns by classifying the historical space-time precipitation records. We applied the hierarchical ascending clustering method to analyze the precipitation records from 1960 to 2008 at the six rainfall stations located in Lan-yang catchment at the northeast of the island. Our results identify the four primary space-time precipitation types which may result from distinct driving forces from the changes of atmospheric variables and topology at different space-time scales. This study also presents an important application of the statistical downscaling to combine large-scale upper-air circulation with local space-time precipitation patterns.
Anharmonic effects in the quantum cluster equilibrium method
von Domaros, Michael; Perlt, Eva
2017-03-01
The well-established quantum cluster equilibrium (QCE) model provides a statistical thermodynamic framework to apply high-level ab initio calculations of finite cluster structures to macroscopic liquid phases using the partition function. So far, the harmonic approximation has been applied throughout the calculations. In this article, we apply an important correction in the evaluation of the one-particle partition function and account for anharmonicity. Therefore, we implemented an analytical approximation to the Morse partition function and the derivatives of its logarithm with respect to temperature, which are required for the evaluation of thermodynamic quantities. This anharmonic QCE approach has been applied to liquid hydrogen chloride and cluster distributions, and the molar volume, the volumetric thermal expansion coefficient, and the isobaric heat capacity have been calculated. An improved description for all properties is observed if anharmonic effects are considered.
A crystalline cluster method for deep impurities in insulators
International Nuclear Information System (INIS)
Guimaraes, P.S.
1983-01-01
An 'ab initio' self-consistent-field crystalline-cluster approach to the study of deep impurity states in insulators is proposed. It is shown that, in spite of being a cluster calculation, the interaction of the impurity with the crystal environment is fully taken into account. It is also shown that the present representation of the impurity states is, at least, as precise as the crystalline cluster representation of the pure crystal electronic structure. The procedure has been tested by performing the calculation of the electronic structure of the U center in a sodium chloride crystal, and it has been observed that the calculated GAMMA 1 - GAMMA 15 absorption energy is in good agreement with experiment. (Author) [pt
A crystalline cluster method for deep impurities in insulators
International Nuclear Information System (INIS)
Guimaraes, P.S.
1983-01-01
An ''ab initio'' self-consistent-field crysttalline-cluster approach to the study of deep impurity states in insulators is proposed. It is shown that, in spite of being a cluster calculation, the interaction of the impurity with the crystal environment is fully taken into account. It is also shown that the present representation of the impurity states is, at least, as precise as the crystalline cluster representation of the pure crystal electronic structure. The procedure has been tested by performing the calculation of the electronic structure of the U center in a sodium chloride crystal, and it has been observed that the calculated γ 1 - γ 15 absorption energy is in good agreement with experiment. (author) [pt
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.
A dynamic lattice searching method with rotation operation for optimization of large clusters
International Nuclear Information System (INIS)
Wu Xia; Cai Wensheng; Shao Xueguang
2009-01-01
Global optimization of large clusters has been a difficult task, though much effort has been paid and many efficient methods have been proposed. During our works, a rotation operation (RO) is designed to realize the structural transformation from decahedra to icosahedra for the optimization of large clusters, by rotating the atoms below the center atom with a definite degree around the fivefold axis. Based on the RO, a development of the previous dynamic lattice searching with constructed core (DLSc), named as DLSc-RO, is presented. With an investigation of the method for the optimization of Lennard-Jones (LJ) clusters, i.e., LJ 500 , LJ 561 , LJ 600 , LJ 665-667 , LJ 670 , LJ 685 , and LJ 923 , Morse clusters, silver clusters by Gupta potential, and aluminum clusters by NP-B potential, it was found that both the global minima with icosahedral and decahedral motifs can be obtained, and the method is proved to be efficient and universal.
Luo, Junhai; Fu, Liang
2017-06-09
With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.
Directory of Open Access Journals (Sweden)
Junhai Luo
2017-06-01
Full Text Available With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS, which is collected from Access Points (APs. The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.
A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization.
He, Sheng; Samara, Petros; Burgers, Jan; Schomaker, Lambert
2016-11-01
It is of essential importance for historians to know the date and place of origin of the documents they study. It would be a huge advancement for historical scholars if it would be possible to automatically estimate the geographical and temporal provenance of a handwritten document by inferring them from the handwriting style of such a document. We propose a multiple-label guided clustering algorithm to discover the correlations between the concrete low-level visual elements in historical documents and abstract labels, such as date and location. First, a novel descriptor, called histogram of orientations of handwritten strokes, is proposed to extract and describe the visual elements, which is built on a scale-invariant polar-feature space. In addition, the multi-label self-organizing map (MLSOM) is proposed to discover the correlations between the low-level visual elements and their labels in a single framework. Our proposed MLSOM can be used to predict the labels directly. Moreover, the MLSOM can also be considered as a pre-structured clustering method to build a codebook, which contains more discriminative information on date and geography. The experimental results on the medieval paleographic scale data set demonstrate that our method achieves state-of-the-art results.
Evaluation of local corrosion life by statistical method
International Nuclear Information System (INIS)
Kato, Shunji; Kurosawa, Tatsuo; Takaku, Hiroshi; Kusanagi, Hideo; Hirano, Hideo; Kimura, Hideo; Hide, Koichiro; Kawasaki, Masayuki
1987-01-01
In this paper, for the purpose of achievement of life extension of light water reactor, we examined the evaluation of local corrosion by satistical method and its application of nuclear power plant components. There are many evaluation examples of maximum cracking depth of local corrosion by dowbly exponential distribution. This evaluation method has been established. But, it has not been established that we evaluate service lifes of construction materials by satistical method. In order to establish of service life evaluation by satistical method, we must strive to collect local corrosion dates and its analytical researchs. (author)
Wide-banded NTC radiation: local to remote observations by the four Cluster satellites
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P. M. E. Décréau
2015-10-01
Full Text Available The Cluster multi-point mission offers a unique collection of non-thermal continuum (NTC radio waves observed in the 2–80 kHz frequency range over almost 15 years, from various view points over the radiating plasmasphere. Here we present rather infrequent case events, such as when primary electrostatic sources of such waves are embedded within the plasmapause boundary far from the magnetic equatorial plane. The spectral signature of the emitted electromagnetic waves is structured as a series of wide harmonic bands within the range covered by the step in plasma frequency encountered at the boundary. Developing the concept that the frequency distance df between harmonic bands measures the magnetic field magnitude B at the source (df = Fce, electron gyrofrequency, we analyse three selected events. The first one (studied in Grimald et al., 2008 presents electric field signatures observed by a Cluster constellation of small size (~ 200 to 1000 km spacecraft separation placed in the vicinity of sources. The electric field frequency spectra display frequency peaks placed at frequencies fs = n df (n being an integer, with df of the order of Fce values encountered at the plasmapause by the spacecraft. The second event, taken from the Cluster tilt campaign, leads to a 3-D view of NTC waves ray path orientations and to a localization of a global source region at several Earth radii (RE from Cluster (Décréau et al., 2013. The measured spectra present successive peaks placed at fs ~ (n+ 1/2 df. Next, considering if both situations might be two facets of the same phenomenon, we analyze a third event. The Cluster fleet, configured into a constellation of large size (~ 8000 to 25 000 km spacecraft separation, allows us to observe wide-banded NTC waves at different distances from their sources. Two new findings can be derived from our analysis. First, we point out that a large portion of the plasmasphere boundary layer, covering a large range of magnetic
Identification of rural landscape classes through a GIS clustering method
Directory of Open Access Journals (Sweden)
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.
A Local Search Algorithm for Clustering in Software as a Service Networks
J.P. van der Gaast (Jelmer); C.A. Rietveld (Niels); A.F. Gabor (Adriana); Y. Zhang (Yingqian)
2011-01-01
textabstractIn this paper we present and analyze a model for clustering in networks that offer Software as a Service (SaaS). In this problem, organizations requesting a set of applications have to be assigned to clusters such that the costs of opening clusters and installing the necessary
A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis
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Huanhuan Li
2017-08-01
Full Text Available The Shipboard Automatic Identification System (AIS is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW, a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our
A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis.
Li, Huanhuan; Liu, Jingxian; Liu, Ryan Wen; Xiong, Naixue; Wu, Kefeng; Kim, Tai-Hoon
2017-08-04
The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with
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.
Local Convergence and Radius of Convergence for Modified Newton Method
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Măruşter Ştefan
2017-12-01
Full Text Available We investigate the local convergence of modified Newton method, i.e., the classical Newton method in which the derivative is periodically re-evaluated. Based on the convergence properties of Picard iteration for demicontractive mappings, we give an algorithm to estimate the local radius of convergence for considered method. Numerical experiments show that the proposed algorithm gives estimated radii which are very close to or even equal with the best ones.
Elsässer, Thilo
Exposure to radiation of high-energy and highly charged ions (HZE) causes a major risk to human beings, since in long term space explorations about 10 protons per month and about one HZE particle per month hit each cell nucleus (1). Despite the larger number of light ions, the high ionisation power of HZE particles and its corresponding more complex damage represents a major hazard for astronauts. Therefore, in order to get a reasonable risk estimate, it is necessary to take into account the entire mixed radiation field. Frequently, neoplastic cell transformation serves as an indicator for the oncogenic potential of radiation exposure. It can be measured for a small number of ion and energy combinations. However, due to the complexity of the radiation field it is necessary to know the contribution to the radiation damage of each ion species for the entire range of energies. Therefore, a model is required which transfers the few experimental data to other particles with different LETs. We use the Local Effect Model (LEM) (2) with its cluster extension (3) to calculate the relative biological effectiveness (RBE) of neoplastic transformation. It was originally developed in the framework of hadrontherapy and is applicable for a large range of ions and energies. The input parameters for the model include the linear-quadratic parameters for the induction of lethal events as well as for the induction of transformation events per surviving cell. Both processes of cell inactivation and neoplastic transformation per viable cell are combined to eventually yield the RBE for cell transformation. We show that the Local Effect Model is capable of predicting the RBE of neoplastic cell transformation for a broad range of ions and energies. The comparison of experimental data (4) with model calculations shows a reasonable agreement. We find that the cluster extension results in a better representation of the measured RBE values. With this model it should be possible to better
Consensus of satellite cluster flight using an energy-matching optimal control method
Luo, Jianjun; Zhou, Liang; Zhang, Bo
2017-11-01
This paper presents an optimal control method for consensus of satellite cluster flight under a kind of energy matching condition. Firstly, the relation between energy matching and satellite periodically bounded relative motion is analyzed, and the satellite energy matching principle is applied to configure the initial conditions. Then, period-delayed errors are adopted as state variables to establish the period-delayed errors dynamics models of a single satellite and the cluster. Next a novel satellite cluster feedback control protocol with coupling gain is designed, so that the satellite cluster periodically bounded relative motion consensus problem (period-delayed errors state consensus problem) is transformed to the stability of a set of matrices with the same low dimension. Based on the consensus region theory in the research of multi-agent system consensus issues, the coupling gain can be obtained to satisfy the requirement of consensus region and decouple the satellite cluster information topology and the feedback control gain matrix, which can be determined by Linear quadratic regulator (LQR) optimal method. This method can realize the consensus of satellite cluster period-delayed errors, leading to the consistency of semi-major axes (SMA) and the energy-matching of satellite cluster. Then satellites can emerge the global coordinative cluster behavior. Finally the feasibility and effectiveness of the present energy-matching optimal consensus for satellite cluster flight is verified through numerical simulations.
An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
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XiuLi Zhao
2015-01-01
Full Text Available 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.
Reply to ``Comment on `Cluster methods for strongly correlated electron systems' ''
Biroli, G.; Kotliar, G.
2005-01-01
We reply to the Comment by Aryanpour, Maier, and Jarrell [Phys. Rev. B 71, 037101 (2005)] on our paper [Phys. Rev. B 65, 155112 (2002)]. We demonstrate, using general arguments and explicit examples, that whenever the correlation length is finite, local observables converge exponentially fast in the cluster size Lc within cellular dynamical mean field theory. This is a faster rate of convergence than the 1/ L2c behavior of the dynamical cluster approximation, thus refuting the central assertion of their Comment.
Energy-Based Acoustic Source Localization Methods: A Survey
Directory of Open Access Journals (Sweden)
Wei Meng
2017-02-01
Full Text Available Energy-based source localization is an important problem in wireless sensor networks (WSNs, which has been studied actively in the literature. Numerous localization algorithms, e.g., maximum likelihood estimation (MLE and nonlinear-least-squares (NLS methods, have been reported. In the literature, there are relevant review papers for localization in WSNs, e.g., for distance-based localization. However, not much work related to energy-based source localization is covered in the existing review papers. Energy-based methods are proposed and specially designed for a WSN due to its limited sensor capabilities. This paper aims to give a comprehensive review of these different algorithms for energy-based single and multiple source localization problems, their merits and demerits and to point out possible future research directions.
Comparison of Localization Methods for a Robot Soccer Team
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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.
Comparison of Localization Methods for a Robot Soccer Team
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Hatice Kose
2006-12-01
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.
Directory of Open Access Journals (Sweden)
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.
A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES
Energy Technology Data Exchange (ETDEWEB)
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.
Investigation of the cluster formation in lithium niobate crystals by computer modeling method
Energy Technology Data Exchange (ETDEWEB)
Voskresenskii, V. M.; Starodub, O. R., E-mail: ol-star@mail.ru; Sidorov, N. V.; Palatnikov, M. N. [Russian Academy of Sciences, Tananaev Institute of Chemistry and Technology of Rare Earth Elements and Mineral Raw Materials, Kola Science Centre (Russian Federation)
2017-03-15
The processes occurring upon the formation of energetically equilibrium oxygen-octahedral clusters in the ferroelectric phase of a stoichiometric lithium niobate (LiNbO{sub 3}) crystal have been investigated by the computer modeling method within the semiclassical atomistic model. An energetically favorable cluster size (at which a structure similar to that of a congruent crystal is organized) is shown to exist. A stoichiometric cluster cannot exist because of the electroneutrality loss. The most energetically favorable cluster is that with a Li/Nb ratio of about 0.945, a value close to the lithium-to-niobium ratio for a congruent crystal.
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…
An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.
Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei
2013-05-01
Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.
Performance Analysis of Entropy Methods on K Means in Clustering Process
Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib
2017-12-01
K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.
Directory of Open Access Journals (Sweden)
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.
Traditional methods of social control in Afikpo north local ...
African Journals Online (AJOL)
Traditional methods of social control in Afikpo north local government area, Ebonyi state south eastern Nigeria. ... Journal of Religion and Human Relations ... simple percentage was used in presenting and interpreting the quantitative data.
Local defect correction for boundary integral equation methods
Kakuba, G.; Anthonissen, M.J.H.
2013-01-01
This paper presents a new approach to gridding for problems with localised regions of high activity. The technique of local defect correction has been studied for other methods as ¿nite difference methods and ¿nite volume methods. In this paper we develop the technique for the boundary element
A two-stage method for microcalcification cluster segmentation in mammography by deformable models
International Nuclear Information System (INIS)
Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.; Karahaliou, A.; Costaridou, L.; Vassiou, K.
2015-01-01
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 cluster , average of minimum distance—AMINDIST cluster ) and the area overlap measure (AOM 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 tenfold cross
The swift UVOT stars survey. I. Methods and test clusters
Energy Technology Data Exchange (ETDEWEB)
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.
The swift UVOT stars survey. I. Methods and test clusters
International Nuclear Information System (INIS)
Siegel, Michael H.; Porterfield, Blair L.; Linevsky, Jacquelyn S.; Bond, Howard E.; Hoversten, Erik A.; Berrier, Joshua L.; Gronwall, Caryl A.; Holland, Stephen T.; Breeveld, Alice A.; Brown, Peter J.
2014-01-01
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.
Zhang, Ying; Moges, Semu; Block, Paul
2018-01-01
Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.
Directory of Open Access Journals (Sweden)
Mário Mestria
2013-08-01
Full Text Available The Clustered Traveling Salesman Problem (CTSP is a generalization of the Traveling Salesman Problem (TSP in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. The CTSP is NP-hard and, in this context, we are proposed heuristic methods for the CTSP using GRASP, Path Relinking and Variable Neighborhood Descent (VND. The heuristic methods were tested using Euclidean instances with up to 2000 vertices and clusters varying between 4 to 150 vertices. The computational tests were performed to compare the performance of the heuristic methods with an exact algorithm using the Parallel CPLEX software. The computational results showed that the hybrid heuristic method using VND outperforms other heuristic methods.
International Nuclear Information System (INIS)
Anderson, Ryan B.; Bell, James F.; Wiens, Roger C.; Morris, Richard V.; Clegg, Samuel M.
2012-01-01
We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO 2 at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by ∼ 3 wt.%. The statistical significance of these improvements was ∼ 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and specifically
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.
Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke
2017-01-01
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.
Song, Lei; Gao, Jungang; Wang, Sheng; Hu, Huasi; Guo, Youmin
2017-01-01
Estimation of the pleural effusion's volume is an important clinical issue. The existing methods cannot assess it accurately when there is large volume of liquid in the pleural cavity and/or the patient has some other disease (e.g. pneumonia). In order to help solve this issue, the objective of this study is to develop and test a novel algorithm using B-spline and local clustering level set method jointly, namely BLL. The BLL algorithm was applied to a dataset involving 27 pleural effusions detected on chest CT examination of 18 adult patients with the presence of free pleural effusion. Study results showed that average volumes of pleural effusion computed using the BLL algorithm and assessed manually by the physicians were 586 ml±339 ml and 604±352 ml, respectively. For the same patient, the volume of the pleural effusion, segmented semi-automatically, was 101.8% ±4.6% of that was segmented manually. Dice similarity was found to be 0.917±0.031. The study demonstrated feasibility of applying the new BLL algorithm to accurately measure the volume of pleural effusion.
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.
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. PMID:27124610
International Nuclear Information System (INIS)
Valotti, Andrea
2016-01-01
Cosmology is one of the fundamental pillars of astrophysics, as such it contains many unsolved puzzles. To investigate some of those puzzles, we analyze X-ray surveys of galaxy clusters. These surveys are possible thanks to the bremsstrahlung emission of the intra-cluster medium. The simultaneous fit of cluster counts as a function of mass and distance provides an independent measure of cosmological parameters such as Ω m , σ s , and the dark energy equation of state w0. A novel approach to cosmological analysis using galaxy cluster data, called top-down, was developed in N. Clerc et al. (2012). This top-down approach is based purely on instrumental observables that are considered in a two-dimensional X-ray color-magnitude diagram. The method self-consistently includes selection effects and scaling relationships. It also provides a means of bypassing the computation of individual cluster masses. My work presents an extension of the top-down method by introducing the apparent size of the cluster, creating a three-dimensional X-ray cluster diagram. The size of a cluster is sensitive to both the cluster mass and its angular diameter, so it must also be included in the assessment of selection effects. The performance of this new method is investigated using a Fisher analysis. In parallel, I have studied the effects of the intrinsic scatter in the cluster size scaling relation on the sample selection as well as on the obtained cosmological parameters. To validate the method, I estimate uncertainties of cosmological parameters with MCMC method Amoeba minimization routine and using two simulated XMM surveys that have an increasing level of complexity. The first simulated survey is a set of toy catalogues of 100 and 10000 deg 2 , whereas the second is a 1000 deg 2 catalogue that was generated using an Aardvark semi-analytical N-body simulation. This comparison corroborates the conclusions of the Fisher analysis. In conclusion, I find that a cluster diagram that accounts
International Nuclear Information System (INIS)
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.
2016-01-01
We show that cluster magnetism in ferromagnetic amorphous Fe_6_7Cr_1_8B_1_5 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 Fe_3B clusters and subcluster atomic α-(Fe, Cr, B) groupings, D=10–20 Å, in disordered intercluster medium. For initial sample and irradiated one (Φ=1.5×10"1"8 ions/cm"2) superconductivity exists in the cluster shells of metallic α-(Fe, Cr) phase where ferromagnetism of iron is counterbalanced by antiferromagnetism of chromium. At Φ=3×10"1"8 ions/cm"2, 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)~T"2 is determined by the electron scattering on quantum defects. In strongly inhomogeneous samples, after irradiation by fluence Φ=1.5×10"1"8 ions/cm"2, the transition to a dependence ρ(T)~T"1"/"2 is caused by the effects of weak localization. In more homogeneous samples, at Φ=3×10"1"8 ions/cm"2, a return to the dependence ρ(T)~T"2 is observed. - Highlights: • The samples at high dose of ion irradiation become more homogeneous. • Gigantic increase in density of the samples (twice as much) is observed. • Ferromagnetism in large Fe–Cr clusters is restored. • Ferromagnetism of Fe–Cr clusters suppresses local superconductivity in them. • The participation of quantum defects in scattering of electrons is returned.
A clustering based method to evaluate soil corrosivity for pipeline external integrity management
International Nuclear Information System (INIS)
Yajima, Ayako; Wang, Hui; Liang, Robert Y.; Castaneda, Homero
2015-01-01
One important category of transportation infrastructure is underground pipelines. Corrosion of these buried pipeline systems may cause pipeline failures with the attendant hazards of property loss and fatalities. Therefore, developing the capability to estimate the soil corrosivity is important for designing and preserving materials and for risk assessment. The deterioration rate of metal is highly influenced by the physicochemical characteristics of a material and the environment of its surroundings. In this study, the field data obtained from the southeast region of Mexico was examined using various data mining techniques to determine the usefulness of these techniques for clustering soil corrosivity level. Specifically, the soil was classified into different corrosivity level clusters by k-means and Gaussian mixture model (GMM). In terms of physical space, GMM shows better separability; therefore, the distributions of the material loss of the buried petroleum pipeline walls were estimated via the empirical density within GMM clusters. The soil corrosivity levels of the clusters were determined based on the medians of metal loss. The proposed clustering method was demonstrated to be capable of classifying the soil into different levels of corrosivity severity. - Highlights: • The clustering approach is applied to the data extracted from a real-life pipeline system. • Soil properties in the right-of-way are analyzed via clustering techniques to assess corrosivity. • GMM is selected as the preferred method for detecting the hidden pattern of in-situ data. • K–W test is performed for significant difference of corrosivity level between clusters
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].
Directory of Open Access Journals (Sweden)
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].
Global/local methods for probabilistic structural analysis
Millwater, H. R.; Wu, Y.-T.
1993-04-01
A probabilistic global/local method is proposed to reduce the computational requirements of probabilistic structural analysis. A coarser global model is used for most of the computations with a local more refined model used only at key probabilistic conditions. The global model is used to establish the cumulative distribution function (cdf) and the Most Probable Point (MPP). The local model then uses the predicted MPP to adjust the cdf value. The global/local method is used within the advanced mean value probabilistic algorithm. The local model can be more refined with respect to the g1obal model in terms of finer mesh, smaller time step, tighter tolerances, etc. and can be used with linear or nonlinear models. The basis for this approach is described in terms of the correlation between the global and local models which can be estimated from the global and local MPPs. A numerical example is presented using the NESSUS probabilistic structural analysis program with the finite element method used for the structural modeling. The results clearly indicate a significant computer savings with minimal loss in accuracy.
clues: An R Package for Nonparametric Clustering Based on Local Shrinking
Directory of Open Access Journals (Sweden)
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.
Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization
Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li
2018-04-01
Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.
Phenotypic clustering: a novel method for microglial morphology analysis.
Verdonk, Franck; Roux, Pascal; Flamant, Patricia; Fiette, Laurence; Bozza, Fernando A; Simard, Sébastien; Lemaire, Marc; Plaud, Benoit; Shorte, Spencer L; Sharshar, Tarek; Chrétien, Fabrice; Danckaert, Anne
2016-06-17
Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex "dendritic-like" aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters. Using the knock-in mouse model CX3CR1(GFP/+), we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations. Our results on more than 20,000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge. Our results counteract the classical view of a homogenous regional resting
Rapacioli, Mathias; Spiegelman, Fernand; Talbi, Dahbia; Mineva, Tzonka; Goursot, Annick; Heine, Thomas; Seifert, Gotthard
2009-06-01
The density functional based tight binding (DFTB) is a semiempirical method derived from the density functional theory (DFT). It inherits therefore its problems in treating van der Waals clusters. A major error comes from dispersion forces, which are poorly described by commonly used DFT functionals, but which can be accounted for by an a posteriori treatment DFT-D. This correction is used for DFTB. The self-consistent charge (SCC) DFTB is built on Mulliken charges which are known to give a poor representation of Coulombic intermolecular potential. We propose to calculate this potential using the class IV/charge model 3 definition of atomic charges. The self-consistent calculation of these charges is introduced in the SCC procedure and corresponding nuclear forces are derived. Benzene dimer is then studied as a benchmark system with this corrected DFTB (c-DFTB-D) method, but also, for comparison, with the DFT-D. Both methods give similar results and are in agreement with references calculations (CCSD(T) and symmetry adapted perturbation theory) calculations. As a first application, pyrene dimer is studied with the c-DFTB-D and DFT-D methods. For coronene clusters, only the c-DFTB-D approach is used, which finds the sandwich configurations to be more stable than the T-shaped ones.
MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.
Tuta, Jure; Juric, Matjaz B
2018-03-24
This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.
MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method
Directory of Open Access Journals (Sweden)
Jure Tuta
2018-03-01
Full Text Available This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method, a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.. Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.
Novel Clustering Method Based on K-Medoids and Mobility Metric
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Y. Hamzaoui
2018-06-01
Full Text Available The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head.
A simple and fast method to determine the parameters for fuzzy c-means cluster analysis
DEFF Research Database (Denmark)
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...... of algorithm parameters. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random dataset but which detects cluster...... formation with maximum resolution on the edge of randomness. RESULTS: Estimation of the optimal parameter values is achieved by evaluation of the results of the clustering procedure applied to randomized datasets. In this case, the optimal value of the fuzzifier follows common rules that depend only...
A semi-supervised method to detect seismic random noise with fuzzy GK clustering
International Nuclear Information System (INIS)
Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert
2008-01-01
We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise
Kinetic methods for measuring the temperature of clusters and nanoparticles in molecular beams
International Nuclear Information System (INIS)
Makarov, Grigorii N
2011-01-01
The temperature (internal energy) of clusters and nanoparticles is an important physical parameter which affects many of their properties and the character of processes they are involved in. At the same time, determining the temperature of free clusters and nanoparticles in molecular beams is a rather complicated problem because the temperature of small particles depends on their size. In this paper, recently developed kinetic methods for measuring the temperature of clusters and nanoparticles in molecular beams are reviewed. The definition of temperature in the present context is given, and how the temperature affects the properties of and the processes involving the particles is discussed. The temperature behavior of clusters and nanoparticles near a phase transition point is analyzed. Early methods for measuring the temperature of large clusters are briefly described. It is shown that, compared to other methods, new kinetic methods are more universal and applicable for determining the temperature of clusters and nanoparticles of practically any size and composition. The future development and applications of these methods are outlined. (reviews of topical problems)
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.
Ben-Shimon, Lilach; Paul, Viktoria D; David-Kadoch, Galit; Volpe, Marina; Stümpfig, Martin; Bill, Eckhard; Mühlenhoff, Ulrich; Lill, Roland; Ben-Aroya, Shay
2018-05-30
Fe-S clusters act as co-factors of proteins with diverse functions, e.g. in DNA repair. Down-regulation of the cytosolic iron-sulfur protein assembly (CIA) machinery promotes genomic instability by the inactivation of multiple DNA repair pathways. Furthermore, CIA deficiencies are associated with so far unexplained mitotic defects. Here, we show that CIA2B and MMS19, constituents of the CIA targeting complex involved in facilitating Fe-S cluster insertion into cytosolic and nuclear target proteins, co-localize with components of the mitotic machinery. Down-regulation of CIA2B and MMS19 impairs the mitotic cycle. We identify the chromokinesin KIF4A as a mitotic component involved in these effects. KIF4A binds a Fe-S cluster in vitro through its conserved cysteine-rich domain. We demonstrate in vivo that this domain is required for the mitosis-related KIF4A localization and for the mitotic defects associated with KIF4A knockout. KIF4A is the first identified mitotic component carrying such a post-translational modification. These findings suggest that the lack of Fe-S clusters in KIF4A upon down-regulation of the CIA targeting complex contributes to the mitotic defects. © 2018. Published by The Company of Biologists Ltd.
Adaptive ACMS: A robust localized Approximated Component Mode Synthesis Method
Madureira, Alexandre L.; Sarkis, Marcus
2017-01-01
We consider finite element methods of multiscale type to approximate solutions for two-dimensional symmetric elliptic partial differential equations with heterogeneous $L^\\infty$ coefficients. The methods are of Galerkin type and follows the Variational Multiscale and Localized Orthogonal Decomposition--LOD approaches in the sense that it decouples spaces into multiscale and fine subspaces. In a first method, the multiscale basis functions are obtained by mapping coarse basis functions, based...
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. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Akamatsu, Ken; Shikazono, Naoya; Saito, Takeshi
2017-11-01
We have developed a new method for estimating the localization of DNA damage such as apurinic/apyrimidinic sites (APs) on DNA using fluorescence anisotropy. This method is aimed at characterizing clustered DNA damage produced by DNA-damaging agents such as ionizing radiation and genotoxic chemicals. A fluorescent probe with an aminooxy group (AlexaFluor488) was used to label APs. We prepared a pUC19 plasmid with APs by heating under acidic conditions as a model for damaged DNA, and subsequently labeled the APs. We found that the observed fluorescence anisotropy (r obs ) decreases as averaged AP density (λ AP : number of APs per base pair) increases due to homo-FRET, and that the APs were randomly distributed. We applied this method to three DNA-damaging agents, 60 Co γ-rays, methyl methanesulfonate (MMS), and neocarzinostatin (NCS). We found that r obs -λ AP relationships differed significantly between MMS and NCS. At low AP density (λ AP < 0.001), the APs induced by MMS seemed to not be closely distributed, whereas those induced by NCS were remarkably clustered. In contrast, the AP clustering induced by 60 Co γ-rays was similar to, but potentially more likely to occur than, random distribution. This simple method can be used to estimate mutagenicity of ionizing radiation and genotoxic chemicals. Copyright © 2017 Elsevier Inc. All rights reserved.
Evaluation and comparison of mammalian subcellular localization prediction methods
Directory of Open Access Journals (Sweden)
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
An improved K-means clustering method for cDNA microarray image segmentation.
Wang, T N; Li, T J; Shao, G F; Wu, S X
2015-07-14
Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.
Application Of WIMS Code To Calculation Kartini Reactor Parameters By Pin-Cell And Cluster Method
International Nuclear Information System (INIS)
Sumarsono, Bambang; Tjiptono, T.W.
1996-01-01
Analysis UZrH fuel element parameters calculation in Kartini Reactor by WIMS Code has been done. The analysis is done by pin cell and cluster method. The pin cell method is done as a function percent burn-up and by 8 group 3 region analysis and cluster method by 8 group 12 region analysis. From analysis and calculation resulted K ∼ = 1.3687 by pin cell method and K ∼ = 1.3162 by cluster method and so deviation is 3.83%. By pin cell analysis as a function percent burn-up at the percent burn-up greater than 59.50%, the multiplication factor is less than one (k ∼ < 1) it is mean that the fuel element reactivity is negative
Local coding based matching kernel method for image classification.
Directory of Open Access Journals (Sweden)
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.
Improving the local wavenumber method by automatic DEXP transformation
Abbas, Mahmoud Ahmed; Fedi, Maurizio; Florio, Giovanni
2014-12-01
In this paper we present a new method for source parameter estimation, based on the local wavenumber function. We make use of the stable properties of the Depth from EXtreme Points (DEXP) method, in which the depth to the source is determined at the extreme points of the field scaled with a power-law of the altitude. Thus the method results particularly suited to deal with local wavenumber of high-order, as it is able to overcome its known instability caused by the use of high-order derivatives. The DEXP transformation enjoys a relevant feature when applied to the local wavenumber function: the scaling-law is in fact independent of the structural index. So, differently from the DEXP transformation applied directly to potential fields, the Local Wavenumber DEXP transformation is fully automatic and may be implemented as a very fast imaging method, mapping every kind of source at the correct depth. Also the simultaneous presence of sources with different homogeneity degree can be easily and correctly treated. The method was applied to synthetic and real examples from Bulgaria and Italy and the results agree well with known information about the causative sources.
Oña, Ofelia B; Alcoba, Diego R; Torre, Alicia; Lain, Luis; Torres-Vega, Juan J; Tiznado, William
2013-12-05
A recently proposed molecular orbital localization procedure, based on the electron localization function (ELF) technique, has been used to describe chemical bonding in the cluster series Sin(BH)(5-n)(2-) (n = 0-5). The method combines the chemically intuitive information obtained from the traditional ELF analysis with the flexibility and generality of canonical molecular orbital theory. This procedure attempts to localize the molecular orbitals in regions that have the highest probability for finding a pair of electrons, providing a chemical bonding description according to the classical Lewis theory. The results confirm that conservation of the structures upon isoelectronic replacement of a B-H group by a Si atom, allowing evolution from B5H5(2-) to Si5(2-), is in total agreement with the preservation of the chemical bonding pattern.
The use of different clustering methods in the evaluation of genetic diversity in upland cotton
Directory of Open Access Journals (Sweden)
Laíse Ferreira de Araújo
Full Text Available The continuous development and evaluation of new genotypes through crop breeding is essential in order to obtain new cultivars. The objective of this work was to evaluate the genetic divergences between cultivars of upland cotton (Gossypium hirsutum L. using the agronomic and technological characteristics of the fibre, in order to select superior parent plants. The experiment was set up during 2010 at the Federal University of Ceará in Fortaleza, Ceará, Brazil. Eleven cultivars of upland cotton were used in an experimental design of randomised blocks with three replications. In order to evaluate the genetic diversity among cultivars, the generalised Mahalanobis distance matrix was calculated, with cluster analysis then being applied, employing various methods: single linkage, Ward, complete linkage, median, average linkage within a cluster and average linkage between clusters. Genetic variability exists among the evaluated genotypes. The most consistant clustering method was that employing average linkage between clusters. Among the characteristics assessed, mean boll weight presented the highest contribution to genetic diversity, followed by elongation at rupture. Employing the method of mean linkage between clusters, the cultivars with greater genetic divergence were BRS Acacia and LD Frego; those of greater similarity were BRS Itaúba and BRS Araripe.
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.
An online substructure identification method for local structural health monitoring
International Nuclear Information System (INIS)
Hou, Jilin; Ou, Jinping; Jankowski, Łukasz
2013-01-01
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. (paper)
Directory of Open Access Journals (Sweden)
Y. Zhang
2018-01-01
Full Text Available Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS values of up to 0.5 and 33 %, respectively. The general skill (after bias correction of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.
A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation
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Jian-Yong Lou
2015-01-01
error of 7.18% for well-defined masses (or 8.06% for ill-defined masses was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55% and 6.14% (or 5.27% improvements as compared to the standard DA and fuzzy c-means methods.
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...
Optic disk localization by a robust fusion method
Zhang, Jielin; Yin, Fengshou; Wong, Damon W. K.; Liu, Jiang; Baskaran, Mani; Cheng, Ching-Yu; Wong, Tien Yin
2013-02-01
The optic disk localization plays an important role in developing computer-aided diagnosis (CAD) systems for ocular diseases such as glaucoma, diabetic retinopathy and age-related macula degeneration. In this paper, we propose an intelligent fusion of methods for the localization of the optic disk in retinal fundus images. Three different approaches are developed to detect the location of the optic disk separately. The first method is the maximum vessel crossing method, which finds the region with the most number of blood vessel crossing points. The second one is the multichannel thresholding method, targeting the area with the highest intensity. The final method searches the vertical and horizontal region-of-interest separately on the basis of blood vessel structure and neighborhood entropy profile. Finally, these three methods are combined using an intelligent fusion method to improve the overall accuracy. The proposed algorithm was tested on the STARE database and the ORIGAlight database, each consisting of images with various pathologies. The preliminary result on the STARE database can achieve 81.5%, while a higher result of 99% can be obtained for the ORIGAlight database. The proposed method outperforms each individual approach and state-of-the-art method which utilizes an intensity-based approach. The result demonstrates a high potential for this method to be used in retinal CAD systems.
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.
Robustness of serial clustering of extratropical cyclones to the choice of tracking method
Directory of Open Access Journals (Sweden)
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.
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.
MHCcluster, a method for functional clustering of MHC molecules
DEFF Research Database (Denmark)
Thomsen, Martin Christen Frølund; Lundegaard, Claus; Buus, Søren
2013-01-01
The identification of peptides binding to major histocompatibility complexes (MHC) is a critical step in the understanding of T cell immune responses. The human MHC genomic region (HLA) is extremely polymorphic comprising several thousand alleles, many encoding a distinct molecule. The potentially...... 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...
Larsen, S. S.; Brodie, J. P.; Wasserman, A.; Strader, J.
2018-06-01
Context. Globular clusters (GCs) are emerging as powerful tracers of the chemical composition of extragalactic stellar populations. Aims: We present new abundance measurements for 11 GCs in the Local Group galaxies NGC 147, NGC 6822, and Messier 33. These are combined with previously published observations of four GCs in the Fornax and Wolf-Lundmark-Melotte (WLM) galaxies. Methods: The abundances were determined from analyses of integrated-light spectra obtained with the HIRES spectrograph on the Keck I telescope and with UVES on the Very Large Telescope (VLT). We used our analysis technique that was developed for this purpose and tested on Milky Way GCs. Results: We find that the clusters with [Fe/H] -1.5, the GCs in M33 are also α-enhanced, while the GCs that belong to dwarfs (NGC 6822 SC7 and Fornax 4) have closer to solar-scaled α-element abundances. The abundance patterns in SC7 are remarkably similar to those in the Galactic GC Ruprecht 106, including significantly subsolar [Na/Fe] and [Ni/Fe] ratios. In NGC 147, the GCs with [Fe/H] account for about 6% of the total luminosity of stars in the same metallicity range, a lower fraction than those previously found in the Fornax and WLM galaxies, but substantially higher than in the Milky Way halo. Conclusions: At low metallicities, the abundance patterns suggest that GCs in the Milky Way, dwarf galaxies, and M33 experienced similar enrichment histories and/or processes. At higher metallicities, the lower levels of α-enhancement in the GCs found in dwarf galaxies resemble the abundance patterns observed in field stars in nearby dwarfs. Constraining the presence of multiple populations in these GCs is complicated by lack of information about detailed abundances in field stars of the corresponding metallicities. We suggest that correlations such as [Na/Fe] versus [Ni/Fe] may prove useful for this purpose if an accuracy of 0.1 dex or better can be reached for integrated-light measurements. Tables A.1-A.15
Pseudo-potential method for taking into account the Pauli principle in cluster systems
International Nuclear Information System (INIS)
Krasnopol'skii, V.M.; Kukulin, V.I.
1975-01-01
In order to take account of the Pauli principle in cluster systems (such as 3α, α + α + n) a convenient method of renormalization of the cluster-cluster deep attractive potentials with forbidden states is suggested. The renormalization consists of adding projectors upon the occupied states with an infinite coupling constant to the initial deep potential which means that we pass to pseudo-potentials. The pseudo-potential approach in projecting upon the noneigenstates is shown to be equivalent to the orthogonality condition model of Saito et al. The orthogonality of the many-particle wave function to the forbidden states of each two-cluster sub-system is clearly demonstrated
Test computations on the dynamical evolution of star clusters. [Fluid dynamic method
Energy Technology Data Exchange (ETDEWEB)
Angeletti, L; Giannone, P. (Rome Univ. (Italy))
1977-01-01
Test calculations have been carried out on the evolution of star clusters using the fluid-dynamical method devised by Larson (1970). Large systems of stars have been considered with specific concern with globular clusters. With reference to the analogous 'standard' model by Larson, the influence of varying in turn the various free parameters (cluster mass, star mass, tidal radius, mass concentration of the initial model) has been studied for the results. Furthermore, the partial release of some simplifying assumptions with regard to the relaxation time and distribution of the 'target' stars has been considered. The change of the structural properties is discussed, and the variation of the evolutionary time scale is outlined. An indicative agreement of the results obtained here with structural properties of globular clusters as deduced from previous theoretical models is pointed out.
The resonating group method three cluster approach to the ground state 9 Li nucleus structure
International Nuclear Information System (INIS)
Filippov, G.F.; Pozdnyakov, Yu.A.; Terenetsky, K.O.; Verbitsky, V.P.
1994-01-01
The three-cluster approach for light atomic nuclei is formulated in frame of the algebraic version of resonating group method. Overlap integral and Hamiltonian matrix elements on generating functions are obtained for 9 Li nucleus. All permissible by Pauli principle 9 Li different cluster nucleon permutations were taken into account in the calculations. The results obtained can be easily generalised on any three-cluster system up to 12 C. Matrix elements obtained in the work were used in the variational calculations of the ground state energetic and geometric 9 Li characteristics. It is shown that 9 Li ground state is not adequate to the shell model limit and has pronounced three-cluster structure. (author). 16 refs., 4 tab., 2 figs
A New Soft Computing Method for K-Harmonic Means Clustering.
Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe
2016-01-01
The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.
Localization of sources of the hyperinsulinism through the image methods
International Nuclear Information System (INIS)
Abath, C.G.A.
1990-01-01
Pancreatic insulinomas are small tumours, manifested early by the high hormonal production. Microscopic changes, like islet cell hyperplasia or nesidioblastosis, are also sources of hyperinsulinism. The pre-operative localization of the lesions is important, avoiding unnecessary or insufficient blind pancreatectomies. It is presented the experience with 26 patients with hyperinsulinism, of whom six were examined by ultrasound, nine by computed tomography, 25 by angiography and 16 by pancreatic venous sampling for hormone assay, in order to localize the lesions. Percutaneous transhepatic portal and pancreatic vein catheterization with measurement of insuline concentrations was the most reliable and sensitive method for detecting the lesions, including those non-palpable during the surgical exploration (author)
Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification
Directory of Open Access Journals (Sweden)
Yajie Zou
2017-01-01
Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.
Cluster Analysis of the Newcastle Electronic Corpus of Tyneside English: A Comparison of Methods
Moisl, Hermann; Jones, Valerie M.
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
Cluster Analysis of the Newcastle Electronic Corpus of Tyneside English: In A Comparison of Methods
Moisl, Hermann; Jones, Valerie M.
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
System and Method for Outlier Detection via Estimating Clusters
Iverson, David J. (Inventor)
2016-01-01
An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.
A method of detecting spatial clustering of disease
International Nuclear Information System (INIS)
Openshaw, S.; Wilkie, D.; Binks, K.; Wakeford, R.; Gerrard, M.H.; Croasdale, M.R.
1989-01-01
A statistical technique has been developed to identify extreme groupings of a disease and is being applied to childhood cancers, initially to acute lymphoblastic leukaemia incidence in the Northern and North-Western Regions of England. The method covers the area with a square grid, the size of which is varied over a wide range and whose origin is moved in small increments in two directions. The population at risk within any square is estimated using the 1971 and 1981 censuses. The significance of an excess of disease is determined by random simulation. In addition, tests to detect a general departure from a background Poisson process are carried out. Available results will be presented at the conference. (author)
Los cluster tecnológicos en México y Argentina: una estrategia para el desarrollo local
Directory of Open Access Journals (Sweden)
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.
A method for determining the radius of an open cluster from stellar proper motions
Sánchez, Néstor; Alfaro, Emilio J.; López-Martínez, Fátima
2018-04-01
We propose a method for calculating the radius of an open cluster in an objective way from an astrometric catalogue containing, at least, positions and proper motions. It uses the minimum spanning tree in the proper motion space to discriminate cluster stars from field stars and it quantifies the strength of the cluster-field separation by means of a statistical parameter defined for the first time in this paper. This is done for a range of different sampling radii from where the cluster radius is obtained as the size at which the best cluster-field separation is achieved. The novelty of this strategy is that the cluster radius is obtained independently of how its stars are spatially distributed. We test the reliability and robustness of the method with both simulated and real data from a well-studied open cluster (NGC 188), and apply it to UCAC4 data for five other open clusters with different catalogued radius values. NGC 188, NGC 1647, NGC 6603, and Ruprecht 155 yielded unambiguous radius values of 15.2 ± 1.8, 29.4 ± 3.4, 4.2 ± 1.7, and 7.0 ± 0.3 arcmin, respectively. ASCC 19 and Collinder 471 showed more than one possible solution, but it is not possible to know whether this is due to the involved uncertainties or due to the presence of complex patterns in their proper motion distributions, something that could be inherent to the physical object or due to the way in which the catalogue was sampled.
Directory of Open Access Journals (Sweden)
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
DEFF Research Database (Denmark)
Poutanen, T.; Natoli, P.; Polenta, G.
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
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 >
Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods
DEFF Research Database (Denmark)
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......-transformation of the cost data and apply the normal theory on these data. Again we try to account for the cluster effect. The performance of these two methods is investigated in a simulation study. The advantages and disadvantages of the different approaches are discussed....... 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...
Dong, Skye T; Costa, Daniel S J; Butow, Phyllis N; Lovell, Melanie R; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C; Clarke, Stephen J; van der Hoek, Kim; King, Madeleine T; Fayers, Peter M
2016-01-01
Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Anderson, Ryan B., E-mail: randerson@astro.cornell.edu [Cornell University Department of Astronomy, 406 Space Sciences Building, Ithaca, NY 14853 (United States); Bell, James F., E-mail: Jim.Bell@asu.edu [Arizona State University School of Earth and Space Exploration, Bldg.: INTDS-A, Room: 115B, Box 871404, Tempe, AZ 85287 (United States); Wiens, Roger C., E-mail: rwiens@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States); Morris, Richard V., E-mail: richard.v.morris@nasa.gov [NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX 77058 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States)
2012-04-15
We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO{sub 2} at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by {approx} 3 wt.%. The statistical significance of these improvements was {approx} 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and
Li, Mengtian; Zhang, Ruisheng; Hu, Rongjing; Yang, Fan; Yao, Yabing; Yuan, Yongna
2018-03-01
Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible-infectious-recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.
Energy Technology Data Exchange (ETDEWEB)
Bo, Maolin; Huang, Yongli; Zhang, Ting [Key Laboratory of Low-Dimensional Materials and Application Technologies, Xiangtan University, Hunan 411105 (China); Wang, Yan, E-mail: ywang8@hnust.edu.cn, E-mail: ecqsun@ntu.edu.sg [Key Laboratory of Low-Dimensional Materials and Application Technologies, Xiangtan University, Hunan 411105 (China); School of Information and Electronic Engineering, Hunan University of Science and Technology, Hunan 411201 (China); Zhang, Xi [School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (Singapore); Li, Can [Center for Coordination Bond Engineering, School of Materials Science and Engineering, China Jiliang University, Hangzhou 330018 (China); Sun, Chang Q., E-mail: ywang8@hnust.edu.cn, E-mail: ecqsun@ntu.edu.sg [Key Laboratory of Low-Dimensional Materials and Application Technologies, Xiangtan University, Hunan 411105 (China); School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (Singapore); Center for Coordination Bond Engineering, School of Materials Science and Engineering, China Jiliang University, Hangzhou 330018 (China)
2014-04-14
Consistency between x-ray photoelectron spectroscopy measurements and density-function theory calculations confirms our bond order-length-strength notation-incorporated tight-binding theory predictions on the quantum entrapment of Si solid skin and atomic clusters. It has been revealed that bond-order deficiency shortens and strengthens the Si-Si bond, which results in the local densification and quantum entrapment of the core and valence electrons. Unifying Si clusters and Si(001) and (111) skins, this mechanism has led to quantification of the 2p binding energy of 96.089 eV for an isolated Si atom, and their bulk shifts of 2.461 eV. Findings evidence the significance of atomic undercoordination that is of great importance to device performance.
Enhanced Methods for Local Ancestry Assignment in Sequenced Admixed Individuals
Brown, Robert; Pasaniuc, Bogdan
2014-01-01
Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs. PMID:24743331
Directory of Open Access Journals (Sweden)
Qiao Wei
2017-01-01
Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.
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
AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
Directory of Open Access Journals (Sweden)
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.
ClubSub-P: Cluster-based subcellular localization prediction for Gram-negative bacteria and Archaea.
Directory of Open Access Journals (Sweden)
Nagarajan eParamasivam
2011-11-01
Full Text Available The subcellular localization of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned subcellular localizations. This and other problems in subcellular localization prediction, such as the relatively high false positive and false negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing subcellular localization prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false positive and false negative predictions. ClubSub-P can assign the subcellular localization of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the subcellular localization prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/
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
A local level set method based on a finite element method for unstructured meshes
International Nuclear Information System (INIS)
Ngo, Long Cu; Choi, Hyoung Gwon
2016-01-01
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
A local level set method based on a finite element method for unstructured meshes
Energy Technology Data Exchange (ETDEWEB)
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.
International Nuclear Information System (INIS)
Zhou, Hongming; Soh, Yeng Chai; Wu, Xiaoying
2015-01-01
Maintaining a desired comfort level while minimizing the total energy consumed is an interesting optimization problem in Heating, ventilating and air conditioning (HVAC) system control. This paper proposes a localized control strategy that uses Computational Fluid Dynamics (CFD) simulation results and K-means clustering algorithm to optimally partition an air-conditioned room into different zones. The temperature and air velocity results from CFD simulation are combined in two ways: 1) based on the relationship indicated in predicted mean vote (PMV) formula; 2) based on the relationship extracted from ASHRAE RP-884 database using extreme learning machine (ELM). Localized control can then be effected in which each of the zones can be treated individually and an optimal control strategy can be developed based on the partitioning result. - Highlights: • The paper provides a visual guideline for thermal comfort analysis. • CFD, K-means, PMV and ELM are used to analyze thermal conditions within a room. • Localized control strategy could be developed based on our clustering results
Spatial abundance and clustering of Culicoides (Diptera: Ceratopogonidae) on a local scale
DEFF Research Database (Denmark)
Kirkeby, Carsten; Bødker, Rene; Stockmarr, Anders
2013-01-01
, 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...... 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...... of Culicoides moved around in a dynamic pattern varying between catch nights. This conforms with the modeling but was not explained by any of the tested covariates. The mean abundance within these clusters was up to 11 times higher for the Obsoletus group and 4 times higher for the Pulicaris group compared...
Local Gaussian approximation in the generator coordinate method
International Nuclear Information System (INIS)
Onishi, Naoki; Une, Tsutomu.
1975-01-01
A transformation from a non-orthogonal representation to an orthogonal representation of wave functions is studied in the generator coordinate method. A differential equation can be obtained by the transformation for a case that the eigenvalue equation of the overlap kernel is solvable. By assuming local Gaussian overlap, we derive a Schroedinger-type equation for the collective motion from the Hill-Wheeler integral equation. (auth.)
Local Gaussian approximation in the generator coordinate method
Energy Technology Data Exchange (ETDEWEB)
Onishi, N [Tokyo Univ. (Japan). Coll. of General Education; Une, Tsutomu
1975-02-01
A transformation from a non-orthogonal representation to an orthogonal representation of wave functions is studied in the generator coordinate method. A differential equation can be obtained by the transformation for a case that the eigenvalue equation of the overlap kernel is solvable. By assuming local Gaussian overlap, we derive a Schroedinger-type equation for the collective motion from the Hill-Wheeler integral equation.
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.
Communication: Time-dependent optimized coupled-cluster method for multielectron dynamics
Sato, Takeshi; Pathak, Himadri; Orimo, Yuki; Ishikawa, Kenichi L.
2018-02-01
Time-dependent coupled-cluster method with time-varying orbital functions, called time-dependent optimized coupled-cluster (TD-OCC) method, is formulated for multielectron dynamics in an intense laser field. We have successfully derived the equations of motion for CC amplitudes and orthonormal orbital functions based on the real action functional, and implemented the method including double excitations (TD-OCCD) and double and triple excitations (TD-OCCDT) within the optimized active orbitals. The present method is size extensive and gauge invariant, a polynomial cost-scaling alternative to the time-dependent multiconfiguration self-consistent-field method. The first application of the TD-OCC method of intense-laser driven correlated electron dynamics in Ar atom is reported.
Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification
Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang
2017-12-01
To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.
Bonell, Chris; Mathiot, Anne; Allen, Elizabeth; Bevilacqua, Leonardo; Christie, Deborah; Elbourne, Diana; Fletcher, Adam; Grieve, Richard; Legood, Rosa; Scott, Stephen; Warren, Emily; Wiggins, Meg; Viner, Russell M
2017-05-25
Systematic reviews suggest that multi-component interventions are effective in reducing bullying victimisation and perpetration. We are undertaking a phase III randomised trial of the INCLUSIVE multi-component intervention. This trial aims to assess the effectiveness and cost-effectiveness of the INCLUSIVE intervention in reducing aggression and bullying victimisation in English secondary schools. This paper updates the original trial protocol published in 2014 (Trials 15:381, 2014) and presents the changes in the process evaluation protocol and the secondary outcome data collection. The methods are summarised as follows. cluster randomised trial. 40 state secondary schools. Outcomes assessed among the cohort of students at the end of year 7 (n = 6667) at baseline. INCLUSIVE is a multi-component school intervention including a social and emotional learning curriculum, changes to school environment (an action group comprising staff and students reviews local data on needs to review rules and policies and determine other local actions) and staff training in restorative practice. The intervention will be delivered by schools supported in the first two years by educational facilitators independent of the research team, with a third intervention year involving no external facilitation but all other elements. Comparator: normal practice. Primary: Two primary outcomes at student level assessed at baseline and at 36 months: 1. Aggressive behaviours in school: Edinburgh Study of Youth Transitions and Crime school misbehaviour subscale (ESYTC) 2. Bullying and victimisation: Gatehouse Bullying Scale (GBS) Secondary outcomes assessed at baseline, 24 and 36 months will include measures relating to the economic evaluation, psychosocial outcomes in students and staff and school-level truancy and exclusion rates. 20 schools per arm will provide 90% power to identify an effect size of 0.25 SD with a 5% significance level. Randomisation: eligible consenting schools were
Clustering of attitudes towards obesity: a mixed methods study of Australian parents and children.
Olds, Tim; Thomas, Samantha; Lewis, Sophie; Petkov, John
2013-10-12
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. 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. 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 less extreme than the other clusters
Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method
DEFF Research Database (Denmark)
Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels
2014-01-01
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 the probabi......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...... the probability distribution of the Y-STR haplotypes. Creating a consistent statistical model of the haplotypes enables us to perform a wide range of analyses. Previously, haplotype frequency estimation using the discrete Laplace method has been validated. In this paper we investigate how the discrete Laplace...... 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...
Detecting and extracting clusters in atom probe data: A simple, automated method using Voronoi cells
International Nuclear Information System (INIS)
Felfer, P.; Ceguerra, A.V.; Ringer, S.P.; Cairney, J.M.
2015-01-01
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. - 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
Meshfree Local Radial Basis Function Collocation Method with Image Nodes
Energy Technology Data Exchange (ETDEWEB)
Baek, Seung Ki; Kim, Minjae [Pukyong National University, Busan (Korea, Republic of)
2017-07-15
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
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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.
Extension of local front reconstruction method with controlled coalescence model
Rajkotwala, A. H.; Mirsandi, H.; Peters, E. A. J. F.; Baltussen, M. W.; van der Geld, C. W. M.; Kuerten, J. G. M.; Kuipers, J. A. M.
2018-02-01
The physics of droplet collisions involves a wide range of length scales. This poses a challenge to accurately simulate such flows with standard fixed grid methods due to their inability to resolve all relevant scales with an affordable number of computational grid cells. A solution is to couple a fixed grid method with subgrid models that account for microscale effects. In this paper, we improved and extended the Local Front Reconstruction Method (LFRM) with a film drainage model of Zang and Law [Phys. Fluids 23, 042102 (2011)]. The new framework is first validated by (near) head-on collision of two equal tetradecane droplets using experimental film drainage times. When the experimental film drainage times are used, the LFRM method is better in predicting the droplet collisions, especially at high velocity in comparison with other fixed grid methods (i.e., the front tracking method and the coupled level set and volume of fluid method). When the film drainage model is invoked, the method shows a good qualitative match with experiments, but a quantitative correspondence of the predicted film drainage time with the experimental drainage time is not obtained indicating that further development of film drainage model is required. However, it can be safely concluded that the LFRM coupled with film drainage models is much better in predicting the collision dynamics than the traditional methods.
The modal surface interpolation method for damage localization
Pina Limongelli, Maria
2017-05-01
The Interpolation Method (IM) has been previously proposed and successfully applied for damage localization in plate like structures. The method is based on the detection of localized reductions of smoothness in the Operational Deformed Shapes (ODSs) of the structure. The IM can be applied to any type of structure provided the ODSs are estimated accurately in the original and in the damaged configurations. If the latter circumstance fails to occur, for example when the structure is subjected to an unknown input(s) or if the structural responses are strongly corrupted by noise, both false and missing alarms occur when the IM is applied to localize a concentrated damage. In order to overcome these drawbacks a modification of the method is herein investigated. An ODS is the deformed shape of a structure subjected to a harmonic excitation: at resonances the ODS are dominated by the relevant mode shapes. The effect of noise at resonance is usually lower with respect to other frequency values hence the relevant ODS are estimated with higher reliability. Several methods have been proposed to reliably estimate modal shapes in case of unknown input. These two circumstances can be exploited to improve the reliability of the IM. In order to reduce or eliminate the drawbacks related to the estimation of the ODSs in case of noisy signals, in this paper is investigated a modified version of the method based on a damage feature calculated considering the interpolation error relevant only to the modal shapes and not to all the operational shapes in the significant frequency range. Herein will be reported the comparison between the results of the IM in its actual version (with the interpolation error calculated summing up the contributions of all the operational shapes) and in the new proposed version (with the estimation of the interpolation error limited to the modal shapes).
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.
A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data
DEFF Research Database (Denmark)
Kent, Peter; Jensen, Rikke K; Kongsted, Alice
2014-01-01
). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold...... LCA and SNOB LCA). METHODS: The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program...... classify individuals into those subgroups. CONCLUSIONS: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data...
International Nuclear Information System (INIS)
Kang, Hyeonggon; Attota, Ravikiran; Tondare, Vipin; Vladár, András E.; Kavuri, Premsagar
2015-01-01
We present a method that uses conventional optical microscopes to determine the number of nanoparticles in a cluster, which is typically not possible using traditional image-based optical methods due to the diffraction limit. The method, called through-focus scanning optical microscopy (TSOM), uses a series of optical images taken at varying focus levels to achieve this. The optical images cannot directly resolve the individual nanoparticles, but contain information related to the number of particles. The TSOM method makes use of this information to determine the number of nanoparticles in a cluster. Initial good agreement between the simulations and the measurements is also presented. The TSOM method can be applied to fluorescent and non-fluorescent as well as metallic and non-metallic nano-scale materials, including soft materials, making it attractive for tag-less, high-speed, optical analysis of nanoparticles down to 45 nm diameter
Clustering method to process signals from a CdZnTe detector
International Nuclear Information System (INIS)
Zhang, Lan; Takahashi, Hiroyuki; Fukuda, Daiji; Nakazawa, Masaharu
2001-01-01
The poor mobility of holes in a compound semiconductor detector results in the imperfect collection of the primary charge deposited in the detector. Furthermore the fluctuation of the charge loss efficiency due to the change in the hole collection path length seriously degrades the energy resolution of the detector. Since the charge collection efficiency varies with the signal waveform, we can expect the improvement of the energy resolution through a proper waveform signal processing method. We developed a new digital signal processing technique, a clustering method which derives typical patterns containing the information on the real situation inside a detector from measured signals. The obtained typical patterns for the detector are then used for the pattern matching method. Measured signals are classified through analyzing the practical waveform variation due to the charge trapping, the electric field and the crystal defect etc. Signals with similar shape are placed into the same cluster. For each cluster we calculate an average waveform as a reference pattern. Using these reference patterns obtained from all the clusters, we can classify other measured signal waveforms from the same detector. Then signals are independently processed according to the classified category and form corresponding spectra. Finally these spectra are merged into one spectrum by multiplying normalization coefficients. The effectiveness of this method was verified with a CdZnTe detector of 2 mm thick and a 137 Cs gamma-ray source. The obtained energy resolution as improved to about 8 keV (FWHM). Because the clustering method is only related to the measured waveforms, it can be applied to any type and size of detectors and compatible with any type of filtering methods. (author)
Statistical method on nonrandom clustering with application to somatic mutations in cancer
Directory of Open Access Journals (Sweden)
Rejto Paul A
2010-01-01
Full Text Available Abstract Background Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention. Results We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors. Conclusions Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.
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.
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
International Nuclear Information System (INIS)
Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)
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. 30 references
Local Field Response Method Phenomenologically Introducing Spin Correlations
Tomaru, Tatsuya
2018-03-01
The local field response (LFR) method is a way of searching for the ground state in a similar manner to quantum annealing. However, the LFR method operates on a classical machine, and quantum effects are introduced through a priori information and through phenomenological means reflecting the states during the computations. The LFR method has been treated with a one-body approximation, and therefore, the effect of entanglement has not been sufficiently taken into account. In this report, spin correlations are phenomenologically introduced as one of the effects of entanglement, by which multiple tunneling at anticrossing points is taken into account. As a result, the accuracy of solutions for a 128-bit system increases by 31% compared with that without spin correlations.
Research of the Space Clustering Method for the Airport Noise Data Minings
Directory of Open Access Journals (Sweden)
Jiwen Xie
2014-03-01
Full Text Available Mining the distribution pattern and evolution of the airport noise from the airport noise data and the geographic information of the monitoring points is of great significance for the scientific and rational governance of airport noise pollution problem. However, most of the traditional clustering methods are based on the closeness of space location or the similarity of non-spatial features, which split the duality of space elements, resulting in that the clustering result has difficult in satisfying both the closeness of space location and the similarity of non-spatial features. This paper, therefore, proposes a spatial clustering algorithm based on dual-distance. This algorithm uses a distance function as the similarity measure function in which spatial features and non-spatial features are combined. The experimental results show that the proposed algorithm can discover the noise distribution pattern around the airport effectively.
Implementation of K-Means Clustering Method for Electronic Learning Model
Latipa Sari, Herlina; Suranti Mrs., Dewi; Natalia Zulita, Leni
2017-12-01
Teaching and Learning process at SMK Negeri 2 Bengkulu Tengah has applied e-learning system for teachers and students. The e-learning was based on the classification of normative, productive, and adaptive subjects. SMK Negeri 2 Bengkulu Tengah consisted of 394 students and 60 teachers with 16 subjects. The record of e-learning database was used in this research to observe students’ activity pattern in attending class. K-Means algorithm in this research was used to classify students’ learning activities using e-learning, so that it was obtained cluster of students’ activity and improvement of student’s ability. Implementation of K-Means Clustering method for electronic learning model at SMK Negeri 2 Bengkulu Tengah was conducted by observing 10 students’ activities, namely participation of students in the classroom, submit assignment, view assignment, add discussion, view discussion, add comment, download course materials, view article, view test, and submit test. In the e-learning model, the testing was conducted toward 10 students that yielded 2 clusters of membership data (C1 and C2). Cluster 1: with membership percentage of 70% and it consisted of 6 members, namely 1112438 Anggi Julian, 1112439 Anis Maulita, 1112441 Ardi Febriansyah, 1112452 Berlian Sinurat, 1112460 Dewi Anugrah Anwar and 1112467 Eka Tri Oktavia Sari. Cluster 2:with membership percentage of 30% and it consisted of 4 members, namely 1112463 Dosita Afriyani, 1112471 Erda Novita, 1112474 Eskardi and 1112477 Fachrur Rozi.
Study of methods to increase cluster/dislocation loop densities in electrodes
Yang, Xiaoling; Miley, George H.
2009-03-01
Recent research has developed a technique for imbedding ultra-high density deuterium ``clusters'' (50 to 100 atoms per cluster) in various metals such as Palladium (Pd), Beryllium (Be) and Lithium (Li). It was found the thermally dehydrogenated PdHx retained the clusters and exhibited up to 12 percent lower resistance compared to the virginal Pd samplesootnotetextA. G. Lipson, et al. Phys. Solid State. 39 (1997) 1891. SQUID measurements showed that in Pd these condensed matter clusters approach metallic conditions, exhibiting superconducting propertiesootnotetextA. Lipson, et al. Phys. Rev. B 72, 212507 (2005ootnotetextA. G. Lipson, et al. Phys. Lett. A 339, (2005) 414-423. If the fabrication methods under study are successful, a large packing fraction of nuclear reactive clusters can be developed in the electrodes by electrolyte or high pressure gas loading. This will provide a much higher low-energy-nuclear- reaction (LENR) rate than achieved with earlier electrodeootnotetextCastano, C.H., et al. Proc. ICCF-9, Beijing, China 19-24 May, 2002..
Directory of Open Access Journals (Sweden)
Marion Eryilmaz
2018-01-01
Full Text Available In radiation biophysics, it is a subject of nowadays research to investigate DNA strand break repair in detail after damage induction by ionizing radiation. It is a subject of debate as to what makes up the cell’s decision to use a certain repair pathway and how the repair machinery recruited in repair foci is spatially and temporarily organized. Single-molecule localization microscopy (SMLM allows super-resolution analysis by precise localization of single fluorescent molecule tags, resulting in nuclear structure analysis with a spatial resolution in the 10 nm regime. Here, we used SMLM to study MRE11 foci. MRE11 is one of three proteins involved in the MRN-complex (MRE11-RAD50-NBS1 complex, a prominent DNA strand resection and broken end bridging component involved in homologous recombination repair (HRR and alternative non-homologous end joining (a-NHEJ. We analyzed the spatial arrangements of antibody-labelled MRE11 proteins in the nuclei of a breast cancer and a skin fibroblast cell line along a time-course of repair (up to 48 h after irradiation with a dose of 2 Gy. Different kinetics for cluster formation and relaxation were determined. Changes in the internal nano-scaled structure of the clusters were quantified and compared between the two cell types. The results indicate a cell type-dependent DNA damage response concerning MRE11 recruitment and cluster formation. The MRE11 data were compared to H2AX phosphorylation detected by γH2AX molecule distribution. These data suggested modulations of MRE11 signal frequencies that were not directly correlated to DNA damage induction. The application of SMLM in radiation biophysics offers new possibilities to investigate spatial foci organization after DNA damaging and during subsequent repair.
Eryilmaz, Marion; Schmitt, Eberhard; Krufczik, Matthias; Theda, Franziska; Lee, Jin-Ho; Cremer, Christoph; Bestvater, Felix; Schaufler, Wladimir; Hausmann, Michael; Hildenbrand, Georg
2018-01-22
In radiation biophysics, it is a subject of nowadays research to investigate DNA strand break repair in detail after damage induction by ionizing radiation. It is a subject of debate as to what makes up the cell's decision to use a certain repair pathway and how the repair machinery recruited in repair foci is spatially and temporarily organized. Single-molecule localization microscopy (SMLM) allows super-resolution analysis by precise localization of single fluorescent molecule tags, resulting in nuclear structure analysis with a spatial resolution in the 10 nm regime. Here, we used SMLM to study MRE11 foci. MRE11 is one of three proteins involved in the MRN-complex (MRE11-RAD50-NBS1 complex), a prominent DNA strand resection and broken end bridging component involved in homologous recombination repair (HRR) and alternative non-homologous end joining (a-NHEJ). We analyzed the spatial arrangements of antibody-labelled MRE11 proteins in the nuclei of a breast cancer and a skin fibroblast cell line along a time-course of repair (up to 48 h) after irradiation with a dose of 2 Gy. Different kinetics for cluster formation and relaxation were determined. Changes in the internal nano-scaled structure of the clusters were quantified and compared between the two cell types. The results indicate a cell type-dependent DNA damage response concerning MRE11 recruitment and cluster formation. The MRE11 data were compared to H2AX phosphorylation detected by γH2AX molecule distribution. These data suggested modulations of MRE11 signal frequencies that were not directly correlated to DNA damage induction. The application of SMLM in radiation biophysics offers new possibilities to investigate spatial foci organization after DNA damaging and during subsequent repair.
IP2P K-means: an efficient method for data clustering on sensor networks
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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.
Method for Determining Appropriate Clustering Criteria of Location-Sensing Data
Directory of Open Access Journals (Sweden)
Youngmin Lee
2016-08-01
Full Text Available Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.
Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling
Energy Technology Data Exchange (ETDEWEB)
Peterson, John R.; Marshall, P.J.; /KIPAC, Menlo Park; Andersson, K.; /Stockholm U. /SLAC
2005-08-05
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 the form of adaptively binned photon lists via a two-sample likelihood statistic and iterated via Markov Chain Monte Carlo. The complex cluster model is propagated through the X-ray instrument response using direct sampling Monte Carlo methods. Using this approach the method can reproduce many of the features observed in the X-ray emission in a less assumption-dependent way that traditional analyses, and it allows for a more detailed characterization of the density, temperature, and metal abundance structure of clusters. Multi-instrument X-ray analyses and simultaneous X-ray, Sunyaev-Zeldovich (SZ), and lensing analyses are a straight-forward extension of this methodology. Significant challenges still exist in understanding the degeneracy in these models and the statistical noise induced by the complexity of the models.
Relativistic rise measurement by cluster counting method in time expansion chamber
International Nuclear Information System (INIS)
Rehak, P.; Walenta, A.H.
1979-10-01
A new approach to the measurement of the ionization energy loss for the charged particle identification in the region of the relativistic rise was tested experimentally. The method consists of determining in a special drift chamber (TEC) the number of clusters of the primary ionization. The method gives almost the full relativistic rise and narrower landau distribution. The consequences for a practical detector are discussed
Directory of Open Access Journals (Sweden)
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.
Direct comparison of enzyme histochemical and immunohistochemical methods to localize an enzyme
van Noorden, Cornelis J. F.
2002-01-01
Immunohistochemical localization of enzymes is compared directly with localization of enzyme activity with (catalytic) enzyme histochemical methods. The two approaches demonstrate principally different aspects of an enzyme. The immunohistochemical method localizes the enzyme protein whether it is
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
Directory of Open Access Journals (Sweden)
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
Pre-crash scenarios at road junctions: A clustering method for car crash data.
Nitsche, Philippe; Thomas, Pete; Stuetz, Rainer; Welsh, Ruth
2017-10-01
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.
Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.
Lee, Hung-Wen; Chang, Chun-Ming; Hsing, Cheng-Rong
2017-02-28
The puzzle of the magnetic moments of small nickel clusters arises from the discrepancy between values predicted using density functional theory (DFT) and experimental measurements. Traditional DFT approaches underestimate the magnetic moments of nickel clusters. Two fundamental problems are associated with this puzzle, namely, calculating the exchange-correlation interaction accurately and determining the global minimum structures of the clusters. Theoretically, the two problems can be solved using quantum Monte Carlo (QMC) calculations and the ab initio random structure searching (AIRSS) method correspondingly. Therefore, we combined the fixed-moment AIRSS and QMC methods to investigate the magnetic properties of Ni n (n = 5-9) clusters. The spin moments of the diffusion Monte Carlo (DMC) ground states are higher than those of the Perdew-Burke-Ernzerhof ground states and, in the case of Ni 8-9 , two new ground-state structures have been discovered using the DMC calculations. The predicted results are closer to the experimental findings, unlike the results predicted in previous standard DFT studies.
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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.
Devasenapathy, Deepa; Kannan, Kathiravan
2015-01-01
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.
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.
Directory of Open Access Journals (Sweden)
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.
Water Quality Evaluation of the Yellow River Basin Based on Gray Clustering Method
Fu, X. Q.; Zou, Z. H.
2018-03-01
Evaluating the water quality of 12 monitoring sections in the Yellow River Basin comprehensively by grey clustering method based on the water quality monitoring data from the Ministry of environmental protection of China in May 2016 and the environmental quality standard of surface water. The results can reflect the water quality of the Yellow River Basin objectively. Furthermore, the evaluation results are basically the same when compared with the fuzzy comprehensive evaluation method. The results also show that the overall water quality of the Yellow River Basin is good and coincident with the actual situation of the Yellow River basin. Overall, gray clustering method for water quality evaluation is reasonable and feasible and it is also convenient to calculate.
Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm
Karaca, Yeliz; Cattani, Carlo
Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.
Threshold selection for classification of MR brain images by clustering method
Energy Technology Data Exchange (ETDEWEB)
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.
New Methods for Crafting Locally Decision-Relevant Scenarios
Lempert, R. J.
2015-12-01
Scenarios can play an important role in helping decision makers to imagine future worlds, both good and bad, different than the one with which we are familiar and to take concrete steps now to address the risks generated by climate change. At their best, scenarios can effectively represent deep uncertainty; integrate over multiple domains; and enable parties with different expectation and values to expand the range of futures they consider, to see the world from different points of view, and to grapple seriously with the potential implications of surprising or inconvenient futures. These attributes of scenario processes can prove crucial in helping craft effective responses to climate change. But traditional scenario methods can also fail to overcome difficulties related to choosing, communicating, and using scenarios to identify, evaluate, and reach consensus on appropriate policies. Such challenges can limit scenario's impact in broad public discourse. This talk will demonstrate how new decision support approaches can employ new quantitative tools that allow scenarios to emerge from a process of deliberation with analysis among stakeholders, rather than serve as inputs to it, thereby increasing the impacts of scenarios on decision making. This talk will demonstrate these methods in the design of a decision support tool to help residents of low lying coastal cities grapple with the long-term risks of sea level rise. In particular, this talk will show how information from the IPCC SSP's can be combined with local information to provide a rich set of locally decision-relevant information.
A New Method for Local Energy Planning in Developing Countries
International Nuclear Information System (INIS)
Van Beeck, N.
2001-01-01
Energy planning is an essential tool in the economic development of industrialized as well as developing countries. Energy planning in this paper is restricted to the selection of new energy systems for the production of proper energy forms in order to meet increased energy demand. This demand is actually the desire for certain energy services, which are the starting point of the new decision support method for local energy planning presented in this paper. In the decision making process concerning energy planning at the local level it is important to include context-related issues because the context determines for a large part the viability of the technologies or systems used. The context, in turn, is represented by the aims of the relevant actors, which are translated into measurable indicators to compare the different options. The impact assessment must allow for inclusion of all the indicators, either quantitative or qualitative in order to find the most appropriate technology for a region rather than the technically best or economically most optimal one. Appropriateness is defined by the context and is thus case specific, but the framework described in this paper is generally applicable within the given limitations. Note that the new method described in this paper is a decision support tool, implying that it does not decide for the energy planner which actions to take. The ultimate decision must be made by the planners themselves
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
Symmetrized partial-wave method for density-functional cluster calculations
International Nuclear Information System (INIS)
Averill, F.W.; Painter, G.S.
1994-01-01
The computational advantage and accuracy of the Harris method is linked to the simplicity and adequacy of the reference-density model. In an earlier paper, we investigated one way the Harris functional could be extended to systems outside the limits of weakly interacting atoms by making the charge density of the interacting atoms self-consistent within the constraints of overlapping spherical atomic densities. In the present study, a method is presented for augmenting the interacting atom charge densities with symmetrized partial-wave expansions on each atomic site. The added variational freedom of the partial waves leads to a scheme capable of giving exact results within a given exchange-correlation approximation while maintaining many of the desirable convergence and stability properties of the original Harris method. Incorporation of the symmetry of the cluster in the partial-wave construction further reduces the level of computational effort. This partial-wave cluster method is illustrated by its application to the dimer C 2 , the hypothetical atomic cluster Fe 6 Al 8 , and the benzene molecule
Structure and bonding in clusters
International Nuclear Information System (INIS)
Kumar, V.
1991-10-01
We review here the recent progress made in the understanding of the electronic and atomic structure of small clusters of s-p bonded materials using the density functional molecular dynamics technique within the local density approximation. Starting with a brief description of the method, results are presented for alkali metal clusters, clusters of divalent metals such as Mg and Be which show a transition from van der Waals or weak chemical bonding to metallic behaviour as the cluster size grows and clusters of Al, Sn and Sb. In the case of semiconductors, we discuss results for Si, Ge and GaAs clusters. Clusters of other materials such as P, C, S, and Se are also briefly discussed. From these and other available results we suggest the possibility of unique structures for the magic clusters. (author). 69 refs, 7 figs, 1 tab
Cluster detection methods applied to the Upper Cape Cod cancer data
Directory of Open Access Journals (Sweden)
Ozonoff David
2005-09-01
Full Text Available Abstract Background A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. Methods We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. Results The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. Conclusion The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.
Directory of Open Access Journals (Sweden)
Mohammad Abuei Ardakan
2010-04-01
Full Text Available The present paper offers a basic introduction to data clustering and demonstrates the application of clustering methods in drawing maps of science. All approaches towards classification and clustering of information are briefly discussed. Their application to the process of visualization of conceptual information and drawing of science maps are illustrated by reviewing similar researches in this field. By implementing aggregated hierarchical clustering algorithm, which is an algorithm based on complete-link method, the map for urban management science as an emerging, interdisciplinary scientific field is analyzed and reviewed.
Fourth-order perturbative extension of the single-double excitation coupled-cluster method
International Nuclear Information System (INIS)
Derevianko, Andrei; Emmons, Erik D.
2002-01-01
Fourth-order many-body corrections to matrix elements for atoms with one valence electron are derived. The obtained diagrams are classified using coupled-cluster-inspired separation into contributions from n-particle excitations from the lowest-order wave function. The complete set of fourth-order diagrams involves only connected single, double, and triple excitations and disconnected quadruple excitations. Approximately half of the fourth-order diagrams are not accounted for by the popular coupled-cluster method truncated at single and double excitations (CCSD). Explicit formulas are tabulated for the entire set of fourth-order diagrams missed by the CCSD method and its linearized version, i.e., contributions from connected triple and disconnected quadruple excitations. A partial summation scheme of the derived fourth-order contributions to all orders of perturbation theory is proposed
Cluster models of light nuclei and the method of hyperspherical harmonics: Successes and challenges
International Nuclear Information System (INIS)
Danilin, B. V.; Shul'gina, N. B.; Ershov, S. N.; Vaagen, J. S.
2009-01-01
Hyperspherical-harmonics method to investigate the lightest nuclei having three-cluster structure is discussed together with recent experiments. Properties of bound states and methods to explore three-body continuum are presented. The challenges created by large neutron excess and halo phenomena are highlighted. Astrophysical aspects of the 7 Li + n → 8 Li + γ reaction and the solar-boron-neutrinos problem are analyzed. Three-cluster structure of highly excited states in 8 Be is shown to be responsible for extreme isospin mixing. Progress in studies of 6 He- and 11 Li-induced inclusive and exclusive nuclear reactions is demonstrated, providing information on the nature of continuum structures of Borromean nuclei.
Directory of Open Access Journals (Sweden)
Diana Janis
2014-01-01
Full Text Available The characterization of nonmetallic inclusions is of importance for the production of clean steel in order to improve the mechanical properties. In this respect, a three-dimensional (3D investigation is considered to be useful for an accurate evaluation of size, number, morphology of inclusions, and elementary distribution in each inclusion particle. In this study, the application of various extraction methods (chemical extraction/etching by acid or halogen-alcohol solutions, electrolysis, sputtering with glow discharge, and so on for 3D estimation of nonmetallic Al2O3 inclusions and clusters in high-alloyed steels was examined and discussed using an Fe-10 mass% Ni alloy and an 18/8 stainless steel deoxidized with Al. Advantages and limitations of different extraction methods for 3D investigations of inclusions and clusters were discussed in comparison to conventional two-dimensional (2D observations on a polished cross section of metal samples.
A New Waveform Signal Processing Method Based on Adaptive Clustering-Genetic Algorithms
International Nuclear Information System (INIS)
Noha Shaaban; Fukuzo Masuda; Hidetsugu Morota
2006-01-01
We present a fast digital signal processing method for numerical analysis of individual pulses from CdZnTe compound semiconductor detectors. Using Maxi-Mini Distance Algorithm and Genetic Algorithms based discrimination technique. A parametric approach has been used for classifying the discriminated waveforms into a set of clusters each has a similar signal shape with a corresponding pulse height spectrum. A corrected total pulse height spectrum was obtained by applying a normalization factor for the full energy peak for each cluster with a highly improvements in the energy spectrum characteristics. This method applied successfully for both simulated and real measured data, it can be applied to any detector suffers from signal shape variation. (authors)
The IMACS Cluster Building Survey. I. Description of the Survey and Analysis Methods
Oemler Jr., Augustus; Dressler, Alan; Gladders, Michael G.; Rigby, Jane R.; Bai, Lei; Kelson, Daniel; Villanueva, Edward; Fritz, Jacopo; Rieke, George; Poggianti, Bianca M.;
2013-01-01
The IMACS Cluster Building Survey uses the wide field spectroscopic capabilities of the IMACS spectrograph on the 6.5 m 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 (SFRs) derived from optical and infrared spectroscopy and photometry. We demonstrate that there is a tight relation between the observed SFR 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 ongoing and recent starbursts in galaxies.
THE IMACS CLUSTER BUILDING SURVEY. I. DESCRIPTION OF THE SURVEY AND ANALYSIS METHODS
Energy Technology Data Exchange (ETDEWEB)
Oemler, Augustus Jr.; Dressler, Alan; Kelson, Daniel; Villanueva, Edward [Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101-1292 (United States); Gladders, Michael G. [Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637 (United States); Rigby, Jane R. [Observational Cosmology Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Bai Lei [Department of Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4 (Canada); Fritz, Jacopo [Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281 S9, B-9000 Gent (Belgium); Rieke, George [Steward Observatory, University of Arizona, Tucson, AZ 8572 (United States); Poggianti, Bianca M.; Vulcani, Benedetta, E-mail: oemler@obs.carnegiescience.edu [INAF-Osservatorio Astronomico di Padova, Vicolo dell' Osservatorio 5, I-35122 Padova (Italy)
2013-06-10
The IMACS Cluster Building Survey uses the wide field spectroscopic capabilities of the IMACS spectrograph on the 6.5 m 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 (SFRs) derived from optical and infrared spectroscopy and photometry. We demonstrate that there is a tight relation between the observed SFR 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 ongoing and recent starbursts in galaxies.
Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie; Brown, Alison E; Gill, O Noel; Tostevin, Anna; Fraser, Christophe; Volz, Erik M
2018-06-01
Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
Formation of Si clusters in AlGaN: A study of local structure
International Nuclear Information System (INIS)
Somogyi, A.; Martinez-Criado, G.; Homs, A.; Hernandez-Fenollosa, M. A.; Vantelon, D.; Ambacher, O.
2007-01-01
In this study, the authors report on the application of synchrotron radiation x-ray microprobe to the study of Si impurities in plasma-induced molecular beam epitaxy grown Al 0.32 Ga 0.68 N. Elemental maps obtained by μ-x-ray fluorescence spectrometry show inhomogeneous distributions of Si, Al, and Ga on the micron scale. X-ray absorption near-edge structure spectra taken at the Si and Al K edges provided information about their local chemical environment and revealed the change of the spectral features as depending on the position compared to the sample surface and on the concentration of Si
Thermodynamics of non-ideal QGP using Mayers cluster expansion method
International Nuclear Information System (INIS)
Prasanth, J.P; Simji, P.; Bannur, Vishnu M.
2013-01-01
The Quark gluon plasma (QGP) is the state in which the individual hadrons dissolve into a system of free (or almost free) quarks and gluons in strongly compressed system at high temperature. The present paper aims to calculate the critical temperature at which a non-ideal three quark plasma condenses into droplet of three quarks (i.e., into a liquid of baryons) using Mayers cluster expansion method
GLOBAL CLASSIFICATION OF DERMATITIS DISEASE WITH K-MEANS CLUSTERING IMAGE SEGMENTATION METHODS
Prafulla N. Aerkewar1 & Dr. G. H. Agrawal2
2018-01-01
The objective of this paper to presents a global technique for classification of different dermatitis disease lesions using the process of k-Means clustering image segmentation method. The word global is used such that the all dermatitis disease having skin lesion on body are classified in to four category using k-means image segmentation and nntool of Matlab. Through the image segmentation technique and nntool can be analyze and study the segmentation properties of skin lesions occurs in...
International Nuclear Information System (INIS)
Azimi, R.; Ghayekhloo, M.; Ghofrani, M.
2016-01-01
Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar
Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation
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Ricardo FAIA
2016-10-01
Full Text Available Artificial Intelligence (AI methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM. In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.
Parks, Renee G; Tabak, Rachel G; Allen, Peg; Baker, Elizabeth A; Stamatakis, Katherine A; Poehler, Allison R; Yan, Yan; Chin, Marshall H; Harris, Jenine K; Dobbins, Maureen; Brownson, Ross C
2017-10-18
The rates of diabetes and prediabetes in the USA are growing, significantly impacting the quality and length of life of those diagnosed and financially burdening society. Premature death and disability can be prevented through implementation of evidence-based programs and policies (EBPPs). Local health departments (LHDs) are uniquely positioned to implement diabetes control EBPPs because of their knowledge of, and focus on, community-level needs, contexts, and resources. There is a significant gap, however, between known diabetes control EBPPs and actual diabetes control activities conducted by LHDs. The purpose of this study is to determine how best to support the use of evidence-based public health for diabetes (and related chronic diseases) control among local-level public health practitioners. This paper describes the methods for a two-phase study with a stepped-wedge cluster randomized trial that will evaluate dissemination strategies to increase the uptake of public health knowledge and EBPPs for diabetes control among LHDs. Phase 1 includes development of measures to assess practitioner views on and organizational supports for evidence-based public health, data collection using a national online survey of LHD chronic disease practitioners, and a needs assessment of factors influencing the uptake of diabetes control EBPPs among LHDs within one state in the USA. Phase 2 involves conducting a stepped-wedge cluster randomized trial to assess effectiveness of dissemination strategies with local-level practitioners at LHDs to enhance capacity and organizational support for evidence-based diabetes prevention and control. Twelve LHDs will be selected and randomly assigned to one of the three groups that cross over from usual practice to receive the intervention (dissemination) strategies at 8-month intervals; the intervention duration for groups ranges from 8 to 24 months. Intervention (dissemination) strategies may include multi-day in-person workshops, electronic
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Reilly John J
2005-06-01
Full Text Available Abstract Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical
Timmerman, Marieke E; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla
2013-12-01
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).
Wendt, Martin; Husser, Tim-Oliver; Kamann, Sebastian; Monreal-Ibero, Ana; Richter, Philipp; Brinchmann, Jarle; Dreizler, Stefan; Weilbacher, Peter M.; Wisotzki, Lutz
2017-11-01
Context. We map the interstellar medium (ISM) including the diffuse interstellar bands (DIBs) in absorption toward the globular cluster NGC 6397 using VLT/MUSE. Assuming the absorbers are located at the rim of the Local Bubble we trace structures on the order of mpc (milliparsec, a few thousand AU). Aims: We aimed to demonstrate the feasibility to map variations of DIBs on small scales with MUSE. The sightlines defined by binned stellar spectra are separated by only a few arcseconds and we probe the absorption within a physically connected region. Methods: This analysis utilized the fitting residuals of individual stellar spectra of NGC 6397 member stars and analyzed lines from neutral species and several DIBs in Voronoi-binned composite spectra with high signal-to-noise ratio (S/N). Results: This pilot study demonstrates the power of MUSE for mapping the local ISM on very small scales which provides a new window for ISM observations. We detect small scale variations in Na I and K I as well as in several DIBs within few arcseconds, or mpc with regard to the Local Bubble. We verify the suitability of the MUSE 3D spectrograph for such measurements and gain new insights by probing a single physical absorber with multiple sight lines.
A study of several CAD methods for classification of clustered microcalcifications
Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.; Jiang, Yulei
2005-04-01
In this paper we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs), aimed to assisting radiologists for more accurate diagnosis of breast cancer in a computer-aided diagnosis (CADx) scheme. The methods we consider include: support vector machine (SVM), kernel Fisher discriminant (KFD), and committee machines (ensemble averaging and AdaBoost), most of which have been developed recently in statistical learning theory. We formulate differentiation of malignant from benign MCs as a supervised learning problem, and apply these learning methods to develop the classification algorithms. As input, these methods use image features automatically extracted from clustered MCs. We test these methods using a database of 697 clinical mammograms from 386 cases, which include a wide spectrum of difficult-to-classify cases. We use receiver operating characteristic (ROC) analysis to evaluate and compare the classification performance by the different methods. In addition, we also investigate how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD) yield the best performance, significantly outperforming a well-established CADx approach based on neural network learning.
Yoshiaki, A; Zhang, Y C
2002-01-01
Embedded nano-Pd particles of 5 nm in size instantly abundant D-atoms more than 250% in the atomic ratio against Pd-atoms at room temperature when they are kept in D sub 2 gas pressurized to less than 10 atm. In such ultrahigh densities, 2-4 D-atoms can be coagulated inside each octahedral space of Pd lattice (pycnodeuterium-lump). When a stimulation energy such as latticequake causing by ultrasonic wave was supplied to those highly deuterated Pd particles, intense deuterium nuclear fusion (''solid fusion'') was generated there and both excess heat and sup 4 He gas were abundantly produced. Naturally, these facts can not be realized at all in bulk Pd. The results show that the nuclear fusion occurs without any hazardous rays in pycnodeuterium-lumps coagulated locally inside the each cell of the host metal lattice. These unit cells correspond to minimum unit of the solid fusion reactor as a ''Lattice Reactor''. (author)
Environmental data processing by clustering methods for energy forecast and planning
Energy Technology Data Exchange (ETDEWEB)
Di Piazza, Annalisa [Dipartimento di Ingegneria Idraulica e Applicazioni Ambientali (DIIAA), viale delle Scienze, Universita degli Studi di Palermo, 90128 Palermo (Italy); Di Piazza, Maria Carmela; Ragusa, Antonella; Vitale, Gianpaolo [Consiglio Nazionale delle Ricerche Istituto di Studi sui Sistemi Intelligenti per l' Automazione (ISSIA - CNR), sezione di Palermo, Via Dante, 12, 90141 Palermo (Italy)
2011-03-15
This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation is concerned, a suitable generator, matching the wind profile of the studied sites, has been selected for the evaluation of the producible energy. With respect to the photovoltaic generation, the irradiance data have been taken from the acquisition system of an actual installation. It is demonstrated, in both cases, that the use of the k-means clustering method allows data that do not contribute to the produced energy to be grouped into a cluster, moreover it simplifies the problem of the energy assessment since it permits to obtain the desired information on energy capability by managing a reduced amount of experimental samples. In the studied cases, the proposed method permitted a reduction of the 50% of the data with a maximum discrepancy of 10% in energy estimation compared to the classical statistical approach. Therefore, the adopted k-means clustering technique represents an useful tool for an appropriate and less demanding energy forecast and planning in distributed generation systems. (author)
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Dumitru Baleanu
2014-01-01
Full Text Available We perform a comparison between the fractional iteration and decomposition methods applied to the wave equation on Cantor set. 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.
Adin, A; Lee, D; Goicoa, T; Ugarte, María Dolores
2018-01-01
Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.
Directory of Open Access Journals (Sweden)
Shizhen Zhao
2018-06-01
Full Text Available Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF, and multivariate Gaussian distribution (MGD is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.
Boriollo, Marcelo Fabiano Gomes; Rosa, Edvaldo Antonio Ribeiro; Gonçalves, Reginaldo Bruno; Höfling, José Francisco
2006-03-01
The typing of C. albicans by MLEE (multilocus enzyme electrophoresis) is dependent on the interpretation of enzyme electrophoretic patterns, and the study of the epidemiological relationships of these yeasts can be conducted by cluster analysis. Therefore, the aims of the present study were to first determine the discriminatory power of genetic interpretation (deduction of the allelic composition of diploid organisms) and numerical interpretation (mere determination of the presence and absence of bands) of MLEE patterns, and then to determine the concordance (Pearson product-moment correlation coefficient) and similarity (Jaccard similarity coefficient) of the groups of strains generated by three cluster analysis models, and the discriminatory power of such models as well [model A: genetic interpretation, genetic distance matrix of Nei (d(ij)) and UPGMA dendrogram; model B: genetic interpretation, Dice similarity matrix (S(D1)) and UPGMA dendrogram; model C: numerical interpretation, Dice similarity matrix (S(D2)) and UPGMA dendrogram]. MLEE was found to be a powerful and reliable tool for the typing of C. albicans due to its high discriminatory power (>0.9). Discriminatory power indicated that numerical interpretation is a method capable of discriminating a greater number of strains (47 versus 43 subtypes), but also pointed to model B as a method capable of providing a greater number of groups, suggesting its use for the typing of C. albicans by MLEE and cluster analysis. Very good agreement was only observed between the elements of the matrices S(D1) and S(D2), but a large majority of the groups generated in the three UPGMA dendrograms showed similarity S(J) between 4.8% and 75%, suggesting disparities in the conclusions obtained by the cluster assays.
Periodic local MP2 method employing orbital specific virtuals
International Nuclear Information System (INIS)
Usvyat, Denis; Schütz, Martin; Maschio, Lorenzo
2015-01-01
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
Periodic local MP2 method employing orbital specific virtuals
Energy Technology Data Exchange (ETDEWEB)
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
A quasiparticle-based multi-reference coupled-cluster method.
Rolik, Zoltán; Kállay, Mihály
2014-10-07
The purpose of this paper is to introduce a quasiparticle-based multi-reference coupled-cluster (MRCC) approach. The quasiparticles are introduced via a unitary transformation which allows us to represent a complete active space reference function and other elements of an orthonormal multi-reference (MR) basis in a determinant-like form. The quasiparticle creation and annihilation operators satisfy the fermion anti-commutation relations. On the basis of these quasiparticles, a generalization of the normal-ordered operator products for the MR case can be introduced as an alternative to the approach of Mukherjee and Kutzelnigg [Recent Prog. Many-Body Theor. 4, 127 (1995); Mukherjee and Kutzelnigg, J. Chem. Phys. 107, 432 (1997)]. Based on the new normal ordering any quasiparticle-based theory can be formulated using the well-known diagram techniques. Beyond the general quasiparticle framework we also present a possible realization of the unitary transformation. The suggested transformation has an exponential form where the parameters, holding exclusively active indices, are defined in a form similar to the wave operator of the unitary coupled-cluster approach. The definition of our quasiparticle-based MRCC approach strictly follows the form of the single-reference coupled-cluster method and retains several of its beneficial properties. Test results for small systems are presented using a pilot implementation of the new approach and compared to those obtained by other MR methods.
Clustering method for counting passengers getting in a bus with single camera
Yang, Tao; Zhang, Yanning; Shao, Dapei; Li, Ying
2010-03-01
Automatic counting of passengers is very important for both business and security applications. We present a single-camera-based vision system that is able to count passengers in a highly crowded situation at the entrance of a traffic bus. The unique characteristics of the proposed system include, First, a novel feature-point-tracking- and online clustering-based passenger counting framework, which performs much better than those of background-modeling-and foreground-blob-tracking-based methods. Second, a simple and highly accurate clustering algorithm is developed that projects the high-dimensional feature point trajectories into a 2-D feature space by their appearance and disappearance times and counts the number of people through online clustering. Finally, all test video sequences in the experiment are captured from a real traffic bus in Shanghai, China. The results show that the system can process two 320×240 video sequences at a frame rate of 25 fps simultaneously, and can count passengers reliably in various difficult scenarios with complex interaction and occlusion among people. The method achieves high accuracy rates up to 96.5%.
Alam, Md Ferdous; Haque, Asadul
2017-10-18
An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM), has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis.
application of single-linkage clustering method in the analysis of ...
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ANALYSIS OF GROWTH RATE OF GROSS DOMESTIC PRODUCT. (GDP) AT ... The end result of the algorithm is a tree of clusters called a dendrogram, which shows how the clusters are ..... Number of cluster sum from from observations of ...
A novel intrusion detection method based on OCSVM and K-means recursive clustering
Directory of Open Access Journals (Sweden)
Leandros A. Maglaras
2015-01-01
Full Text Available In this paper we present an intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition system, based on the combination of One-Class Support Vector Machine (OCSVM with RBF kernel and recursive k-means clustering. Important parameters of OCSVM, such as Gaussian width o and parameter v affect the performance of the classifier. Tuning of these parameters is of great importance in order to avoid false positives and over fitting. The combination of OCSVM with recursive k- means clustering leads the proposed intrusion detection module to distinguish real alarms from possible attacks regardless of the values of parameters o and v, making it ideal for real-time intrusion detection mechanisms for SCADA systems. Extensive simulations have been conducted with datasets extracted from small and medium sized HTB SCADA testbeds, in order to compare the accuracy, false alarm rate and execution time against the base line OCSVM method.
New Target for an Old Method: Hubble Measures Globular Cluster Parallax
Hensley, Kerry
2018-05-01
Measuring precise distances to faraway objects has long been a challenge in astrophysics. Now, one of the earliest techniques used to measure the distance to astrophysical objects has been applied to a metal-poor globular cluster for the first time.A Classic TechniqueAn artists impression of the European Space Agencys Gaia spacecraft. Gaia is on track to map the positions and motions of a billion stars. [ESA]Distances to nearby stars are often measured using the parallax technique tracing the tiny apparent motion of a target star against the background of more distant stars as Earth orbits the Sun. This technique has come a long way since it was first used in the 1800s to measure the distance to stars a few tens of light-years away; with the advent of space observatories like Hipparcos and Gaia, parallax can now be used to map the positions of stars out to thousands of light-years.Precise distance measurements arent only important for setting the scale of the universe, however; they can also help us better understand stellar evolution over the course of cosmic history. Stellar evolution models are often anchored to a reference star cluster, the properties of which must be known precisely. These precise properties can be readily determined for young, nearby open clusters using parallax measurements. But stellar evolution models that anchor on themore-distant, ancient, metal-poor globular clusters have been hampered by theless-precise indirect methods used tomeasure distance to these faraway clusters until now.Top: An image of NGC 6397 overlaid with the area scanned by Hubble (dashed green) and the footprint of the camera (solid green). The blue ellipse represents the parallax motion of a star in the cluster, exaggerated by a factor of ten thousand. Bottom: An example scan from this field. [Adapted from Brown et al. 2018]New Measurement to an Old ClusterThomas Brown (Space Telescope Science Institute) and collaborators used the Hubble Space Telescope todetermine the
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.
Yang, Yan-Pu; Chen, Deng-Kai; Gu, Rong; Gu, Yu-Feng; Yu, Sui-Huai
2016-01-01
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.
Multiple-Features-Based Semisupervised Clustering DDoS Detection Method
Directory of Open Access Journals (Sweden)
Yonghao Gu
2017-01-01
Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.
Directory of Open Access Journals (Sweden)
Marvin Acuña Ortega
2010-12-01
Full Text Available Uno de los rasgos más notables que mejor representan el cambio estructural que ha experimentado la economía costarricense, en los últimos años, fruto de los procesos de apertura y globalización ha sido la creciente importancia relativa del sector servicios, y en particular, del sector turismo en la economía. Este estudio se propone analizar estos cambios estructurales desde una perspectiva territorial, determinando su impacto en el desarrollo económico regional o local. De manera más particular, este estudio busca determinar la estructura de mercado subyacente y el entorno institucional que ha hecho posible que el ecoturismo se haya convertido en La Fortuna de San Carlos una alternativa productiva para el desarrollo de las PyMES, y el impulsor de procesos de desarrollo local, en condiciones relativamente endógenas y un medio para una mejor inserción internacional de esos territorios. El poder probar la existencia de cluster de servicios eco-turísticos en La Fortuna de San Carlos permitirá dar respuesta a la siguiente interrogante: ¿qué tipo de estructuras e instituciones de mercado son capaces de generar economías de aglomeración, rendimientos crecientes y menores costos de transacción, para que las PyMES puedan adoptar un patrón de desarrollo basado en la cooperación, la competitividad y la innovación continua, para así convertirse en artífices del desarrollo local?
International Nuclear Information System (INIS)
Kosarev, E.L.
1980-01-01
A new method to reconstruct spatial star distribution in globular clusters is presented. The method gives both the estimation of unknown spatial distribution and the probable reconstruction error. This error has statistical origin and depends only on the number of stars in a cluster. The method is applied to reconstruct the spatial density of 441 flare stars in Pleiades. The spatial density has a maximum in the centre of the cluster of about 1.6-2.5 pc -3 and with increasing distance from the center smoothly falls down to zero approximately with the Gaussian law with a scale parameter of 3.5 pc
Modeling local extinction in turbulent combustion using an embedding method
Knaus, Robert; Pantano, Carlos
2012-11-01
Local regions of extinction in diffusion flames, called ``flame holes,'' can reduce the efficiency of combustion and increase the production of certain pollutants. At sufficiently high speeds, a flame may also be lifted from the rim of the burner to a downstream location that may be stable. These two phenomena share a common underlying mechanism of propagation related to edge-flame dynamics where chemistry and fluid mechanics are equally important. We present a formulation that describes the formation, propagation, and growth of flames holes on the stoichiometric surface using edge flame dynamics. The boundary separating the flame from the quenched region is modeled using a progress variable defined on the moving stoichiometric surface that is embedded in the three-dimensional space using an extension algorithm. This Cartesian problem is solved using a high-order finite-volume WENO method extended to this nonconservative problem. This algorithm can track the dynamics of flame holes in a turbulent reacting-shear layer and model flame liftoff without requiring full chemistry calculations.
K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering
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Lv Tao
2017-01-01
Full Text Available Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1 the predictive power of a pattern varies a great deal for different shapes and (2 each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.
A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.
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Daniel M de Brito
Full Text Available Genomic Islands (GIs are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.
A new method to cluster genomes based on cumulative Fourier power spectrum.
Dong, Rui; Zhu, Ziyue; Yin, Changchuan; He, Rong L; Yau, Stephen S-T
2018-06-20
Analyzing phylogenetic relationships using mathematical methods has always been of importance in bioinformatics. Quantitative research may interpret the raw biological data in a precise way. Multiple Sequence Alignment (MSA) is used frequently to analyze biological evolutions, but is very time-consuming. When the scale of data is large, alignment methods cannot finish calculation in reasonable time. Therefore, we present a new method using moments of cumulative Fourier power spectrum in clustering the DNA sequences. Each sequence is translated into a vector in Euclidean space. Distances between the vectors can reflect the relationships between sequences. The mapping between the spectra and moment vector is one-to-one, which means that no information is lost in the power spectra during the calculation. We cluster and classify several datasets including Influenza A, primates, and human rhinovirus (HRV) datasets to build up the phylogenetic trees. Results show that the new proposed cumulative Fourier power spectrum is much faster and more accurately than MSA and another alignment-free method known as k-mer. The research provides us new insights in the study of phylogeny, evolution, and efficient DNA comparison algorithms for large genomes. The computer programs of the cumulative Fourier power spectrum are available at GitHub (https://github.com/YaulabTsinghua/cumulative-Fourier-power-spectrum). Copyright © 2018. Published by Elsevier B.V.
Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei
2011-04-01
An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.
Data Clustering on Breast Cancer Data Using Firefly Algorithm with Golden Ratio Method
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DEMIR, M.
2015-05-01
Full Text Available Heuristic methods are problem solving methods. In general, they obtain near-optimal solutions, and they do not take the care of provability of this case. The heuristic methods do not guarantee to obtain the optimal results; however, they guarantee to obtain near-optimal solutions in considerable time. In this paper, an application was performed by using firefly algorithm - one of the heuristic methods. The golden ratio was applied to different steps of firefly algorithm and different parameters of firefly algorithm to develop a new algorithm - called Firefly Algorithm with Golden Ratio (FAGR. It was shown that the golden ratio made firefly algorithm be superior to the firefly algorithm without golden ratio. At this aim, the developed algorithm was applied to WBCD database (breast cancer database to cluster data obtained from breast cancer patients. The highest obtained success rate among all executions is 96% and the highest obtained average success rate in all executions is 94.5%.
Directory of Open Access Journals (Sweden)
Curtis Andrew
2006-03-01
Full Text Available Abstract Background To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May – 31 October 1906 – 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. Results The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. Conclusion The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century.
Peleg, Mor; Asbeh, Nuaman; Kuflik, Tsvi; Schertz, Mitchell
2009-02-01
Children with developmental disorders usually exhibit multiple developmental problems (comorbidities). Hence, such diagnosis needs to revolve on developmental disorder groups. Our objective is to systematically identify developmental disorder groups and represent them in an ontology. We developed a methodology that combines two methods (1) a literature-based ontology that we created, which represents developmental disorders and potential developmental disorder groups, and (2) clustering for detecting comorbid developmental disorders in patient data. The ontology is used to interpret and improve clustering results and the clustering results are used to validate the ontology and suggest directions for its development. We evaluated our methodology by applying it to data of 1175 patients from a child development clinic. We demonstrated that the ontology improves clustering results, bringing them closer to an expert generated gold-standard. We have shown that our methodology successfully combines an ontology with a clustering method to support systematic identification and representation of developmental disorder groups.
Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys
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Rose Angela MC
2007-06-01
Full Text Available Abstract In two-stage cluster surveys, the traditional method used in second-stage sampling (in which the first household in a cluster is selected is time-consuming and may result in biased estimates of the indicator of interest. Firstly, a random direction from the center of the cluster is selected, usually by spinning a pen. The houses along that direction are then counted out to the boundary of the cluster, and one is then selected at random to be the first household surveyed. This process favors households towards the center of the cluster, but it could easily be improved. During a recent meningitis vaccination coverage survey in Maradi, Niger, we compared this method of first household selection to two alternatives in urban zones: 1 using a superimposed grid on the map of the cluster area and randomly selecting an intersection; and 2 drawing the perimeter of the cluster area using a Global Positioning System (GPS and randomly selecting one point within the perimeter. Although we only compared a limited number of clusters using each method, we found the sampling grid method to be the fastest and easiest for field survey teams, although it does require a map of the area. Selecting a random GPS point was also found to be a good method, once adequate training can be provided. Spinning the pen and counting households to the boundary was the most complicated and time-consuming. The two methods tested here represent simpler, quicker and potentially more robust alternatives to spinning the pen for cluster surveys in urban areas. However, in rural areas, these alternatives would favor initial household selection from lower density (or even potentially empty areas. Bearing in mind these limitations, as well as available resources and feasibility, investigators should choose the most appropriate method for their particular survey context.
International Nuclear Information System (INIS)
Farnell, D J J; Zinke, R; Richter, J; Schulenburg, J
2009-01-01
We apply the coupled cluster method (CCM) in order to study the ground-state properties of the (unfrustrated) square-lattice and (frustrated) triangular-lattice spin-half Heisenberg antiferromagnets in the presence of external magnetic fields. Approximate methods are difficult to apply to the triangular-lattice antiferromagnet because of frustration, and so, for example, the quantum Monte Carlo (QMC) method suffers from the 'sign problem'. Results for this model in the presence of magnetic field are rarer than those for the square-lattice system. Here we determine and solve the basic CCM equations by using the localized approximation scheme commonly referred to as the 'LSUBm' approximation scheme and we carry out high-order calculations by using intensive computational methods. We calculate the ground-state energy, the uniform susceptibility, the total (lattice) magnetization and the local (sublattice) magnetizations as a function of the magnetic field strength. Our results for the lattice magnetization of the square-lattice case compare well to the results from QMC approaches for all values of the applied external magnetic field. We find a value for the magnetic susceptibility of χ = 0.070 for the square-lattice antiferromagnet, which is also in agreement with the results from other approximate methods (e.g., χ = 0.0669 obtained via the QMC approach). Our estimate for the range of the extent of the (M/M s =) 1/3 magnetization plateau for the triangular-lattice antiferromagnet is 1.37 SWT = 0.0794. Higher-order calculations are thus suggested for both SWT and CCM LSUBm calculations in order to determine the value of χ for the triangular lattice conclusively.
A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.
Ferrari, Alberto; Comelli, Mario
2016-12-01
In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.
Analytical Energy Gradients for Excited-State Coupled-Cluster Methods
Wladyslawski, Mark; Nooijen, Marcel
The equation-of-motion coupled-cluster (EOM-CC) and similarity transformed equation-of-motion coupled-cluster (STEOM-CC) methods have been firmly established as accurate and routinely applicable extensions of single-reference coupled-cluster theory to describe electronically excited states. An overview of these methods is provided, with emphasis on the many-body similarity transform concept that is the key to a rationalization of their accuracy. The main topic of the paper is the derivation of analytical energy gradients for such non-variational electronic structure approaches, with an ultimate focus on obtaining their detailed algebraic working equations. A general theoretical framework using Lagrange's method of undetermined multipliers is presented, and the method is applied to formulate the EOM-CC and STEOM-CC gradients in abstract operator terms, following the previous work in [P.G. Szalay, Int. J. Quantum Chem. 55 (1995) 151] and [S.R. Gwaltney, R.J. Bartlett, M. Nooijen, J. Chem. Phys. 111 (1999) 58]. Moreover, the systematics of the Lagrange multiplier approach is suitable for automation by computer, enabling the derivation of the detailed derivative equations through a standardized and direct procedure. To this end, we have developed the SMART (Symbolic Manipulation and Regrouping of Tensors) package of automated symbolic algebra routines, written in the Mathematica programming language. The SMART toolkit provides the means to expand, differentiate, and simplify equations by manipulation of the detailed algebraic tensor expressions directly. The Lagrangian multiplier formulation establishes a uniform strategy to perform the automated derivation in a standardized manner: A Lagrange multiplier functional is constructed from the explicit algebraic equations that define the energy in the electronic method; the energy functional is then made fully variational with respect to all of its parameters, and the symbolic differentiations directly yield the explicit
Dancoff factors with partial absorption in cluster geometry by the direct method
International Nuclear Information System (INIS)
Rodrigues, Leticia Jenisch; Leite, Sergio de Queiroz Bogado; Vilhena, Marco Tullio de; Bodmann, Bardo Ernest Josef
2007-01-01
Accurate analysis of resonance absorption in heterogeneous systems is essential in problems like criticality, breeding ratios and fuel depletion calculations. In compact arrays of fuel rods, resonance absorption is strongly affected by the Dancoff factor, defined in this study as the probability that a neutron emitted from the surface of a fuel element, enters another fuel element without any collision in the moderator or cladding. In the original WIMS code, Black Dancoff factors were computed in cluster geometry by the collision probability method, for each one of the symmetrically distinct fuel pin positions in the cell. Recent improvements to the code include a new routine (PIJM) that was created to incorporate a more efficient scheme for computing the collision matrices. In that routine, each system region is considered individually, minimizing convergence problems and reducing the number of neutron track lines required in the in-plane integrations of the Bickley functions for any given accuracy. In the present work, PIJM is extended to compute Grey Dancoff factors for two-dimensional cylindrical cells in cluster geometry. The effectiveness of the method is accessed by comparing Grey Dancoff factors as calculated by PIJM, with those available in the literature by the Monte Carlo method, for the irregular geometry of the Canadian CANDU37 assembly. Dancoff factors at five symmetrically distinct fuel pin positions are found in very good agreement with the literature results (author)
A robust automatic leukocyte recognition method based on island-clustering texture
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Xiaoshun Li
2016-01-01
Full Text Available A leukocyte recognition method for human peripheral blood smear based on island-clustering texture (ICT is proposed. By analyzing the features of the five typical classes of leukocyte images, a new ICT model is established. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands. Secondly, the growing region is employed to create regions of the islands in which the seeds are just these feature points. These islands distribution can describe a new texture. Finally, a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte. Then the five typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3% with a total sample of 1310 leukocytes. Experimental results show the feasibility of the proposed method. Further analysis reveals that the method is robust and results can provide important information for disease diagnosis.
The active titration method for measuring local hydroxyl radical concentration
Sprengnether, Michele; Prinn, Ronald G.
1994-01-01
We are developing a method for measuring ambient OH by monitoring its rate of reaction with a chemical species. Our technique involves the local, instantaneous release of a mixture of saturated cyclic hydrocarbons (titrants) and perfluorocarbons (dispersants). These species must not normally be present in ambient air above the part per trillion concentration. We then track the mixture downwind using a real-time portable ECD tracer instrument. We collect air samples in canisters every few minutes for roughly one hour. We then return to the laboratory and analyze our air samples to determine the ratios of the titrant to dispersant concentrations. The trends in these ratios give us the ambient OH concentration from the relation: dlnR/dt = -k(OH). A successful measurement of OH requires that the trends in these ratios be measureable. We must not perturb ambient OH concentrations. The titrant to dispersant ratio must be spatially invariant. Finally, heterogeneous reactions of our titrant and dispersant species must be negligible relative to the titrant reaction with OH. We have conducted laboratory studies of our ability to measure the titrant to dispersant ratios as a function of concentration down to the few part per trillion concentration. We have subsequently used these results in a gaussian puff model to estimate our expected uncertainty in a field measurement of OH. Our results indicate that under a range of atmospheric conditions we expect to be able to measure OH with a sensitivity of 3x10(exp 5) cm(exp -3). In our most optimistic scenarios, we obtain a sensitivity of 1x10(exp 5) cm(exp -3). These sensitivity values reflect our anticipated ability to measure the ratio trends. However, because we are also using a rate constant to obtain our (OH) from this ratio trend, our accuracy cannot be better than that of the rate constant, which we expect to be about 20 percent.
Guo, Jingyu; Tian, Dehua; McKinney, Brett A.; Hartman, John L.
2010-06-01
of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.
African Journals Online (AJOL)
Background: The importance of local variations in patterns of health and disease are increasingly recognised, but, particularly in the case of tropical infections, available methods and resources for characterising disease clusters in time and space are limited. Whilst the Global Positioning System. (GPS) allows accurate and ...
Directory of Open Access Journals (Sweden)
Yongwei Zhang
2017-01-01
Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.
Directory of Open Access Journals (Sweden)
Aldo Merlino
2007-01-01
Full Text Available Qualitative methods present a wide spectrum of application possibilities as well as opportunities for combining qualitative and quantitative methods. In the social sciences fruitful theoretical discussions and a great deal of empirical research have taken place. This article introduces an empirical investigation which demonstrates the logic of combining methodologies as well as the collection and interpretation, both sequential as simultaneous, of qualitative and quantitative data. Specifically, the investigation process will be described, beginning with a grounded theory methodology and its combination with the techniques of structural semiotics discourse analysis to generate—in a first phase—an instrument for quantitative measuring and to understand—in a second phase—clusters obtained by quantitative analysis. This work illustrates how qualitative methods allow for the comprehension of the discursive and behavioral elements under study, and how they function as support making sense of and giving meaning to quantitative data. URN: urn:nbn:de:0114-fqs0701219
Kent, Peter; Jensen, Rikke K; Kongsted, Alice
2014-10-02
There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets
Kim, H.; Ho, C.; Kim, J.
2008-12-01
This study presents the pattern classification of tropical cyclone (TC) tracks over the western North Pacific (WNP) basin during the typhoon season (June through October) for 1965-2006 (total 42 years) using a fuzzy clustering method. After the fuzzy c-mean clustering algorithm to the TC trajectory interpolated into 20 segments of equivalent length, we divided the whole tracks into 7 patterns. The optimal number of the fuzzy cluster is determined by several validity measures. The classified TC track patterns represent quite different features in the recurving latitudes, genesis locations, and geographical pathways: TCs mainly forming in east-northern part of the WNP and striking Korean and Japan (C1); mainly forming in west-southern part of the WNP, traveling long pathway, and partly striking Japan (C2); mainly striking Taiwan and East China (C3); traveling near the east coast of Japan (C4); traveling the distant ocean east of Japan (C5); moving toward South China and Vietnam straightly (C6); and forming in the South China Sea (C7). Atmospheric environments related to each cluster show physically consistent with each TC track patterns. The straight track pattern is closely linked to a developed anticyclonic circulation to the north of the TC. It implies that this ridge acts as a steering flow forcing TCs to move to the northwest with a more west-oriented track. By contrast, recurving patterns occur commonly under the influence of the strong anomalous westerlies over the TC pathway but there definitely exist characteristic anomalous circulations over the mid- latitudes by pattern. Some clusters are closely related to the well-known large-scale phenomena. The C1 and C2 are highly related to the ENSO phase: The TCs in the C1 (C2) is more active during La Niña (El Niño). The TC activity in the C3 is associated with the WNP summer monsoon. The TCs in the C4 is more (less) vigorous during the easterly (westerly) phase of the stratospheric quasi-biennial oscillation
Jacobs, S G
2000-10-01
The parallax method (image/tube shift method, Clark's rule, Richards' buccal object rule) is recommended to localize unerupted teeth. Richards' contribution to the development of the parallax method is discussed. The favored method for localization uses a rotational panoramic radiograph in combination with an occlusal radiograph involving a vertical shift of the x-ray tube. The use of this combination when localizing teeth and supernumeraries in the premolar region is illustrated. When taking an occlusal radiograph to localize an unerupted maxillary canine, clinical situations are presented where modification of the vertical angulation of the tube of 70 degrees to 75 degrees or of the horizontal position of the tube is warranted. The limitations of axial (true, cross-sectional, vertex) occlusal radiographs are also explored.
Statistical method for determining ages of globular clusters by fitting isochrones
International Nuclear Information System (INIS)
Flannery, B.P.; Johnson, B.C.
1982-01-01
We describe a statistical procedure to compare models of stellar evolution and atmospheres with color-magnitude diagrams of globular clusters. The isochrone depends on five parameters: m-M, age, [Fe/H], Y, and α, but in practice we can only determine m-M and age for an assumed composition. The technique allows us to determine parameters of the model, their uncertainty, and to assess goodness of fit. We test the method, and evaluate the effect of assumptions on an extensive set of Monte Carlo simulations. We apply the method to extensive observations of NGC 6752 and M5, and to smaller data sets for the clusters M3, M5, M15, and M92. We determine age and m-M for two assumed values of helium Y = (0.2, 0.3), and three values of metallicity with a spread in [Fe/H] of +- 0.3 dex. These result in a spread in age of 5-8 Gyr (1 Gyr = 10 9 yr), and a spread in m-M of 0.5 mag. The mean age is generally younger by 2-3 Gyr than previous estimates. Likely uncertainty associated with an individual fit can be small as 0.4 Gyr. Most importantly, we find that two uncalibratable sources of systematic error make the results suspect. These are uncertainty in the stellar temperatures induced by choice of mixing length, and known errors in stellar atmospheres. These effects could reduce age estimates by an additional 5 Gyr. We conclude that observations do not preclude ages as young as 10 Gyr for globular clusters
Multiple instance learning tracking method with local sparse representation
Xie, Chengjun; Tan, Jieqing; Chen, Peng; Zhang, Jie; Helg, Lei
2013-01-01
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
International Nuclear Information System (INIS)
Than, Trung Duc; Alici, Gursel; Zhou, Hao; Li, Weihua; Harvey, Steven
2014-01-01
Purpose: Over the last decade, wireless capsule endoscope has been the tool of choice for noninvasive inspection of the gastrointestinal tract, especially in the small intestine. However, the latest clinical products have not been equipped with a sufficiently accurate localization system which makes it difficult to determine the location of intestinal abnormalities, and to apply follow-up interventions such as biopsy or drug delivery. In this paper, the authors present a novel localization method based on tracking three positron emission markers embedded inside an endoscopic capsule. Methods: Three spherical 22 Na markers with diameters of less than 1 mm are embedded in the cover of the capsule. Gamma ray detectors are arranged around a patient body to detect coincidence gamma rays emitted from the three markers. The position of each marker can then be estimated using the collected data by the authors’ tracking algorithm which consists of four consecutive steps: a method to remove corrupted data, an initialization method, a clustering method based on the Fuzzy C-means clustering algorithm, and a failure prediction method. Results: The tracking algorithm has been implemented inMATLAB utilizing simulation data generated from the Geant4 Application for Emission Tomography toolkit. The results show that this localization method can achieve real-time tracking with an average position error of less than 0.4 mm and an average orientation error of less than 2°. Conclusions: The authors conclude that this study has proven the feasibility and potential of the proposed technique in effectively determining the position and orientation of a robotic endoscopic capsule
Powell, M. C.; Cappelluti, N.; Urry, C. M.; Koss, M.; Finoguenov, A.; Ricci, C.; Trakhtenbrot, B.; Allevato, V.; Ajello, M.; Oh, K.; Schawinski, K.; Secrest, N.
2018-05-01
We characterize the environments of local accreting supermassive black holes by measuring the clustering of AGNs in the Swift/BAT Spectroscopic Survey (BASS). With 548 AGN in the redshift range 0.01 2MASS galaxies, and interpreting it via halo occupation distribution and subhalo-based models, we constrain the occupation statistics of the full sample, as well as in bins of absorbing column density and black hole mass. We find that AGNs tend to reside in galaxy group environments, in agreement with previous studies of AGNs throughout a large range of luminosity and redshift, and that on average they occupy their dark matter halos similar to inactive galaxies of comparable stellar mass. We also find evidence that obscured AGNs tend to reside in denser environments than unobscured AGNs, even when samples were matched in luminosity, redshift, stellar mass, and Eddington ratio. We show that this can be explained either by significantly different halo occupation distributions or statistically different host halo assembly histories. Lastly, we see that massive black holes are slightly more likely to reside in central galaxies than black holes of smaller mass.
ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea
Paramasivam, Nagarajan; Linke, Dirk
2011-01-01
The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/ PMID:22073040
Directory of Open Access Journals (Sweden)
Shiza Anand
2015-08-01
Full Text Available As the number of hypertext documents are increasing continuously day by day on world wide web. Therefore clustering methods will be required to bind documents into the clusters repositories according to the similarity lying between the documents. Various clustering methods exist such as Hierarchical Based K-means Fuzzy Logic Based Centroid Based etc. These keyword based clustering methods takes much more amount of time for creating containers and putting documents in their respective containers. These traditional methods use File Handling techniques of different programming languages for creating repositories and transferring web documents into these containers. In contrast openstack4j SDK is a new technique for creating containers and shifting web documents into these containers according to the similarity in much more less amount of time as compared to the traditional methods. Another benefit of this technique is that this SDK understands and reads all types of files such as jpg html pdf doc etc. This paper compares the time required for clustering of documents by using openstack4j and by traditional methods and suggests various search engines to adopt this technique for clustering so that they give result to the user querries in less amount of time.
Energy Technology Data Exchange (ETDEWEB)
Martini, Paul [Department of Astronomy and Center for Cosmology and Astroparticle Physics, Ohio State University, 140 West 18th Avenue, Columbus, OH 43210 (United States); Miller, E. D.; Bautz, M. [Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 (United States); Brodwin, M. [Department of Physics and Astronomy, University of Missouri, 5110 Rockhill Road, Kansas City, MO 64110 (United States); Stanford, S. A. [Department of Physics, University of California, One Shields Avenue, Davis, CA 95616 (United States); Gonzalez, Anthony H. [Department of Astronomy, University of Florida, Gainesville, FL 32611 (United States); Hickox, R. C. [Department of Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH 03755 (United States); Stern, D.; Eisenhardt, P. R. [Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 (United States); Galametz, A. [INAF-Osservatorio di Roma, Via Frascati 33, I-00040 Monteporzio (Italy); Norman, D.; Dey, A. [NOAO, 950 North Cherry Avenue, Tucson, AZ 85719 (United States); Jannuzi, B. T. [Department of Astronomy and Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721 (United States); Murray, S.; Jones, C. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Brown, M. J. I., E-mail: martini@astronomy.ohio-state.edu [School of Physics, Monash University, Clayton, Victoria 3800 (Australia)
2013-05-01
The fraction of cluster galaxies that host luminous active galactic nuclei (AGNs) is an important probe of AGN fueling processes, the cold interstellar medium at the centers of galaxies, and how tightly black holes and galaxies co-evolve. We present a new measurement of the AGN fraction in a sample of 13 clusters of galaxies (M {>=} 10{sup 14} M{sub Sun }) at 1 < z < 1.5 selected from the Spitzer/IRAC Shallow Cluster Survey, as well as the field fraction in the immediate vicinity of these clusters, and combine these data with measurements from the literature to quantify the relative evolution of cluster and field AGN from the present to z {approx} 3. We estimate that the cluster AGN fraction at 1 < z < 1.5 is f{sub A} = 3.0{sup +2.4}{sub -1.4}% for AGNs with a rest-frame, hard X-ray luminosity greater than L{sub X,{sub H}} {>=} 10{sup 44} erg s{sup -1}. This fraction is measured relative to all cluster galaxies more luminous than M{sup *}{sub 3.6}(z) + 1, where M{sup *}{sub 3.6}(z) is the absolute magnitude of the break in the galaxy luminosity function at the cluster redshift in the IRAC 3.6 {mu}m bandpass. The cluster AGN fraction is 30 times greater than the 3{sigma} upper limit on the value for AGNs of similar luminosity at z {approx} 0.25, as well as more than an order of magnitude greater than the AGN fraction at z {approx} 0.75. AGNs with L{sub X,{sub H}} {>=} 10{sup 43} erg s{sup -1} exhibit similarly pronounced evolution with redshift. In contrast to the local universe, where the luminous AGN fraction is higher in the field than in clusters, the X-ray and MIR-selected AGN fractions in the field and clusters are consistent at 1 < z < 1.5. This is evidence that the cluster AGN population has evolved more rapidly than the field population from z {approx} 1.5 to the present. This environment-dependent AGN evolution mimics the more rapid evolution of star-forming galaxies in clusters relative to the field.
International Nuclear Information System (INIS)
Martini, Paul; Miller, E. D.; Bautz, M.; Brodwin, M.; Stanford, S. A.; Gonzalez, Anthony H.; Hickox, R. C.; Stern, D.; Eisenhardt, P. R.; Galametz, A.; Norman, D.; Dey, A.; Jannuzi, B. T.; Murray, S.; Jones, C.; Brown, M. J. I.
2013-01-01
The fraction of cluster galaxies that host luminous active galactic nuclei (AGNs) is an important probe of AGN fueling processes, the cold interstellar medium at the centers of galaxies, and how tightly black holes and galaxies co-evolve. We present a new measurement of the AGN fraction in a sample of 13 clusters of galaxies (M ≥ 10 14 M ☉ ) at 1 A = 3.0 +2.4 -1.4 % for AGNs with a rest-frame, hard X-ray luminosity greater than L X, H ≥ 10 44 erg s –1 . This fraction is measured relative to all cluster galaxies more luminous than M * 3.6 (z) + 1, where M * 3.6 (z) is the absolute magnitude of the break in the galaxy luminosity function at the cluster redshift in the IRAC 3.6 μm bandpass. The cluster AGN fraction is 30 times greater than the 3σ upper limit on the value for AGNs of similar luminosity at z ∼ 0.25, as well as more than an order of magnitude greater than the AGN fraction at z ∼ 0.75. AGNs with L X, H ≥ 10 43 erg s –1 exhibit similarly pronounced evolution with redshift. In contrast to the local universe, where the luminous AGN fraction is higher in the field than in clusters, the X-ray and MIR-selected AGN fractions in the field and clusters are consistent at 1 < z < 1.5. This is evidence that the cluster AGN population has evolved more rapidly than the field population from z ∼ 1.5 to the present. This environment-dependent AGN evolution mimics the more rapid evolution of star-forming galaxies in clusters relative to the field.
Stability of maximum-likelihood-based clustering methods: exploring the backbone of classifications
International Nuclear Information System (INIS)
Mungan, Muhittin; Ramasco, José J
2010-01-01
Components of complex systems are often classified according to the way they interact with each other. In graph theory such groups are known as clusters or communities. Many different techniques have been recently proposed to detect them, some of which involve inference methods using either Bayesian or maximum likelihood approaches. In this paper, we study a statistical model designed for detecting clusters based on connection similarity. The basic assumption of the model is that the graph was generated by a certain grouping of the nodes and an expectation maximization algorithm is employed to infer that grouping. We show that the method admits further development to yield a stability analysis of the groupings that quantifies the extent to which each node influences its neighbors' group membership. Our approach naturally allows for the identification of the key elements responsible for the grouping and their resilience to changes in the network. Given the generality of the assumptions underlying the statistical model, such nodes are likely to play special roles in the original system. We illustrate this point by analyzing several empirical networks for which further information about the properties of the nodes is available. The search and identification of stabilizing nodes constitutes thus a novel technique to characterize the relevance of nodes in complex networks
Measuring Group Synchrony: A Cluster-Phase Method for Analyzing Multivariate Movement Time-Series
Directory of Open Access Journals (Sweden)
Michael eRichardson
2012-10-01
Full Text Available A new method for assessing group synchrony is introduced as being potentially useful for objectively determining degree of group cohesiveness or entitativity. The cluster-phase method of Frank and Richardson (2010 was used to analyze movement data from the rocking chair movements of six-member groups who rocked their chairs while seated in a circle facing the center. In some trials group members had no information about others’ movements (their eyes were shut or they had their eyes open and gazed at a marker in the center of the group. As predicted, the group level synchrony measure was able to distinguish between situations where synchrony would have been possible and situations where it would be impossible. Moreover, other aspects of the analysis illustrated how the cluster phase measures can be used to determine the type of patterning of group synchrony, and, when integrated with multi-level modeling, can be used to examine individual-level differences in synchrony and dyadic level synchrony as well.
The multi-scattering-Xα method for analysis of the electronic structure of atomic clusters
International Nuclear Information System (INIS)
Bahurmuz, A.A.; Woo, C.H.
1984-12-01
A computer program, MSXALPHA, has been developed to carry out a quantum-mechanical analysis of the electronic structure of molecules and atomic clusters using the Multi-Scattering-Xα (MSXα) method. The MSXALPHA program is based on a code obtained from the University of Alberta; several improvements and new features were incorporated to increase generality and efficiency. The major ones are: (1) minimization of core memory usage, (2) reduction of execution time, (3) introduction of a dynamic core allocation scheme for a large number of arrays, (4) incorporation of an atomic program to generate numerical orbitals used to construct the initial molecular potential, and (5) inclusion of a routine to evaluate total energy. This report is divided into three parts. The first discusses the theory of the MSXα method. The second gives a detailed description of the program, MSXALPHA. The third discusses the results of calculations carried out for the methane molecule (CH 4 ) and a four-atom zirconium cluster (Zr 4 )
International Nuclear Information System (INIS)
Liu Xuan; Ito, Haruhiko; Torikai, Eiko
2012-01-01
We calculate the different geometric isomers of spin clusters composed of a small number of alkali-metal atoms using the UB3LYP density-functional method. The electron density distribution of clusters changes according to the value of total spin. Steric structures as well as planar structures arise when the number of atoms increases. The lowest spin state is the most stable and Li n , Na n , K n , Rb n , and Cs n with n = 2–8 can be formed in higher spin states. In the highest spin state, the preparation of clusters depends on the kind and the number of constituent atoms. The interaction energy between alkali-metal atoms and rare-gas atoms is smaller than the binding energy of spin clusters. Consequently, it is possible to self-organize the alkali-metal-atom clusters on a non-wetting substrate coated with rare-gas atoms.
Energy Technology Data Exchange (ETDEWEB)
Carvalho, Priscilla R.; Munita, Casimiro S.; Lapolli, André L., E-mail: prii.ramos@gmail.com, E-mail: camunita@ipen.br, E-mail: alapolli@ipen.br [Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN-SP), São Paulo, SP (Brazil)
2017-07-01
The literature presents many methods for partitioning of data base, and is difficult choose which is the most suitable, since the various combinations of methods based on different measures of dissimilarity can lead to different patterns of grouping and false interpretations. Nevertheless, little effort has been expended in evaluating these methods empirically using an archaeological data base. In this way, the objective of this work is make a comparative study of the different cluster analysis methods and identify which is the most appropriate. For this, the study was carried out using a data base of the Archaeometric Studies Group from IPEN-CNEN/SP, in which 45 samples of ceramic fragments from three archaeological sites were analyzed by instrumental neutron activation analysis (INAA) which were determinate the mass fraction of 13 elements (As, Ce, Cr, Eu, Fe, Hf, La, Na, Nd, Sc, Sm, Th, U). The methods used for this study were: single linkage, complete linkage, average linkage, centroid and Ward. The validation was done using the cophenetic correlation coefficient and comparing these values the average linkage method obtained better results. A script of the statistical program R with some functions was created to obtain the cophenetic correlation. By means of these values was possible to choose the most appropriate method to be used in the data base. (author)
International Nuclear Information System (INIS)
Carvalho, Priscilla R.; Munita, Casimiro S.; Lapolli, André L.
2017-01-01
The literature presents many methods for partitioning of data base, and is difficult choose which is the most suitable, since the various combinations of methods based on different measures of dissimilarity can lead to different patterns of grouping and false interpretations. Nevertheless, little effort has been expended in evaluating these methods empirically using an archaeological data base. In this way, the objective of this work is make a comparative study of the different cluster analysis methods and identify which is the most appropriate. For this, the study was carried out using a data base of the Archaeometric Studies Group from IPEN-CNEN/SP, in which 45 samples of ceramic fragments from three archaeological sites were analyzed by instrumental neutron activation analysis (INAA) which were determinate the mass fraction of 13 elements (As, Ce, Cr, Eu, Fe, Hf, La, Na, Nd, Sc, Sm, Th, U). The methods used for this study were: single linkage, complete linkage, average linkage, centroid and Ward. The validation was done using the cophenetic correlation coefficient and comparing these values the average linkage method obtained better results. A script of the statistical program R with some functions was created to obtain the cophenetic correlation. By means of these values was possible to choose the most appropriate method to be used in the data base. (author)
Directory of Open Access Journals (Sweden)
Yunfeng Dong
2017-01-01
Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.
Than, Trung Duc; Alici, Gursel; Harvey, Steven; Zhou, Hao; Li, Weihua
2014-07-01
Over the last decade, wireless capsule endoscope has been the tool of choice for noninvasive inspection of the gastrointestinal tract, especially in the small intestine. However, the latest clinical products have not been equipped with a sufficiently accurate localization system which makes it difficult to determine the location of intestinal abnormalities, and to apply follow-up interventions such as biopsy or drug delivery. In this paper, the authors present a novel localization method based on tracking three positron emission markers embedded inside an endoscopic capsule. Three spherical(22)Na markers with diameters of less than 1 mm are embedded in the cover of the capsule. Gamma ray detectors are arranged around a patient body to detect coincidence gamma rays emitted from the three markers. The position of each marker can then be estimated using the collected data by the authors' tracking algorithm which consists of four consecutive steps: a method to remove corrupted data, an initialization method, a clustering method based on the Fuzzy C-means clustering algorithm, and a failure prediction method. The tracking algorithm has been implemented inMATLAB utilizing simulation data generated from the Geant4 Application for Emission Tomography toolkit. The results show that this localization method can achieve real-time tracking with an average position error of less than 0.4 mm and an average orientation error of less than 2°. The authors conclude that this study has proven the feasibility and potential of the proposed technique in effectively determining the position and orientation of a robotic endoscopic capsule.
A Clustering K-Anonymity Privacy-Preserving Method for Wearable IoT Devices
Directory of Open Access Journals (Sweden)
Fang Liu
2018-01-01
Full Text Available Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific data. Generally, data holders (manufacturers of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices’ data. Then we propose a K-anonymity method based on clustering to preserve privacy of wearable IoT devices’ data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method.
Comparison of methods for genomic localization of gene trap sequences
Directory of Open Access Journals (Sweden)
Ferrin Thomas E
2006-09-01
Full Text Available Abstract Background Gene knockouts in a model organism such as mouse provide a valuable resource for the study of basic biology and human disease. Determining which gene has been inactivated by an untargeted gene trapping event poses a challenging annotation problem because gene trap sequence tags, which represent sequence near the vector insertion site of a trapped gene, are typically short and often contain unresolved residues. To understand better the localization of these sequences on the mouse genome, we compared stand-alone versions of the alignment programs BLAT, SSAHA, and MegaBLAST. A set of 3,369 sequence tags was aligned to build 34 of the mouse genome using default parameters for each algorithm. Known genome coordinates for the cognate set of full-length genes (1,659 sequences were used to evaluate localization results. Results In general, all three programs performed well in terms of localizing sequences to a general region of the genome, with only relatively subtle errors identified for a small proportion of the sequence tags. However, large differences in performance were noted with regard to correctly identifying exon boundaries. BLAT correctly identified the vast majority of exon boundaries, while SSAHA and MegaBLAST missed the majority of exon boundaries. SSAHA consistently reported the fewest false positives and is the fastest algorithm. MegaBLAST was comparable to BLAT in speed, but was the most susceptible to localizing sequence tags incorrectly to pseudogenes. Conclusion The differences in performance for sequence tags and full-length reference sequences were surprisingly small. Characteristic variations in localization results for each program were noted that affect the localization of sequence at exon boundaries, in particular.
METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS.
Tignor, Nicole; Wang, Pei; Genes, Nicholas; Rogers, Linda; Hershman, Steven G; Scott, Erick R; Zweig, Micol; Yvonne Chan, Yu-Feng; Schadt, Eric E
2017-01-01
In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important
Zhang, Hongqin; Tian, Xiangjun
2018-04-01
Ensemble-based data assimilation methods often use the so-called localization scheme to improve the representation of the ensemble background error covariance (Be). Extensive research has been undertaken to reduce the computational cost of these methods by using the localized ensemble samples to localize Be by means of a direct decomposition of the local correlation matrix C. However, the computational costs of the direct decomposition of the local correlation matrix C are still extremely high due to its high dimension. In this paper, we propose an efficient local correlation matrix decomposition approach based on the concept of alternating directions. This approach is intended to avoid direct decomposition of the correlation matrix. Instead, we first decompose the correlation matrix into 1-D correlation matrices in the three coordinate directions, then construct their empirical orthogonal function decomposition at low resolution. This procedure is followed by the 1-D spline interpolation process to transform the above decompositions to the high-resolution grid. Finally, an efficient correlation matrix decomposition is achieved by computing the very similar Kronecker product. We conducted a series of comparison experiments to illustrate the validity and accuracy of the proposed local correlation matrix decomposition approach. The effectiveness of the proposed correlation matrix decomposition approach and its efficient localization implementation of the nonlinear least-squares four-dimensional variational assimilation are further demonstrated by several groups of numerical experiments based on the Advanced Research Weather Research and Forecasting model.
Mathematical models and methods of localized interaction theory
Bunimovich, AI
1995-01-01
The interaction of the environment with a moving body is called "localized" if it has been found or assumed that the force or/and thermal influence of the environment on each body surface point is independent and can be determined by the local geometrical and kinematical characteristics of this point as well as by the parameters of the environment and body-environment interactions which are the same for the whole surface of contact.Such models are widespread in aerodynamics and gas dynamics, covering supersonic and hypersonic flows, and rarefied gas flows. They describe the influence of light
Paz, Andrea; Crawford, Andrew J
2012-11-01
Molecular markers offer a universal source of data for quantifying biodiversity. DNA barcoding uses a standardized genetic marker and a curated reference database to identify known species and to reveal cryptic diversity within wellsampled clades. Rapid biological inventories, e.g. rapid assessment programs (RAPs), unlike most barcoding campaigns, are focused on particular geographic localities rather than on clades. Because of the potentially sparse phylogenetic sampling, the addition of DNA barcoding to RAPs may present a greater challenge for the identification of named species or for revealing cryptic diversity. In this article we evaluate the use of DNA barcoding for quantifying lineage diversity within a single sampling site as compared to clade-based sampling, and present examples from amphibians. We compared algorithms for identifying DNA barcode clusters (e.g. species, cryptic species or Evolutionary Significant Units) using previously published DNA barcode data obtained from geography-based sampling at a site in Central Panama, and from clade-based sampling in Madagascar. We found that clustering algorithms based on genetic distance performed similarly on sympatric as well as clade-based barcode data, while a promising coalescent-based method performed poorly on sympatric data. The various clustering algorithms were also compared in terms of speed and software implementation. Although each method has its shortcomings in certain contexts, we recommend the use of the ABGD method, which not only performs fairly well under either sampling method, but does so in a few seconds and with a user-friendly Web interface.
Energy Technology Data Exchange (ETDEWEB)
Okunev, V.D., E-mail: okunev@mail.fti.ac.donetsk.ua [Donetsk Physiko-Technical Institute, Ukrainian Academy of Sciences, av. Nauki 46, 03028 Kiev (Ukraine); Samoilenko, Z.A. [Donetsk Physiko-Technical Institute, Ukrainian Academy of Sciences, av. Nauki 46, 03028 Kiev (Ukraine); Szymczak, H.; Szewczyk, A.; Szymczak, R.; Lewandowski, S.J.; Aleshkevych, P.; Malinowski, A.; Gierłowski, P.; Więckowski, J. [Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw (Poland); Wolny-Marszałek, M.; Jeżabek, M. [Institute of Nuclear Physics, Polish Academy of Sciences, Krakow (Poland); Varyukhin, V.N. [Donetsk Physiko-Technical Institute, Ukrainian Academy of Sciences, av. Nauki 46, 03028 Kiev (Ukraine); Antoshina, I.A. [Obninsk State Technical University of Atomic Energy, 249020 Obninsk (Russian Federation)
2016-02-01
We show that cluster magnetism in ferromagnetic amorphous Fe{sub 67}Cr{sub 18}B{sub 15} 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 Fe{sub 3}B clusters and subcluster atomic α-(Fe, Cr, B) groupings, D=10–20 Å, in disordered intercluster medium. For initial sample and irradiated one (Φ=1.5×10{sup 18} ions/cm{sup 2}) superconductivity exists in the cluster shells of metallic α-(Fe, Cr) phase where ferromagnetism of iron is counterbalanced by antiferromagnetism of chromium. At Φ=3×10{sup 18} ions/cm{sup 2}, 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)~T{sup 2} is determined by the electron scattering on quantum defects. In strongly inhomogeneous samples, after irradiation by fluence Φ=1.5×10{sup 18} ions/cm{sup 2}, the transition to a dependence ρ(T)~T{sup 1/2} is caused by the effects of weak localization. In more homogeneous samples, at Φ=3×10{sup 18} ions/cm{sup 2}, a return to the dependence ρ(T)~T{sup 2} is observed. - Highlights: • The samples at high dose of ion irradiation become more homogeneous. • Gigantic increase in density of the samples (twice as much) is observed. • Ferromagnetism in large Fe–Cr clusters is restored. • Ferromagnetism of Fe–Cr clusters suppresses local superconductivity in them. • The participation of quantum defects in scattering of electrons is returned.
Global and Local Sensitivity Analysis Methods for a Physical System
Morio, Jerome
2011-01-01
Sensitivity analysis is the study of how the different input variations of a mathematical model influence the variability of its output. In this paper, we review the principle of global and local sensitivity analyses of a complex black-box system. A simulated case of application is given at the end of this paper to compare both approaches.…
Understanding Global / Local Cultural Leadership : Issues and Methods
Kolsteeg, Johan
2017-01-01
Cultural leaders sail between the Scylla and Charibdis of aggregated trans- and supranational cultural-political discourses and the cultural needs of local communities. How do these dynamics influence the work of cultural leaders? How can we understand the work of cultural leaders to connect
Another Method for Localization of Radiolucent Urinary Stones ...
African Journals Online (AJOL)
Objectives: 1. To study the feasibility and safety of localization of radiolucent urinary stones during ESWL utilising the refluxing intravesically injected contrast medium along indwelling ureteral stents. 2. To identify the optimum volume of contrast medium and the intravesical pressure at which adequate vesicoureteral reflux ...
Localized atomic basis set in the projector augmented wave method
DEFF Research Database (Denmark)
Larsen, Ask Hjorth; Vanin, Marco; Mortensen, Jens Jørgen
2009-01-01
We present an implementation of localized atomic-orbital basis sets in the projector augmented wave (PAW) formalism within the density-functional theory. The implementation in the real-space GPAW code provides a complementary basis set to the accurate but computationally more demanding grid...
Herd Clustering: A synergistic data clustering approach using collective intelligence
Wong, Kachun
2014-10-01
Traditional data mining methods emphasize on analytical abilities to decipher data, assuming that data are static during a mining process. We challenge this assumption, arguing that we can improve the analysis by vitalizing data. In this paper, this principle is used to develop a new clustering algorithm. Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances are represented by moving particles. Particles attract each other locally and form clusters by themselves as shown in the case studies reported. To demonstrate its effectiveness, the performance of HC is compared to other state-of-the art clustering methods on more than thirty datasets using four performance metrics. An application for DNA motif discovery is also conducted. The results support the effectiveness of HC and thus the underlying philosophy. © 2014 Elsevier B.V.
Comparison of three methods for the estimation of cross-shock electric potential using Cluster data
Directory of Open Access Journals (Sweden)
A. P. Dimmock
2011-05-01
Full Text Available Cluster four point measurements provide a comprehensive dataset for the separation of temporal and spatial variations, which is crucial for the calculation of the cross shock electrostatic potential using electric field measurements. While Cluster is probably the most suited among present and past spacecraft missions to provide such a separation at the terrestrial bow shock, it is far from ideal for a study of the cross shock potential, since only 2 components of the electric field are measured in the spacecraft spin plane. The present paper is devoted to the comparison of 3 different techniques that can be used to estimate the potential with this limitation. The first technique is the estimate taking only into account the projection of the measured components onto the shock normal. The second uses the ideal MHD condition E·B = 0 to estimate the third electric field component. The last method is based on the structure of the electric field in the Normal Incidence Frame (NIF for which only the potential component along the shock normal and the motional electric field exist. All 3 approaches are used to estimate the potential for a single crossing of the terrestrial bow shock that took place on the 31 March 2001. Surprisingly all three methods lead to the same order of magnitude for the cross shock potential. It is argued that the third method must lead to more reliable results. The effect of the shock normal inaccuracy is investigated for this particular shock crossing. The resulting electrostatic potential appears too high in comparison with the theoretical results for low Mach number shocks. This shows the variability of the potential, interpreted in the frame of the non-stationary shock model.
Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D
2017-01-01
Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, Pdepression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
Hadron formation in a non-ideal quark gluon plasma using Mayer's method of cluster expansion
International Nuclear Information System (INIS)
Prasanth, J.P.; Bannur, Vishnu M.
2015-01-01
This work investigates the applicability of using the Mayer's cluster expansion method to derive the equation of state (EoS) of the quark-antiquark plasma. Dissociation of heavier hadrons in QGP is studied. The possibility of the existence of quarkonium after deconfinement at higher temperature than the critical temperature T > T c is investigated. The EoS has been studied by calculating second and third cluster integrals. The results are compared and discussed with available works. (author)
Directory of Open Access Journals (Sweden)
Guo Zheng-Hong
2016-01-01
Full Text Available In this article, the Sumudu transform series expansion method is used to handle the local fractional Laplace equation arising in the steady fractal heat-transfer problem via local fractional calculus.
Method of center localization for objects containing concentric arcs
Kuznetsova, Elena G.; Shvets, Evgeny A.; Nikolaev, Dmitry P.
2015-02-01
This paper proposes a method for automatic center location of objects containing concentric arcs. The method utilizes structure tensor analysis and voting scheme optimized with Fast Hough Transform. Two applications of the proposed method are considered: (i) wheel tracking in video-based system for automatic vehicle classification and (ii) tree growth rings analysis on a tree cross cut image.
Bonell, Chris; Allen, Elizabeth; Christie, Deborah; Elbourne, Diana; Fletcher, Adam; Grieve, Richard; LeGood, Rosa; Mathiot, Anne; Scott, Stephen; Wiggins, Meg; Viner, Russell M
2014-09-30
Systematic reviews suggest that interventions that address school organisation are effective in reducing victimisation and bullying. We successfully piloted a school environment intervention modified from international studies to incorporate 'restorative justice' approaches. This trial aims to establish the effectiveness and cost-effectiveness of the INCLUSIVE intervention in reducing aggression and bullying in English secondary schools. cluster randomised trial. 40 state-supported secondary schools. OUTCOMES assessed among the cohort of students in year 8 (n = approximately 6,000) in intervention year 1. INCLUSIVE is a school-led intervention which combines changes to the school environment with the promotion of social and emotional skills and restorative practices through: the formation of a school action group involving students and staff supported by an external facilitator to review local data on needs, determine priorities, and develop and implement an action plan for revising relevant school policies/rules and other actions to improve relationships at school and reduce aggression; staff training in restorative practices; and a new social and emotional skills curriculum. The intervention will be delivered by schools supported in the first two years by educational facilitators independent of the research team, with a third locally facilitated intervention year.Comparator: normal practice. primary: 2 primary outcomes at student level assessed at baseline and at 36 months:1. Aggressive behaviours in school: Edinburgh Study of Youth Transitions and Crime school misbehaviour subscale (ESYTC)2. Bullying and victimisation: Gatehouse Bullying Scale (GBS)Secondary outcomes assessed at baseline, 24 and 36 months will include measures relating to the economic evaluation, psychosocial outcomes in students and staff and school-level truancy and exclusion rates. 20 schools per arm will provide 90% power to identify an effect size of 0.25 SD with a 5% significance level
Inverse mass matrix via the method of localized lagrange multipliers
Czech Academy of Sciences Publication Activity Database
González, José A.; Kolman, Radek; Cho, S.S.; Felippa, C.A.; Park, K.C.
2018-01-01
Roč. 113, č. 2 (2018), s. 277-295 ISSN 0029-5981 R&D Projects: GA MŠk(CZ) EF15_003/0000493; GA ČR GA17-22615S Institutional support: RVO:61388998 Keywords : explicit time integration * inverse mass matrix * localized Lagrange multipliers * partitioned analysis Subject RIV: BI - Acoustics OBOR OECD: Applied mechanics Impact factor: 2.162, year: 2016 https://onlinelibrary.wiley.com/doi/10.1002/nme.5613
Locally linear approximation for Kernel methods : the Railway Kernel
Muñoz, Alberto; González, Javier
2008-01-01
In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capab...
Bell Operator Method to Classify Local Realistic Theories
International Nuclear Information System (INIS)
Nagata, Koji
2010-01-01
We review the historical fact of multipartite Bell inequalities with an arbitrary number of settings. An explicit local realistic model for the values of a correlation function, given in a two-setting Bell experiment (two-setting model), works only for the specific set of settings in the given experiment, but cannot construct a local realistic model for the values of a correlation function, given in a continuous-infinite settings Bell experiment (infinite-setting model), even though there exist two-setting models for all directions in space. Hence, the two-setting model does not have the property that the infinite-setting model has. Here, we show that an explicit two-setting model cannot construct a local realistic model for the values of a correlation function, given in an M-setting Bell experiment (M-setting model), even though there exist two-setting models for the M measurement directions chosen in the given M-setting experiment. Hence, the two-setting model does not have the property that the M-setting model has. (general)
Sander, Ulrich; Lubbe, Nils
2018-04-01
Intersection accidents are frequent and harmful. The accident types 'straight crossing path' (SCP), 'left turn across path - oncoming direction' (LTAP/OD), and 'left-turn across path - lateral direction' (LTAP/LD) represent around 95% of all intersection accidents and one-third of all police-reported car-to-car accidents in Germany. The European New Car Assessment Program (Euro NCAP) have announced that intersection scenarios will be included in their rating from 2020; however, how these scenarios are to be tested has not been defined. This study investigates whether clustering methods can be used to identify a small number of test scenarios sufficiently representative of the accident dataset to evaluate Intersection Automated Emergency Braking (AEB). Data from the German In-Depth Accident Study (GIDAS) and the GIDAS-based Pre-Crash Matrix (PCM) from 1999 to 2016, containing 784 SCP and 453 LTAP/OD accidents, were analyzed with principal component methods to identify variables that account for the relevant total variances of the sample. Three different methods for data clustering were applied to each of the accident types, two similarity-based approaches, namely Hierarchical Clustering (HC) and Partitioning Around Medoids (PAM), and the probability-based Latent Class Clustering (LCC). The optimum number of clusters was derived for HC and PAM with the silhouette method. The PAM algorithm was both initiated with random start medoid selection and medoids from HC. For LCC, the Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. Test scenarios were defined from optimal cluster medoids weighted by their real-life representation in GIDAS. The set of variables for clustering was further varied to investigate the influence of variable type and character. We quantified how accurately each cluster variation represents real-life AEB performance using pre-crash simulations with PCM data and a generic algorithm for AEB intervention. The
Noniterative Multireference Coupled Cluster Methods on Heterogeneous CPU-GPU Systems
Energy Technology Data Exchange (ETDEWEB)
Bhaskaran-Nair, Kiran; Ma, Wenjing; Krishnamoorthy, Sriram; Villa, Oreste; van Dam, Hubertus JJ; Apra, Edoardo; Kowalski, Karol
2013-04-09
A novel parallel algorithm for non-iterative multireference coupled cluster (MRCC) theories, which merges recently introduced reference-level parallelism (RLP) [K. Bhaskaran-Nair, J.Brabec, E. Aprà, H.J.J. van Dam, J. Pittner, K. Kowalski, J. Chem. Phys. 137, 094112 (2012)] with the possibility of accelerating numerical calculations using graphics processing unit (GPU) is presented. We discuss the performance of this algorithm on the example of the MRCCSD(T) method (iterative singles and doubles and perturbative triples), where the corrections due to triples are added to the diagonal elements of the MRCCSD (iterative singles and doubles) effective Hamiltonian matrix. The performance of the combined RLP/GPU algorithm is illustrated on the example of the Brillouin-Wigner (BW) and Mukherjee (Mk) state-specific MRCCSD(T) formulations.
Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee
2010-07-01
The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.
Equation-of-motion coupled cluster method for high spin double electron attachment calculations
Energy Technology Data Exchange (ETDEWEB)
Musiał, Monika, E-mail: musial@ich.us.edu.pl; Lupa, Łukasz; Kucharski, Stanisław A. [Institute of Chemistry, University of Silesia, Szkolna 9, 40-006 Katowice (Poland)
2014-03-21
The new formulation of the equation-of-motion (EOM) coupled cluster (CC) approach applicable to the calculations of the double electron attachment (DEA) states for the high spin components is proposed. The new EOM equations are derived for the high spin triplet and quintet states. In both cases the new equations are easier to solve but the substantial simplification is observed in the case of quintets. Out of 21 diagrammatic terms contributing to the standard DEA-EOM-CCSDT equations for the R{sub 2} and R{sub 3} amplitudes only four terms survive contributing to the R{sub 3} part. The implemented method has been applied to the calculations of the excited states (singlets, triplets, and quintets) energies of the carbon and silicon atoms and potential energy curves for selected states of the Na{sub 2} (triplets) and B{sub 2} (quintets) molecules.
Novel strategy to implement active-space coupled-cluster methods
Rolik, Zoltán; Kállay, Mihály
2018-03-01
A new approach is presented for the efficient implementation of coupled-cluster (CC) methods including higher excitations based on a molecular orbital space partitioned into active and inactive orbitals. In the new framework, the string representation of amplitudes and intermediates is used as long as it is beneficial, but the contractions are evaluated as matrix products. Using a new diagrammatic technique, the CC equations are represented in a compact form due to the string notations we introduced. As an application of these ideas, a new automated implementation of the single-reference-based multi-reference CC equations is presented for arbitrary excitation levels. The new program can be considered as an improvement over the previous implementations in many respects; e.g., diagram contributions are evaluated by efficient vectorized subroutines. Timings for test calculations for various complete active-space problems are presented. As an application of the new code, the weak interactions in the Be dimer were studied.
Application of the cluster variation method to ordering in an interstitital solid solution
DEFF Research Database (Denmark)
Pekelharing, Marjon I.; Böttger, Amarante; Somers, Marcel A. J.
1999-01-01
The tetrahedron approximation of the cluster variation method (CVM) was applied to describe the ordering on the fcc interstitial sublattice of gamma-Fe[N] and gamma'-Fe4N1-x. A Lennard-Jones potential was used to describe the dominantly strain-induced interactions, caused by misfitting of the N...... atoms in the interstitial octahedral sites. The gamma-Fe[N]/gamma'-Fe4N1-x miscibility gap, short range ordering (SRO), and long-range ordering (LRO) of nitrogen in gamma-Fe[N] and gamma'-Fe4N1-x, respectively, and lattice parameters of gamma and gamm' were calculated. For the first time, N distribution...... parameters,as calculated by CVM, were compared directly to Mössbauer data for specific surroundings of Fe atoms....
Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.
Waugh, William; Allen, John; Wightman, James; Sims, Andrew J; Beale, Thomas A W
2018-01-01
Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.
Okunev, V. D.; Szymczak, R.; Szymczak, H.; Aleshkevych, P.; Glot, A. B.; Bondarchuk, A. N.
2015-10-01
It has been shown that the overlap of tails of the density of states of the valence and conduction bands leads to the formation of a "negative" gap in the In2O3-SrO ceramic with disordered structure and oxygen clusters in nanovoids. Two types of magnetism are observed. One of them caused by the formation of (dangling bond + O- 2 center) complexes has been found in samples saturated with oxygen. The other is associated with the presence of dangling bonds in oxygen-depleted samples. At T conductivity of samples. The effects caused by magnetic phase transitions in clusters of crystal oxygen are manifested at T < 54.8 K. The variations of the resistance of samples in the interval T = 5-300 K correspond to Mott's law under the dependence of the local activation energy for electron hopping on the state of oxygen clusters.
A New Method to Constrain Supernova Fractions Using X-ray Observations of Clusters of Galaxies
Bulbul, Esra; Smith, Randall K.; Loewenstein, Michael
2012-01-01
Supernova (SN) explosions enrich the intracluster medium (ICM) both by creating and dispersing metals. We introduce a method to measure the number of SNe and relative contribution of Type Ia supernovae (SNe Ia) and core-collapse supernovae (SNe cc) by directly fitting X-ray spectral observations. The method has been implemented as an XSPEC model called snapec. snapec utilizes a single-temperature thermal plasma code (apec) to model the spectral emission based on metal abundances calculated using the latest SN yields from SN Ia and SN cc explosion models. This approach provides a self-consistent single set of uncertainties on the total number of SN explosions and relative fraction of SN types in the ICM over the cluster lifetime by directly allowing these parameters to be determined by SN yields provided by simulations. We apply our approach to XMM-Newton European Photon Imaging Camera (EPIC), Reflection Grating Spectrometer (RGS), and 200 ks simulated Astro-H observations of a cooling flow cluster, A3112.We find that various sets of SN yields present in the literature produce an acceptable fit to the EPIC and RGS spectra of A3112. We infer that 30.3% plus or minus 5.4% to 37.1% plus or minus 7.1% of the total SN explosions are SNe Ia, and the total number of SN explosions required to create the observed metals is in the range of (1.06 plus or minus 0.34) x 10(exp 9), to (1.28 plus or minus 0.43) x 10(exp 9), fromsnapec fits to RGS spectra. These values may be compared to the enrichment expected based on well-established empirically measured SN rates per star formed. The proportions of SNe Ia and SNe cc inferred to have enriched the ICM in the inner 52 kiloparsecs of A3112 is consistent with these specific rates, if one applies a correction for the metals locked up in stars. At the same time, the inferred level of SN enrichment corresponds to a star-to-gas mass ratio that is several times greater than the 10% estimated globally for clusters in the A3112 mass range.
A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Baoguo Yu
2016-01-01
Full Text Available In the wireless sensor network (WSN localization methods based on Received Signal Strength Indicator (RSSI, it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.
Directory of Open Access Journals (Sweden)
Sascha Fuerst
2010-06-01
Full Text Available Este artículo utiliza el concepto de “cadena de valor global” para hacer un análisis de cómo se desarrolla en Colombia el cluster de animaciones 3D. Se argumenta que la participación en cadenas de valor globales trae un impacto positivo al crecimiento y la innovación del cluster, e igualmente a sus empresas. El artículo utiliza la representación de diamante presentada por Porter para mostrar las características que influyen positivamente en el desarrollo de este cluster en específico y para identificar recomendaciones a nivel de políticas necesarias que pueden mejorar la inserción del cluster de animaciones 3D en cadenas de valor globales This article draws on the framework of the “global value chain” 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. Porter’s diamond is used to illustrate the characteristics that drive dynamic cluster development in this case and to point out the necessary policy recommendations for further enhancing the linkage of the 3D-animation cluster into global value chains.
Datta, Dipayan; Kossmann, Simone; Neese, Frank
2016-09-01
The domain-based local pair-natural orbital coupled-cluster (DLPNO-CC) theory has recently emerged as an efficient and powerful quantum-chemical method for the calculation of energies of molecules comprised of several hundred atoms. It has been demonstrated that the DLPNO-CC approach attains the accuracy of a standard canonical coupled-cluster calculation to about 99.9% of the basis set correlation energy while realizing linear scaling of the computational cost with respect to system size. This is achieved by combining (a) localized occupied orbitals, (b) large virtual orbital correlation domains spanned by the projected atomic orbitals (PAOs), and (c) compaction of the virtual space through a truncated pair natural orbital (PNO) basis. In this paper, we report on the implementation of an analytic scheme for the calculation of the first derivatives of the DLPNO-CC energy for basis set independent perturbations within the singles and doubles approximation (DLPNO-CCSD) for closed-shell molecules. Perturbation-independent one-particle density matrices have been implemented in order to account for the response of the CC wave function to the external perturbation. Orbital-relaxation effects due to external perturbation are not taken into account in the current implementation. We investigate in detail the dependence of the computed first-order electrical properties (e.g., dipole moment) on the three major truncation parameters used in a DLPNO-CC calculation, namely, the natural orbital occupation number cutoff used for the construction of the PNOs, the weak electron-pair cutoff, and the domain size cutoff. No additional truncation parameter has been introduced for property calculation. We present benchmark calculations on dipole moments for a set of 10 molecules consisting of 20-40 atoms. We demonstrate that 98%-99% accuracy relative to the canonical CCSD results can be consistently achieved in these calculations. However, this comes with the price of tightening the
Study of revitalisation methods on anthropogenic soils - Stara Beta locality
International Nuclear Information System (INIS)
Svec, J.
2003-01-01
Coal mining in Krusne Mts. region is significant anthropogenic pressure. Thus it is necessary to restore land devastated by mining and to bring back its natural functions. Since 2002 locality of Stara Beta, Jan Sverma quarry hopper is being monitored. In 1992 restoration works at Stara Beta were opened. Monitoring is aimed at evaluation development of restoration processes, soil and vegetation caring. Areas where restoration works are realized represent about 60 square kilometres in Most district. The aim is to prepare necessary groundwork for methodology on caring of wood vegetation on restored areas
Use of the Local Variation Methods for Nuclear Design Calculations
International Nuclear Information System (INIS)
Zhukov, A.I.
2006-01-01
A new problem-solving method for steady-state equations, which describe neutron diffusion, is presented. The method bases on a variation principal for steady-state diffusion equations and direct search the minimum of a corresponding functional. Benchmark problem calculation for power of fuel assemblies show ∼ 2% relative accuracy
Energy Technology Data Exchange (ETDEWEB)
Sosa-Hernandez, E.M. [Departamento de Matematicas Aplicadas, Facultad de Contaduria y Administration, Universidad Autonoma de San Luis Potosi, Alvaro Obregon 64, 78000 San Luis Potosi, S.L.P. (Mexico); Alvarado-Leyva, P.G. [Departamento de Fisica, Facultad de Ciencias, Universidad Autonoma de San Luis Potosi Alvaro Obregon 64, 78000 San Luis Potosi, S.L.P. (Mexico)]. E-mail: pal@galia.fc.uaslp.mx
2006-11-09
The magnetic behavior of clusters V{sub 6}-V{sub 9} in bulk Fe is determined by using an electronic Hamiltonian which includes s, p and d electrons. The spin density distribution is calculated self-consistenly in the unrestricted Hartree-Fock approximation. The local magnetic moments are obtained at V and Fe atoms; the magnetic coupling between Fe and V atoms is antiferromagnetic-like. We consider two cases, the first case correspond to non-interacting clusters, the distance between them is infinity, and the another case, when the clusters are interacting, the separation between them is finite; in the first case, the magnetic order in V{sub 6} is ferromagnetic-like whereas for V{sub 9} the magnetic order is antiferromagnetic-like, in the second case we have found that the magnetic order is not well stablished in V{sub 6}. We have found that the magnetic order in the matrix is not broken by the presence of the V atoms, although the local magnetic moments of Fe atoms at the interface cluster-matrix, are reduced respect to Fe bulk magnetization (2.22{mu} {sub B}) [e.g. {mu} {sub Fe}(5) = 1.98{mu} {sub B} in V{sub 6}; {mu} {sub Fe}(3) 1.89{mu} {sub B} in V{sub 9}].
Steepest descent method for set-valued locally accretive mappings
International Nuclear Information System (INIS)
Chidume, C.E.
1993-05-01
Let E be a real q-uniformly smooth Banach space. Suppose T is a set-valued locally strongly accretive map with open domain D(T) in E and that 0 is an element of Tx has a solution x* in D(T). Then there exists a neighbourhood B in D(T) of x* and a real number r 1 >0 such that for any r>r 1 and some real sequence {c n }, any initial guess x 1 is an element of B and any single-valued selection T 0 of T, the sequence {x n } generated from x 1 by x n+1 =x n -c n T 0 x n , n≥1, remains in D(T) and converges strongly to x* with ||x n -x*|| O(n -(q-1)/ q). A related result deals with iterative approximation of a solution of the equation f is an element of x+Ax when A is a locally accretive map. Our theorems generalize important known results and resolve a problem of interest. (author). 39 refs
Directory of Open Access Journals (Sweden)
Masataka Uehara
2015-01-01
Full Text Available The nonsurgical strategies for locally advanced oral cancer are desirable. Superselective intra-arterial infusion with radiotherapy was utilized for this purpose, and there are two types of superselective intra-arterial infusion methods: The Seldinger method and the retrograde superselective intra-arterial chemotherapy (HFT method. In one case, the HFT method was applied to locally advanced tongue cancer, and the Seldinger method was used for additional administration of cisplatin (CDDP to compensate for a lack of drug flow in the HFT method. In another case, the HFT method was applied to locally advanced lower gingival cancer. The Seldinger method was applied to metastatic lymph nodes. In both cases, additional administration of CDDP using the Seldinger method resulted in a complete response. The combination of the HFT and Seldinger methods was useful to eradicate locally advanced oral cancer because each method compensated for the defects of the other.
Method of preliminary localization of the iris in biometric access control systems
Minacova, N.; Petrov, I.
2015-10-01
This paper presents a method of preliminary localization of the iris, based on the stable brightness features of the iris in images of the eye. In tests on images of eyes from publicly available databases method showed good accuracy and speed compared to existing methods preliminary localization.
Damage evolution analysis of coal samples under cyclic loading based on single-link cluster method
Zhang, Zhibo; Wang, Enyuan; Li, Nan; Li, Xuelong; Wang, Xiaoran; Li, Zhonghui
2018-05-01
In this paper, the acoustic emission (AE) response of coal samples under cyclic loading is measured. The results show that there is good positive relation between AE parameters and stress. The AE signal of coal samples under cyclic loading exhibits an obvious Kaiser Effect. The single-link cluster (SLC) method is applied to analyze the spatial evolution characteristics of AE events and the damage evolution process of coal samples. It is found that a subset scale of the SLC structure becomes smaller and smaller when the number of cyclic loading increases, and there is a negative linear relationship between the subset scale and the degree of damage. The spatial correlation length ξ of an SLC structure is calculated. The results show that ξ fluctuates around a certain value from the second cyclic loading process to the fifth cyclic loading process, but spatial correlation length ξ clearly increases in the sixth loading process. Based on the criterion of microcrack density, the coal sample failure process is the transformation from small-scale damage to large-scale damage, which is the reason for changes in the spatial correlation length. Through a systematic analysis, the SLC method is an effective method to research the damage evolution process of coal samples under cyclic loading, and will provide important reference values for studying coal bursts.
International Nuclear Information System (INIS)
Riplinger, Christoph; Pinski, Peter; Becker, Ute; Neese, Frank; Valeev, Edward F.
2016-01-01
Domain based local pair natural orbital coupled cluster theory with single-, double-, and perturbative triple excitations (DLPNO-CCSD(T)) is a highly efficient local correlation method. It is known to be accurate and robust and can be used in a black box fashion in order to obtain coupled cluster quality total energies for large molecules with several hundred atoms. While previous implementations showed near linear scaling up to a few hundred atoms, several nonlinear scaling steps limited the applicability of the method for very large systems. In this work, these limitations are overcome and a linear scaling DLPNO-CCSD(T) method for closed shell systems is reported. The new implementation is based on the concept of sparse maps that was introduced in Part I of this series [P. Pinski, C. Riplinger, E. F. Valeev, and F. Neese, J. Chem. Phys. 143, 034108 (2015)]. Using the sparse map infrastructure, all essential computational steps (integral transformation and storage, initial guess, pair natural orbital construction, amplitude iterations, triples correction) are achieved in a linear scaling fashion. In addition, a number of additional algorithmic improvements are reported that lead to significant speedups of the method. The new, linear-scaling DLPNO-CCSD(T) implementation typically is 7 times faster than the previous implementation and consumes 4 times less disk space for large three-dimensional systems. For linear systems, the performance gains and memory savings are substantially larger. Calculations with more than 20 000 basis functions and 1000 atoms are reported in this work. In all cases, the time required for the coupled cluster step is comparable to or lower than for the preceding Hartree-Fock calculation, even if this is carried out with the efficient resolution-of-the-identity and chain-of-spheres approximations. The new implementation even reduces the error in absolute correlation energies by about a factor of two, compared to the already accurate
A matrix structured LED backlight system with 2D-DHT local dimming method
Liu, Jia; Li, Yang; Du, Sidan
To reduce the number of the drivers in the conventional local dimming method for LCDs, a novel LED backlight local dimming system is proposed in this paper. The backlight of this system is generated by 2D discrete Hadamard transform and its matrix structured LED modules. Compared with the conventional 2D local dimming method, the proposed method costs much fewer drivers but with little degradation.
A fast template matching method for LED chip Localization
Directory of Open Access Journals (Sweden)
Zhong Fuqiang
2015-01-01
Full Text Available Efficiency determines the profits of the semiconductor producers. So the producers spare no effort to enhance the efficiency of every procedure. The purpose of the paper is to present a method to shorten the time to locate the LED chips on wafer. The method consists of 3 steps. Firstly, image segmentation and blob analyzation are used to predict the positions of potential chips. Then predict the orientations of potential chips based on their dominant orientations. Finally, according to the positions and orientations predicted above, locate the chips precisely based on gradient orientation features. Experiments show that the algorithm is faster than the traditional method we choose to locate the LED chips. Besides, even the orientations of the chips on wafer are of big deviation to the orientation of the template, the efficiency of this method won't be affected.
Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes
National Research Council Canada - National Science Library
Ott, Jurg
1998-01-01
.... For a number of variables measured on a trait, a method, principal components of heritability, was developed that combines these variables in such a way that the resulting linear combination has highest heritability...
Qiao Wei; Li Ying; Wu Zhong-Hai
2017-01-01
Deep neural networks (DNNs) have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling ...
Localized surface plasmon resonance mercury detection system and methods
James, Jay; Lucas, Donald; Crosby, Jeffrey Scott; Koshland, Catherine P.
2016-03-22
A mercury detection system that includes a flow cell having a mercury sensor, a light source and a light detector is provided. The mercury sensor includes a transparent substrate and a submonolayer of mercury absorbing nanoparticles, e.g., gold nanoparticles, on a surface of the substrate. Methods of determining whether mercury is present in a sample using the mercury sensors are also provided. The subject mercury detection systems and methods find use in a variety of different applications, including mercury detecting applications.
Dancoff factors with partial neutrons absorption in cluster geometry by the direct method
International Nuclear Information System (INIS)
Rodrigues, Leticia Jenisch
2007-01-01
Accurate analysis of resonance absorption in heterogeneous systems is essential in problems like criticality, breeding ratios and fuel depletion calculations. In compact arrays of fuel rods, resonance absorption is strongly affected by the Dancoff factor, defined in mis study as the probability that a neutron emitted from the surface of a fuel element, enters another fuel element without any collusion in the moderator or cladding. In fact, in the most practical cases of irregular cells, it is observed that inaccuracies in computing both Grey and Black Dancoff factors, i.e. for partially and perfectly absorbing fuel rods, can lead to considerable errors in the calculated values of such integral quantities. For this reason, much effort has been made in the past decades to further improve the models for calculating Dancoff factors, a task that has been accomplished in connection with the development of faster computers. In the WIMS code, Black Dancoff factors based on the above mentioned collusion probability definition are computed in cluster geometry, for each one of the symmetrically distinct fuel pin positions in the cell. Sets of equally-spaced parallel lines are drawn in subroutine PIJ, at a number of discrete equally-incremented azimuthal angles, covering the whole system and forming a mesh over which the in-plane integrations of the Bickley functions are carried out by simple trapezoidal rule, leading to the first-flight collusion matrices. Although fast, the method in PIJ is inefficient, since the constructed mesh does not depended on the system details, so that regions of small relative volumes are crossed out by relatively few lines, which affects the convergence of the calculated probabilities. A new routine (PIJM) was then created to incorporate a more efficient integration scheme considering each system region individually, minimizing convergence problems and reducing the number of neutron track lines required in the in-plane integrations for any given
An ensemble method for predicting subnuclear localizations from primary protein structures.
Directory of Open Access Journals (Sweden)
Guo Sheng Han
Full Text Available BACKGROUND: Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. METHODOLOGY/PRINCIPAL FINDINGS: A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis. CONCLUSIONS: It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method
Formulation analysis and computation of an optimization-based local-to-nonlocal coupling method.
Energy Technology Data Exchange (ETDEWEB)
D' Elia, Marta [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research; Bochev, Pavel Blagoveston [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
2017-01-01
In this paper, we present an optimization-based coupling method for local and nonlocal continuum models. Our approach couches the coupling of the models into a control problem where the states are the solutions of the nonlocal and local equations, the objective is to minimize their mismatch on the overlap of the local and nonlocal problem domains, and the virtual controls are the nonlocal volume constraint and the local boundary condition. We present the method in the context of Local-to-Nonlocal di usion coupling. Numerical examples illustrate the theoretical properties of the approach.
An efficient implementation of parallel molecular dynamics method on SMP cluster architecture
International Nuclear Information System (INIS)
Suzuki, Masaaki; Okuda, Hiroshi; Yagawa, Genki
2003-01-01
The authors have applied MPI/OpenMP hybrid parallel programming model to parallelize a molecular dynamics (MD) method on a symmetric multiprocessor (SMP) cluster architecture. In that architecture, it can be expected that the hybrid parallel programming model, which uses the message passing library such as MPI for inter-SMP node communication and the loop directive such as OpenMP for intra-SNP node parallelization, is the most effective one. In this study, the parallel performance of the hybrid style has been compared with that of conventional flat parallel programming style, which uses only MPI, both in cases the fast multipole method (FMM) is employed for computing long-distance interactions and that is not employed. The computer environments used here are Hitachi SR8000/MPP placed at the University of Tokyo. The results of calculation are as follows. Without FMM, the parallel efficiency using 16 SMP nodes (128 PEs) is: 90% with the hybrid style, 75% with the flat-MPI style for MD simulation with 33,402 atoms. With FMM, the parallel efficiency using 16 SMP nodes (128 PEs) is: 60% with the hybrid style, 48% with the flat-MPI style for MD simulation with 117,649 atoms. (author)
Energy Technology Data Exchange (ETDEWEB)
Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife
2001-07-01
Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.
International Nuclear Information System (INIS)
Romli
1997-01-01
Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods
Directory of Open Access Journals (Sweden)
Rita De Sanctis
2018-01-01
Full Text Available The aim of this report is to unveil specific prognostic factors for retroperitoneal sarcoma (RPS patients by univariate and multivariate statistical techniques. A phase I-II study on localized RPS treated with high-dose ifosfamide and radiotherapy followed by surgery (ISG-STS 0303 protocol demonstrated that chemo/radiotherapy was safe and increased the 3-year relapse-free survival (RFS with respect to historical controls. Of 70 patients, twenty-six developed local, 10 distant, and 5 combined relapse. Median disease-free interval (DFI was 29.47 months. According to a discriminant function analysis, DFI, histology, relapse pattern, and the first treatment approach at relapse had a statistically significant prognostic impact. Based on scientific literature and clinical expertise, clinicopathological data were analyzed using both a supervised and an unsupervised classification method to predict the prognosis, with similar sample sizes (66 and 65, resp., in casewise approach and 70 in mean-substitution one. This is the first attempt to predict patients’ prognosis by means of multivariate statistics, and in this light, it looks noticable that (i some clinical data have a well-defined prognostic value, (ii the unsupervised model produced comparable results with respect to the supervised one, and (iii the appropriate combination of both models appears fruitful and easily extensible to different clinical contexts.
Gene cluster statistics with gene families.
Raghupathy, Narayanan; Durand, Dannie
2009-05-01
Identifying genomic regions that descended from a common ancestor is important for understanding the function and evolution of genomes. In distantly related genomes, clusters of homologous gene pairs are evidence of candidate homologous regions. Demonstrating the statistical significance of such "gene clusters" is an essential component of comparative genomic analyses. However, currently there are no practical statistical tests for gene clusters that model the influence of the number of homologs in each gene family on cluster significance. In this work, we demonstrate empirically that failure to incorporate gene family size in gene cluster statistics results in overestimation of significance, leading to incorrect conclusions. We further present novel analytical methods for estimating gene cluster significance that take gene family size into account. Our methods do not require complete genome data and are suitable for testing individual clusters found in local regions, such as contigs in an unfinished assembly. We consider pairs of regions drawn from the same genome (paralogous clusters), as well as regions drawn from two different genomes (orthologous clusters). Determining cluster significance under general models of gene family size is computationally intractable. By assuming that all gene families are of equal size, we obtain analytical expressions that allow fast approximation of cluster probabilities. We evaluate the accuracy of this approximation by comparing the resulting gene cluster probabilities with cluster probabilities obtained by simulating a realistic, power-law distributed model of gene family size, with parameters inferred from genomic data. Surprisingly, despite the simplicity of the underlying assumption, our method accurately approximates the true cluster probabilities. It slightly overestimates these probabilities, yielding a conservative test. We present additional simulation results indicating the best choice of parameter values for data
Non-Hierarchical Clustering as a method to analyse an open-ended ...
African Journals Online (AJOL)
Apple
Keywords: algebraic thinking; cluster analysis; mathematics education; quantitative analysis. Introduction. Extensive ..... C1, C2 and C3 represent the three centroids of the three clusters formed. .... 6ALd. All these strategies are algebraic and 'high- ... 1995), of the didactical aspects related to teaching .... Brazil, 18-23 July.
Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models
Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim
2013-01-01
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
Comparison of clustering methods for tracking features in RGB-D images
CSIR Research Space (South Africa)
Pancham, Ardhisha
2016-10-01
Full Text Available difficult to track individually over an image sequence. Clustering techniques have been recommended and used to cluster image features to improve tracking results. New and affordable RGB-D cameras, provide both color and depth information. This paper...
Piotrowski, Maurício J; Piquini, Paulo; Cândido, Ladir; Da Silva, Juarez L F
2011-10-14
The crystalline structure of transition-metals (TM) has been widely known for several decades, however, our knowledge on the atomic structure of TM clusters is still far from satisfactory, which compromises an atomistic understanding of the reactivity of TM clusters. For example, almost all density functional theory (DFT) calculations for TM clusters have been based on local (local density approximation--LDA) and semilocal (generalized gradient approximation--GGA) exchange-correlation functionals, however, it is well known that plain DFT fails to correct the self-interaction error, which affects the properties of several systems. To improve our basic understanding of the atomic and electronic properties of TM clusters, we report a DFT study within two nonlocal functionals, namely, the hybrid HSE (Heyd, Scuseria, and Ernzerhof) and GGA+U functionals, of the structural and electronic properties of the Co(13), Rh(13), and Hf(13) clusters. For Co(13) and Rh(13), we found that improved exchange-correlation functionals decrease the stability of open structures such as the hexagonal bilayer (HBL) and double simple-cubic (DSC) compared with the compact icosahedron (ICO) structure, however, DFT-GGA, DFT-GGA+U, and DFT-HSE yield very similar results for Hf(13). Thus, our results suggest that the DSC structure obtained by several plain DFT calculations for Rh(13) can be improved by the use of improved functionals. Using the sd hybridization analysis, we found that a strong hybridization favors compact structures, and hence, a correct description of the sd hybridization is crucial for the relative energy stability. For example, the sd hybridization decreases for HBL and DSC and increases for ICO in the case of Co(13) and Rh(13), while for Hf(13), the