An event driven algorithm for fractal cluster formation

NARCIS (Netherlands)

González, S.; Gonzalez Briones, Sebastián; Thornton, Anthony Richard; Luding, Stefan

2011-01-01

A new cluster based event-driven algorithm is developed to simulate the formation of clusters in a two dimensional gas: particles move freely until they collide and "stick" together irreversibly. These clusters aggregate into bigger structures in an isotompic way, forming fractal structures whose

An event driven algorithm for fractal cluster formation

NARCIS (Netherlands)

González, S.; Thornton, Anthony Richard; Luding, Stefan

2010-01-01

A new cluster based event-driven algorithm is developed to simulate the formation of clusters in a two dimensional gas: particles move freely until they collide and "stick" together irreversibly. These clusters aggregate into bigger structures in an isotompic way, forming fractal structures whose

Effects of oxygen addition in reactive cluster beam deposition of tungsten by magnetron sputtering with gas aggregation

Energy Technology Data Exchange (ETDEWEB)

Polášek, J., E-mail: xpolasekj@seznam.cz [Department of Surface and Plasma Science, Faculty of Mathematics and Physic, Charles University, V Holešovičkách 2, Prague 8, CZ-18000 (Czech Republic); Mašek, K. [Department of Surface and Plasma Science, Faculty of Mathematics and Physic, Charles University, V Holešovičkách 2, Prague 8, CZ-18000 (Czech Republic); Marek, A.; Vyskočil, J. [HVM Plasma Ltd., Na Hutmance 2, Prague 5, CZ-158 00 (Czech Republic)

2015-09-30

In this work, we investigated the possibilities of tungsten and tungsten oxide nanoclusters generation by means of non-reactive and reactive magnetron sputtering with gas aggregation. It was found that in pure argon atmosphere, cluster aggregation proceeded in two regimes depending on argon pressure in the aggregation chamber. At the lower pressure, cluster generation was dominated by two-body collisions yielding larger clusters (about 5.5 nm in diameter) at lower rate. At higher pressures, cluster generation was dominated by three-body collisions yielding smaller clusters (3–4 nm in diameter) at higher rate. The small amount of oxygen admixture in the aggregation chamber had considerable influence on cluster aggregation process. At certain critical pressure, the presence of oxygen led to the raise of deposition rate and cluster size. Resulting clusters were composed mostly of tungsten trioxide. The oxygen pressure higher than critical led to the target poisoning and the decrease in the sputtering rate. Critical oxygen pressure decreased with increasing argon pressure, suggesting that cluster aggregation process was influenced by atomic oxygen species (namely, O{sup −} ion) generated by oxygen–argon collisions in the magnetron plasma. - Highlights: • Formation of tungsten and tungsten oxide clusters was observed. • Two modes of cluster aggregation in pure argon atmosphere were found. • Dependence of cluster deposition speed and size on oxygen admixture was observed. • Changes of dependence on oxygen with changing argon pressure were described.

Effects of oxygen addition in reactive cluster beam deposition of tungsten by magnetron sputtering with gas aggregation

International Nuclear Information System (INIS)

Polášek, J.; Mašek, K.; Marek, A.; Vyskočil, J.

2015-01-01

In this work, we investigated the possibilities of tungsten and tungsten oxide nanoclusters generation by means of non-reactive and reactive magnetron sputtering with gas aggregation. It was found that in pure argon atmosphere, cluster aggregation proceeded in two regimes depending on argon pressure in the aggregation chamber. At the lower pressure, cluster generation was dominated by two-body collisions yielding larger clusters (about 5.5 nm in diameter) at lower rate. At higher pressures, cluster generation was dominated by three-body collisions yielding smaller clusters (3–4 nm in diameter) at higher rate. The small amount of oxygen admixture in the aggregation chamber had considerable influence on cluster aggregation process. At certain critical pressure, the presence of oxygen led to the raise of deposition rate and cluster size. Resulting clusters were composed mostly of tungsten trioxide. The oxygen pressure higher than critical led to the target poisoning and the decrease in the sputtering rate. Critical oxygen pressure decreased with increasing argon pressure, suggesting that cluster aggregation process was influenced by atomic oxygen species (namely, O"− ion) generated by oxygen–argon collisions in the magnetron plasma. - Highlights: • Formation of tungsten and tungsten oxide clusters was observed. • Two modes of cluster aggregation in pure argon atmosphere were found. • Dependence of cluster deposition speed and size on oxygen admixture was observed. • Changes of dependence on oxygen with changing argon pressure were described.

Aggregation Number in Water/n-Hexanol Molecular Clusters Formed in Cyclohexane at Different Water/n-Hexanol/Cyclohexane Compositions Calculated by Titration 1H NMR.

Science.gov (United States)

Flores, Mario E; Shibue, Toshimichi; Sugimura, Natsuhiko; Nishide, Hiroyuki; Moreno-Villoslada, Ignacio

2017-11-09

Upon titration of n-hexanol/cyclohexane mixtures of different molar compositions with water, water/n-hexanol clusters are formed in cyclohexane. Here, we develop a new method to estimate the water and n-hexanol aggregation numbers in the clusters that combines integration analysis in one-dimensional 1 H NMR spectra, diffusion coefficients calculated by diffusion-ordered NMR spectroscopy, and further application of the Stokes-Einstein equation to calculate the hydrodynamic volume of the clusters. Aggregation numbers of 5-15 molecules of n-hexanol per cluster in the absence of water were observed in the whole range of n-hexanol/cyclohexane molar fractions studied. After saturation with water, aggregation numbers of 6-13 n-hexanol and 0.5-5 water molecules per cluster were found. O-H and O-O atom distances related to hydrogen bonds between donor/acceptor molecules were theoretically calculated using density functional theory. The results show that at low n-hexanol molar fractions, where a robust hydrogen-bond network is held between n-hexanol molecules, addition of water makes the intermolecular O-O atom distance shorter, reinforcing molecular association in the clusters, whereas at high n-hexanol molar fractions, where dipole-dipole interactions dominate, addition of water makes the intermolecular O-O atom distance longer, weakening the cluster structure. This correlates with experimental NMR results, which show an increase in the size and aggregation number in the clusters upon addition of water at low n-hexanol molar fractions, and a decrease of these magnitudes at high n-hexanol molar fractions. In addition, water produces an increase in the proton exchange rate between donor/acceptor molecules at all n-hexanol molar fractions.

Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms

OpenAIRE

Chen, Pin-Yu; Hero, Alfred O.

2017-01-01

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a multilayer spectral graph clustering (SGC) framework that performs convex layer aggregation. Under a multilayer signal plus noise model, we provide a phase transition analys...

Cluster-cluster correlations in the two-dimensional stationary Ising-model

International Nuclear Information System (INIS)

Klassmann, A.

1997-01-01

In numerical integration of the Cahn-Hillard equation, which describes Oswald rising in a two-phase matrix, N. Masbaum showed that spatial correlations between clusters scale with respect to the mean cluster size (itself a function of time). T. B. Liverpool showed by Monte Carlo simulations for the Ising model that the analogous correlations have a similar form. Both demonstrated that immediately around each cluster there is some depletion area followed by something like a ring of clusters of the same size as the original one. More precisely, it has been shown that the distribution of clusters around a given cluster looks like a sinus-curve decaying exponentially with respect to the distance to a constant value

Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

International Nuclear Information System (INIS)

Zhang, Jianbao; Ma, Zhongjun; Zhang, Gang

2013-01-01

This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network

Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

Science.gov (United States)

Zhang, Jianbao; Ma, Zhongjun; Zhang, Gang

2013-12-01

This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network.

A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network.

Science.gov (United States)

Chen, Yuzhong; Weng, Shining; Guo, Wenzhong; Xiong, Naixue

2016-02-19

Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.

A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network

Directory of Open Access Journals (Sweden)

Yuzhong Chen

2016-02-01

Full Text Available Vehicular ad hoc networks (VANETs have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.

Voting-based consensus clustering for combining multiple clusterings of chemical structures

Directory of Open Access Journals (Sweden)

Saeed Faisal

2012-12-01

Full Text Available Abstract Background Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Results The cumulative voting-based aggregation algorithm (CVAA, cluster-based similarity partitioning algorithm (CSPA and hyper-graph partitioning algorithm (HGPA were examined. The F-measure and Quality Partition Index method (QPI were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria. Conclusions The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA was the method of choice among consensus clustering methods.

Molecular clusters of 3D and lower magnetic dimensionality

International Nuclear Information System (INIS)

Papaefthymiou, G.C

1991-01-01

Controlled polymerization of iron leads to the synthesis of molecular clusters of ever-increasing size, tending to extended structures. Polymerization of oxo-bridged octahedrally coordinated iron leads to clusters with 3D magnetic interactions between iron ions, while sulfide- and selenide-bridged tetrahedrally coordinated iron ions produce clusters of lower magnetic dimensionality. In this paper the magnetic properties of the resulting large molecular clusters with N ≥ 17 (where N = the number of iron ions in the cluster) are investigated for the presence of collective magnetic correlations associated with the solid state

Interplay between experiments and calculations for organometallic clusters and caged clusters

International Nuclear Information System (INIS)

Nakajima, Atsushi

2015-01-01

Clusters consisting of 10-1000 atoms exhibit size-dependent electronic and geometric properties. In particular, composite clusters consisting of several elements and/or components provide a promising way for a bottom-up approach for designing functional advanced materials, because the functionality of the composite clusters can be optimized not only by the cluster size but also by their compositions. In the formation of composite clusters, their geometric symmetry and dimensionality are emphasized to control the physical and chemical properties, because selective and anisotropic enhancements for optical, chemical, and magnetic properties can be expected. Organometallic clusters and caged clusters are demonstrated as a representative example of designing the functionality of the composite clusters. Organometallic vanadium-benzene forms a one dimensional sandwich structure showing ferromagnetic behaviors and anomalously large HOMO-LUMO gap differences of two spin orbitals, which can be regarded as spin-filter components for cluster-based spintronic devices. Caged clusters of aluminum (Al) are well stabilized both geometrically and electronically at Al 12 X, behaving as a “superatom”

Aggregation kinetics and structure of cryoimmunoglobulins clusters

CERN Document Server

De Spirito, M; Bassi, F A; Di Stasio, E; Giardina, B; Arcovito, G

2002-01-01

Cryoimmunoglobulins are pathological antibodies characterized by a temperature-dependent reversible insolubility. Rheumatoid factors (RF) are immunoglobulins possessing anti-immunoglobulin activity and usually consist of an IgM antibody that recognizes IgG as antigen. These proteins are present in sera of patients affected by a large variety of different pathologies, such as HCV infection, neoplastic and autoimmune diseases. Aggregation and precipitation of cryoimmunoglobulins, leading to vasculiti, are physical phenomena behind such pathologies. A deep knowledge of the physico-chemical mechanisms regulating such phenomena plays a fundamental role in biological and clinical applications. In this work, a preliminary investigation of the aggregation kinetics and of the final macro- molecular structure of the aggregates is presented. Through static light scattering techniques, the gyration radius R/sub g/ and the fractal dimension D/sub m/ of the growing clusters have been determined. However, while the initial ...

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms.

Science.gov (United States)

McGarvey, Richard; Burch, Paul; Matthews, Janet M

2016-01-01

Natural populations of plants and animals spatially cluster because (1) suitable habitat is patchy, and (2) within suitable habitat, individuals aggregate further into clusters of higher density. We compare the precision of random and systematic field sampling survey designs under these two processes of species clustering. Second, we evaluate the performance of 13 estimators for the variance of the sample mean from a systematic survey. Replicated simulated surveys, as counts from 100 transects, allocated either randomly or systematically within the study region, were used to estimate population density in six spatial point populations including habitat patches and Matérn circular clustered aggregations of organisms, together and in combination. The standard one-start aligned systematic survey design, a uniform 10 x 10 grid of transects, was much more precise. Variances of the 10 000 replicated systematic survey mean densities were one-third to one-fifth of those from randomly allocated transects, implying transect sample sizes giving equivalent precision by random survey would need to be three to five times larger. Organisms being restricted to patches of habitat was alone sufficient to yield this precision advantage for the systematic design. But this improved precision for systematic sampling in clustered populations is underestimated by standard variance estimators used to compute confidence intervals. True variance for the survey sample mean was computed from the variance of 10 000 simulated survey mean estimates. Testing 10 published and three newly proposed variance estimators, the two variance estimators (v) that corrected for inter-transect correlation (ν₈ and ν(W)) were the most accurate and also the most precise in clustered populations. These greatly outperformed the two "post-stratification" variance estimators (ν₂ and ν₃) that are now more commonly applied in systematic surveys. Similar variance estimator performance rankings were found with

Bitwise dimensional co-clustering for analytical workloads

NARCIS (Netherlands)

Baumann, Stephan; Boncz, Peter; Sattler, Kai Uwe

2016-01-01

Analytical workloads in data warehouses often include heavy joins where queries involve multiple fact tables in addition to the typical star-patterns, dimensional grouping and selections. In this paper we propose a new processing and storage framework called bitwise dimensional co-clustering (BDCC)

Evaluating Clustering in Subspace Projections of High Dimensional Data

DEFF Research Database (Denmark)

Müller, Emmanuel; Günnemann, Stephan; Assent, Ira

2009-01-01

Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation...... and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects...... of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention. In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering...

A DISTRIBUTED ENERGY EFFICIENT CLUSTERING ALGORITHM FOR DATA AGGREGATION IN WIRELESS SENSOR NETWORKS

Directory of Open Access Journals (Sweden)

Seyed Mohammad Bagher Musavi Shirazi

2018-06-01

Full Text Available Wireless sensor networks (WSNs are a new generation of networks typically consisting of a large number of inexpensive nodes with wireless communications. The main purpose of these networks is to collect information from the environment for further processing. Nodes in the network have been equipped with limited battery lifetime, so energy saving is one of the major issues in WSNs. If we balance the load among cluster heads and prevent having an extra load on just a few nodes in the network, we can reach longer network lifetime. One solution to control energy consumption and balance the load among nodes is to use clustering techniques. In this paper, we propose a new distributed energy-efficient clustering algorithm for data aggregation in wireless sensor networks, called Distributed Clustering for Data Aggregation (DCDA. In our new approach, an optimal transmission tree is constructed among sensor nodes with a new greedy method. Base station (BS is the root, cluster heads (CHs and relay nodes are intermediate nodes, and other nodes (cluster member nodes are the leaves of this transmission tree. DCDA balances load among CHs in intra-cluster and inter-cluster data communications using different cluster sizes. For efficient inter-cluster communications, some relay nodes will transfer data between CHs. Energy consumption, distance to the base station, and cluster heads’ centric metric are three main adjustment parameters for the cluster heads election. Simulation results show that the proposed protocol leads to the reduction of individual sensor nodes’ energy consumption and prolongs network lifetime, in comparison with other known methods. ABSTRAK: Rangkaian sensor wayarles (WSN adalah rangkaian generasi baru yang terdiri daripada nod-nod murah komunikasi wayarles. Tujuan rangkaian-rangkaian ini adalah bagi mengumpul maklumat sekeliling untuk proses seterusnya. Nod dalam rangkaian ini dilengkapi bateri kurang jangka hayat, jadi simpanan tenaga

Membership determination of open clusters based on a spectral clustering method

Science.gov (United States)

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.

On clusters and clustering from atoms to fractals

CERN Document Server

Reynolds, PJ

1993-01-01

This book attempts to answer why there is so much interest in clusters. Clusters occur on all length scales, and as a result occur in a variety of fields. Clusters are interesting scientifically, but they also have important consequences technologically. The division of the book into three parts roughly separates the field into small, intermediate, and large-scale clusters. Small clusters are the regime of atomic and molecular physics and chemistry. The intermediate regime is the transitional regime, with its characteristics including the onset of bulk-like behavior, growth and aggregation, a

Superfluid response of two-dimensional parahydrogen clusters in confinement

Energy Technology Data Exchange (ETDEWEB)

Idowu, Saheed; Boninsegni, Massimo [Department of Physics, University of Alberta, Edmonton, Alberta T6G 2E7 (Canada)

2015-04-07

We study by computer simulations the effect of confinement on the superfluid properties of small two-dimensional (2D) parahydrogen clusters. For clusters of fewer than twenty molecules, the superfluid response in the low temperature limit is found to remain comparable in magnitude to that of free clusters, within a rather wide range of depth and size of the confining well. The resilience of the superfluid response is attributable to the “supersolid” character of these clusters. We investigate the possibility of establishing a bulk 2D superfluid “cluster crystal” phase of p-H{sub 2}, in which a global superfluid response would arise from tunnelling of molecules across adjacent unit cells. The computed energetics suggests that for clusters of about ten molecules, such a phase may be thermodynamically stable against the formation of the equilibrium insulating crystal, for values of the cluster crystal lattice constant possibly allowing tunnelling across adjacent unit cells.

Polymer depletion-driven cluster aggregation and initial phase separation in charged nanosized colloids

Science.gov (United States)

Gögelein, Christoph; Nägele, Gerhard; Buitenhuis, Johan; Tuinier, Remco; Dhont, Jan K. G.

2009-05-01

We study polymer depletion-driven cluster aggregation and initial phase separation in aqueous dispersions of charge-stabilized silica spheres, where the ionic strength and polymer (dextran) concentration are systematically varied, using dynamic light scattering and visual observation. Without polymers and for increasing salt and colloid content, the dispersions become increasingly unstable against irreversible cluster formation. By adding nonadsorbing polymers, a depletion-driven attraction is induced, which lowers the stabilizing Coulomb barrier and enhances the cluster growth rate. The initial growth rate increases with increasing polymer concentration and decreases with increasing polymer molar mass. These observations can be quantitatively understood by an irreversible dimer formation theory based on the classical Derjaguin, Landau, Verwey, and Overbeek pair potential, with the depletion attraction modeled by the Asakura-Oosawa-Vrij potential. At low colloid concentration, we observe an exponential cluster growth rate for all polymer concentrations considered, indicating a reaction-limited aggregation mechanism. At sufficiently high polymer and colloid concentrations, and lower salt content, a gas-liquidlike demixing is observed initially. Later on, the system separates into a gel and fluidlike phase. The experimental time-dependent state diagram is compared to the theoretical equilibrium phase diagram obtained from a generalized free-volume theory and is discussed in terms of an initial reversible phase separation process in combination with irreversible aggregation at later times.

High-dimensional cluster analysis with the Masked EM Algorithm

Science.gov (United States)

Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

2014-01-01

Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming.

Science.gov (United States)

Wang, Haizhou; Song, Mingzhou

2011-12-01

The heuristic k -means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp . We demonstrate its advantage in optimality and runtime over the standard iterative k -means algorithm.

Fuzzy Clustering

DEFF Research Database (Denmark)

Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan

2000-01-01

A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...

Uplink multi-cluster scheduling with MU-MIMO for LTE-advanced with carrier aggregation

DEFF Research Database (Denmark)

Wang, Hua; Nguyen, Hung Tuan; Rosa, Claudio

2012-01-01

-Advanced requirements and are being considered as part of LTE Release 10. In this paper, some of the physical layer enhancement techniques for LTE-Advanced have been studied including carrier aggregation (CA), uplink multi-cluster scheduling, and uplink multi-user multiple-input multiple-output (MU-MIMO) transmission....... A system-level simulation was conducted to investigate the performance gains that can be achieved in uplink CA with multi-cluster scheduling and MU-MIMO. Simulation results show that with proper separation between power-limited and non-power-limited LTE-A users, multi-cluster scheduling with CA has similar...

Cluster–cluster aggregation with particle replication and chemotaxy: a simple model for the growth of animal cells in culture

International Nuclear Information System (INIS)

Alves, S G; Martins, M L

2010-01-01

Aggregation of animal cells in culture comprises a series of motility, collision and adhesion processes of basic relevance for tissue engineering, bioseparations, oncology research and in vitro drug testing. In the present paper, a cluster–cluster aggregation model with stochastic particle replication and chemotactically driven motility is investigated as a model for the growth of animal cells in culture. The focus is on the scaling laws governing the aggregation kinetics. Our simulations reveal that in the absence of chemotaxy the mean cluster size and the total number of clusters scale in time as stretched exponentials dependent on the particle replication rate. Also, the dynamical cluster size distribution functions are represented by a scaling relation in which the scaling function involves a stretched exponential of the time. The introduction of chemoattraction among the particles leads to distribution functions decaying as power laws with exponents that decrease in time. The fractal dimensions and size distributions of the simulated clusters are qualitatively discussed in terms of those determined experimentally for several normal and tumoral cell lines growing in culture. It is shown that particle replication and chemotaxy account for the simplest cluster size distributions of cellular aggregates observed in culture

Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

Directory of Open Access Journals (Sweden)

Thenmozhi Srinivasan

2015-01-01

Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

Science.gov (United States)

Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Crossover from the coffee-ring effect to the uniform deposit caused by irreversible cluster-cluster aggregation

Science.gov (United States)

Crivoi, A.; Zhong, X.; Duan, Fei

2015-09-01

The coffee-ring effect for particle deposition near the three-phase line after drying a pinned sessile colloidal droplet has been suppressed or attenuated in many recent studies. However, there have been few attempts to simulate the mitigation of the effect in the presence of strong particle-particle attraction forces. We develop a three-dimensional stochastic model to investigate the drying process of a pinned colloidal sessile droplet by considering the sticking between particles, which was observed in the experiments. The Monte Carlo simulation results show that by solely promoting the particle-particle attraction in the model, the final deposit shape is transformed from the coffee ring to the uniform film deposition. This phenomenon is modeled using the colloidal aggregation technique and explained by the "Tetris principle," meaning that unevenly shaped or branched particle clusters rapidly build up a sparse structure spanning throughout the entire domain in the drying process. The influence of the controlled parameters is analyzed as well. The simulation is reflected by the drying patterns of the nanofluid droplets through the surfactant control in the experiments.

Clustervision: Visual Supervision of Unsupervised Clustering.

Science.gov (United States)

Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

2018-01-01

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

Science.gov (United States)

Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

2015-10-01

Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

Cluster dynamics models of irradiation damage accumulation in ferritic iron. II. Effects of reaction dimensionality

Energy Technology Data Exchange (ETDEWEB)

Kohnert, Aaron A.; Wirth, Brian D. [University of Tennessee, Knoxville, Tennessee 37996-2300 (United States)

2015-04-21

The black dot damage features which develop in iron at low temperatures exhibit significant mobility during in situ irradiation experiments via a series of discrete, intermittent, long range hops. By incorporating this mobility into cluster dynamics models, the temperature dependence of such damage structures can be explained with a surprising degree of accuracy. Such motion, however, is one dimensional in nature. This aspect of the physics has not been fully considered in prior models. This article describes one dimensional reaction kinetics in the context of cluster dynamics and applies them to the black dot problem. This allows both a more detailed description of the mechanisms by which defects execute irradiation-induced hops while allowing a full examination of the importance of kinetic assumptions in accurately assessing the development of this irradiation microstructure. Results are presented to demonstrate whether one dimensional diffusion alters the dependence of the defect population on factors such as temperature and defect hop length. Finally, the size of interstitial loops that develop is shown to depend on the extent of the reaction volumes between interstitial clusters, as well as the dimensionality of these interactions.

Ligand combination strategy for the preparation of novel low-dimensional and open-framework metal cluster materials

Science.gov (United States)

Anokhina, Ekaterina V.

Low-dimensional and open-framework materials containing transition metals have a wide range of applications in redox catalysis, solid-state batteries, and electronic and magnetic devices. This dissertation reports on research carried out with the goal to develop a strategy for the preparation of low-dimensional and open-framework materials using octahedral metal clusters as building blocks. Our approach takes its roots from crystal engineering principles where the desired framework topologies are achieved through building block design. The key idea of this work is to induce directional bonding preferences in the cluster units using a combination of ligands with a large difference in charge density. This investigation led to the preparation and characterization of a new family of niobium oxychloride cluster compounds with original structure types exhibiting 1ow-dimensional or open-framework character. Most of these materials have framework topologies unprecedented in compounds containing octahedral clusters. Comparative analysis of their structural features indicates that the novel cluster connectivity patterns in these systems are the result of complex interplay between the effects of anisotropic ligand arrangement in the cluster unit and optimization of ligand-counterion electrostatic interactions. The important role played by these factors sets niobium oxychloride systems apart from cluster compounds with one ligand type or statistical ligand distribution where the main structure-determining factor is the total number of ligands. These results provide a blueprint for expanding the ligand combination strategy to other transition metal cluster systems and for the future rational design of cluster-based materials.

A general treatment of one- to three-dimensional diffusion reaction kinetics of interstitial clusters: Implications for the evolution of voids

International Nuclear Information System (INIS)

Trinkaus, H.; Singh, B.N.; Golubov, S.I.

2008-05-01

In recent years, it has been shown that a number of striking features in the microstructural evolution occurring in metals under cascade damage generating irradiation (e.g. enhanced swelling near grain boundaries, decoration of dislocations with SIA loops, saturation of void growth and void lattice formation) can be rationalised in terms of intra-cascade clustering of vacancies and self-interstitial atoms (SIAs), differences in the thermal stability and mobility of the resulting clusters and one-dimensional (1D) glide diffusion of SIA clusters ('production bias model'). The 1D diffusion of SIA clusters is generally disturbed by changes between equivalent 1D diffusion paths and by transversal diffusion by self-climb, resulting in diffusion reaction kinetics between the 1D and 3D limiting cases. In this paper, a general treatment of such kinetics operating in systems containing random distributions of sinks is presented. The complicated partial sink strengths of different components of the system for the annihilation of SIA clusters are expressed by those for the simple 1D and 3D limiting cases. The effects of direction changes and transversal diffusion are first considered separately and are then combined. The significance of the present treatment for damage accumulation under cascade damage conditions is illustrated by applying it to the discussion of void growth characteristics, particularly of the conditions for saturation of void growth. (au)

A general treatment of one- to three-dimensional diffusion reaction kinetics of interstitial clusters: Implications for the evolution of voids

Energy Technology Data Exchange (ETDEWEB)

Trinkaus, H. (Inst. Festkoerperforschung, Forschungszentrum Juelich (Germany)); Singh, B.N. (Risoe DTU, Roskilde (Denmark)); Golubov, S.I. (Oak Ridge National Lab., Materials Science and Technology Div., TN (United States))

2008-05-15

In recent years, it has been shown that a number of striking features in the microstructural evolution occurring in metals under cascade damage generating irradiation (e.g. enhanced swelling near grain boundaries, decoration of dislocations with SIA loops, saturation of void growth and void lattice formation) can be rationalised in terms of intra-cascade clustering of vacancies and self-interstitial atoms (SIAs), differences in the thermal stability and mobility of the resulting clusters and one-dimensional (1D) glide diffusion of SIA clusters ('production bias model'). The 1D diffusion of SIA clusters is generally disturbed by changes between equivalent 1D diffusion paths and by transversal diffusion by self-climb, resulting in diffusion reaction kinetics between the 1D and 3D limiting cases. In this paper, a general treatment of such kinetics operating in systems containing random distributions of sinks is presented. The complicated partial sink strengths of different components of the system for the annihilation of SIA clusters are expressed by those for the simple 1D and 3D limiting cases. The effects of direction changes and transversal diffusion are first considered separately and are then combined. The significance of the present treatment for damage accumulation under cascade damage conditions is illustrated by applying it to the discussion of void growth characteristics, particularly of the conditions for saturation of void growth. (au)

Dimensional scale effects on surface enhanced Raman scattering efficiency of self-assembled silver nanoparticle clusters

International Nuclear Information System (INIS)

Fasolato, C.; Domenici, F.; De Angelis, L.; Luongo, F.; Postorino, P.; Sennato, S.; Mura, F.; Costantini, F.; Bordi, F.

2014-01-01

A study of the Surface Enhanced Raman Scattering (SERS) from micrometric metallic nanoparticle aggregates is presented. The sample is obtained from the self-assembly on glass slides of micro-clusters of silver nanoparticles (60 and 100 nm diameter), functionalized with the organic molecule 4-aminothiophenol in water solution. For nanoparticle clusters at the micron scale, a maximum enhancement factor of 10 9 is estimated from the SERS over the Raman intensity ratio normalized to the single molecule contribution. Atomic force microscopy, correlated to spatially resolved Raman measurements, allows highlighting the connection between morphology and efficiency of the plasmonic system. The correlation between geometric features and SERS response of the metallic structures reveals a linear trend of the cluster maximum scattered intensity as a function of the surface area of the aggregate. On given clusters, the intensity turns out to be also influenced by the number of stacking planes of the aggregate, thus suggesting a plasmonic waveguide effect. The linear dependence results weakened for the largest area clusters, suggesting 30 μm 2 as the upper limit for exploiting the coherence over large scale of the plasmonic response.

ClusterSignificance: A bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data

DEFF Research Database (Denmark)

Serviss, Jason T.; Gådin, Jesper R.; Eriksson, Per

2017-01-01

, e.g. genes in a specific pathway, alone can separate samples into these established classes. Despite this, the evaluation of class separations is often subjective and performed via visualization. Here we present the ClusterSignificance package; a set of tools designed to assess the statistical...... significance of class separations downstream of dimensionality reduction algorithms. In addition, we demonstrate the design and utility of the ClusterSignificance package and utilize it to determine the importance of long non-coding RNA expression in the identity of multiple hematological malignancies....

AN EFFECTIVE MULTI-CLUSTERING ANONYMIZATION APPROACH USING DISCRETE COMPONENT TASK FOR NON-BINARY HIGH DIMENSIONAL DATA SPACES

Directory of Open Access Journals (Sweden)

L.V. Arun Shalin

2016-01-01

Full Text Available Clustering is a process of grouping elements together, designed in such a way that the elements assigned to similar data points in a cluster are more comparable to each other than the remaining data points in a cluster. During clustering certain difficulties related when dealing with high dimensional data are ubiquitous and abundant. Works concentrated using anonymization method for high dimensional data spaces failed to address the problem related to dimensionality reduction during the inclusion of non-binary databases. In this work we study methods for dimensionality reduction for non-binary database. By analyzing the behavior of dimensionality reduction for non-binary database, results in performance improvement with the help of tag based feature. An effective multi-clustering anonymization approach called Discrete Component Task Specific Multi-Clustering (DCTSM is presented for dimensionality reduction on non-binary database. To start with we present the analysis of attribute in the non-binary database and cluster projection identifies the sparseness degree of dimensions. Additionally with the quantum distribution on multi-cluster dimension, the solution for relevancy of attribute and redundancy on non-binary data spaces is provided resulting in performance improvement on the basis of tag based feature. Multi-clustering tag based feature reduction extracts individual features and are correspondingly replaced by the equivalent feature clusters (i.e. tag clusters. During training, the DCTSM approach uses multi-clusters instead of individual tag features and then during decoding individual features is replaced by corresponding multi-clusters. To measure the effectiveness of the method, experiments are conducted on existing anonymization method for high dimensional data spaces and compared with the DCTSM approach using Statlog German Credit Data Set. Improved tag feature extraction and minimum error rate compared to conventional anonymization

Accounting for One-Group Clustering in Effect-Size Estimation

Science.gov (United States)

Citkowicz, Martyna; Hedges, Larry V.

2013-01-01

In some instances, intentionally or not, study designs are such that there is clustering in one group but not in the other. This paper describes methods for computing effect size estimates and their variances when there is clustering in only one group and the analysis has not taken that clustering into account. The authors provide the effect size…

Cluster properties of the one-dimensional lattice gas: the microscopic meaning of grand potential.

Science.gov (United States)

Fronczak, Agata

2013-02-01

Using a concrete example, we demonstrate how the combinatorial approach to a general system of particles, which was introduced in detail in an earlier paper [Fronczak, Phys. Rev. E 86, 041139 (2012)], works and where this approach provides a genuine extension of results obtained through more traditional methods of statistical mechanics. We study the cluster properties of a one-dimensional lattice gas with nearest-neighbor interactions. Three cases (the infinite temperature limit, the range of finite temperatures, and the zero temperature limit) are discussed separately, yielding interesting results and providing alternative proof of known results. In particular, the closed-form expression for the grand partition function in the zero temperature limit is obtained, which results in the nonanalytic behavior of the grand potential, in accordance with the Yang-Lee theory.

BCS superconductivity for weakly coupled clusters

International Nuclear Information System (INIS)

Friedel, J.

1992-01-01

BCS superconductivity is expected to have fairly high critical temperatures when clusters of moderate sizes are weakly coupled to form a crystal. This remark extends to quasi zerodimensional cases, a remark initially made by Labbe for quasi one-dimensional ones and by Hirsch, Bok and Labbe for quasi twodimensional ones. Possible applications are envisaged for twodimensional clusters (fullerene) or threedimensional ones (metal clusters, Chevrel phases). Conditions for optimal applicability of the scheme are somewhat restricted. (orig.)

Cluster-cluster aggregation kinetics and primary particle growth of soot nanoparticles in flame by light scattering and numerical simulations.

Science.gov (United States)

di Stasio, Stefano; Konstandopoulos, Athanasios G; Kostoglou, Margaritis

2002-03-01

The agglomeration kinetics of growing soot generated in a diffusion atmospheric flame are here studied in situ by light scattering technique to infer cluster morphology and size (fractal dimension D(f) and radius of gyration R(g)). SEM analysis is used as a standard reference to obtain primary particle size D(P) at different residence times. The number N(P) of primary particles per aggregate and the number concentration n(A) of clusters are evaluated on the basis of the measured angular patterns of the scattered light intensity. The major finding is that the kinetics of the coagulation process that yields to the formation of chain-like aggregates by soot primary particles (size 10 to 40 nm) can be described with a constant coagulation kernel beta(c,exp)=2.37x10(-9) cm3/s (coagulation constant tau(c) approximately = 0.28 ms). This result is in nice accord with the Smoluchowski coagulation equation in the free molecular regime, and, vice versa, it is in contrast with previous studies conducted by invasive (ex situ) techniques, which claimed the evidence in flames of coagulation rates much larger than the kinetic theory predictions. Thereafter, a number of numerical simulations is implemented to compare with the experimental results on primary particle growth rate and on the process of aggregate reshaping that is observed by light scattering at later residence times. The restructuring process is conjectured to occur, for not well understood reasons, as a direct consequence of the atomic rearrangement in the solid phase carbon due to the prolonged residence time within the flame. Thus, on one side, it is shown that the numerical simulations of primary size history compare well with the values of primary size from SEM experiment with a growth rate constant of primary diameter about 1 nm/s. On the other side, the evolution of aggregate morphology is found to be predictable by the numerical simulations when the onset of a first-order "thermal" restructuring mechanism is

Clustering by reordering of similarity and Laplacian matrices: Application to galaxy clusters

Science.gov (United States)

Mahmoud, E.; Shoukry, A.; Takey, A.

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values

Three-dimensional cluster formation and structure in heterogeneous dose distribution of intensity modulated radiation therapy.

Science.gov (United States)

Chao, Ming; Wei, Jie; Narayanasamy, Ganesh; Yuan, Yading; Lo, Yeh-Chi; Peñagarícano, José A

2018-05-01

To investigate three-dimensional cluster structure and its correlation to clinical endpoint in heterogeneous dose distributions from intensity modulated radiation therapy. Twenty-five clinical plans from twenty-one head and neck (HN) patients were used for a phenomenological study of the cluster structure formed from the dose distributions of organs at risks (OARs) close to the planning target volumes (PTVs). Initially, OAR clusters were searched to examine the pattern consistence among ten HN patients and five clinically similar plans from another HN patient. Second, clusters of the esophagus from another ten HN patients were scrutinized to correlate their sizes to radiobiological parameters. Finally, an extensive Monte Carlo (MC) procedure was implemented to gain deeper insights into the behavioral properties of the cluster formation. Clinical studies showed that OAR clusters had drastic differences despite similar PTV coverage among different patients, and the radiobiological parameters failed to positively correlate with the cluster sizes. MC study demonstrated the inverse relationship between the cluster size and the cluster connectivity, and the nonlinear changes in cluster size with dose thresholds. In addition, the clusters were insensitive to the shape of OARs. The results demonstrated that the cluster size could serve as an insightful index of normal tissue damage. The clinical outcome of the same dose-volume might be potentially different. Copyright © 2018 Elsevier B.V. All rights reserved.

Observations and calculations of two-dimensional angular optical scattering (TAOS) patterns of a single levitated cluster of two and four microspheres

International Nuclear Information System (INIS)

Krieger, U.K.; Meier, P.

2011-01-01

We use single bi-sphere particles levitated in an electrodynamic balance to record two-dimensional angular scattering patterns at different angles of the coordinate system of the aggregate relative to the incident laser beam. Due to Brownian motion the particle covers the whole set of possible angles with time and allows to select patterns with high symmetry for analysis. These are qualitatively compared to numerical calculations. A small cluster of four spheres shows complex scattering patterns, comparison with computations suggest a low compactness for these clusters. An experimental procedure is proposed for studying restructuring effects occurring in mixed particles upon evaporation. - Research highlights: → Single levitated bi-sphere particle. → Two-dimensional angular scattering pattern. → Comparison experiment with computations.

Small gold clusters on graphene, their mobility and clustering: a DFT study

International Nuclear Information System (INIS)

Amft, Martin; Sanyal, Biplab; Eriksson, Olle; Skorodumova, Natalia V

2011-01-01

Motivated by the experimentally observed high mobility of gold atoms on graphene and their tendency to form nanometer-sized clusters, we present a density functional theory study of the ground state structures of small gold clusters on graphene, their mobility and clustering. Our detailed analysis of the electronic structures identifies the opportunity to form strong gold-gold bonds and the graphene-mediated interaction of the pre-adsorbed fragments as the driving forces behind gold's tendency to aggregate on graphene. While clusters containing up to three gold atoms have one unambiguous ground state structure, both gas phase isomers of a cluster with four gold atoms can be found on graphene. In the gas phase the diamond-shaped Au 4 D cluster is the ground state structure, whereas the Y-shaped Au 4 Y becomes the actual ground state when adsorbed on graphene. As we show, both clusters can be produced on graphene by two distinct clustering processes. We also studied in detail the stepwise formation of a gold dimer out of two pre-adsorbed adatoms, as well as the formation of Au 3 . All reactions are exothermic and no further activation barriers, apart from the diffusion barriers, were found. The diffusion barriers of all studied clusters range from 4 to 36 meV only, and are substantially exceeded by the adsorption energies of - 0.1 to - 0.59 eV. This explains the high mobility of Au 1-4 on graphene along the C-C bonds.

Charging of nanoparticles in stationary plasma in a gas aggregation cluster source

Science.gov (United States)

Blažek, J.; Kousal, J.; Biederman, H.; Kylián, O.; Hanuš, J.; Slavínská, D.

2015-10-01

Clusters that grow into nanoparticles near the magnetron target of the gas aggregation cluster source (GAS) may acquire electric charge by collecting electrons and ions or through other mechanisms like secondary- or photo-electron emissions. The region of the GAS close to magnetron may be considered as stationary plasma. The steady state charge distribution on nanoparticles can be determined by means of three possible models—fluid model, kinetic model and model employing Monte Carlo simulations—of cluster charging. In the paper the mathematical and numerical aspects of these models are analyzed in detail and close links between them are clarified. Among others it is shown that Monte Carlo simulation may be considered as a particular numerical technique of solving kinetic equations. Similarly the equations of the fluid model result, after some approximation, from averaged kinetic equations. A new algorithm solving an in principle unlimited set of kinetic equations is suggested. Its efficiency is verified on physical models based on experimental input data.

Root uptake and phytotoxicity of nanosized molybdenum octahedral clusters

International Nuclear Information System (INIS)

Aubert, Tangi; Burel, Agnès; Esnault, Marie-Andrée; Cordier, Stéphane; Grasset, Fabien; Cabello-Hurtado, Francisco

2012-01-01

Highlights: ► We investigated the effect of nanosized Mo 6 clusters on the growth of rapeseed plants. ► The aggregation state of the clusters depends on the dispersion medium. ► The concentration-dependant toxicity of the clusters depends on aggregation state. ► We took into account the possible contribution to toxicity of dissolved ionic species. ► The root uptake of the clusters was followed by NanoSIMS. - Abstract: Here are examined the root uptake and phytotoxicity of octahedral hexamolybdenum clusters on rapeseed plants using the solid state compound Cs 2 Mo 6 Br 14 as cluster precursor. [Mo 6 Br 14 ] 2− cluster units are nanosized entities offering a strong and stable emission in the near-infrared region with numerous applications in biotechnology. To investigate cluster toxicity on rapeseed plants, two different culture systems have been set up, using either a water-sorbing suspension of cluster aggregates or an ethanol-sorbing solution of dispersed nanosized clusters. Size, shape, surface area and state of clusters in both medium were analyzed by FE-SEM, BET and XPS. The potential contribution of cluster dissolution to phytotoxicity was evaluated by ICP-OES and toxicity analysis of Mo, Br and Cs. We showed that the clusters did not affect seed germination but greatly inhibited plant growth. This inhibition was much more important when plants were treated with nanosized entities than with microsized cluster aggregates. In addition, nanosized clusters affected the root morphology in a different manner than microsized cluster aggregates, as shown by FE-SEM observations. The root penetration of the clusters was followed by secondary ion mass spectroscopy with high spatial resolution (NanoSIMS) and was also found to be much more important for treatments with nanosized clusters.

Root uptake and phytotoxicity of nanosized molybdenum octahedral clusters

Energy Technology Data Exchange (ETDEWEB)

Aubert, Tangi [Solid State Chemistry and Materials Group, UMR CNRS 6226 Sciences Chimiques de Rennes, University of Rennes 1, 263 av. du General Leclerc, Campus de Beaulieu, 35042 Rennes (France); Burel, Agnes [Electronic Microscopy Department, University of Rennes 1, 2 av. du Professeur Leon-Bernard, Campus de Villejean, 35043 Rennes (France); Esnault, Marie-Andree [Mechanisms at the Origin of Biodiversity Team, UMR CNRS 6553 Ecobio, University of Rennes 1, 263 av. du General Leclerc, Campus de Beaulieu, 35042 Rennes (France); Cordier, Stephane; Grasset, Fabien [Solid State Chemistry and Materials Group, UMR CNRS 6226 Sciences Chimiques de Rennes, University of Rennes 1, 263 av. du General Leclerc, Campus de Beaulieu, 35042 Rennes (France); Cabello-Hurtado, Francisco, E-mail: francisco.cabello@univ-rennes1.fr [Mechanisms at the Origin of Biodiversity Team, UMR CNRS 6553 Ecobio, University of Rennes 1, 263 av. du General Leclerc, Campus de Beaulieu, 35042 Rennes (France)

2012-06-15

Highlights: Black-Right-Pointing-Pointer We investigated the effect of nanosized Mo{sub 6} clusters on the growth of rapeseed plants. Black-Right-Pointing-Pointer The aggregation state of the clusters depends on the dispersion medium. Black-Right-Pointing-Pointer The concentration-dependant toxicity of the clusters depends on aggregation state. Black-Right-Pointing-Pointer We took into account the possible contribution to toxicity of dissolved ionic species. Black-Right-Pointing-Pointer The root uptake of the clusters was followed by NanoSIMS. - Abstract: Here are examined the root uptake and phytotoxicity of octahedral hexamolybdenum clusters on rapeseed plants using the solid state compound Cs{sub 2}Mo{sub 6}Br{sub 14} as cluster precursor. [Mo{sub 6}Br{sub 14}]{sup 2-} cluster units are nanosized entities offering a strong and stable emission in the near-infrared region with numerous applications in biotechnology. To investigate cluster toxicity on rapeseed plants, two different culture systems have been set up, using either a water-sorbing suspension of cluster aggregates or an ethanol-sorbing solution of dispersed nanosized clusters. Size, shape, surface area and state of clusters in both medium were analyzed by FE-SEM, BET and XPS. The potential contribution of cluster dissolution to phytotoxicity was evaluated by ICP-OES and toxicity analysis of Mo, Br and Cs. We showed that the clusters did not affect seed germination but greatly inhibited plant growth. This inhibition was much more important when plants were treated with nanosized entities than with microsized cluster aggregates. In addition, nanosized clusters affected the root morphology in a different manner than microsized cluster aggregates, as shown by FE-SEM observations. The root penetration of the clusters was followed by secondary ion mass spectroscopy with high spatial resolution (NanoSIMS) and was also found to be much more important for treatments with nanosized clusters.

Relevant Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2009-01-01

Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

Spectral embedded clustering: a framework for in-sample and out-of-sample spectral clustering.

Science.gov (United States)

Nie, Feiping; Zeng, Zinan; Tsang, Ivor W; Xu, Dong; Zhang, Changshui

2011-11-01

Spectral clustering (SC) methods have been successfully applied to many real-world applications. The success of these SC methods is largely based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. However, such an assumption might not always hold on high-dimensional data. When the data do not exhibit a clear low-dimensional manifold structure (e.g., high-dimensional and sparse data), the clustering performance of SC will be degraded and become even worse than K -means clustering. In this paper, motivated by the observation that the true cluster assignment matrix for high-dimensional data can be always embedded in a linear space spanned by the data, we propose the spectral embedded clustering (SEC) framework, in which a linearity regularization is explicitly added into the objective function of SC methods. More importantly, the proposed SEC framework can naturally deal with out-of-sample data. We also present a new Laplacian matrix constructed from a local regression of each pattern and incorporate it into our SEC framework to capture both local and global discriminative information for clustering. Comprehensive experiments on eight real-world high-dimensional datasets demonstrate the effectiveness and advantages of our SEC framework over existing SC methods and K-means-based clustering methods. Our SEC framework significantly outperforms SC using the Nyström algorithm on unseen data.

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

Directory of Open Access Journals (Sweden)

Ji-wook Jeong

2016-01-01

Full Text Available We propose computer-aided detection (CADe algorithm for microcalcification (MC clusters in reconstructed digital breast tomosynthesis (DBT images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

Applying Clustering to Statistical Analysis of Student Reasoning about Two-Dimensional Kinematics

Science.gov (United States)

Springuel, R. Padraic; Wittman, Michael C.; Thompson, John R.

2007-01-01

We use clustering, an analysis method not presently common to the physics education research community, to group and characterize student responses to written questions about two-dimensional kinematics. Previously, clustering has been used to analyze multiple-choice data; we analyze free-response data that includes both sketches of vectors and…

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

Science.gov (United States)

Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

2013-03-01

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

Minimalist's linux cluster

International Nuclear Information System (INIS)

Choi, Chang-Yeong; Kim, Jeong-Hyun; Kim, Seyong

2004-01-01

Using barebone PC components and NIC's, we construct a linux cluster which has 2-dimensional mesh structure. This cluster has smaller footprint, is less expensive, and use less power compared to conventional linux cluster. Here, we report our experience in building such a machine and discuss our current lattice project on the machine

Accelerating three-dimensional FDTD calculations on GPU clusters for electromagnetic field simulation.

Science.gov (United States)

Nagaoka, Tomoaki; Watanabe, Soichi

2012-01-01

Electromagnetic simulation with anatomically realistic computational human model using the finite-difference time domain (FDTD) method has recently been performed in a number of fields in biomedical engineering. To improve the method's calculation speed and realize large-scale computing with the computational human model, we adapt three-dimensional FDTD code to a multi-GPU cluster environment with Compute Unified Device Architecture and Message Passing Interface. Our multi-GPU cluster system consists of three nodes. The seven GPU boards (NVIDIA Tesla C2070) are mounted on each node. We examined the performance of the FDTD calculation on multi-GPU cluster environment. We confirmed that the FDTD calculation on the multi-GPU clusters is faster than that on a multi-GPU (a single workstation), and we also found that the GPU cluster system calculate faster than a vector supercomputer. In addition, our GPU cluster system allowed us to perform the large-scale FDTD calculation because were able to use GPU memory of over 100 GB.

Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes

Science.gov (United States)

Ahmadzadeh, Ezat; Jaferzadeh, Keyvan; Lee, Jieun; Moon, Inkyu

2017-07-01

We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis.

Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

Science.gov (United States)

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.

Photo-induced transformation process at gold clusters-semiconductor interface: Implications for the complexity of gold clusters-based photocatalysis

Science.gov (United States)

Liu, Siqi; Xu, Yi-Jun

2016-03-01

The recent thrust in utilizing atomically precise organic ligands protected gold clusters (Au clusters) as photosensitizer coupled with semiconductors for nano-catalysts has led to the claims of improved efficiency in photocatalysis. Nonetheless, the influence of photo-stability of organic ligands protected-Au clusters at the Au/semiconductor interface on the photocatalytic properties remains rather elusive. Taking Au clusters-TiO2 composites as a prototype, we for the first time demonstrate the photo-induced transformation of small molecular-like Au clusters to larger metallic Au nanoparticles under different illumination conditions, which leads to the diverse photocatalytic reaction mechanism. This transformation process undergoes a diffusion/aggregation mechanism accompanied with the onslaught of Au clusters by active oxygen species and holes resulting from photo-excited TiO2 and Au clusters. However, such Au clusters aggregation can be efficiently inhibited by tuning reaction conditions. This work would trigger rational structural design and fine condition control of organic ligands protected-metal clusters-semiconductor composites for diverse photocatalytic applications with long-term photo-stability.

A hybridized K-means clustering approach for high dimensional ...

African Journals Online (AJOL)

International Journal of Engineering, Science and Technology ... Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays ... Recently cluster analysis is a popularly used data analysis method in number of areas.

Three-Dimensional Modeling of Fracture Clusters in Geothermal Reservoirs

Energy Technology Data Exchange (ETDEWEB)

Ghassemi, Ahmad [Univ. of Oklahoma, Norman, OK (United States)

2017-08-11

The objective of this is to develop a 3-D numerical model for simulating mode I, II, and III (tensile, shear, and out-of-plane) propagation of multiple fractures and fracture clusters to accurately predict geothermal reservoir stimulation using the virtual multi-dimensional internal bond (VMIB). Effective development of enhanced geothermal systems can significantly benefit from improved modeling of hydraulic fracturing. In geothermal reservoirs, where the temperature can reach or exceed 350oC, thermal and poro-mechanical processes play an important role in fracture initiation and propagation. In this project hydraulic fracturing of hot subsurface rock mass will be numerically modeled by extending the virtual multiple internal bond theory and implementing it in a finite element code, WARP3D, a three-dimensional finite element code for solid mechanics. The new constitutive model along with the poro-thermoelastic computational algorithms will allow modeling the initiation and propagation of clusters of fractures, and extension of pre-existing fractures. The work will enable the industry to realistically model stimulation of geothermal reservoirs. The project addresses the Geothermal Technologies Office objective of accurately predicting geothermal reservoir stimulation (GTO technology priority item). The project goal will be attained by: (i) development of the VMIB method for application to 3D analysis of fracture clusters; (ii) development of poro- and thermoelastic material sub-routines for use in 3D finite element code WARP3D; (iii) implementation of VMIB and the new material routines in WARP3D to enable simulation of clusters of fractures while accounting for the effects of the pore pressure, thermal stress and inelastic deformation; (iv) simulation of 3D fracture propagation and coalescence and formation of clusters, and comparison with laboratory compression tests; and (v) application of the model to interpretation of injection experiments (planned by our

Vibronic coupling in molecular crystals: A Franck-Condon Herzberg-Teller model of H-aggregate fluorescence based on quantum chemical cluster calculations

Energy Technology Data Exchange (ETDEWEB)

Wykes, M., E-mail: mikewykes@gmail.com; Parambil, R.; Gierschner, J. [Madrid Institute for Advanced Studies, IMDEA Nanoscience, Calle Faraday 9, Campus Cantoblanco, 28049 Madrid (Spain); Beljonne, D. [Laboratory for Chemistry of Novel Materials, University of Mons, Place du Parc 20, 7000 Mons (Belgium)

2015-09-21

Here, we present a general approach to treating vibronic coupling in molecular crystals based on atomistic simulations of large clusters. Such clusters comprise model aggregates treated at the quantum chemical level embedded within a realistic environment treated at the molecular mechanics level. As we calculate ground and excited state equilibrium geometries and vibrational modes of model aggregates, our approach is able to capture effects arising from coupling to intermolecular degrees of freedom, absent from existing models relying on geometries and normal modes of single molecules. Using the geometries and vibrational modes of clusters, we are able to simulate the fluorescence spectra of aggregates for which the lowest excited state bears negligible oscillator strength (as is the case, e.g., ideal H-aggregates) by including both Franck-Condon (FC) and Herzberg-Teller (HT) vibronic transitions. The latter terms allow the adiabatic excited state of the cluster to couple with vibrations in a perturbative fashion via derivatives of the transition dipole moment along nuclear coordinates. While vibronic coupling simulations employing FC and HT terms are well established for single-molecules, to our knowledge this is the first time they are applied to molecular aggregates. Here, we apply this approach to the simulation of the low-temperature fluorescence spectrum of para-distyrylbenzene single-crystal H-aggregates and draw comparisons with coarse-grained Frenkel-Holstein approaches previously extensively applied to such systems.

Support Vector Data Descriptions and k-Means Clustering: One Class?

Science.gov (United States)

Gornitz, Nico; Lima, Luiz Alberto; Muller, Klaus-Robert; Kloft, Marius; Nakajima, Shinichi

2017-09-27

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data.

Science.gov (United States)

Vera, J Fernando; Macías, Rodrigo

2017-06-01

One of the main problems in cluster analysis is that of determining the number of groups in the data. In general, the approach taken depends on the cluster method used. For K-means, some of the most widely employed criteria are formulated in terms of the decomposition of the total point scatter, regarding a two-mode data set of N points in p dimensions, which are optimally arranged into K classes. This paper addresses the formulation of criteria to determine the number of clusters, in the general situation in which the available information for clustering is a one-mode [Formula: see text] dissimilarity matrix describing the objects. In this framework, p and the coordinates of points are usually unknown, and the application of criteria originally formulated for two-mode data sets is dependent on their possible reformulation in the one-mode situation. The decomposition of the variability of the clustered objects is proposed in terms of the corresponding block-shaped partition of the dissimilarity matrix. Within-block and between-block dispersion values for the partitioned dissimilarity matrix are derived, and variance-based criteria are subsequently formulated in order to determine the number of groups in the data. A Monte Carlo experiment was carried out to study the performance of the proposed criteria. For simulated clustered points in p dimensions, greater efficiency in recovering the number of clusters is obtained when the criteria are calculated from the related Euclidean distances instead of the known two-mode data set, in general, for unequal-sized clusters and for low dimensionality situations. For simulated dissimilarity data sets, the proposed criteria always outperform the results obtained when these criteria are calculated from their original formulation, using dissimilarities instead of distances.

Chiral Silver-Lanthanide Metal-Organic Frameworks Comprised of One-Dimensional Triple Right-Handed Helical Chains Based on [Ln7(μ3-OH)8]13+ Clusters.

Science.gov (United States)

Guo, Yan; Zhang, Lijuan; Muhammad, Nadeem; Xu, Yan; Zhou, Yunshan; Tang, Fang; Yang, Shaowei

2018-02-05

Three new isostructural chiral silver-lanthanide heterometal-organic frameworks [Ag 3 Ln 7 (μ 3 -OH) 8 (bpdc) 6 (NO 3 ) 3 (H 2 O) 6 ](NO 3 )·2H 2 O [Ln = Eu (1), Tb (2, Sm (3); H 2 bpdc = 2,2'-bipyridine-3,3'-dicarboxylic acid] based on heptanuclear lanthanide clusters [Ln 7 (μ 3 -OH) 8 ] 13+ comprised of one-dimensional triple right-handed helical chains were hydrothermally synthesized. Various means such as UV-vis spectroscopy, IR spectroscopy, elemental analysis, powder X-ray diffraction, and thermogravimetric/differential thermal analysis were used to characterize the compounds, wherein compound 3 was crystallographically characterized. In the structure of compound 3, eight μ 3 -OH - groups link seven Sm 3+ ions, forming a heptanuclear cluster, [Sm 7 (μ 3 -OH) 8 ] 13+ , and the adjacent [Sm 7 (μ 3 -OH) 8 ] 13+ clusters are linked by the carboxylic groups of bpdc 2- ligands, leading to the formation of a one-dimensional triple right-handed helical chain. The adjacent triple right-handed helical chains are further joined together by coordinating the pyridyl N atoms of the bpdc 2- ligands with Ag + , resulting in a chiral three-dimensional silver(I)-lanthanide(III) heterometal-organic framework with one-dimensional channels wherein NO 3 - anions and crystal lattice H 2 O molecules are trapped. The compounds were studied systematically with respect to their photoluminescence properties and energy-transfer mechanism, and it was found that H 2 bpdc (the energy level for the triplet states of the ligand H 2 bpdc is 21505 cm -1 ) can sensitize Eu 3+ luminescence more effectively than Tb 3+ and Sm 3+ luminescence because of effective energy transfer from bpdc 2- to Eu 3+ under excitation in compound 1.

Two Tier Cluster Based Data Aggregation (TTCDA) in Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2012-01-01

Wireless Sensor Network (WSN) often used for monitoring and control applications where sensor nodes collect data and send it to the sink. Most of the nodes consume their energy in transmission of data packets without aggregation to sink, which may be located at single or multi hop distance....... The direct transmission of data packets to the sink from nodes in the network causes increased communication costs in terms of energy, average delay and network lifetime. In this context, the data aggregation techniques minimize the communication cost with efficient bandwidth utilization by decreasing...... the packet count reached at the sink. Here, we propose Two Tier Cluster based Data Aggregation (TTCDA) algorithm for the randomly distributed nodes to minimize computation and communication cost. The TTCDA is energy and bandwidth efficient since it reduces the transmission of the number of packets...

Greedy subspace clustering.

Science.gov (United States)

2016-09-01

We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets ...

Synthesis of molecular hexatechnetium clusters by means of dimensional reduction of their polymeric complexes

International Nuclear Information System (INIS)

Ikai, T.; Yoshimura, T.; Shinohara, A.; Takayama, T.; Sekine, T.

2006-01-01

Selenide capping hexatechnetium cluster complex [Tc 6 (μ 3 -Se) 8 CN 6 ] 4- (1) was prepared by the reactions of one-dimensional polymer complex [Tc 6 (μ 3 -Se) 8 Br 4 ] 2- and cyanides at high temperature. Similar reaction of sulfide capping hexatechnetium cluster complex, [Tc 6 (μ 3 -S) 8 Br 6 ] 4- with cyanide gave the terminal substituted complex [Tc 6 (μ 3 -S) 8 CN 6 ] 4- (2). The single-crystal X-ray analysis of 1 and 2, showed that the Tc-Tc bond lengths become longer with lager ionic radius of the face capping ligands in the order S -1 , and that of 2 showed it at 2119 cm -1 . Each of cyclic voltammogram of 1 and 2 showed a reversible one electron redox wave assignable to the Tc 6 III /Tc 5 III Tc IV process. These redox potentials shift to the positive about 0.4V compared to those of the Re cluster analogs. (author)

Neutrosophic Hierarchical Clustering Algoritms

Directory of Open Access Journals (Sweden)

Rıdvan Şahin

2014-03-01

Full Text Available Interval neutrosophic set (INS is a generalization of interval valued intuitionistic fuzzy set (IVIFS, whose the membership and non-membership values of elements consist of fuzzy range, while single valued neutrosophic set (SVNS is regarded as extension of intuitionistic fuzzy set (IFS. In this paper, we extend the hierarchical clustering techniques proposed for IFSs and IVIFSs to SVNSs and INSs respectively. Based on the traditional hierarchical clustering procedure, the single valued neutrosophic aggregation operator, and the basic distance measures between SVNSs, we define a single valued neutrosophic hierarchical clustering algorithm for clustering SVNSs. Then we extend the algorithm to classify an interval neutrosophic data. Finally, we present some numerical examples in order to show the effectiveness and availability of the developed clustering algorithms.

Diffusion Monte Carlo simulations of gas phase and adsorbed D2-(H2)n clusters

Science.gov (United States)

Curotto, E.; Mella, M.

2018-03-01

We have computed ground state energies and analyzed radial distributions for several gas phase and adsorbed D2(H2)n and HD(H2)n clusters. An external model potential designed to mimic ionic adsorption sites inside porous materials is used [M. Mella and E. Curotto, J. Phys. Chem. A 121, 5005 (2017)]. The isotopic substitution lowers the ground state energies by the expected amount based on the mass differences when these are compared with the energies of the pure clusters in the gas phase. A similar impact is found for adsorbed aggregates. The dissociation energy of D2 from the adsorbed clusters is always much higher than that of H2 from both pure and doped aggregates. Radial distributions of D2 and H2 are compared for both the gas phase and adsorbed species. For the gas phase clusters, two types of hydrogen-hydrogen interactions are considered: one based on the assumption that rotations and translations are adiabatically decoupled and the other based on nonisotropic four-dimensional potential. In the gas phase clusters of sufficiently large size, we find the heavier isotopomer more likely to be near the center of mass. However, there is a considerable overlap among the radial distributions of the two species. For the adsorbed clusters, we invariably find the heavy isotope located closer to the attractive interaction source than H2, and at the periphery of the aggregate, H2 molecules being substantially excluded from the interaction with the source. This finding rationalizes the dissociation energy results. For D2-(H2)n clusters with n ≥12 , such preference leads to the desorption of D2 from the aggregate, a phenomenon driven by the minimization of the total energy that can be obtained by reducing the confinement of (H2)12. The same happens for (H2)13, indicating that such an effect may be quite general and impact on the absorption of quantum species inside porous materials.

Calculations of light scattering matrices for stochastic ensembles of nanosphere clusters

International Nuclear Information System (INIS)

Bunkin, N.F.; Shkirin, A.V.; Suyazov, N.V.; Starosvetskiy, A.V.

2013-01-01

Results of the calculation of the light scattering matrices for systems of stochastic nanosphere clusters are presented. A mathematical model of spherical particle clustering with allowance for cluster–cluster aggregation is used. The fractal properties of cluster structures are explored at different values of the model parameter that governs cluster–cluster interaction. General properties of the light scattering matrices of nanosphere-cluster ensembles as dependent on their mean fractal dimension have been found. The scattering-matrix calculations were performed for finite samples of 10 3 random clusters, made up of polydisperse spherical nanoparticles, having lognormal size distribution with the effective radius 50 nm and effective variance 0.02; the mean number of monomers in a cluster and its standard deviation were set to 500 and 70, respectively. The implemented computation environment, modeling the scattering matrices for overall sequences of clusters, is based upon T-matrix program code for a given single cluster of spheres, which was developed in [1]. The ensemble-averaged results have been compared with orientation-averaged ones calculated for individual clusters. -- Highlights: ► We suggested a hierarchical model of cluster growth allowing for cluster–cluster aggregation. ► We analyzed the light scattering by whole ensembles of nanosphere clusters. ► We studied the evolution of the light scattering matrix when changing the fractal dimension

Clustering of near clusters versus cluster compactness

International Nuclear Information System (INIS)

Yu Gao; Yipeng Jing

1989-01-01

The clustering properties of near Zwicky clusters are studied by using the two-point angular correlation function. The angular correlation functions for compact and medium compact clusters, for open clusters, and for all near Zwicky clusters are estimated. The results show much stronger clustering for compact and medium compact clusters than for open clusters, and that open clusters have nearly the same clustering strength as galaxies. A detailed study of the compactness-dependence of correlation function strength is worth investigating. (author)

Cluster-cluster clustering

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

Polyelectrolyte-induced aggregation of liposomes: a new cluster phase with interesting applications

International Nuclear Information System (INIS)

Bordi, F; Sennato, S; Truzzolillo, D

2009-01-01

Different charged colloidal particles have been shown to be able to self-assemble, when mixed in an aqueous solvent with oppositely charged linear polyelectrolytes, forming long-lived finite-size mesoscopic aggregates. On increasing the polyelectrolyte content, with the progressive reduction of the net charge of the primary polyelectrolyte-decorated particles, larger and larger clusters are observed. Close to the isoelectric point, where the charge of the adsorbed polyelectrolytes neutralizes the original charge of the particles' surface, the aggregates reach their maximum size, while beyond this point any further increase of the polyelectrolyte-particle charge ratio causes the formation of aggregates whose size is progressively reduced. This re-entrant condensation behavior is accompanied by a significant overcharging. Overcharging, or charge inversion, occurs when more polyelectrolyte chains adsorb on a particle than are needed to neutralize its original charge so that, eventually, the sign of the net charge of the polymer-decorated particle is inverted. The stability of the finite-size long-lived clusters that this aggregation process yields results from a fine balance between long-range repulsive and short-range attractive interactions, both of electrostatic nature. For the latter, besides the ubiquitous dispersion forces, whose supply becomes relevant only at high ionic strength, the main contribution appears due to the non-uniform correlated distribution of the charge on the surface of the polyelectrolyte-decorated particles ('charge-patch' attraction). The interesting phenomenology shown by these system has a high potential for biotechnological applications, particularly when the primary colloidal particles are bio-compatible lipid vesicles. Possible applications of these systems as multi-compartment vectors for the simultaneous intra-cellular delivery of different pharmacologically active substances will be briefly discussed. (topical review)

Variational cluster perturbation theory for Bose-Hubbard models

International Nuclear Information System (INIS)

Koller, W; Dupuis, N

2006-01-01

We discuss the application of the variational cluster perturbation theory (VCPT) to the Mott-insulator-to-superfluid transition in the Bose-Hubbard model. We show how the VCPT can be formulated in such a way that it gives a translation invariant excitation spectrum-free of spurious gaps-despite the fact that it formally breaks translation invariance. The phase diagram and the single-particle Green function in the insulating phase are obtained for one-dimensional systems. When the chemical potential of the cluster is taken as a variational parameter, the VCPT reproduces the dimensional dependence of the phase diagram even for one-site clusters. We find a good quantitative agreement with the results of the density-matrix renormalization group when the number of sites in the cluster becomes of order 10. The extension of the method to the superfluid phase is discussed

Multiple Clustering Views via Constrained Projections

DEFF Research Database (Denmark)

Dang, Xuan-Hong; Assent, Ira; Bailey, James

2012-01-01

Clustering, the grouping of data based on mutual similarity, is often used as one of principal tools to analyze and understand data. Unfortunately, most conventional techniques aim at finding only a single clustering over the data. For many practical applications, especially those being described...... in high dimensional data, it is common to see that the data can be grouped into different yet meaningful ways. This gives rise to the recently emerging research area of discovering alternative clusterings. In this preliminary work, we propose a novel framework to generate multiple clustering views....... The framework relies on a constrained data projection approach by which we ensure that a novel alternative clustering being found is not only qualitatively strong but also distinctively different from a reference clustering solution. We demonstrate the potential of the proposed framework using both synthetic...

Study of two-dimensional Debye clusters using Brownian motion

International Nuclear Information System (INIS)

Sheridan, T.E.; Theisen, W.L.

2006-01-01

A two-dimensional Debye cluster is a system of n identical particles confined in a parabolic well and interacting through a screened Coulomb (i.e., a Debye-Hueckel or Yukawa) potential with a Debye length λ. Experiments were performed for 27 clusters with n=3-63 particles (9 μm diam) in a capacitively coupled 9 W rf discharge at a neutral argon pressure of 13.6 mTorr. In the strong-coupling regime each particle exhibits small amplitude Brownian motion about its equilibrium position. These motions were projected onto the center-of-mass and breathing modes and Fourier analyzed to give resonance curves from which the mode frequencies, amplitudes, and damping rates were determined. The ratio of the breathing frequency to the center-of-mass frequency was compared with theory to self-consistently determine the Debye shielding parameter κ, Debye length λ, particle charge q, and mode temperatures. It is found that 1 < or approx. κ < or approx. 2, and κ decreases weakly with n. The particle charge averaged over all measurements is -14 200±200 e, and q decreases slightly with n. The two center-of-mass modes and the breathing mode are found to have the same temperature, indicating that the clusters are in thermal equilibrium with the neutral gas. The average cluster temperature is 399±5 K

Universality and clustering in 1 + 1 dimensional superstring-bit models

International Nuclear Information System (INIS)

Bergman, O.; Thorn, C.B.

1996-01-01

We construct a 1+1 dimensional superstring-bit model for D=3 Type IIB superstring. This low dimension model escapes the problem encountered in higher dimension models: (1) It possesses full Galilean supersymmetry; (2) For noninteracting Polymers of bits, the exactly soluble linear superpotential describing bit interactions is in a large universality class of superpotentials which includes ones bounded at spatial infinity; (3) The latter are used to construct a superstring-bit model with the clustering properties needed to define an S-matrix for closed polymers of superstring-bits

Semi-Supervised Clustering for High-Dimensional and Sparse Features

Science.gov (United States)

Yan, Su

2010-01-01

Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

Applying clustering to statistical analysis of student reasoning about two-dimensional kinematics

Directory of Open Access Journals (Sweden)

R. Padraic Springuel

2007-12-01

Full Text Available We use clustering, an analysis method not presently common to the physics education research community, to group and characterize student responses to written questions about two-dimensional kinematics. Previously, clustering has been used to analyze multiple-choice data; we analyze free-response data that includes both sketches of vectors and written elements. The primary goal of this paper is to describe the methodology itself; we include a brief overview of relevant results.

Excess electrons in methanol clusters: Beyond the one-electron picture

Science.gov (United States)

Pohl, Gábor; Mones, Letif; Turi, László

2016-10-01

We performed a series of comparative quantum chemical calculations on various size negatively charged methanol clusters, ("separators=" CH 3 OH ) n - . The clusters are examined in their optimized geometries (n = 2-4), and in geometries taken from mixed quantum-classical molecular dynamics simulations at finite temperature (n = 2-128). These latter structures model potential electron binding sites in methanol clusters and in bulk methanol. In particular, we compute the vertical detachment energy (VDE) of an excess electron from increasing size methanol cluster anions using quantum chemical computations at various levels of theory including a one-electron pseudopotential model, several density functional theory (DFT) based methods, MP2 and coupled-cluster CCSD(T) calculations. The results suggest that at least four methanol molecules are needed to bind an excess electron on a hydrogen bonded methanol chain in a dipole bound state. Larger methanol clusters are able to form stronger interactions with an excess electron. The two simulated excess electron binding motifs in methanol clusters, interior and surface states, correlate well with distinct, experimentally found VDE tendencies with size. Interior states in a solvent cavity are stabilized significantly stronger than electron states on cluster surfaces. Although we find that all the examined quantum chemistry methods more or less overestimate the strength of the experimental excess electron stabilization, MP2, LC-BLYP, and BHandHLYP methods with diffuse basis sets provide a significantly better estimate of the VDE than traditional DFT methods (BLYP, B3LYP, X3LYP, PBE0). A comparison to the better performing many electron methods indicates that the examined one-electron pseudopotential can be reasonably used in simulations for systems of larger size.

Scaling in patterns produces by cluster deposition

DEFF Research Database (Denmark)

Kyhle, Anders; Sørensen, Alexis Hammer; Oddershede, Lene

1997-01-01

Cluster deposition on flat substrates can lead to surprising patterns. This pattern formation can be related either to phenomena taking place at the substrate surface or to dynamics in the cluster beam. We describe the observation of a pattern of particles each being an aggregate of Cu clusters. ...

Synchronous Firefly Algorithm for Cluster Head Selection in WSN

Directory of Open Access Journals (Sweden)

Madhusudhanan Baskaran

2015-01-01

Full Text Available Wireless Sensor Network (WSN consists of small low-cost, low-power multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Cluster-based approaches use some nodes as Cluster Heads (CHs and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NP-Hard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energy-efficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC.

Stacking and Branching in Self-Aggregation of Caffeine in Aqueous Solution: From the Supramolecular to Atomic Scale Clustering.

Science.gov (United States)

Tavagnacco, Letizia; Gerelli, Yuri; Cesàro, Attilio; Brady, John W

2016-09-22

The dynamical and structural properties of caffeine solutions at the solubility limit have been investigated as a function of temperature by means of MD simulations, static and dynamic light scattering, and small angle neutron scattering experiments. A clear picture unambiguously supported by both experiment and simulation emerges: caffeine self-aggregation promotes the formation of two distinct types of clusters: linear aggregates of stacked molecules, formed by 2-14 caffeine molecules depending on the thermodynamic conditions and disordered branched aggregates with a size in the range 1000-3000 Å. While the first type of association is well-known to occur under room temperature conditions for both caffeine and other purine systems, such as nucleotides, the presence of the supramolecular aggregates has not been reported previously. MD simulations indicate that branched structures are formed by caffeine molecules in a T-shaped arrangement. An increase of the solubility limit (higher temperature but also higher concentration) broadens the distribution of cluster sizes, promoting the formation of stacked aggregates composed by a larger number of caffeine molecules. Surprisingly, the effect on the branched aggregates is rather limited. Their internal structure and size do not change considerably in the range of solubility limits investigated.

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

Directory of Open Access Journals (Sweden)

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.

Science.gov (United States)

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

Percolation with multiple giant clusters

International Nuclear Information System (INIS)

Ben-Naim, E; Krapivsky, P L

2005-01-01

We study mean-field percolation with freezing. Specifically, we consider cluster formation via two competing processes: irreversible aggregation and freezing. We find that when the freezing rate exceeds a certain threshold, the percolation transition is suppressed. Below this threshold, the system undergoes a series of percolation transitions with multiple giant clusters ('gels') formed. Giant clusters are not self-averaging as their total number and their sizes fluctuate from realization to realization. The size distribution F k , of frozen clusters of size k, has a universal tail, F k ∼ k -3 . We propose freezing as a practical mechanism for controlling the gel size. (letter to the editor)

Cluster management.

Science.gov (United States)

Katz, R

1992-11-01

Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

Three-dimensional visualization of cultural clusters in the 1878 yellow fever epidemic of New Orleans.

Science.gov (United States)

Curtis, Andrew J

2008-08-22

An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level) of investigation. Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in turn might mean traditional quarantine measures should be

Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

Science.gov (United States)

Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

2018-05-01

Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

Science.gov (United States)

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Scalable Density-Based Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2011-01-01

For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering...... method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real...... and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well....

Air void clustering.

Science.gov (United States)

2015-06-01

Air void clustering around coarse aggregate in concrete has been identified as a potential source of : low strengths in concrete mixes by several Departments of Transportation around the country. Research was : carried out to (1) develop a quantitati...

Morphology of clusters of attractive dry and wet self-propelled spherical particle suspensions.

Science.gov (United States)

Alarcón, Francisco; Valeriani, Chantal; Pagonabarraga, Ignacio

2017-01-25

In order to assess the effect of hydrodynamics in the assembly of active attractive spheres, we simulate a semi-dilute suspension of attractive self-propelled spherical particles in a quasi-two dimensional geometry comparing the case with and without hydrodynamics interactions. To start with, independent of the presence of hydrodynamics, we observe that depending on the ratio between attraction and propulsion, particles either coarsen or aggregate forming finite-size clusters. Focusing on the clustering regime, we characterize two different cluster parameters, i.e. their morphology and orientational order, and compare the case when active particles behave either as pushers or pullers (always in the regime where inter-particle attractions compete with self-propulsion). Studying cluster phases for squirmers with respect to those obtained for active Brownian disks (indicated as ABPs), we have shown that hydrodynamics alone can sustain a cluster phase of active swimmers (pullers), while ABPs form cluster phases due to the competition between attraction and self-propulsion. The structural properties of the cluster phases of squirmers and ABPs are similar, although squirmers show sensitivity to active stresses. Active Brownian disks resemble weakly pusher squirmer suspensions in terms of cluster size distribution, structure of the radius of gyration on the cluster size and degree of cluster polarity.

Projection-based curve clustering

International Nuclear Information System (INIS)

Auder, Benjamin; Fischer, Aurelie

2012-01-01

This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat a l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU time-consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, the CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centres found by the clustering method based on projections, compared with the 'true' ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem. (authors)

Electron Tomography of Nanoparticle Clusters: Implications for Atmospheric Lifetimes and Radiative Forcing of Soot

Science.gov (United States)

vanPoppel, Laura H.; Friedrich, Heiner; Spinsby, Jacob; Chung, Serena H.; Seinfeld, John H.; Buseck, Peter R.

2005-01-01

Nanoparticles are ubiquitous in nature. Their large surface areas and consequent chemical reactivity typically result in their aggregation into clusters. Their chemical and physical properties depend on cluster shapes, which are commonly complex and unknown. This is the first application of electron tomography with a transmission electron microscope to quantitatively determine the three-dimensional (3D) shapes, volumes, and surface areas of nanoparticle clusters. We use soot (black carbon, BC) nanoparticles as an example because it is a major contributor to environmental degradation and global climate change. To the extent that our samples are representative, we find that quantitative measurements of soot surface areas and volumes derived from electron tomograms differ from geometrically derived values by, respectively, almost one and two orders of magnitude. Global sensitivity studies suggest that the global burden and direct radiative forcing of fractal BC are only about 60% of the value if it is assumed that BC has a spherical shape.

Operational limit of a planar DC magnetron cluster source due to target erosion

International Nuclear Information System (INIS)

Rai, A.; Mutzke, A.; Bandelow, G.; Schneider, R.; Ganeva, M.; Pipa, A.V.; Hippler, R.

2013-01-01

The binary collision-based two dimensional SDTrimSP-2D model has been used to simulate the erosion process of a Cu target and its influence on the operational limit of a planar DC magnetron nanocluster source. The density of free metal atoms in the aggregation region influences the cluster formation and cluster intensity during the target lifetime. The density of the free metal atoms in the aggregation region can only be predicted by taking into account (i) the angular distribution of the sputtered flux from the primary target source and (ii) relative downwards shift of the primary source of sputtered atoms during the erosion process. It is shown that the flux of the sputtered atoms smoothly decreases with the target erosion

Three-Dimensional Hermite—Bessel—Gaussian Soliton Clusters in Strongly Nonlocal Media

International Nuclear Information System (INIS)

Jin Hai-Qin; Yi Lin; Liang Jian-Chu; Cai Ze-Bin; Liu Fei

2012-01-01

We analytically and numerically demonstrate the existence of Hermite—Bessel—Gaussian spatial soliton clusters in three-dimensional strongly nonlocal media. It is found that the soliton clusters display the vortex, dipole azimuthon and quadrupole azimuthon in geometry, and the total number of solitons in the necklaces depends on the quantum number n and m of the Hermite functions and generalized Bessel polynomials. The numerical simulation is basically identical to the analytical solution, and white noise does not lead to collapse of the soliton, which confirms the stability of the soliton waves. The theoretical predictions may give new insights into low-energetic spatial soliton transmission with high fidelity

Formation and stability of sputtered clusters

International Nuclear Information System (INIS)

Andersen, H.H.

1989-01-01

Current theory for the formation of sputtered clusters states that either atoms are sputtered individually and aggregate after having left the surface or they are sputtered as complete clusters. There is no totally sharp boundary between the two interpretations, but experimental evidence is mainly thought to favour the latter model. Both theories demand a criterion for the stability of the clusters. In computer simulations of sputtering, the idea has been to use the same interaction potential as in the lattice computations to judge the stability. More qualitatively, simple geometrical shapes have also been looked for. It is found here, that evidence for 'magic numbers' and electron parity effects in clusters have existed in the sputtering literature for a long time, making more sophisticated stability criteria necessary. The breakdown of originally sputtered metastable clusters into stable clusters gives strong support to the 'sputtered as clusters' hypothesis. (author)

Robust continuous clustering.

Science.gov (United States)

Shah, Sohil Atul; Koltun, Vladlen

2017-09-12

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank.

Computer simulation of defect cluster

Energy Technology Data Exchange (ETDEWEB)

Kuramoto, Eiichi [Kyushu Univ., Kasuga, Fukuoka (Japan). Research Inst. for Applied Mechanics

1996-04-01

In order to elucidate individual element process of various defects and defect clusters of used materials under irradiation environments, interatomic potential with reliability was investigated. And for comparison with experimental results, it is often required to adopt the temperature effect and to investigate in details mechanism of one dimensional motion of micro conversion loop and so forth using the molecular dynamic (MD) method. Furthermore, temperature effect is also supposed for stable structure of defects and defect clusters, and many problems relating to alloy element are also remained. And, simulation on photon life at the defects and defect clusters thought to be important under comparison with equipment can also be supposed an improvement of effectiveness due to relation to theses products. In this paper, some topics in such flow was extracted to explain them. In particular, future important problems will be potential preparation of alloy, structure, dynamic behavior and limited temperature of intralattice atomic cluster. (G.K.)

Weighted voting-based consensus clustering for chemical structure databases

Science.gov (United States)

Saeed, Faisal; Ahmed, Ali; Shamsir, Mohd Shahir; Salim, Naomie

2014-06-01

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

Three-dimensional visualization of cultural clusters in the 1878 yellow fever epidemic of New Orleans

Directory of Open Access Journals (Sweden)

Curtis Andrew J

2008-08-01

Full Text Available Abstract Background An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Results Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level of investigation. Conclusion Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in

THE SWIFT AGN AND CLUSTER SURVEY. II. CLUSTER CONFIRMATION WITH SDSS DATA

International Nuclear Information System (INIS)

Griffin, Rhiannon D.; Dai, Xinyu; Kochanek, Christopher S.; Bregman, Joel N.

2016-01-01

We study 203 (of 442) Swift AGN and Cluster Survey extended X-ray sources located in the SDSS DR8 footprint to search for galaxy over-densities in three-dimensional space using SDSS galaxy photometric redshifts and positions near the Swift cluster candidates. We find 104 Swift clusters with a >3σ galaxy over-density. The remaining targets are potentially located at higher redshifts and require deeper optical follow-up observations for confirmation as galaxy clusters. We present a series of cluster properties including the redshift, brightest cluster galaxy (BCG) magnitude, BCG-to-X-ray center offset, optical richness, and X-ray luminosity. We also detect red sequences in ∼85% of the 104 confirmed clusters. The X-ray luminosity and optical richness for the SDSS confirmed Swift clusters are correlated and follow previously established relations. The distribution of the separations between the X-ray centroids and the most likely BCG is also consistent with expectation. We compare the observed redshift distribution of the sample with a theoretical model, and find that our sample is complete for z ≲ 0.3 and is still 80% complete up to z ≃ 0.4, consistent with the SDSS survey depth. These analysis results suggest that our Swift cluster selection algorithm has yielded a statistically well-defined cluster sample for further study of cluster evolution and cosmology. We also match our SDSS confirmed Swift clusters to existing cluster catalogs, and find 42, 23, and 1 matches in optical, X-ray, and Sunyaev–Zel’dovich catalogs, respectively, and so the majority of these clusters are new detections

A Dissimilarity Measure for Clustering High- and Infinite Dimensional Data that Satisfies the Triangle Inequality

Science.gov (United States)

Socolovsky, Eduardo A.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

The cosine or correlation measures of similarity used to cluster high dimensional data are interpreted as projections, and the orthogonal components are used to define a complementary dissimilarity measure to form a similarity-dissimilarity measure pair. Using a geometrical approach, a number of properties of this pair is established. This approach is also extended to general inner-product spaces of any dimension. These properties include the triangle inequality for the defined dissimilarity measure, error estimates for the triangle inequality and bounds on both measures that can be obtained with a few floating-point operations from previously computed values of the measures. The bounds and error estimates for the similarity and dissimilarity measures can be used to reduce the computational complexity of clustering algorithms and enhance their scalability, and the triangle inequality allows the design of clustering algorithms for high dimensional distributed data.

Efficient computation of k-Nearest Neighbour Graphs for large high-dimensional data sets on GPU clusters.

Directory of Open Access Journals (Sweden)

Ali Dashti

Full Text Available This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG construction for ultra-large high-dimensional data cloud. The proposed method uses Graphics Processing Units (GPUs and is scalable with multi-levels of parallelism (between nodes of a cluster, between different GPUs on a single node, and within a GPU. The method is applicable to homogeneous computing clusters with a varying number of nodes and GPUs per node. We achieve a 6-fold speedup in data processing as compared with an optimized method running on a cluster of CPUs and bring a hitherto impossible [Formula: see text]-NNG generation for a dataset of twenty million images with 15 k dimensionality into the realm of practical possibility.

MHBCDA: Mobility and Heterogeneity aware Bandwidth Efficient Cluster based Data Aggregation for Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2013-01-01

Wireless Sensor Network (WSN) offers a variety of novel applications for mobile targets. It generates the large amount of redundant sensing data. The data aggregation techniques are extensively used to reduce the energy consumption and increase the network lifetime, although it has the side effect...... efficient. It exploits correlation of data packets generated by varying the packet generation rate. It prevents transmission of redundant data packets by improving energy consumption by 4.11% and prolongs the network life by 34.45% as compared with state-of-the-art solutions.......-based Data Aggregation (MHBCDA) algorithm for the randomly distributed nodes. It considers the mobile sink based packet aggregation for the heterogeneous WSN. It uses predefined region for the aggregation at cluster head to minimize computation and communication cost. The MHBCDA is energy and bandwidth...

One- and two-cluster synchronized dynamics of non-diffusively coupled Tchebycheff map networks

International Nuclear Information System (INIS)

Schäfer, Mirko; Greiner, Martin

2012-01-01

We use the master stability formalism to discuss one- and two-cluster synchronization of coupled Tchebycheff map networks. For diffusively coupled map systems, the one-cluster synchronized dynamics is given by the behaviour of the individual maps, and the coupling only determines the stability of the coherent state. For the case of non-diffusive coupling and for two-cluster synchronization, the synchronized dynamics on networks is different from the behaviour of the single individual map. Depending on the coupling, we study numerically the characteristics of various forms of the resulting synchronized dynamics. The stability properties of the respective one-cluster synchronized states are discussed for arbitrary network structures. For the case of two-cluster synchronization on bipartite networks we also present analytical expressions for fixed points and zig-zag patterns, and explicitly determine the linear stability of these orbits for the special case of ring-networks.

Shape characteristics of equilibrium and non-equilibrium fractal clusters.

Science.gov (United States)

Mansfield, Marc L; Douglas, Jack F

2013-07-28

It is often difficult in practice to discriminate between equilibrium and non-equilibrium nanoparticle or colloidal-particle clusters that form through aggregation in gas or solution phases. Scattering studies often permit the determination of an apparent fractal dimension, but both equilibrium and non-equilibrium clusters in three dimensions frequently have fractal dimensions near 2, so that it is often not possible to discriminate on the basis of this geometrical property. A survey of the anisotropy of a wide variety of polymeric structures (linear and ring random and self-avoiding random walks, percolation clusters, lattice animals, diffusion-limited aggregates, and Eden clusters) based on the principal components of both the radius of gyration and electric polarizability tensor indicates, perhaps counter-intuitively, that self-similar equilibrium clusters tend to be intrinsically anisotropic at all sizes, while non-equilibrium processes such as diffusion-limited aggregation or Eden growth tend to be isotropic in the large-mass limit, providing a potential means of discriminating these clusters experimentally if anisotropy could be determined along with the fractal dimension. Equilibrium polymer structures, such as flexible polymer chains, are normally self-similar due to the existence of only a single relevant length scale, and are thus anisotropic at all length scales, while non-equilibrium polymer structures that grow irreversibly in time eventually become isotropic if there is no difference in the average growth rates in different directions. There is apparently no proof of these general trends and little theoretical insight into what controls the universal anisotropy in equilibrium polymer structures of various kinds. This is an obvious topic of theoretical investigation, as well as a matter of practical interest. To address this general problem, we consider two experimentally accessible ratios, one between the hydrodynamic and gyration radii, the other

Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach

DEFF Research Database (Denmark)

Xu, Guandong; Zong, Yu; Dolog, Peter

2010-01-01

Web clustering is an approach for aggregating Web objects into various groups according to underlying relationships among them. Finding co-clusters of Web objects is an interesting topic in the context of Web usage mining, which is able to capture the underlying user navigational interest...... and content preference simultaneously. In this paper we will present an algorithm using bipartite spectral clustering to co-cluster Web users and pages. The usage data of users visiting Web sites is modeled as a bipartite graph and the spectral clustering is then applied to the graph representation of usage...... data. The proposed approach is evaluated by experiments performed on real datasets, and the impact of using various clustering algorithms is also investigated. Experimental results have demonstrated the employed method can effectively reveal the subset aggregates of Web users and pages which...

Clustering methods for the optimization of atomic cluster structure

Science.gov (United States)

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.

Dimension dependence of clustering dynamics in models of ballistic aggregation and freely cooling granular gas

Science.gov (United States)

Paul, Subhajit; Das, Subir K.

2018-03-01

Via event-driven molecular dynamics simulations we study kinetics of clustering in assemblies of inelastic particles in various space dimensions. We consider two models, viz., the ballistic aggregation model (BAM) and the freely cooling granular gas model (GGM), for each of which we quantify the time dependence of kinetic energy and average mass of clusters (that form due to inelastic collisions). These quantities, for both the models, exhibit power-law behavior, at least in the long time limit. For the BAM, corresponding exponents exhibit strong dimension dependence and follow a hyperscaling relation. In addition, in the high packing fraction limit the behavior of these quantities become consistent with a scaling theory that predicts an inverse relation between energy and mass. On the other hand, in the case of the GGM we do not find any evidence for such a picture. In this case, even though the energy decay, irrespective of packing fraction, matches quantitatively with that for the high packing fraction picture of the BAM, it is inversely proportional to the growth of mass only in one dimension, and the growth appears to be rather insensitive to the choice of the dimension, unlike the BAM.

Periodic cluster mutations and related integrable maps

International Nuclear Information System (INIS)

Fordy, Allan P

2014-01-01

One of the remarkable properties of cluster algebras is that any cluster, obtained from a sequence of mutations from an initial cluster, can be written as a Laurent polynomial in the initial cluster (known as the ‘Laurent phenomenon’). There are many nonlinear recurrences which exhibit the Laurent phenomenon and thus unexpectedly generate integer sequences. The mutation of a typical quiver will not generate a recurrence, but rather an erratic sequence of exchange relations. How do we ‘design’ a quiver which gives rise to a given recurrence? A key role is played by the concept of ‘periodic cluster mutation’, introduced in 2009. Each recurrence corresponds to a finite dimensional map. In the context of cluster mutations, these are called ‘cluster maps’. What properties do cluster maps have? Are they integrable in some standard sense?In this review I describe how integrable maps arise in the context of cluster mutations. I first explain the concept of ‘periodic cluster mutation’, giving some classification results. I then give a review of what is meant by an integrable map and apply this to cluster maps. Two classes of integrable maps are related to interesting monodromy problems, which generate interesting Poisson algebras of functions, used to prove complete integrability and a linearization. A connection to the Hirota–Miwa equation is explained. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Cluster algebras in mathematical physics’. (review)

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

Directory of Open Access Journals (Sweden)

Morris John H

2011-11-01

Full Text Available Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view, k-means, k-medoid, SCPS, AutoSOME, and native (Java MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin cluster

Ion-streaming induced order transition in three-dimensional dust clusters

International Nuclear Information System (INIS)

Ludwig, Patrick; Kählert, Hanno; Bonitz, Michael

2012-01-01

Dust dynamics simulations utilizing a dynamical screening approach are performed to study the effect of ion-streaming on the self-organized structures in a three-dimensional spherically confined complex (dusty) plasma. Varying the Mach number M, the ratio of ion drift velocity to the sound velocity, the simulations reproduce the experimentally observed cluster configurations in the two limiting cases: at M = 0 strongly correlated crystalline structures consisting of nested spherical shells (Yukawa balls) and, for M ⩾ 1, flow-aligned dust chains, respectively. In addition, our simulations reveal a discontinuous transition between these two limits. It is found that already a moderate ion drift velocity (M ≈ 0.1 for the plasma conditions considered here) destabilizes the highly ordered Yukawa balls and initiates an abrupt melting transition. The critical value of M is found to be independent of the cluster size. (paper)

Scanning probe microscopy investigation of gold clusters deposited on atomically flat substrates

International Nuclear Information System (INIS)

Vandamme, N; Janssens, E; Vanhoutte, F; Lievens, P; Haesendonck, C van

2003-01-01

We systematically studied the influence of the substrate on the shape, mobility, and stability of deposited gold clusters. The Au n clusters were produced in a laser vaporization source and deposited with low kinetic energy (∼0.4 eV/atom) on atomically flat substrates (graphite, mica, and gold and silver films on mica) under UHV conditions. Their size distribution is probed with time-of-flight mass spectrometry and ranges from dimers to several hundreds of atoms. Scanning probe microscopy is used to characterize the deposited clusters and the formation of islands by cluster aggregation. On all substrates, Au n islands can be clearly distinguished and the islands are flattened despite the small impact energy. The shape and size of the island configurations are strongly system dependent. Gold clusters deposited on Au(111) and Ag(111) films grown on mica do not aggregate, but deform due to strong cluster-substrate interactions. The clusters tend to grow epitaxially on these surfaces. On graphite and on mica, deposited clusters do diffuse and aggregate. On the graphite surface, large ramified islands are formed by juxtaposition of small islands and trapping of the clusters at the step edges. On the other hand, the diffusion of the clusters on mica results in a total coalescence of the Au n clusters into compact islands

ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS

Directory of Open Access Journals (Sweden)

T. SHANKAR

2014-04-01

Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.

Statistical Significance for Hierarchical Clustering

Science.gov (United States)

Kimes, Patrick K.; Liu, Yufeng; Hayes, D. Neil; Marron, J. S.

2017-01-01

Summary Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this paper, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets. PMID:28099990

Information diffusion, Facebook clusters, and the simplicial model of social aggregation: a computational simulation of simplicial diffusers for community health interventions.

Science.gov (United States)

Kee, Kerk F; Sparks, Lisa; Struppa, Daniele C; Mannucci, Mirco A; Damiano, Alberto

2016-01-01

By integrating the simplicial model of social aggregation with existing research on opinion leadership and diffusion networks, this article introduces the constructs of simplicial diffusers (mathematically defined as nodes embedded in simplexes; a simplex is a socially bonded cluster) and simplicial diffusing sets (mathematically defined as minimal covers of a simplicial complex; a simplicial complex is a social aggregation in which socially bonded clusters are embedded) to propose a strategic approach for information diffusion of cancer screenings as a health intervention on Facebook for community cancer prevention and control. This approach is novel in its incorporation of interpersonally bonded clusters, culturally distinct subgroups, and different united social entities that coexist within a larger community into a computational simulation to select sets of simplicial diffusers with the highest degree of information diffusion for health intervention dissemination. The unique contributions of the article also include seven propositions and five algorithmic steps for computationally modeling the simplicial model with Facebook data.

Cluster Based Text Classification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

2011-01-01

We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

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

The C4 clustering algorithm: Clusters of galaxies in the Sloan Digital Sky Survey

Energy Technology Data Exchange (ETDEWEB)

Miller, Christopher J.; Nichol, Robert; Reichart, Dan; Wechsler, Risa H.; Evrard, August; Annis, James; McKay, Timothy; Bahcall, Neta; Bernardi, Mariangela; Boehringer,; Connolly, Andrew; Goto, Tomo; Kniazev, Alexie; Lamb, Donald; Postman, Marc; Schneider, Donald; Sheth, Ravi; Voges, Wolfgang; /Cerro-Tololo InterAmerican Obs. /Portsmouth U.,

2005-03-01

We present the ''C4 Cluster Catalog'', a new sample of 748 clusters of galaxies identified in the spectroscopic sample of the Second Data Release (DR2) of the Sloan Digital Sky Survey (SDSS). The C4 cluster-finding algorithm identifies clusters as overdensities in a seven-dimensional position and color space, thus minimizing projection effects that have plagued previous optical cluster selection. The present C4 catalog covers {approx}2600 square degrees of sky and ranges in redshift from z = 0.02 to z = 0.17. The mean cluster membership is 36 galaxies (with redshifts) brighter than r = 17.7, but the catalog includes a range of systems, from groups containing 10 members to massive clusters with over 200 cluster members with redshifts. The catalog provides a large number of measured cluster properties including sky location, mean redshift, galaxy membership, summed r-band optical luminosity (L{sub r}), velocity dispersion, as well as quantitative measures of substructure and the surrounding large-scale environment. We use new, multi-color mock SDSS galaxy catalogs, empirically constructed from the {Lambda}CDM Hubble Volume (HV) Sky Survey output, to investigate the sensitivity of the C4 catalog to the various algorithm parameters (detection threshold, choice of passbands and search aperture), as well as to quantify the purity and completeness of the C4 cluster catalog. These mock catalogs indicate that the C4 catalog is {approx_equal}90% complete and 95% pure above M{sub 200} = 1 x 10{sup 14} h{sup -1}M{sub {circle_dot}} and within 0.03 {le} z {le} 0.12. Using the SDSS DR2 data, we show that the C4 algorithm finds 98% of X-ray identified clusters and 90% of Abell clusters within 0.03 {le} z {le} 0.12. Using the mock galaxy catalogs and the full HV dark matter simulations, we show that the L{sub r} of a cluster is a more robust estimator of the halo mass (M{sub 200}) than the galaxy line-of-sight velocity dispersion or the richness of the cluster

Familial aggregation of suicide explained by cluster B traits: a three-group family study of suicide controlling for major depressive disorder.

Science.gov (United States)

McGirr, Alexander; Alda, Martin; Séguin, Monique; Cabot, Sophie; Lesage, Alain; Turecki, Gustavo

2009-10-01

There is substantial evidence suggesting that suicide aggregates in families. However, the extent of overlap between the liability to suicide and psychiatric disorders, particularly major depressive disorder, remains an important issue. Similarly, factors that account for the familial transmission of suicidal behavior remain unclear. Thus, through direct and blind assessment of first-degree relatives, the authors conducted a family study of suicide by examining three proband groups: probands who committed suicide in the context of major depressive disorder, living depressed probands with no history of suicidal behavior, and psychiatrically normal community comparison probands. Participants were 718 first-degree relatives from 120 families: 296 relatives of 51 depressed probands who committed suicide, 185 relatives of 34 nonsuicidal depressed probands, and 237 relatives of 35 community comparison subjects. Psychopathology, suicidal behavior, and behavioral measures were assessed via interviews. The relatives of probands who committed suicide had higher levels of suicidal behavior (10.8%) than the relatives of nonsuicidal depressed probands (6.5%) and community comparison probands (3.4%). Testing cluster B traits as intermediate phenotypes of suicide showed that the relatives of depressed probands who committed suicide had elevated levels of cluster B traits; familial predisposition to suicide was associated with increased levels of cluster B traits; cluster B traits demonstrated familial aggregation and were associated with suicide attempts among relatives; and cluster B traits mediated, at least in part, the relationship between familial predisposition and suicide attempts among relatives. Analyses were repeated for severity of attempts, where cluster B traits also met criteria for endophenotypes of suicide. Familial transmission of suicide and major depression, while partially overlapping, are distinct. Cluster B traits and impulsive-aggressive behavior represent

Beams of mass-selected clusters: realization and first experiments

International Nuclear Information System (INIS)

Kamalou, O.

2007-04-01

The main objective of this work concerns the production of beams of mass-selected clusters of metallic and semiconductor materials. Clusters are produced in magnetron sputtering source combined with a gas aggregation chamber, cooled by liquid nitrogen circulation. Downstream of the cluster source, a Wiley-McLaren time-of-flight setup allows to select a given cluster size or a narrow size range. The pulsed mass-selected cluster ion beam is separated from the continuous neutral one by an electrostatic 90-quadrupole deflector. After the deflector, the density of the pulsed beam amounts to about 10 3 particles/cm 3 . Preliminary deposition experiments of mass-selected copper clusters with a deposition energy of about 0.5 eV/atom have ben performed on highly oriented pyrolytic graphite (HOPG) substrates, indicating that copper clusters are evidently mobile on the HOPG-surface until they reach cleavage steps, dislocation lines or other surface defects. In order to lower the cluster mobility on the HOPG-surface, we have first irradiated HOPG samples with slow highly charged ions (high dose) in order to create superficial defects. In a second step we have deposited mass-selected copper clusters on these pre-irradiated samples. The first analysis by AFM (Atomic Force Microscopy) techniques showed that the copper clusters are trapped on the defects produced by the highly charged ions. (author)

The effects of one-dimensional migration of self-interstitial clusters on the formation of void lattices

DEFF Research Database (Denmark)

Heinisch, H.L.; Singh, B.N.

2002-01-01

under pure 3-D SIA migration, but they are extremely stable, relative to random arrays of voids, under 1-D SIA migration. Void lattices remain stable even under the condition of fairly frequent changes in the Burgers vectors of the 1-D migrating SIA clusters. Clusters with average 1-D path segments...

HDclassif : An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

Directory of Open Access Journals (Sweden)

Laurent Berge

2012-01-01

Full Text Available This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this parametrization is called high dimensional discriminant analysis (HDDA. In a similar manner, the associated clustering method iscalled high dimensional data clustering (HDDC and uses the expectation-maximization algorithm for inference. In order to correctly t the data, both methods estimate the specific subspace and the intrinsic dimension of the groups. Due to the constraints on the covariance matrices, the number of parameters to estimate is significantly lower than other model-based methods and this allows the methods to be stable and efficient in high dimensions. Two introductory examples illustrated with R codes allow the user to discover the hdda and hddc functions. Experiments on simulated and real datasets also compare HDDC and HDDA with existing classification methods on high-dimensional datasets. HDclassif is a free software and distributed under the general public license, as part of the R software project.

The GALAH survey: chemical tagging of star clusters and new members in the Pleiades

Science.gov (United States)

Kos, Janez; Bland-Hawthorn, Joss; Freeman, Ken; Buder, Sven; Traven, Gregor; De Silva, Gayandhi M.; Sharma, Sanjib; Asplund, Martin; Duong, Ly; Lin, Jane; Lind, Karin; Martell, Sarah; Simpson, Jeffrey D.; Stello, Dennis; Zucker, Daniel B.; Zwitter, Tomaž; Anguiano, Borja; Da Costa, Gary; D'Orazi, Valentina; Horner, Jonathan; Kafle, Prajwal R.; Lewis, Geraint; Munari, Ulisse; Nataf, David M.; Ness, Melissa; Reid, Warren; Schlesinger, Katie; Ting, Yuan-Sen; Wyse, Rosemary

2018-02-01

The technique of chemical tagging uses the elemental abundances of stellar atmospheres to 'reconstruct' chemically homogeneous star clusters that have long since dispersed. The GALAH spectroscopic survey - which aims to observe one million stars using the Anglo-Australian Telescope - allows us to measure up to 30 elements or dimensions in the stellar chemical abundance space, many of which are not independent. How to find clustering reliably in a noisy high-dimensional space is a difficult problem that remains largely unsolved. Here, we explore t-distributed stochastic neighbour embedding (t-SNE) - which identifies an optimal mapping of a high-dimensional space into fewer dimensions - whilst conserving the original clustering information. Typically, the projection is made to a 2D space to aid recognition of clusters by eye. We show that this method is a reliable tool for chemical tagging because it can: (i) resolve clustering in chemical space alone, (ii) recover known open and globular clusters with high efficiency and low contamination, and (iii) relate field stars to known clusters. t-SNE also provides a useful visualization of a high-dimensional space. We demonstrate the method on a data set of 13 abundances measured in the spectra of 187 000 stars by the GALAH survey. We recover seven of the nine observed clusters (six globular and three open clusters) in chemical space with minimal contamination from field stars and low numbers of outliers. With chemical tagging, we also identify two Pleiades supercluster members (which we confirm kinematically), one as far as 6° - one tidal radius away from the cluster centre.

Support Policies in Clusters: Prioritization of Support Needs by Cluster Members According to Cluster Life Cycle

Directory of Open Access Journals (Sweden)

Gulcin Salıngan

2012-07-01

Full Text Available Economic development has always been a moving target. Both the national and local governments have been facing the challenge of implementing the effective and efficient economic policy and program in order to best utilize their limited resources. One of the recent approaches in this area is called cluster-based economic analysis and strategy development. This study reviews key literature and some of the cluster based economic policies adopted by different governments. Based on this review, it proposes “the cluster life cycle” as a determining factor to identify the support requirements of clusters. A survey, designed based on literature review of International Cluster support programs, was conducted with 30 participants from 3 clusters with different maturity stage. This paper discusses the results of this study conducted among the cluster members in Eskişehir- Bilecik-Kütahya Region in Turkey on the requirement of the support to foster the development of related clusters.

AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

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.

Microassembly using a Cluster of Paramagnetic Microparticles

NARCIS (Netherlands)

Khalil, I.S.M.; Brink, F.V; Sardan Sukas, Ö.; Misra, Sarthak

2013-01-01

We use a cluster of paramagnetic microparticles to carry out a wireless two-dimensional microassembly operation. A magnetic-based manipulation system is used to control the motion of the cluster under the influence of the applied magnetic fields. Wireless motion control of the cluster is implemented

Globular clusters, old and young

International Nuclear Information System (INIS)

Samus', N.N.

1984-01-01

The problem of similarity of and difference in the globular and scattered star clusters is considered. Star clusters in astronomy are related either to globular or to scattered ones according to the structure of Hertzsprung-Russell diagram constructed for star clusters, but not according to the appearance. The qlobular clusters in the Galaxy are composed of giants and subgiants, which testifies to the old age of the globular clusters. The Globular clusters in the Magellanic clouds are classified into ''red'' ones - similar to the globular clusters of the Galaxy, and ''blue'' ones - similar to them in appearance but differing extremely by the star composition and so by the age. The old star clusters are suggested to be called globular ones, while another name (''populous'', for example) is suggested to be used for other clusters similar to globular ones only in appearance

Structural properties of gold clusters at different temperatures

CSIR Research Space (South Africa)

Mahladisa, MA

2005-09-01

Full Text Available A series of gold clusters consisting of aggregates of from 13 to 147 atoms was studied using the Sutton-Chen type many-body potential in molecular dynamics simulations. The properties of these clusters at temperatures from 10 K to 1000 K were...

Some properties of ion and cluster plasma

International Nuclear Information System (INIS)

Gudzenko, L.I.; Derzhiev, V.I.; Yakovlenko, S.I.

1982-01-01

The aggregate of problems connected with the physics of ion and cluster plasma is qualitatively considered. Such a plasma can exist when a dense gas is ionized by a hard ionizer. The conditions for the formation of an ion plasma and the difference between its characteristics and those of an ordinary electron plasma are discussed; a solvated-ion model and the distribution of the clusters with respect to the number of solvated molecules are considered. The recombination rate of the positively and negatively charged clusters is roughly estimated. The parameters of a ball-lightning plasma are estimated on the basis of the cluster model

Major cluster mergers and the location of the brightest cluster galaxy

International Nuclear Information System (INIS)

Martel, Hugo; Robichaud, Fidèle; Barai, Paramita

2014-01-01

Using a large N-body cosmological simulation combined with a subgrid treatment of galaxy formation, merging, and tidal destruction, we study the formation and evolution of the galaxy and cluster population in a comoving volume (100 Mpc) 3 in a ΛCDM universe. At z = 0, our computational volume contains 1788 clusters with mass M cl > 1.1 × 10 12 M ☉ , including 18 massive clusters with M cl > 10 14 M ☉ . It also contains 1, 088, 797 galaxies with mass M gal ≥ 2 × 10 9 M ☉ and luminosity L > 9.5 × 10 5 L ☉ . For each cluster, we identified the brightest cluster galaxy (BCG). We then computed two separate statistics: the fraction f BNC of clusters in which the BCG is not the closest galaxy to the center of the cluster in projection, and the ratio Δv/σ, where Δv is the difference in radial velocity between the BCG and the whole cluster and σ is the radial velocity dispersion of the cluster. We found that f BNC increases from 0.05 for low-mass clusters (M cl ∼ 10 12 M ☉ ) to 0.5 for high-mass clusters (M cl > 10 14 M ☉ ) with very little dependence on cluster redshift. Most of this result turns out to be a projection effect and when we consider three-dimensional distances instead of projected distances, f BNC increases only to 0.2 at high-cluster mass. The values of Δv/σ vary from 0 to 1.8, with median values in the range 0.03-0.15 when considering all clusters, and 0.12-0.31 when considering only massive clusters. These results are consistent with previous observational studies and indicate that the central galaxy paradigm, which states that the BCG should be at rest at the center of the cluster, is usually valid, but exceptions are too common to be ignored. We built merger trees for the 18 most massive clusters in the simulation. Analysis of these trees reveal that 16 of these clusters have experienced 1 or several major or semi-major mergers in the past. These mergers leave each cluster in a non-equilibrium state, but eventually the cluster

Frequent Pattern Mining Algorithms for Data Clustering

DEFF Research Database (Denmark)

Zimek, Arthur; Assent, Ira; Vreeken, Jilles

2014-01-01

that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed into an independent research field. In this chapter, we discuss how frequent pattern mining algorithms have been extended and generalized towards the discovery of local clusters in high-dimensional data......Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say....... In particular, we discuss several example algorithms for subspace clustering or projected clustering as well as point out recent research questions and open topics in this area relevant to researchers in either clustering or pattern mining...

On the applicability of one- and many-electron quantum chemistry models for hydrated electron clusters

Science.gov (United States)

Turi, László

2016-04-01

We evaluate the applicability of a hierarchy of quantum models in characterizing the binding energy of excess electrons to water clusters. In particular, we calculate the vertical detachment energy of an excess electron from water cluster anions with methods that include one-electron pseudopotential calculations, density functional theory (DFT) based calculations, and ab initio quantum chemistry using MP2 and eom-EA-CCSD levels of theory. The examined clusters range from the smallest cluster size (n = 2) up to nearly nanosize clusters with n = 1000 molecules. The examined cluster configurations are extracted from mixed quantum-classical molecular dynamics trajectories of cluster anions with n = 1000 water molecules using two different one-electron pseudopotenial models. We find that while MP2 calculations with large diffuse basis set provide a reasonable description for the hydrated electron system, DFT methods should be used with precaution and only after careful benchmarking. Strictly tested one-electron psudopotentials can still be considered as reasonable alternatives to DFT methods, especially in large systems. The results of quantum chemistry calculations performed on configurations, that represent possible excess electron binding motifs in the clusters, appear to be consistent with the results using a cavity structure preferring one-electron pseudopotential for the hydrated electron, while they are in sharp disagreement with the structural predictions of a non-cavity model.

On the applicability of one- and many-electron quantum chemistry models for hydrated electron clusters

Energy Technology Data Exchange (ETDEWEB)

Turi, László, E-mail: turi@chem.elte.hu [Department of Physical Chemistry, Eötvös Loránd University, P.O. Box 32, H-1518 Budapest 112 (Hungary)

2016-04-21

We evaluate the applicability of a hierarchy of quantum models in characterizing the binding energy of excess electrons to water clusters. In particular, we calculate the vertical detachment energy of an excess electron from water cluster anions with methods that include one-electron pseudopotential calculations, density functional theory (DFT) based calculations, and ab initio quantum chemistry using MP2 and eom-EA-CCSD levels of theory. The examined clusters range from the smallest cluster size (n = 2) up to nearly nanosize clusters with n = 1000 molecules. The examined cluster configurations are extracted from mixed quantum-classical molecular dynamics trajectories of cluster anions with n = 1000 water molecules using two different one-electron pseudopotenial models. We find that while MP2 calculations with large diffuse basis set provide a reasonable description for the hydrated electron system, DFT methods should be used with precaution and only after careful benchmarking. Strictly tested one-electron psudopotentials can still be considered as reasonable alternatives to DFT methods, especially in large systems. The results of quantum chemistry calculations performed on configurations, that represent possible excess electron binding motifs in the clusters, appear to be consistent with the results using a cavity structure preferring one-electron pseudopotential for the hydrated electron, while they are in sharp disagreement with the structural predictions of a non-cavity model.

Cluster Ions and Atmospheric Processes

Science.gov (United States)

D'Auria, R.; Turco, R. P.

We investigate the properties and possible roles of naturally occurring ions under at- mospheric conditions. Among other things, the formation of stable charged molecular clusters represents the initial stages of aerosol nucleation [e.g., Keesee and Castle- man, 1982], while the conversion of vapor to aggregates is the first step in certain atmospheric phase transitions [e.g. Hamill and Turco, 2000]. We analyze the stability and size distributions of common ionic clusters by solving the differential equations describing their growth and loss. The necessary reaction rate coefficients are deter- mined using kinetic and thermodynamic data. The latter are derived from direct labo- ratory measurements of equilibrium constants, from the classical charged liquid drop model applied to large aggregates (i.e., the Thomson model [Thomson, 1906]), and from quantum mechanical calculations of the thermodynamic potentials associated with the cluster structures. This approach allows us to characterize molecular clusters across the entire size range from true molecular species to larger aggregates exhibiting macroscopic behavior [D'Auria, 2001]. Cluster systems discussed in this talk include the proton hydrates (PHs) and nitrate-water and nitrate-nitric acid series [D'Auria and Turco, 2001]. These ions have frequently been detected in the stratosphere and tropo- sphere [e.g., Arnold et al., 1977; Viggiano and Arnold, 1981]. We show how the pro- posed hybrid cluster model can be extended to a wide range of ion systems, including non-proton hydrates (NPHs), mixed-ligand clusters such as nitrate-water-nitric acid and sulfate-sulfuric acid-water, as well as more exotic species containing ammonia, pyridine and other organic compounds found on ions [e.g., Eisele, 1988; Tanner and Eisele, 1991]. References: Arnold, F., D. Krankowsky and K. H. Marien, First mass spectrometric measurements of posi- tive ions in the stratosphere, Nature, 267, 30-32, 1977. D'Auria, R., A study of ionic

Triggered cluster formation in the RMC

Science.gov (United States)

Li, Jin Zeng; Smith, Michael D.

An investigation based on data from the spatially complete 2MASS Survey reveals that a remarkable burst of clustered star formation is taking place throughout the south-east quadrant of the Rosette Molecular Cloud. Compact clusters are forming in a multi-seeded mode, in parallel and at various places. In addition, sparse aggregates of embedded young stars are extensively distributed. Here we present the primary results and implications for high-mass and clustered star formation in this giant molecular cloud. In particular, we incorporate for the first time the birth of medium to low-mass stars into the scenario of sequential formation of OB clusters. Following the emergence of the young OB cluster NGC 2244, a variety of manifestations of forming clusters of medium to high mass appear in the vicinity of the swept-up layer of the H II region as well as further into the molecular cloud. The embedded clusters appear to form in a structured manner, which suggests they follow tracks laid out by the decay of macroturbulence. We address the possible origins of the turbulence. This leads us to propose a tree model to interpret the neat spatial distribution of clusters within a large section of the Rosette complex. Prominent new generation OB clusters are identified at the root of the tree pattern.

A Flocking Based algorithm for Document Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

2006-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

Cluster-based Data Gathering in Long-Strip Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

FANG, W.

2012-02-01

Full Text Available This paper investigates a special class of wireless sensor networks that are different from traditional ones in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a long-strip topology. According to hardware capabilities of current sensor nodes, a cluster-based protocol for reliable and efficient data gathering in long-strip wireless sensor networks (LSWSN is proposed. A well-distributed cluster-based architecture is first formed in the whole network through contention-based cluster head election. Cluster heads are responsible for coordination among the nodes within their clusters and aggregation of their sensory data, as well as transmission the data to the sink node on behalf of their own clusters. The intra-cluster coordination is based on the traditional TDMA schedule, in which the inter-cluster interference caused by the border nodes is solved by the multi-channel communication technique. The cluster reporting is based on the CSMA contention, in which a connected overlay network is formed by relay nodes to forward the data from the cluster heads through multi-hops to the sink node. The relay nodes are non-uniformly deployed to resolve the energy-hole problem which is extremely serious in the LSWSN. Extensive simulation results illuminate the distinguished performance of the proposed protocol.

Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia

Directory of Open Access Journals (Sweden)

Mohammad Nur Shodiq

2016-03-01

Full Text Available Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System, for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014. Keywords: Clustering, visualization, multidimensional data, seismic parameters.

Particle coagulation in molten metal based on three-dimensional analysis of cluster by x-ray micro-computer tomography (CT)

International Nuclear Information System (INIS)

Li, Tao; Shimasaki, Shin-ichi; Taniguchi, Shoji; Narita, Shunsuke; Uesugi, Kentaro

2013-01-01

Particle coagulation plays a key role in steel refining process to remove inclusions. Many research works focus on the behaviors of particle coagulation. To reveal its mechanism water model experiments have been performed by some researchers including the authors' group. In this paper, experiments of particle coagulation were carried out with molten Al including SiC particles in a mechanically agitated crucible with two baffles. Particle coagulation and formation of clusters were observed on the microscopy images of as-polished samples. Three-dimensional (3D) analysis of the clusters in solidified Al was implemented by X-ray micro CT available at SPring-8. The methods to distinguish clusters on two-dimensional (2D) cross-sectional images were discussed, which were established in the previous works by the present authors' group. The characteristics of the 3D SiC clusters and their 2D cross-sections were analyzed. The statistical ranges of the parameters for 2D clusters were used as criterions to distinguish the clusters on 2D microscopy images from the as-polished samples. The kinetics of SiC particle coagulation was studied by the measured cluster number density and size using our program to distinguish cluster in 2D cross-sectional images according to 3D information (DC-2D-3D). The calculated and experimental results of the SiC particle coagulation in molten Al agree well with each other. (author)

Data Clustering

Science.gov (United States)

Wagstaff, Kiri L.

2012-03-01

On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

Clustering of correlated networks

OpenAIRE

Dorogovtsev, S. N.

2003-01-01

We obtain the clustering coefficient, the degree-dependent local clustering, and the mean clustering of networks with arbitrary correlations between the degrees of the nearest-neighbor vertices. The resulting formulas allow one to determine the nature of the clustering of a network.

Clustering Game Behavior Data

DEFF Research Database (Denmark)

Bauckhage, C.; Drachen, Anders; Sifa, Rafet

2015-01-01

of the causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering...... and other techniques for player profiling and play style analysis have, therefore, become popular in the nascent field of game analytics. However, the proper use of clustering techniques requires expertise and an understanding of games is essential to evaluate results. With this paper, we address game data...... scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game analytics...

Semi-supervised consensus clustering for gene expression data analysis

OpenAIRE

Wang, Yunli; Pan, Youlian

2014-01-01

Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

A scalable and practical one-pass clustering algorithm for recommender system

Science.gov (United States)

Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali

2015-12-01

KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.

Electron attenuation in free, neutral ethane clusters.

Science.gov (United States)

Winkler, M; Myrseth, V; Harnes, J; Børve, K J

2014-10-28

The electron effective attenuation length (EAL) in free, neutral ethane clusters has been determined at 40 eV kinetic energy by combining carbon 1s x-ray photoelectron spectroscopy and theoretical lineshape modeling. More specifically, theory is employed to form model spectra on a grid in cluster size (N) and EAL (λ), allowing N and λ to be determined by optimizing the goodness-of-fit χ(2)(N, λ) between model and observed spectra. Experimentally, the clusters were produced in an adiabatic-expansion setup using helium as the driving gas, spanning a range of 100-600 molecules in mean cluster size. The effective attenuation length was determined to be 8.4 ± 1.9 Å, in good agreement with an independent estimate of 10 Å formed on the basis of molecular electron-scattering data and Monte Carlo simulations. The aggregation state of the clusters as well as the cluster temperature and its importance to the derived EAL value are discussed in some depth.

Kinetics of aggregation with choice.

Science.gov (United States)

Ben-Naim, E; Krapivsky, P L

2016-12-01

We generalize the ordinary aggregation process to allow for choice. In ordinary aggregation, two random clusters merge and form a larger aggregate. In our implementation of choice, a target cluster and two candidate clusters are randomly selected and the target cluster merges with the larger of the two candidate clusters. We study the long-time asymptotic behavior and find that as in ordinary aggregation, the size density adheres to the standard scaling form. However, aggregation with choice exhibits a number of different features. First, the density of the smallest clusters exhibits anomalous scaling. Second, both the small-size and the large-size tails of the density are overpopulated, at the expense of the density of moderate-size clusters. We also study the complementary case where the smaller candidate cluster participates in the aggregation process and find an abundance of moderate clusters at the expense of small and large clusters. Additionally, we investigate aggregation processes with choice among multiple candidate clusters and a symmetric implementation where the choice is between two pairs of clusters.

Metalloid Aluminum Clusters with Fluorine

Science.gov (United States)

2016-12-01

metal clusters containing Al4 units. The Al4 was evaluated when attached to an alkaline or transitional metals, namely Na, Li, Be, Cu and Zn. Mandado...i i i n r r r   and therefore the dimensionality goes as 3 3N . This changes the problem to a many one electron problem. Recall that

A Distributed Agent Implementation of Multiple Species Flocking Model for Document Partitioning Clustering

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

2006-01-01

The Flocking model, first proposed by Craig Reynolds, is one of the first bio-inspired computational collective behavior models that has many popular applications, such as animation. Our early research has resulted in a flock clustering algorithm that can achieve better performance than the Kmeans or the Ant clustering algorithms for data clustering. This algorithm generates a clustering of a given set of data through the embedding of the highdimensional data items on a two-dimensional grid for efficient clustering result retrieval and visualization. In this paper, we propose a bio-inspired clustering model, the Multiple Species Flocking clustering model (MSF), and present a distributed multi-agent MSF approach for document clustering.

The Effects of One-Dimensional Glide on the Reaction Kinetics of Interstitial Clusters

International Nuclear Information System (INIS)

Heinisch, Howard L.; Singh, B N.; Golubov, S I.

2000-01-01

Collision cascades in metals produce small interstitial clusters and perfect dislocation loops that glide in thermally activated one-dimensional (1D) random walks. These gliding defects can change their Burgers vectors by thermal activation or by interactions with other defects. Their migration is therefore''mixed 1D/3D migration'' along a 3D path consisting of 1D segments. The defect reaction kinetics under mixed 1D/3D diffusion are different from pure 1D diffusion and pure 3D diffusion, both of which can be formulated within analytical rate theory models of microstructure evolution under irradiation. Atomic-scale kinetic Monte Carlo (kMC) defect migration simulations are used to investigate the effects of mixed 1D/3D migration on defect reaction kinetics as a guide for implementing mixed 1D/3D migration into the analytical rate theory. The functional dependence of the sink strength on the sixe and concentration of sinks under mixed 1D/3D migration is shown to lie between that for pure 1D and pure 3D migration and varies with L, the average distance between direction changes of the gliding defects. It is shown that the sink strength in simulations for spherical sinks of radius R under mixed 1D/3D migration for values of L greater than R can be approximated by an expression that varies directly as R2. For small L, the form of the transition from mixed 1D/3D to pure 3D diffusion as L decreases is demonstrated in the simulations, the results of which can be used in the future development of an analytical expression describing this transition region

Co-clustering for Weblogs in Semantic Space

DEFF Research Database (Denmark)

Zong, Yu; Xu, Guandong; Dolog, Peter

2010-01-01

Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able to capture the underlying user navigational...... interest and content preference simultaneously. In this paper we will present a novel web co-clustering algorithm named Co-Clustering in Semantic space (COCS) to simultaneously partition web users and pages via a latent semantic analysis approach. In COCS, we first, train the latent semantic space...... of weblog data by using Probabilistic Latent Semantic Analysis (PLSA) model, and then, project all weblog data objects into this semantic space with probability distribution to capture the relationship among web pages and web users, at last, propose a clustering algorithm to generate the co...

ONE THOUSAND AND ONE CLUSTERS: MEASURING THE BULK FLOW WITH THE PLANCK ESZ AND X-RAY-SELECTED GALAXY CLUSTER CATALOGS

Energy Technology Data Exchange (ETDEWEB)

Mody, Krishnan [Mathematics Department, Princeton University, Princeton, NJ 08544 (United States); Hajian, Amir, E-mail: kmody@princeton.edu, E-mail: ahajian@cita.utoronto.ca [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada)

2012-10-10

We present our measurement of the 'bulk flow' using the kinetic Sunyaev-Zel'dovich (kSZ) effect in the Wilkinson Microwave Anisotropy Probe (WMAP) seven-year data. As the tracer of peculiar velocities, we use Planck Early Sunyaev-Zel'dovich Detected Cluster Catalog and a compilation of X-ray-detected galaxy cluster catalogs based on ROSAT All-Sky Survey. We build a full-sky kSZ template and fit it to the WMAP data in W band. Using a Wiener filter we maximize the signal-to-noise ratio of the kSZ cluster signal in the data. We find no significant detection of the bulk flow, and our results are consistent with the {Lambda}CDM prediction.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

Science.gov (United States)

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

OBSERVED SCALING RELATIONS FOR STRONG LENSING CLUSTERS: CONSEQUENCES FOR COSMOLOGY AND CLUSTER ASSEMBLY

International Nuclear Information System (INIS)

Comerford, Julia M.; Moustakas, Leonidas A.; Natarajan, Priyamvada

2010-01-01

Scaling relations of observed galaxy cluster properties are useful tools for constraining cosmological parameters as well as cluster formation histories. One of the key cosmological parameters, σ 8 , is constrained using observed clusters of galaxies, although current estimates of σ 8 from the scaling relations of dynamically relaxed galaxy clusters are limited by the large scatter in the observed cluster mass-temperature (M-T) relation. With a sample of eight strong lensing clusters at 0.3 8 , but combining the cluster concentration-mass relation with the M-T relation enables the inclusion of unrelaxed clusters as well. Thus, the resultant gains in the accuracy of σ 8 measurements from clusters are twofold: the errors on σ 8 are reduced and the cluster sample size is increased. Therefore, the statistics on σ 8 determination from clusters are greatly improved by the inclusion of unrelaxed clusters. Exploring cluster scaling relations further, we find that the correlation between brightest cluster galaxy (BCG) luminosity and cluster mass offers insight into the assembly histories of clusters. We find preliminary evidence for a steeper BCG luminosity-cluster mass relation for strong lensing clusters than the general cluster population, hinting that strong lensing clusters may have had more active merging histories.

Model-based Clustering of High-Dimensional Data in Astrophysics

Science.gov (United States)

Bouveyron, C.

2016-05-01

The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.

Progeny Clustering: A Method to Identify Biological Phenotypes

Science.gov (United States)

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

Air void clustering : [technical summary].

Science.gov (United States)

2015-06-01

Air void clustering around coarse aggregate in concrete has been : identified as a potential source of low strengths in concrete mixes by : several Departments of Transportation around the country. Research : was carried out to (1) develop a quantita...

Brightest Cluster Galaxies in REXCESS Clusters

Science.gov (United States)

Haarsma, Deborah B.; Leisman, L.; Bruch, S.; Donahue, M.

2009-01-01

Most galaxy clusters contain a Brightest Cluster Galaxy (BCG) which is larger than the other cluster ellipticals and has a more extended profile. In the hierarchical model, the BCG forms through many galaxy mergers in the crowded center of the cluster, and thus its properties give insight into the assembly of the cluster as a whole. In this project, we are working with the Representative XMM-Newton Cluster Structure Survey (REXCESS) team (Boehringer et al 2007) to study BCGs in 33 X-ray luminous galaxy clusters, 0.055 < z < 0.183. We are imaging the BCGs in R band at the Southern Observatory for Astrophysical Research (SOAR) in Chile. In this poster, we discuss our methods and give preliminary measurements of the BCG magnitudes, morphology, and stellar mass. We compare these BCG properties with the properties of their host clusters, particularly of the X-ray emitting gas.

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering

Science.gov (United States)

2013-01-01

Background The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. Results In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Conclusions Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship. PMID:23845024

CBHRP: A Cluster Based Routing Protocol for Wireless Sensor Network

OpenAIRE

Rashed, M. G.; Kabir, M. Hasnat; Rahim, M. Sajjadur; Ullah, Sk. Enayet

2012-01-01

A new two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP) is proposed in this paper. It is an extension of LEACH routing protocol. We introduce cluster head-set idea for cluster-based routing where several clusters are formed with the deployed sensors to collect information from target field. On rotation basis, a head-set member receives data from the neighbor nodes and transmits the aggregated results to the distance base station. This protocol ...

Magic numbers and isotopic effect of ion clusters

International Nuclear Information System (INIS)

Wang Guanghou

1989-04-01

The magic numbers and isotopic effect as well as stable configurations in relation to the charge state of the clusters are discussed. Ionic (atomic) clusters are small atomic aggregates, a physical state between gas and solid states, and have many interesting properties, some of them are more or less similar to those in nuclei

Macroeconomic Dimensions in the Clusterization Processes: Lithuanian Biomass Cluster Case

Directory of Open Access Journals (Sweden)

Navickas Valentinas

2017-03-01

Full Text Available The Future production systems’ increasing significance will impose work, which maintains not a competitive, but a collaboration basis, with concentrated resources and expertise, which can help to reach the general purpose. One form of collaboration among medium-size business organizations is work in clusters. Clusterization as a phenomenon has been known from quite a long time, but it offers simple benefits to researches at micro and medium levels. The clusterization process evaluation in macroeconomic dimensions has been comparatively little investigated. Thereby, in this article, the clusterization processes is analysed by concentrating our attention on macroeconomic factor researches. The authors analyse clusterization’s influence on country’s macroeconomic growth; they apply a structure research methodology for clusterization’s macroeconomic influence evaluation and propose that clusterization processes benefit macroeconomic analysis. The theoretical model of clusterization processes was validated by referring to a biomass cluster case. Because biomass cluster case is a new phenomenon, currently there are no other scientific approaches to them. The authors’ accomplished researches show that clusterization allows the achievement of a large positive slip in macroeconomics, which proves to lead to a high value added to creation, a faster country economic growth, and social situation amelioration.

Thermodynamics of Pore Filling Metal Clusters in Metal Organic Frameworks: Pd in UiO-66

DEFF Research Database (Denmark)

Vilhelmsen, Lasse; Sholl, David S.

2012-01-01

Metal organic frameworks (MOFs) have experimentally been demonstrated to be capable of supporting isolated transition-metal clusters, but the stability of these clusters with respect to aggregation is unclear. In this letter we use a genetic algorithm together with density functional theory...... calculations to predict the structure of Pd clusters in UiO-66. The cluster sizes examined are far larger than those in any previous modeling studies of metal clusters in MOFs and allow us to test the hypothesis that the physically separated cavities in UiO-66 could stabilize isolated Pd clusters. Our...... calculations show that Pd clusters in UiO-66 are, at best, metastable and will aggregate into connected pore filling structures at equilibrium....

Clusters in nuclei

CERN Document Server

Following the pioneering discovery of alpha clustering and of molecular resonances, the field of nuclear clustering is today one of those domains of heavy-ion nuclear physics that faces the greatest challenges, yet also contains the greatest opportunities. After many summer schools and workshops, in particular over the last decade, the community of nuclear molecular physicists has decided to collaborate in producing a comprehensive collection of lectures and tutorial reviews covering the field. This third volume follows the successful Lect. Notes Phys. 818 (Vol. 1) and 848 (Vol. 2), and comprises six extensive lectures covering the following topics:  - Gamma Rays and Molecular Structure - Faddeev Equation Approach for Three Cluster Nuclear Reactions - Tomography of the Cluster Structure of Light Nuclei Via Relativistic Dissociation - Clustering Effects Within the Dinuclear Model : From Light to Hyper-heavy Molecules in Dynamical Mean-field Approach - Clusterization in Ternary Fission - Clusters in Light N...

Spatial cluster modelling

CERN Document Server

Lawson, Andrew B

2002-01-01

Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

Metal cluster compounds - chemistry and importance; clusters containing isolated main group element atoms, large metal cluster compounds, cluster fluxionality

International Nuclear Information System (INIS)

Walther, B.

1988-01-01

This part of the review on metal cluster compounds deals with clusters containing isolated main group element atoms, with high nuclearity clusters and metal cluster fluxionality. It will be obvious that main group element atoms strongly influence the geometry, stability and reactivity of the clusters. High nuclearity clusters are of interest in there own due to the diversity of the structures adopted, but their intermediate position between molecules and the metallic state makes them a fascinating research object too. These both sites of the metal cluster chemistry as well as the frequently observed ligand and core fluxionality are related to the cluster metal and surface analogy. (author)

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yihang Yin

2015-08-01

Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

Science.gov (United States)

Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

2015-08-07

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

Semantic based cluster content discovery in description first clustering algorithm

International Nuclear Information System (INIS)

Khan, M.W.; Asif, H.M.S.

2017-01-01

In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm) is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing); an IR (Information Retrieval) technique for induction of meaningful labels for clusters and VSM (Vector Space Model) for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase. (author)

Traveling-cluster approximation for uncorrelated amorphous systems

International Nuclear Information System (INIS)

Sen, A.K.; Mills, R.; Kaplan, T.; Gray, L.J.

1984-01-01

We have developed a formalism for including cluster effects in the one-electron Green's function for a positionally disordered (liquid or amorphous) system without any correlation among the scattering sites. This method is an extension of the technique known as the traveling-cluster approximation (TCA) originally obtained and applied to a substitutional alloy by Mills and Ratanavararaksa. We have also proved the appropriate fixed-point theorem, which guarantees, for a bounded local potential, that the self-consistent equations always converge upon iteration to a unique, Herglotz solution. To our knowledge, this is the only analytic theory for considering cluster effects. Furthermore, we have performed some computer calculations in the pair TCA, for the model case of delta-function potentials on a one-dimensional random chain. These results have been compared with ''exact calculations'' (which, in principle, take into account all cluster effects) and with the coherent-potential approximation (CPA), which is the single-site TCA. The density of states for the pair TCA clearly shows some improvement over the CPA and yet, apparently, the pair approximation distorts some of the features of the exact results

Three-dimensional reconstruction of clustered microcalcifications from two digitized mammograms

Science.gov (United States)

Stotzka, Rainer; Mueller, Tim O.; Epper, Wolfgang; Gemmeke, Hartmut

1998-06-01

X-ray mammography is one of the most significant diagnosis methods in early detection of breast cancer. Usually two X- ray images from different angles are taken from each mamma to make even overlapping structures visible. X-ray mammography has a very high spatial resolution and can show microcalcifications of 50 - 200 micron in size. Clusters of microcalcifications are one of the most important and often the only indicator for malignant tumors. These calcifications are in some cases extremely difficult to detect. Computer assisted diagnosis of digitized mammograms may improve detection and interpretation of microcalcifications and cause more reliable diagnostic findings. We build a low-cost mammography workstation to detect and classify clusters of microcalcifications and tissue densities automatically. New in this approach is the estimation of the 3D formation of segmented microcalcifications and its visualization which will put additional diagnostic information at the radiologists disposal. The real problem using only two or three projections for reconstruction is the big loss of volume information. Therefore the arrangement of a cluster is estimated using only the positions of segmented microcalcifications. The arrangement of microcalcifications is visualized to the physician by rotating.

PREFACE: Nuclear Cluster Conference; Cluster'07

Science.gov (United States)

Freer, Martin

2008-05-01

The Cluster Conference is a long-running conference series dating back to the 1960's, the first being initiated by Wildermuth in Bochum, Germany, in 1969. The most recent meeting was held in Nara, Japan, in 2003, and in 2007 the 9th Cluster Conference was held in Stratford-upon-Avon, UK. As the name suggests the town of Stratford lies upon the River Avon, and shortly before the conference, due to unprecedented rainfall in the area (approximately 10 cm within half a day), lay in the River Avon! Stratford is the birthplace of the `Bard of Avon' William Shakespeare, and this formed an intriguing conference backdrop. The meeting was attended by some 90 delegates and the programme contained 65 70 oral presentations, and was opened by a historical perspective presented by Professor Brink (Oxford) and closed by Professor Horiuchi (RCNP) with an overview of the conference and future perspectives. In between, the conference covered aspects of clustering in exotic nuclei (both neutron and proton-rich), molecular structures in which valence neutrons are exchanged between cluster cores, condensates in nuclei, neutron-clusters, superheavy nuclei, clusters in nuclear astrophysical processes and exotic cluster decays such as 2p and ternary cluster decay. The field of nuclear clustering has become strongly influenced by the physics of radioactive beam facilities (reflected in the programme), and by the excitement that clustering may have an important impact on the structure of nuclei at the neutron drip-line. It was clear that since Nara the field had progressed substantially and that new themes had emerged and others had crystallized. Two particular topics resonated strongly condensates and nuclear molecules. These topics are thus likely to be central in the next cluster conference which will be held in 2011 in the Hungarian city of Debrechen. Martin Freer Participants and Cluster'07

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data.

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Ali Seyed Shirkhorshidi

Full Text Available Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.

Homological methods, representation theory, and cluster algebras

CERN Document Server

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...

Semi-supervised clustering methods.

Science.gov (United States)

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.

Fluorescent Thiol-Derivatized Gold Clusters Embedded in Polymers

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G. Carotenuto

2013-01-01

Full Text Available Owing to aurophilic interactions, linear and/or planar Au(I-thiolate molecules spontaneously aggregate, leading to molecular gold clusters passivated by a thiolate monolayer coating. Differently from the thiolate precursors, such cluster compounds show very intensive visible fluorescence characteristics that can be tuned by alloying the gold clusters with silver atoms or by conjugating the electronic structure of the metallic core with unsaturated electronic structures in the organic ligand through the sulphur atom. Here, the photoluminescence features of some examples of these systems are shortly described.

Formation of stable products from cluster-cluster collisions

International Nuclear Information System (INIS)

Alamanova, Denitsa; Grigoryan, Valeri G; Springborg, Michael

2007-01-01

The formation of stable products from copper cluster-cluster collisions is investigated by using classical molecular-dynamics simulations in combination with an embedded-atom potential. The dependence of the product clusters on impact energy, relative orientation of the clusters, and size of the clusters is studied. The structures and total energies of the product clusters are analysed and compared with those of the colliding clusters before impact. These results, together with the internal temperature, are used in obtaining an increased understanding of cluster fusion processes

Interaction of intense ultrashort pulse lasers with clusters

International Nuclear Information System (INIS)

Petrov, G. M.; Davis, J.

2008-01-01

The dynamics of clusters composed of different material irradiated by a high-intensity ultrashort pulse laser was studied using a fully relativistic three-dimensional molecular dynamics model. Key parameters of the cluster evolution such as particle positions, energy absorption, and cluster explosion were simulated. By a direct comparison of these parameters for clusters of equal initial radius but made of different material (deuterium, neon, argon, and xenon), the main stages and attributes of cluster evolution were elucidated. The simulations showed that clusters made of different material act alike, especially those of heavy elements. Clusters made of heavy elements (neon, argon, and xenon) differentiate from clusters made of light elements (deuterium) by the magnitude of the absorbed energy per cluster and the final mean energy of exploding ions. What most distinguishes clusters composed of different material is the amount of emitted radiation and its spectral range

Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy

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Ning Li

2016-01-01

Full Text Available A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.

Pili-Induced Clustering of N. gonorrhoeae Bacteria.

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Johannes Taktikos

Full Text Available Type IV pili (Tfp are prokaryotic retractable appendages known to mediate surface attachment, motility, and subsequent clustering of cells. Tfp are the main means of motility for Neisseria gonorrhoeae, the causative agent of gonorrhea. Tfp are also involved in formation of the microcolonies, which play a crucial role in the progression of the disease. While motility of individual cells is relatively well understood, little is known about the dynamics of N. gonorrhoeae aggregation. We investigate how individual N. gonorrhoeae cells, initially uniformly dispersed on flat plastic or glass surfaces, agglomerate into spherical microcolonies within hours. We quantify the clustering process by measuring the area fraction covered by the cells, number of cell aggregates, and their average size as a function of time. We observe that the microcolonies are also able to move but their mobility rapidly vanishes as the size of the colony increases. After a certain critical size they become immobile. We propose a simple theoretical model which assumes a pili-pili interaction of cells as the main clustering mechanism. Numerical simulations of the model quantitatively reproduce the experimental data on clustering and thus suggest that the agglomeration process can be entirely explained by the Tfp-mediated interactions. In agreement with this hypothesis mutants lacking pili are not able to form colonies. Moreover, cells with deficient quorum sensing mechanism show similar aggregation as the wild-type bacteria. Therefore, our results demonstrate that pili provide an essential mechanism for colony formation, while additional chemical cues, for example quorum sensing, might be of secondary importance.

Clustering in a quark gas

International Nuclear Information System (INIS)

Welke, G.M.; Heiss, W.D.

1986-01-01

In an infinite one-dimensional quark gas it is shown that a static color force, which increases at large distance, leads to a density fluctuation in the ground state. A self-consistent mean field can only be found for an effectively attractive quark-quark interaction that increases less than linearly at large distances. For a fixed coupling constant, the clustering disappears at high quark density

Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

OpenAIRE

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...

A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

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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.

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

Science.gov (United States)

Mammu, Aboobeker Sidhik Koyamparambil; Hernandez-Jayo, Unai; Sainz, Nekane; de la Iglesia, Idoia

2015-04-09

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Cross-Layer Cluster-Based Energy-Efficient Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Aboobeker Sidhik Koyamparambil Mammu

2015-04-01

Full Text Available Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs. One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs and a cluster head (CH. The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH and hybrid energy-efficient distributed clustering (HEED.

Marketing research cluster analysis

OpenAIRE

Marić Nebojša

2002-01-01

One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

Nuclear clustering - a cluster core model study

International Nuclear Information System (INIS)

Paul Selvi, G.; Nandhini, N.; Balasubramaniam, M.

2015-01-01

Nuclear clustering, similar to other clustering phenomenon in nature is a much warranted study, since it would help us in understanding the nature of binding of the nucleons inside the nucleus, closed shell behaviour when the system is highly deformed, dynamics and structure at extremes. Several models account for the clustering phenomenon of nuclei. We present in this work, a cluster core model study of nuclear clustering in light mass nuclei

Uniform deposition of size-selected clusters using Lissajous scanning

International Nuclear Information System (INIS)

Beniya, Atsushi; Watanabe, Yoshihide; Hirata, Hirohito

2016-01-01

Size-selected clusters can be deposited on the surface using size-selected cluster ion beams. However, because of the cross-sectional intensity distribution of the ion beam, it is difficult to define the coverage of the deposited clusters. The aggregation probability of the cluster depends on coverage, whereas cluster size on the surface depends on the position, despite the size-selected clusters are deposited. It is crucial, therefore, to deposit clusters uniformly on the surface. In this study, size-selected clusters were deposited uniformly on surfaces by scanning the cluster ions in the form of Lissajous pattern. Two sets of deflector electrodes set in orthogonal directions were placed in front of the sample surface. Triangular waves were applied to the electrodes with an irrational frequency ratio to ensure that the ion trajectory filled the sample surface. The advantages of this method are simplicity and low cost of setup compared with raster scanning method. The authors further investigated CO adsorption on size-selected Pt n (n = 7, 15, 20) clusters uniformly deposited on the Al 2 O 3 /NiAl(110) surface and demonstrated the importance of uniform deposition.

Uniform deposition of size-selected clusters using Lissajous scanning

Energy Technology Data Exchange (ETDEWEB)

Beniya, Atsushi; Watanabe, Yoshihide, E-mail: e0827@mosk.tytlabs.co.jp [Toyota Central R& D Labs., Inc., 41-1 Yokomichi, Nagakute, Aichi 480-1192 (Japan); Hirata, Hirohito [Toyota Motor Corporation, 1200 Mishuku, Susono, Shizuoka 410-1193 (Japan)

2016-05-15

Size-selected clusters can be deposited on the surface using size-selected cluster ion beams. However, because of the cross-sectional intensity distribution of the ion beam, it is difficult to define the coverage of the deposited clusters. The aggregation probability of the cluster depends on coverage, whereas cluster size on the surface depends on the position, despite the size-selected clusters are deposited. It is crucial, therefore, to deposit clusters uniformly on the surface. In this study, size-selected clusters were deposited uniformly on surfaces by scanning the cluster ions in the form of Lissajous pattern. Two sets of deflector electrodes set in orthogonal directions were placed in front of the sample surface. Triangular waves were applied to the electrodes with an irrational frequency ratio to ensure that the ion trajectory filled the sample surface. The advantages of this method are simplicity and low cost of setup compared with raster scanning method. The authors further investigated CO adsorption on size-selected Pt{sub n} (n = 7, 15, 20) clusters uniformly deposited on the Al{sub 2}O{sub 3}/NiAl(110) surface and demonstrated the importance of uniform deposition.

Changing cluster composition in cluster randomised controlled trials: design and analysis considerations

Science.gov (United States)

2014-01-01

Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations

MANAGEMENT APPROACH BETWEEN BUSINESS CLUSTER SUCCESS AND SOFT LEADER CHARACTERISTICS

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Robert Lippert

2014-05-01

Full Text Available One of the potential aspects of economic growth lies in focusing on furtherance the development of business clusters. By linking the complementary competencies of profit oriented enterprises, NGO-s, universities, research institutes and local authorities, the innovation potential and the productivity are significantly increased. The present study investigates a specific and challenging managerial activity, the role of the cluster manager. The aim of the research is to reveal the intrinsic motivation of cluster operations and to demonstrate the importance of the manager in the efficient and sustainable operation. An empirical research has been conducted involving cluster managers and member organisations through an extensive questionnaire survey in Hungary. First, determinant factors of cluster success have been identified. By using these factors, as the operational activity of the cluster, as well as the satisfaction of the members in the field of innovation and productivity, a new continuous three-dimensional maturity model has been introduced to evaluate the cluster success. Mapping the soft factors, organisational culture and leadership roles have been assessed by applying Competing Values Framework method. The results of the research depict the correlation found between soft leader characteristics and cluster success.

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2006-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2008-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Robust Pseudo-Hierarchical Support Vector Clustering

DEFF Research Database (Denmark)

Hansen, Michael Sass; Sjöstrand, Karl; Olafsdóttir, Hildur

2007-01-01

Support vector clustering (SVC) has proven an efficient algorithm for clustering of noisy and high-dimensional data sets, with applications within many fields of research. An inherent problem, however, has been setting the parameters of the SVC algorithm. Using the recent emergence of a method...... for calculating the entire regularization path of the support vector domain description, we propose a fast method for robust pseudo-hierarchical support vector clustering (HSVC). The method is demonstrated to work well on generated data, as well as for detecting ischemic segments from multidimensional myocardial...

Marketing research cluster analysis

Directory of Open Access Journals (Sweden)

Marić Nebojša

2002-01-01

Full Text Available One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

Understanding 3D human torso shape via manifold clustering

Science.gov (United States)

Li, Sheng; Li, Peng; Fu, Yun

2013-05-01

Discovering the variations in human torso shape plays a key role in many design-oriented applications, such as suit designing. With recent advances in 3D surface imaging technologies, people can obtain 3D human torso data that provide more information than traditional measurements. However, how to find different human shapes from 3D torso data is still an open problem. In this paper, we propose to use spectral clustering approach on torso manifold to address this problem. We first represent high-dimensional torso data in a low-dimensional space using manifold learning algorithm. Then the spectral clustering method is performed to get several disjoint clusters. Experimental results show that the clusters discovered by our approach can describe the discrepancies in both genders and human shapes, and our approach achieves better performance than the compared clustering method.

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

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Ayad Hendalianpour

2016-11-01

Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

Potential and limits to cluster-state quantum computing using probabilistic gates

International Nuclear Information System (INIS)

Gross, D.; Kieling, K.; Eisert, J.

2006-01-01

We establish bounds to the necessary resource consumption when building up cluster states for one-way computing using probabilistic gates. Emphasis is put on state preparation with linear optical gates, as the probabilistic character is unavoidable here. We identify rigorous general bounds to the necessary consumption of initially available maximally entangled pairs when building up one-dimensional cluster states with individually acting linear optical quantum gates, entangled pairs, and vacuum modes. As the known linear optics gates have a limited maximum success probability, as we show, this amounts to finding the optimal classical strategy of fusing pieces of linear cluster states. A formal notion of classical configurations and strategies is introduced for probabilistic nonfaulty gates. We study the asymptotic performance of strategies that can be simply described, and prove ultimate bounds to the performance of the globally optimal strategy. The arguments employ methods of random walks and convex optimization. This optimal strategy is also the one that requires the shortest storage time, and necessitates the fewest invocations of probabilistic gates. For two-dimensional cluster states, we find, for any elementary success probability, an essentially deterministic preparation of a cluster state with quadratic, hence optimal, asymptotic scaling in the use of entangled pairs. We also identify a percolation effect in state preparation, in that from a threshold probability on, almost all preparations will be either successful or fail. We outline the implications on linear optical architectures and fault-tolerant computations

IntroductionThe Cluster mission

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M. Fehringer

Full Text Available The Cluster mission, ESA’s first cornerstone project, together with the SOHO mission, dating back to the first proposals in 1982, was finally launched in the summer of 2000. On 16 July and 9 August, respectively, two Russian Soyuz rockets blasted off from the Russian cosmodrome in Baikonour to deliver two Cluster spacecraft, each into their proper orbit. By the end of August 2000, the four Cluster satellites had reached their final tetrahedral constellation. The commissioning of 44 instruments, both individually and as an ensemble of complementary tools, was completed five months later to ensure the optimal use of their combined observational potential. On 1 February 2001, the mission was declared operational. The main goal of the Cluster mission is to study the small-scale plasma structures in three dimensions in key plasma regions, such as the solar wind, bow shock, magnetopause, polar cusps, magnetotail and the auroral zones. With its unique capabilities of three-dimensional spatial resolution, Cluster plays a major role in the International Solar Terrestrial Program (ISTP, where Cluster and the Solar and Heliospheric Observatory (SOHO are the European contributions. Cluster’s payload consists of state-of-the-art plasma instrumentation to measure electric and magnetic fields from the quasi-static up to high frequencies, and electron and ion distribution functions from energies of nearly 0 eV to a few MeV. The science operations are coordinated by the Joint Science Operations Centre (JSOC, at the Rutherford Appleton Laboratory (UK, and implemented by the European Space Operations Centre (ESOC, in Darmstadt, Germany. A network of eight national data centres has been set up for raw data processing, for the production of physical parameters, and their distribution to end users all over the world. The latest information on the Cluster mission can be found at http://sci.esa.int/cluster/.

IntroductionThe Cluster mission

Directory of Open Access Journals (Sweden)

C. P. Escoubet

2001-09-01

Full Text Available The Cluster mission, ESA’s first cornerstone project, together with the SOHO mission, dating back to the first proposals in 1982, was finally launched in the summer of 2000. On 16 July and 9 August, respectively, two Russian Soyuz rockets blasted off from the Russian cosmodrome in Baikonour to deliver two Cluster spacecraft, each into their proper orbit. By the end of August 2000, the four Cluster satellites had reached their final tetrahedral constellation. The commissioning of 44 instruments, both individually and as an ensemble of complementary tools, was completed five months later to ensure the optimal use of their combined observational potential. On 1 February 2001, the mission was declared operational. The main goal of the Cluster mission is to study the small-scale plasma structures in three dimensions in key plasma regions, such as the solar wind, bow shock, magnetopause, polar cusps, magnetotail and the auroral zones. With its unique capabilities of three-dimensional spatial resolution, Cluster plays a major role in the International Solar Terrestrial Program (ISTP, where Cluster and the Solar and Heliospheric Observatory (SOHO are the European contributions. Cluster’s payload consists of state-of-the-art plasma instrumentation to measure electric and magnetic fields from the quasi-static up to high frequencies, and electron and ion distribution functions from energies of nearly 0 eV to a few MeV. The science operations are coordinated by the Joint Science Operations Centre (JSOC, at the Rutherford Appleton Laboratory (UK, and implemented by the European Space Operations Centre (ESOC, in Darmstadt, Germany. A network of eight national data centres has been set up for raw data processing, for the production of physical parameters, and their distribution to end users all over the world. The latest information on the Cluster mission can be found at http://sci.esa.int/cluster/.

Peak clustering in two-dimensional gas chromatography with mass spectrometric detection based on theoretical calculation of two-dimensional peak shapes: the 2DAid approach.

Science.gov (United States)

van Stee, Leo L P; Brinkman, Udo A Th

2011-10-28

A method is presented to facilitate the non-target analysis of data obtained in temperature-programmed comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-ToF-MS). One main difficulty of GC×GC data analysis is that each peak is usually modulated several times and therefore appears as a series of peaks (or peaklets) in the one-dimensionally recorded data. The proposed method, 2DAid, uses basic chromatographic laws to calculate the theoretical shape of a 2D peak (a cluster of peaklets originating from the same analyte) in order to define the area in which the peaklets of each individual compound can be expected to show up. Based on analyte-identity information obtained by means of mass spectral library searching, the individual peaklets are then combined into a single 2D peak. The method is applied, amongst others, to a complex mixture containing 362 analytes. It is demonstrated that the 2D peak shapes can be accurately predicted and that clustering and further processing can reduce the final peak list to a manageable size. Copyright © 2011 Elsevier B.V. All rights reserved.

Comprehensive cluster analysis with Transitivity Clustering.

Science.gov (United States)

Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

2011-03-01

Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

Cluster-cluster correlations and constraints on the correlation hierarchy

Science.gov (United States)

Hamilton, A. J. S.; Gott, J. R., III

1988-01-01

The hypothesis that galaxies cluster around clusters at least as strongly as they cluster around galaxies imposes constraints on the hierarchy of correlation amplitudes in hierachical clustering models. The distributions which saturate these constraints are the Rayleigh-Levy random walk fractals proposed by Mandelbrot; for these fractal distributions cluster-cluster correlations are all identically equal to galaxy-galaxy correlations. If correlation amplitudes exceed the constraints, as is observed, then cluster-cluster correlations must exceed galaxy-galaxy correlations, as is observed.

CONSTRAINING CLUSTER PHYSICS WITH THE SHAPE OF X-RAY CLUSTERS: COMPARISON OF LOCAL X-RAY CLUSTERS VERSUS ΛCDM CLUSTERS

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

Semi-supervised clustering methods

Science.gov (United States)

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

Transient plasma injections in the dayside magnetosphere: one-to-one correlated observations by Cluster and SuperDARN

Directory of Open Access Journals (Sweden)

A. Marchaudon

2004-01-01

Full Text Available Conjunctions in the cusp between the four Cluster spacecraft and SuperDARN ground-based radars offer unique opportunities to compare the signatures of transient plasma injections simultaneously in the high-altitude dayside magnetosphere and in the ionosphere. We report here on such observations on 17 March 2001, when the IMF initially northward and duskward, turns southward and dawnward for a short period. The changes in the convection direction at Cluster are well correlated with the interplanetary magnetic field (IMF By variations. Moreover, the changes in the ionosphere follow those in the magnetosphere, with a 2–3min delay. When mapped into the ionosphere, the convection velocity at Cluster is about 1.5 times larger than measured by SuperDARN. In the high-altitude cusp, field and particle observations by Cluster display the characteristic signatures of plasma injections into the magnetosphere suggestive of Flux Transfer Events (FTEs. Simultaneous impulsive and localized convection plasma flows are observed in the ionospheric cusp by the HF radars. A clear one-to-one correlation is observed for three successive injections, with a 2–3min delay between the magnetospheric and ionospheric observations. For each event, the drift velocity of reconnected flux tubes (phase velocity has been compared in the magnetosphere and in the ionosphere. The drift velocity measured at Cluster is of the order of 400–600ms–1 when mapped into the ionosphere, in qualitative agreement with SuperDARN observations. Finally, the reconnected flux tubes are elongated in the north-south direction, with an east-west dimension of 30–60km in the ionosphere from mapped Cluster observations, which is consistent with SuperDARN observations, although slightly smaller. Key words. Ionosphere (plasma convection – Magnetospheric physics (magnetopause, cusp, and boundary layers; magnetosphere-ionosphere interactions

New spectroscopic tool for cluster science: Nonexponential laser fluence dependence of photofragmentation

International Nuclear Information System (INIS)

Haberland, H.; Issendorff, B.v.

1996-01-01

The photodestruction of Hg 7 ++ and Hg 9 ++ has been measured as a function of photon flux. A polarization dependent deviation from a purely exponential intensity decrease was observed in both cases. This effect, which in essence is an alignment phenomenon, can be used to characterize dissociating electronic transitions of molecules and clusters. For the clusters studied it is due to a one-dimensional transition dipole moment having a fixed direction within the cluster. The effect is expected to play a role in many photoabsorption experiments where molecule/cluster ionization or fragmentation is studied under high photon fluxes. copyright 1996 The American Physical Society

THE HST/ACS COMA CLUSTER SURVEY. IV. INTERGALACTIC GLOBULAR CLUSTERS AND THE MASSIVE GLOBULAR CLUSTER SYSTEM AT THE CORE OF THE COMA GALAXY CLUSTER

International Nuclear Information System (INIS)

Peng, Eric W.; Ferguson, Henry C.; Goudfrooij, Paul; Hammer, Derek; Lucey, John R.; Marzke, Ronald O.; Puzia, Thomas H.; Carter, David; Balcells, Marc; Bridges, Terry; Chiboucas, Kristin; Del Burgo, Carlos; Graham, Alister W.; Guzman, Rafael; Hudson, Michael J.; Matkovic, Ana

2011-01-01

Intracluster stellar populations are a natural result of tidal interactions in galaxy clusters. Measuring these populations is difficult, but important for understanding the assembly of the most massive galaxies. The Coma cluster of galaxies is one of the nearest truly massive galaxy clusters and is host to a correspondingly large system of globular clusters (GCs). We use imaging from the HST/ACS Coma Cluster Survey to present the first definitive detection of a large population of intracluster GCs (IGCs) that fills the Coma cluster core and is not associated with individual galaxies. The GC surface density profile around the central massive elliptical galaxy, NGC 4874, is dominated at large radii by a population of IGCs that extend to the limit of our data (R +4000 -5000 (systematic) IGCs out to this radius, and that they make up ∼70% of the central GC system, making this the largest GC system in the nearby universe. Even including the GC systems of other cluster galaxies, the IGCs still make up ∼30%-45% of the GCs in the cluster core. Observational limits from previous studies of the intracluster light (ICL) suggest that the IGC population has a high specific frequency. If the IGC population has a specific frequency similar to high-S N dwarf galaxies, then the ICL has a mean surface brightness of μ V ∼ 27 mag arcsec -2 and a total stellar mass of roughly 10 12 M sun within the cluster core. The ICL makes up approximately half of the stellar luminosity and one-third of the stellar mass of the central (NGC 4874+ICL) system. The color distribution of the IGC population is bimodal, with blue, metal-poor GCs outnumbering red, metal-rich GCs by a ratio of 4:1. The inner GCs associated with NGC 4874 also have a bimodal distribution in color, but with a redder metal-poor population. The fraction of red IGCs (20%), and the red color of those GCs, implies that IGCs can originate from the halos of relatively massive, L* galaxies, and not solely from the disruption of

Negotiating Cluster Boundaries

DEFF Research Database (Denmark)

Giacomin, Valeria

2017-01-01

Palm oil was introduced to Malay(si)a as an alternative to natural rubber, inheriting its cluster organizational structure. In the late 1960s, Malaysia became the world’s largest palm oil exporter. Based on archival material from British colonial institutions and agency houses, this paper focuses...... on the governance dynamics that drove institutional change within this cluster during decolonization. The analysis presents three main findings: (i) cluster boundaries are defined by continuous tug-of-war style negotiations between public and private actors; (ii) this interaction produces institutional change...... within the cluster, in the form of cumulative ‘institutional rounds’ – the correction or disruption of existing institutions or the creation of new ones; and (iii) this process leads to a broader inclusion of local actors in the original cluster configuration. The paper challenges the prevalent argument...

Cluster generator

Science.gov (United States)

Donchev, Todor I [Urbana, IL; Petrov, Ivan G [Champaign, IL

2011-05-31

Described herein is an apparatus and a method for producing atom clusters based on a gas discharge within a hollow cathode. The hollow cathode includes one or more walls. The one or more walls define a sputtering chamber within the hollow cathode and include a material to be sputtered. A hollow anode is positioned at an end of the sputtering chamber, and atom clusters are formed when a gas discharge is generated between the hollow anode and the hollow cathode.

Diffusion of He interstitial and di-He cluster at grain boundaries in α-Fe

International Nuclear Information System (INIS)

Gao, F.; Heinisch, H.L.; Kurtz, R.J.

2007-01-01

A systematic molecular dynamics study of the diffusion mechanisms of He interstitial and di-He cluster at two representative interfaces has been carried out in α-Fe. The diffusion coefficient of a He interstitial and the effective migration energies were determined. The He atom diffuses along the Σ11 grain boundary one-dimensionally along specific directions, while it migrates two-dimensionally at low temperatures, and three-dimensionally at higher temperatures, in the Σ3 grain boundary. The di-He interstitial cluster can migrate rapidly along the Σ3 interface at low temperatures, but not at the Σ11 interface. It has been observed that a di-He interstitial cluster can kick out a self interstitial atom (SIA) at high temperatures, forming a He 2 V complex. The SIA migrates rapidly near interfaces, whereas the He 2 V complex is immobile at the temperatures considered. This small cluster may serve as the smallest nucleation for the formation of helium bubbles at interfaces

Swarm v2: highly-scalable and high-resolution amplicon clustering.

Science.gov (United States)

Mahé, Frédéric; Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2015-01-01

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

Swarm v2: highly-scalable and high-resolution amplicon clustering

Directory of Open Access Journals (Sweden)

Frédéric Mahé

2015-12-01

Full Text Available Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs, free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d, followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1 a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2 the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

Defining objective clusters for rabies virus sequences using affinity propagation clustering.

Directory of Open Access Journals (Sweden)

Susanne Fischer

2018-01-01

Full Text Available Rabies is caused by lyssaviruses, and is one of the oldest known zoonoses. In recent years, more than 21,000 nucleotide sequences of rabies viruses (RABV, from the prototype species rabies lyssavirus, have been deposited in public databases. Subsequent phylogenetic analyses in combination with metadata suggest geographic distributions of RABV. However, these analyses somewhat experience technical difficulties in defining verifiable criteria for cluster allocations in phylogenetic trees inviting for a more rational approach. Therefore, we applied a relatively new mathematical clustering algorythm named 'affinity propagation clustering' (AP to propose a standardized sub-species classification utilizing full-genome RABV sequences. Because AP has the advantage that it is computationally fast and works for any meaningful measure of similarity between data samples, it has previously been applied successfully in bioinformatics, for analysis of microarray and gene expression data, however, cluster analysis of sequences is still in its infancy. Existing (516 and original (46 full genome RABV sequences were used to demonstrate the application of AP for RABV clustering. On a global scale, AP proposed four clusters, i.e. New World cluster, Arctic/Arctic-like, Cosmopolitan, and Asian as previously assigned by phylogenetic studies. By combining AP with established phylogenetic analyses, it is possible to resolve phylogenetic relationships between verifiably determined clusters and sequences. This workflow will be useful in confirming cluster distributions in a uniform transparent manner, not only for RABV, but also for other comparative sequence analyses.

Cluster algebras in mathematical physics

International Nuclear Information System (INIS)

Francesco, Philippe Di; Gekhtman, Michael; Kuniba, Atsuo; Yamazaki, Masahito

2014-01-01

This special issue of Journal of Physics A: Mathematical and Theoretical contains reviews and original research articles on cluster algebras and their applications to mathematical physics. Cluster algebras were introduced by S Fomin and A Zelevinsky around 2000 as a tool for studying total positivity and dual canonical bases in Lie theory. Since then the theory has found diverse applications in mathematics and mathematical physics. Cluster algebras are axiomatically defined commutative rings equipped with a distinguished set of generators (cluster variables) subdivided into overlapping subsets (clusters) of the same cardinality subject to certain polynomial relations. A cluster algebra of rank n can be viewed as a subring of the field of rational functions in n variables. Rather than being presented, at the outset, by a complete set of generators and relations, it is constructed from the initial seed via an iterative procedure called mutation producing new seeds successively to generate the whole algebra. A seed consists of an n-tuple of rational functions called cluster variables and an exchange matrix controlling the mutation. Relations of cluster algebra type can be observed in many areas of mathematics (Plücker and Ptolemy relations, Stokes curves and wall-crossing phenomena, Feynman integrals, Somos sequences and Hirota equations to name just a few examples). The cluster variables enjoy a remarkable combinatorial pattern; in particular, they exhibit the Laurent phenomenon: they are expressed as Laurent polynomials rather than more general rational functions in terms of the cluster variables in any seed. These characteristic features are often referred to as the cluster algebra structure. In the last decade, it became apparent that cluster structures are ubiquitous in mathematical physics. Examples include supersymmetric gauge theories, Poisson geometry, integrable systems, statistical mechanics, fusion products in infinite dimensional algebras, dilogarithm

Subspace K-means clustering.

Science.gov (United States)

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).

Relative efficiency and sample size for cluster randomized trials with variable cluster sizes.

Science.gov (United States)

You, Zhiying; Williams, O Dale; Aban, Inmaculada; Kabagambe, Edmond Kato; Tiwari, Hemant K; Cutter, Gary

2011-02-01

The statistical power of cluster randomized trials depends on two sample size components, the number of clusters per group and the numbers of individuals within clusters (cluster size). Variable cluster sizes are common and this variation alone may have significant impact on study power. Previous approaches have taken this into account by either adjusting total sample size using a designated design effect or adjusting the number of clusters according to an assessment of the relative efficiency of unequal versus equal cluster sizes. This article defines a relative efficiency of unequal versus equal cluster sizes using noncentrality parameters, investigates properties of this measure, and proposes an approach for adjusting the required sample size accordingly. We focus on comparing two groups with normally distributed outcomes using t-test, and use the noncentrality parameter to define the relative efficiency of unequal versus equal cluster sizes and show that statistical power depends only on this parameter for a given number of clusters. We calculate the sample size required for an unequal cluster sizes trial to have the same power as one with equal cluster sizes. Relative efficiency based on the noncentrality parameter is straightforward to calculate and easy to interpret. It connects the required mean cluster size directly to the required sample size with equal cluster sizes. Consequently, our approach first determines the sample size requirements with equal cluster sizes for a pre-specified study power and then calculates the required mean cluster size while keeping the number of clusters unchanged. Our approach allows adjustment in mean cluster size alone or simultaneous adjustment in mean cluster size and number of clusters, and is a flexible alternative to and a useful complement to existing methods. Comparison indicated that we have defined a relative efficiency that is greater than the relative efficiency in the literature under some conditions. Our measure

Core level photoelectron spectroscopy probed heterogeneous xenon/neon clusters

International Nuclear Information System (INIS)

Pokapanich, Wandared; Björneholm, Olle; Öhrwall, Gunnar; Tchaplyguine, Maxim

2017-01-01

Binary rare gas clusters; xenon and neon which have a significant contrariety between sizes, produced by a co-expansion set up and have been studied using synchrotron radiation based x-ray photoelectron spectroscopy. Concentration ratios of the heterogeneous clusters; 1%, 3%, 5% and 10% were controlled. The core level spectra were used to determine structure of the mixed cluster and analyzed by considering screening mechanisms. Furthermore, electron binding energy shift calculations demonstrated cluster aggregation models which may occur in such process. The results showed that in the case of low mixing ratios of 3% and 5% of xenon in neon, the geometric structures exhibit xenon in the center and xenon/neon interfaced in the outer shells. However, neon cluster vanished when the concentration of xenon was increased to 10%. - Highlights: • Co-expansion setup is suitable for producing binary Xe/Ne clusters. • Appropriate temperature, pressure, and mixing ratios should be strictly controlled. • Low mixing ratio, Xe formed in the core and Xe/Ne interfacing in the outer shell. • High mixing ratio, only pure Xe clusters were detected.

A cluster analysis investigation of workaholism as a syndrome.

Science.gov (United States)

Aziz, Shahnaz; Zickar, Michael J

2006-01-01

Workaholism has been conceptualized as a syndrome although there have been few tests that explicitly consider its syndrome status. The authors analyzed a three-dimensional scale of workaholism developed by Spence and Robbins (1992) using cluster analysis. The authors identified three clusters of individuals, one of which corresponded to Spence and Robbins's profile of the workaholic (high work involvement, high drive to work, low work enjoyment). Consistent with previously conjectured relations with workaholism, individuals in the workaholic cluster were more likely to label themselves as workaholics, more likely to have acquaintances label them as workaholics, and more likely to have lower life satisfaction and higher work-life imbalance. The importance of considering workaholism as a syndrome and the implications for effective interventions are discussed. Copyright 2006 APA.

Four-dimensional reconstruction of cultural heritage sites based on photogrammetry and clustering

Science.gov (United States)

Voulodimos, Athanasios; Doulamis, Nikolaos; Fritsch, Dieter; Makantasis, Konstantinos; Doulamis, Anastasios; Klein, Michael

2017-01-01

A system designed and developed for the three-dimensional (3-D) reconstruction of cultural heritage (CH) assets is presented. Two basic approaches are presented. The first one, resulting in an "approximate" 3-D model, uses images retrieved in online multimedia collections; it employs a clustering-based technique to perform content-based filtering and eliminate outliers that significantly reduce the performance of 3-D reconstruction frameworks. The second one is based on input image data acquired through terrestrial laser scanning, as well as close range and airborne photogrammetry; it follows a sophisticated multistep strategy, which leads to a "precise" 3-D model. Furthermore, the concept of change history maps is proposed to address the computational limitations involved in four-dimensional (4-D) modeling, i.e., capturing 3-D models of a CH landmark or site at different time instances. The system also comprises a presentation viewer, which manages the display of the multifaceted CH content collected and created. The described methods have been successfully applied and evaluated in challenging real-world scenarios, including the 4-D reconstruction of the historic Market Square of the German city of Calw in the context of the 4-D-CH-World EU project.

Graph-based clustering and data visualization algorithms

CERN Document Server

Vathy-Fogarassy, Ágnes

2013-01-01

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on

Lifting to cluster-tilting objects in higher cluster categories

OpenAIRE

Liu, Pin

2008-01-01

In this note, we consider the $d$-cluster-tilted algebras, the endomorphism algebras of $d$-cluster-tilting objects in $d$-cluster categories. We show that a tilting module over such an algebra lifts to a $d$-cluster-tilting object in this $d$-cluster category.

Clustering impact regime with shocks in freely evolving granular gas

Science.gov (United States)

Isobe, Masaharu

2017-06-01

A freely cooling granular gas without any external force evolves from the initial homogeneous state to the inhomogeneous clustering state, at which the energy decay deviates from the Haff's law. The asymptotic behavior of energy in the inelastic hard sphere model have been predicted by several theories, which are based on the mode coupling theory or extension of inelastic hard rods gas. In this study, we revisited the clustering regime of freely evolving granular gas via large-scale molecular dynamics simulation with up to 16.7 million inelastic hard disks. We found novel regime regarding on collisions between "clusters" spontaneously appearing after clustering regime, which can only be identified more than a few million particles system. The volumetric dilatation pattern of semicircular shape originated from density shock propagation are well characterized on the appearing of "cluster impact" during the aggregation process of clusters.

Does one-dimensional (1D) adatom and cluster diffusion of Pt on the Pt(110)-(1 x 2) surface lead to 1D ripening?

International Nuclear Information System (INIS)

Linderoth, T R; Horch, S; Petersen, L; Laegsgaard, E; Stensgaard, I; Besenbacher, F

2005-01-01

The technique of scanning tunnelling microscopy (STM) uniquely allows dynamic processes on surfaces to be followed directly in real space and at atomic resolution. Results for the 551225 surface diffusion of Pt adatoms and clusters on the anisotropic, missing row reconstructed Pt(110)-(1 x 2) surface are briefly reviewed. Mass transport in this system is entirely one-dimensional (1D) since, at low adatom coverage, atoms and clusters are confined to the missing row troughs. In this paper, we therefore address the question if Pt/Pt(110)-(1 x 2) is a 1D model system to study late stage growth phenomena such as island ripening? From STM measurements, we quantify the morphology changes resulting from annealing a surface configuration with small 1D Pt islands in the missing row troughs to temperatures in the interval 369-395 K. Interestingly, the resulting increase in island sizes (ripening) cannot be accounted for by the known island and adatom mobilities within a 1D model. An explanation is provided from dynamic, time-resolved 'STM-movies' that directly reveal two novel island-mediated mechanisms for inter-trough mass transport which cause the Pt/Pt(110)-(1 x 2) system not to be purely 1D at the higher surface coverage used in the annealing experiments

Traveling cluster approximation for uncorrelated amorphous systems

International Nuclear Information System (INIS)

Kaplan, T.; Sen, A.K.; Gray, L.J.; Mills, R.

1985-01-01

In this paper, the authors apply the TCA concepts to spatially disordered, uncorrelated systems (e.g., fluids or amorphous metals without short-range order). This is the first approximation scheme for amorphous systems that takes cluster effects into account while preserving the Herglotz property for any amount of disorder. They have performed some computer calculations for the pair TCA, for the model case of delta-function potentials on a one-dimensional random chain. These results are compared with exact calculations (which, in principle, taken into account all cluster effects) and with the CPA, which is the single-site TCA. The density of states for the pair TCA clearly shows some improvement over the CPA, and yet, apparently, the pair approximation distorts some of the features of the exact results. They conclude that the effects of large clusters are much more important in an uncorrelated liquid metal than in a substitutional alloy. As a result, the pair TCA, which does quite a nice job for alloys, is not adequate for the liquid. Larger clusters must be treated exactly, and therefore an n-TCA with n > 2 must be used

Uncertainty of a detected spatial cluster in 1D: quantification and visualization

KAUST Repository

Lee, Junho; Gangnon, Ronald E.; Zhu, Jun; Liang, Jingjing

2017-01-01

Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

Uncertainty of a detected spatial cluster in 1D: quantification and visualization

KAUST Repository

Lee, Junho

2017-10-19

Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

Dense Fe cluster-assembled films by energetic cluster deposition

International Nuclear Information System (INIS)

Peng, D.L.; Yamada, H.; Hihara, T.; Uchida, T.; Sumiyama, K.

2004-01-01

High-density Fe cluster-assembled films were produced at room temperature by an energetic cluster deposition. Though cluster-assemblies are usually sooty and porous, the present Fe cluster-assembled films are lustrous and dense, revealing a soft magnetic behavior. Size-monodispersed Fe clusters with the mean cluster size d=9 nm were synthesized using a plasma-gas-condensation technique. Ionized clusters are accelerated electrically and deposited onto the substrate together with neutral clusters from the same cluster source. Packing fraction and saturation magnetic flux density increase rapidly and magnetic coercivity decreases remarkably with increasing acceleration voltage. The Fe cluster-assembled film obtained at the acceleration voltage of -20 kV has a packing fraction of 0.86±0.03, saturation magnetic flux density of 1.78±0.05 Wb/m 2 , and coercivity value smaller than 80 A/m. The resistivity at room temperature is ten times larger than that of bulk Fe metal

Stable dissipative optical vortex clusters by inhomogeneous effective diffusion.

Science.gov (United States)

Li, Huishan; Lai, Shiquan; Qui, Yunli; Zhu, Xing; Xie, Jianing; Mihalache, Dumitru; He, Yingji

2017-10-30

We numerically show the generation of robust vortex clusters embedded in a two-dimensional beam propagating in a dissipative medium described by the generic cubic-quintic complex Ginzburg-Landau equation with an inhomogeneous effective diffusion term, which is asymmetrical in the two transverse directions and periodically modulated in the longitudinal direction. We show the generation of stable optical vortex clusters for different values of the winding number (topological charge) of the input optical beam. We have found that the number of individual vortex solitons that form the robust vortex cluster is equal to the winding number of the input beam. We have obtained the relationships between the amplitudes and oscillation periods of the inhomogeneous effective diffusion and the cubic gain and diffusion (viscosity) parameters, which depict the regions of existence and stability of vortex clusters. The obtained results offer a method to form robust vortex clusters embedded in two-dimensional optical beams, and we envisage potential applications in the area of structured light.

Four-cluster chimera state in non-locally coupled phase oscillator systems with an external potential

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)

Ensemble Clustering using Semidefinite Programming with Applications.

Science.gov (United States)

Singh, Vikas; Mukherjee, Lopamudra; Peng, Jiming; Xu, Jinhui

2010-05-01

In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

Hydrodynamic time scales for intense laser-heated clusters

International Nuclear Information System (INIS)

Parra, Enrique; Alexeev, Ilya; Fan, Jingyun; Kim, Kiong Y.; McNaught, Stuart J.; Milchberg, Howard M.

2003-01-01

Measurements are presented of x-ray (>1.5 keV) and extreme ultraviolet (EUV, λ equal to 2-44 nm) emission from argon clusters irradiated with constant-energy (50 mJ), variable-width laser pulses ranging from 100 fs to 10 ns. The results for clusters can be understood in terms of two time scales: a short time scale for optimal resonant absorption at the critical-density layer in the expanding plasma, and a longer time scale for the plasma to drop below critical density. We present a one-dimensional hydrodynamic model of the intense laser-cluster interaction in which the laser field is treated self-consistently. We find that nonuniform expansion of the heated material results in long-time resonance of the laser field at the critical-density plasma layer. These simulations explain the dependence of generation efficiency on laser pulse width

Early-stage aggregation in three-dimensional charged granular gas

Science.gov (United States)

Singh, Chamkor; Mazza, Marco G.

2018-02-01

Neutral grains made of the same dielectric material can attain considerable charges due to collisions and generate long-range interactions. We perform molecular dynamic simulations in three dimensions for a dilute, freely cooling granular gas of viscoelastic particles that exchange charges during collisions. As compared to the case of clustering of viscoelastic particles solely due to dissipation, we find that the electrostatic interactions due to collisional charging alter the characteristic size, morphology, and growth rate of the clusters. The average cluster size grows with time as a power law, whose exponent is relatively larger in the charged gas than the neutral case. The growth of the average cluster size is found to be independent of the ratio of characteristic Coulomb to kinetic energy, or equivalently, of the typical Bjerrum length. However, this ratio alters the crossover time of the growth. Both simulations and mean-field calculations based on Smoluchowski's equation suggest that a suppression of particle diffusion due to the electrostatic interactions helps in the aggregation process.

Cluster Physics with Merging Galaxy Clusters

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Sandor M. Molnar

2016-02-01

Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

Performance of a Real-time Multipurpose 2-Dimensional Clustering Algorithm Developed for the ATLAS Experiment

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00372074; The ATLAS collaboration; Sotiropoulou, Calliope Louisa; Annovi, Alberto; Kordas, Kostantinos

2016-01-01

In this paper the performance of the 2D pixel clustering algorithm developed for the Input Mezzanine card of the ATLAS Fast TracKer system is presented. Fast TracKer is an approved ATLAS upgrade that has the goal to provide a complete list of tracks to the ATLAS High Level Trigger for each level-1 accepted event, at up to 100 kHz event rate with a very small latency, in the order of 100µs. The Input Mezzanine card is the input stage of the Fast TracKer system. Its role is to receive data from the silicon detector and perform real time clustering, thus to reduce the amount of data propagated to the subsequent processing levels with minimal information loss. We focus on the most challenging component on the Input Mezzanine card, the 2D clustering algorithm executed on the pixel data. We compare two different implementations of the algorithm. The first is one called the ideal one which searches clusters of pixels in the whole silicon module at once and calculates the cluster centroids exploiting the whole avail...

Performance of a Real-time Multipurpose 2-Dimensional Clustering Algorithm Developed for the ATLAS Experiment

CERN Document Server

Gkaitatzis, Stamatios; The ATLAS collaboration

2016-01-01

In this paper the performance of the 2D pixel clustering algorithm developed for the Input Mezzanine card of the ATLAS Fast TracKer system is presented. Fast TracKer is an approved ATLAS upgrade that has the goal to provide a complete list of tracks to the ATLAS High Level Trigger for each level-1 accepted event, at up to 100 kHz event rate with a very small latency, in the order of 100 µs. The Input Mezzanine card is the input stage of the Fast TracKer system. Its role is to receive data from the silicon detector and perform real time clustering, thus to reduce the amount of data propagated to the subsequent processing levels with minimal information loss. We focus on the most challenging component on the Input Mezzanine card, the 2D clustering algorithm executed on the pixel data. We compare two different implementations of the algorithm. The first is one called the ideal one which searches clusters of pixels in the whole silicon module at once and calculates the cluster centroids exploiting the whole avai...

Surfactant 1-Hexadecyl-3-methylimidazolium Chloride Can Convert One-Dimensional Viologen Bromoplumbate into Zero-Dimensional.

Science.gov (United States)

Liu, Guangfeng; Liu, Jie; Nie, Lina; Ban, Rui; Armatas, Gerasimos S; Tao, Xutang; Zhang, Qichun

2017-05-15

A zero-dimensional N,N'-dibutyl-4,4'-dipyridinium bromoplumbate, [BV] 6 [Pb 9 Br 30 ], with unusual discrete [Pb 9 Br 30 ] 12- anionic clusters was prepared via a facile surfactant-mediated solvothermal process. This bromoplumbate exhibits a narrower optical band gap relative to the congeneric one-dimensional viologen bromoplumbates.

Deployment Strategies and Clustering Protocols Efficiency

Directory of Open Access Journals (Sweden)

Chérif Diallo

2017-06-01

Full Text Available Wireless sensor networks face significant design challenges due to limited computing and storage capacities and, most importantly, dependence on limited battery power. Energy is a critical resource and is often an important issue to the deployment of sensor applications that claim to be omnipresent in the world of future. Thus optimizing the deployment of sensors becomes a major constraint in the design and implementation of a WSN in order to ensure better network operations. In wireless networking, clustering techniques add scalability, reduce the computation complexity of routing protocols, allow data aggregation and then enhance the network performance. The well-known MaxMin clustering algorithm was previously generalized, corrected and validated. Then, in a previous work we have improved MaxMin by proposing a Single- node Cluster Reduction (SNCR mechanism which eliminates single-node clusters and then improve energy efficiency. In this paper, we show that MaxMin, because of its original pathological case, does not support the grid deployment topology, which is frequently used in WSN architectures. The unreliability feature of the wireless links could have negative impacts on Link Quality Indicator (LQI based clustering protocols. So, in the second part of this paper we show how our distributed Link Quality based d- Clustering Protocol (LQI-DCP has good performance in both stable and high unreliable link environments. Finally, performance evaluation results also show that LQI-DCP fully supports the grid deployment topology and is more energy efficient than MaxMin.

Space-time clustering characteristics of tuberculosis in China, 2005-2011.

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Fei Zhao

Full Text Available OBJECTIVES: China is one of the 22 tuberculosis (TB high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics. METHODS: Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+ notification rate and total TB notification rate. Global Moran's I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011. RESULTS: Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01. Global Moran's I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01. The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces. CONCLUSION: The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.

Clustering phenomena in nuclear matter below the saturation density

International Nuclear Information System (INIS)

Takemoto, Hiroki; Fukushima, Masahiro; Chiba, Satoshi; Horiuchi, Hisashi; Akaishi, Yoshinori; Tohsaki, Akihiro

2004-01-01

We investigate density-fluctuated states of nuclear matter as a result of clustering below the saturation density ρ 0 by description in terms of the Bloch function. The Bloch description has the advantage of a unified representation for a density-fluctuated state from an aggregate of uncorrelated clusters in extremely low-density regions to the plane-wave state of uniform matter in relatively high-density regions. We treat the density-fluctuated states due to α and 16 O clustering in symmetric nuclear matter and due to 10 He clustering in asymmetric nuclear matter. The density-fluctuated states develop as the density of matter decreases below each critical density around 0.2-0.4 ρ 0 which depends on what kind of effective force we use

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

Science.gov (United States)

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

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Wills Rachael A

2009-05-01

Full Text Available Abstract Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones, rather than objective reality. Bayesian analysis is (arguably a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

Estimation of cluster stability using the theory of electron density functional

International Nuclear Information System (INIS)

Borisov, Yu.A.

1985-01-01

Prospects of using simple versions of the electron density functional for studying the energy characteristics of cluster compounds Was discussed. These types of cluster compounds were considered: clusters of Cs, Be, B, Sr, Cd, Sc, In, V, Tl, I elements as intermediate form between molecule and solid body, metalloorganic Mo, W, Tc, Re, Rn clusters and elementoorganic compounds of nido-cluster type. The problem concerning changes in the binding energy of homoatomic clusters depending on their size and three-dimensional structure was analysed

Clusters and how to make it work : Cluster Strategy Toolkit

NARCIS (Netherlands)

Manickam, Anu; van Berkel, Karel

2014-01-01

Clusters are the magic answer to regional economic development. Firms in clusters are more innovative; cluster policy dominates EU policy; ‘top-sectors’ and excellence are the choice of national policy makers; clusters are ‘in’. But, clusters are complex, clusters are ‘messy’; there is no clear

Cluster dynamics at different cluster size and incident laser wavelengths

International Nuclear Information System (INIS)

Desai, Tara; Bernardinello, Andrea

2002-01-01

X-ray emission spectra from aluminum clusters of diameter -0.4 μm and gold clusters of dia. ∼1.25 μm are experimentally studied by irradiating the cluster foil targets with 1.06 μm laser, 10 ns (FWHM) at an intensity ∼10 12 W/cm 2 . Aluminum clusters show a different spectra compared to bulk material whereas gold cluster evolve towards bulk gold. Experimental data are analyzed on the basis of cluster dimension, laser wavelength and pulse duration. PIC simulations are performed to study the behavior of clusters at higher intensity I≥10 17 W/cm 2 for different size of the clusters irradiated at different laser wavelengths. Results indicate the dependence of cluster dynamics on cluster size and incident laser wavelength

A COMPARISON OF TWO FUZZY CLUSTERING TECHNIQUES

Directory of Open Access Journals (Sweden)

Samarjit Das

2013-10-01

Full Text Available - In fuzzy clustering, unlike hard clustering, depending on the membership value, a single object may belong exactly to one cluster or partially to more than one cluster. Out of a number of fuzzy clustering techniques Bezdek’s Fuzzy C-Means and GustafsonKessel clustering techniques are well known where Euclidian distance and Mahalanobis distance are used respectively as a measure of similarity. We have applied these two fuzzy clustering techniques on a dataset of individual differences consisting of fifty feature vectors of dimension (feature three. Based on some validity measures we have tried to see the performances of these two clustering techniques from three different aspects- first, by initializing the membership values of the feature vectors considering the values of the three features separately one at a time, secondly, by changing the number of the predefined clusters and thirdly, by changing the size of the dataset.

AES based secure low energy adaptive clustering hierarchy for WSNs

Science.gov (United States)

Kishore, K. R.; Sarma, N. V. S. N.

2013-01-01

Wireless sensor networks (WSNs) provide a low cost solution in diversified application areas. The wireless sensor nodes are inexpensive tiny devices with limited storage, computational capability and power. They are being deployed in large scale in both military and civilian applications. Security of the data is one of the key concerns where large numbers of nodes are deployed. Here, an energy-efficient secure routing protocol, secure-LEACH (Low Energy Adaptive Clustering Hierarchy) for WSNs based on the Advanced Encryption Standard (AES) is being proposed. This crypto system is a session based one and a new session key is assigned for each new session. The network (WSN) is divided into number of groups or clusters and a cluster head (CH) is selected among the member nodes of each cluster. The measured data from the nodes is aggregated by the respective CH's and then each CH relays this data to another CH towards the gateway node in the WSN which in turn sends the same to the Base station (BS). In order to maintain confidentiality of data while being transmitted, it is necessary to encrypt the data before sending at every hop, from a node to the CH and from the CH to another CH or to the gateway node.

Text Clustering Algorithm Based on Random Cluster Core

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Huang Long-Jun

2016-01-01

Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

Cluster model of the nucleus

International Nuclear Information System (INIS)

Horiuchi, H.; Ikeda, K.

1986-01-01

This article reviews the development of the cluster model study. The stress is put on two points; one is how the cluster structure has come to be regarded as a fundamental structure in light nuclei together with the shell-model structure, and the other is how at present the cluster model is extended to and connected with the studies of the various subjects many of which are in the neighbouring fields. The authors the present the main theme with detailed explanations of the fundamentals of the microscopic cluster model which have promoted the development of the cluster mode. Examples of the microscopic cluster model study of light nuclear structure are given

The next generation Virgo cluster survey. VIII. The spatial distribution of globular clusters in the Virgo cluster

Energy Technology Data Exchange (ETDEWEB)

Durrell, Patrick R.; Accetta, Katharine [Department of Physics and Astronomy, Youngstown State University, Youngstown, OH 44555 (United States); Côté, Patrick; Blakeslee, John P.; Ferrarese, Laura; McConnachie, Alan; Gwyn, Stephen [Herzberg Astronomy and Astrophysics, National Research Council, 5071 West Saanich Road, Victoria, BC V9E 2E7 (Canada); Peng, Eric W.; Zhang, Hongxin [Department of Astronomy, Peking University, Beijing 100871 (China); Mihos, J. Christopher [Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106 (United States); Puzia, Thomas H.; Jordán, Andrés [Institute of Astrophysics, Pontificia Universidad Catolica, Av. Vicu' a Mackenna 4860, Macul 7820436, Santiago (Chile); Lançon, Ariane [Observatoire astronomique de Strasbourg, Université de Strasbourg, CNRS, UMR 7550, 11 rue de l' Université, F-67000 Strasbourg (France); Liu, Chengze [Center for Astronomy and Astrophysics, Department of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240 (China); Cuillandre, Jean-Charles [Canada-France-Hawaii Telescope Corporation, Kamuela, HI 96743 (United States); Boissier, Samuel; Boselli, Alessandro [Aix Marseille Université, CNRS, LAM (Laboratoire d' Astrophysique de Marseille) UMR 7326, F-13388 Marseille (France); Courteau, Stéphane [Department of Physics, Engineering Physics and Astronomy, Queen' s University, Kingston, ON K7L 3N6 (Canada); Duc, Pierre-Alain [AIM Paris Saclay, CNRS/INSU, CEA/Irfu, Université Paris Diderot, Orme des Merisiers, F-91191 Gif sur Yvette cedex (France); Emsellem, Eric [Université de Lyon 1, CRAL, Observatoire de Lyon, 9 av. Charles André, F-69230 Saint-Genis Laval (France); CNRS, UMR 5574, ENS de Lyon (France); and others

2014-10-20

We report on a large-scale study of the distribution of globular clusters (GCs) throughout the Virgo cluster, based on photometry from the Next Generation Virgo Cluster Survey (NGVS), a large imaging survey covering Virgo's primary subclusters (Virgo A = M87 and Virgo B = M49) out to their virial radii. Using the g{sub o}{sup ′}, (g' – i') {sub o} color-magnitude diagram of unresolved and marginally resolved sources within the NGVS, we have constructed two-dimensional maps of the (irregular) GC distribution over 100 deg{sup 2} to a depth of g{sub o}{sup ′} = 24. We present the clearest evidence to date showing the difference in concentration between red and blue GCs over the full extent of the cluster, where the red (more metal-rich) GCs are largely located around the massive early-type galaxies in Virgo, while the blue (metal-poor) GCs have a much more extended spatial distribution with significant populations still present beyond 83' (∼215 kpc) along the major axes of both M49 and M87. A comparison of our GC maps to the diffuse light in the outermost regions of M49 and M87 show remarkable agreement in the shape, ellipticity, and boxiness of both luminous systems. We also find evidence for spatial enhancements of GCs surrounding M87 that may be indicative of recent interactions or an ongoing merger history. We compare the GC map to that of the locations of Virgo galaxies and the X-ray intracluster gas, and find generally good agreement between these various baryonic structures. We calculate the Virgo cluster contains a total population of N {sub GC} = 67, 300 ± 14, 400, of which 35% are located in M87 and M49 alone. For the first time, we compute a cluster-wide specific frequency S {sub N,} {sub CL} = 2.8 ± 0.7, after correcting for Virgo's diffuse light. We also find a GC-to-baryonic mass fraction ε {sub b} = 5.7 ± 1.1 × 10{sup –4} and a GC-to-total cluster mass formation efficiency ε {sub t} = 2.9 ± 0.5 × 10{sup –5

Trend analysis using non-stationary time series clustering based on the finite element method

OpenAIRE

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 ...

Modified genetic algorithms to model cluster structures in medium-size silicon clusters

International Nuclear Information System (INIS)

Bazterra, Victor E.; Ona, Ofelia; Caputo, Maria C.; Ferraro, Marta B.; Fuentealba, Patricio; Facelli, Julio C.

2004-01-01

This paper presents the results obtained using a genetic algorithm (GA) to search for stable structures of medium size silicon clusters. In this work the GA uses a semiempirical energy function to find the best cluster structures, which are further optimized using density-functional theory. For small clusters our results agree well with previously reported structures, but for larger ones different structures appear. This is the case of Si 36 where we report a different structure, with significant lower energy than those previously found using limited search approaches on common structural motifs. This demonstrates the need for global optimization schemes when searching for stable structures of medium-size silicon clusters

CHARACTERISTICS OF THE SECONDARY BUBBLE CLUSTER PRODUCED BY AN ELECTROHYDRAULIC SHOCK WAVE LITHOTRIPTER

Science.gov (United States)

Zhou, Yufeng; Qin, Jun; Zhong, Pei

2013-01-01

This study investigated the characteristics of the secondary bubble cluster produced by an electrohydraulic lithotripter using high-speed imaging and passive cavitation detection techniques. The results showed that (i) the discrepancy of the collapse time between near a flat rigid boundary and in a free field of the secondary bubble cluster was not as significant as that by the primary one; (ii) the secondary bubble clusters were small but in a high bubble density and nonuniform in distribution, and they did not expand and aggregate significantly near a rigid boundary; and (iii) the corresponding bubble collapse was weaker with few microjet formation and bubble rebound. By applying a strong suction flow near the electrode tip, the production of the secondary shock wave (SW) and induced bubble cluster could be disturbed significantly, but without influence on the primary ones. Consequently, stone fragmentation efficiency was reduced from 41.2 ± 7.1% to 32.2 ± 3.5% after 250 shocks (p <0.05). Altogether, these observations suggest that the secondary bubble cluster produced by an electrohydraulic lithotripter may contribute to its ability for effective stone fragmentation. PMID:22390990

Cluster evolution and critical cluster sizes for the square and triangular lattice Ising models using lattice animals and Monte Carlo simulations

NARCIS (Netherlands)

Eising, G.; Kooi, B. J.

2012-01-01

Growth and decay of clusters at temperatures below T-c have been studied for a two-dimensional Ising model for both square and triangular lattices using Monte Carlo (MC) simulations and the enumeration of lattice animals. For the lattice animals, all unique cluster configurations with their internal

Anomalous properties of technetium clusters

International Nuclear Information System (INIS)

Kryuchkov, S.V.

1985-01-01

On the basis of critical evaluation of literature data in the field of chemistry of technetium cluster compounds with ligands of a weak field a conclusion is made on specific, ''anomalous'' properties of technetium cluster complexes which consist in an increased ability of the given element to the formation of a series of binuclear and multinuclear clusters, similar in composition and structure and easily transforming in each other. The majority of technetium clusters unlike similar compounds of other elements are paramagnetic with one unpaired electron on ''metallic'' MO of loosening type. All theoretical conceptions known today on the electronic structure of technetium clusters are considered. It is pointed out, that the best results in the explanation of ''anomalous'' properties of technetium clusters can be obtained in the framework of nonempirical methods of self-consistent field taking into account configuration interactions. It is also shown, that certain properties of technetium clusters can be explained on the basis of qualitative model of Coulomb repulsion of metal atoms in clusters. The conclusion is made, that technetium position in the Periodic table, as well as recently detected technetium property to the decrease of effective charge on its atoms during M-M bond formation promote a high ability of the element to cluster formation both with weak field ligands and with strong field one

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Science.gov (United States)

Tokuda, Tomoki; Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Directory of Open Access Journals (Sweden)

Tomoki Tokuda

Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

Science.gov (United States)

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

Clusters in nuclei. Vol. 1

International Nuclear Information System (INIS)

Beck, Christian

2010-01-01

Following the pioneering discovery of alpha clustering and of molecular resonances, the field of nuclear clustering is presently one of the domains of heavy-ion nuclear physics facing both the greatest challenges and opportunities. After many summer schools and workshops, in particular over the last decade, the community of nuclear molecular physics decided to team up in producing a comprehensive collection of lectures and tutorial reviews covering the field. This first volume, gathering seven extensive lectures, covers the follow topics: - Cluster Radioactivity - Cluster States and Mean Field Theories - Alpha Clustering and Alpha Condensates - Clustering in Neutron-rich Nuclei - Di-neutron Clustering - Collective Clusterization in Nuclei - Giant Nuclear Molecules By promoting new ideas and developments while retaining a pedagogical nature of presentation throughout, these lectures will both serve as a reference and as advanced teaching material for future courses and schools in the fields of nuclear physics and nuclear astrophysics. (orig.)

GibbsCluster: unsupervised clustering and alignment of peptide sequences

DEFF Research Database (Denmark)

Andreatta, Massimo; Alvarez, Bruno; Nielsen, Morten

2017-01-01

motif characterizing each cluster. Several parameters are available to customize cluster analysis, including adjustable penalties for small clusters and overlapping groups and a trash cluster to remove outliers. As an example application, we used the server to deconvolute multiple specificities in large......-scale peptidome data generated by mass spectrometry. The server is available at http://www.cbs.dtu.dk/services/GibbsCluster-2.0....

Diametrical clustering for identifying anti-correlated gene clusters.

Science.gov (United States)

Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

2003-09-01

Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

Clustering of resting state networks.

Directory of Open Access Journals (Sweden)

Megan H Lee

Full Text Available The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

Cluster size matters: Size-driven performance of subnanometer clusters in catalysis, electrocatalysis and Li-air batteries

Science.gov (United States)

Vajda, Stefan

2015-03-01

This paper discusses the strongly size-dependent performance of subnanometer cluster based catalysts in 1) heterogeneous catalysis, 2) electrocatalysis and 3) Li-air batteries. The experimental studies are based on I. fabrication of ultrasmall clusters with atomic precision control of particle size and their deposition on oxide and carbon based supports; II. test of performance, III. in situand ex situ X-ray characterization of cluster size, shape and oxidation state; and IV.electron microscopies. Heterogeneous catalysis. The pronounced effect of cluster size and support on the performance of the catalyst (catalyst activity and the yield of Cn products) will be illustrated on the example of nickel and cobalt clusters in Fischer-Tropsch reaction. Electrocatalysis. The study of the oxygen evolution reaction (OER) on size-selected palladium clusters supported on ultrananocrystalline diamond show pronounced size effects. While Pd4 clusters show no reaction, Pd6 and Pd17 clusters are among the most active catalysts known (in in terms of turnover rate per Pd atom). The system (soft-landed Pd4, Pd6, or Pd17 clusters on an UNCD Si coated electrode) shows stable electrochemical potentials over several cycles, and the characterization of the electrodes show no evidence for evolution or dissolution of either the support Theoretical calculations suggest that this striking difference may be a demonstration that bridging Pd-Pd sites, which are only present in three-dimensional clusters, are active for the oxygen evolution reaction in Pd6O6. Li-air batteries. The studies show that sub-nm silver clusters have dramatic size-dependent effect on the lowering of the overpotential, charge capacity, morphology of the discharge products, as well as on the morphology of the nm size building blocks of the discharge products. The results suggest that by precise control of the active surface sites on the cathode, the performance of Li-air cells can be significantly improved

THE M33 GLOBULAR CLUSTER SYSTEM WITH PAndAS DATA: THE LAST OUTER HALO CLUSTER?

International Nuclear Information System (INIS)

Cockcroft, Robert; Harris, William E.; Ferguson, Annette M. N.

2011-01-01

We use CFHT/MegaCam data to search for outer halo star clusters in M33 as part of the Pan-Andromeda Archaeological Survey. This work extends previous studies out to a projected radius of 50 kpc and covers over 40 deg 2 . We find only one new unambiguous star cluster in addition to the five previously known in the M33 outer halo (10 kpc ≤ r ≤ 50 kpc). Although we identify 2440 cluster candidates of various degrees of confidence from our objective image search procedure, almost all of these are likely background contaminants, mostly faint unresolved galaxies. We measure the luminosity, color, and structural parameters of the new cluster in addition to the five previously known outer halo clusters. At a projected radius of 22 kpc, the new cluster is slightly smaller, fainter, and redder than all but one of the other outer halo clusters, and has g' ∼ 19.9, (g' - i') ∼ 0.6, concentration parameter c ∼ 1.0, a core radius r c ∼ 3.5 pc, and a half-light radius r h ∼ 5.5 pc. For M33 to have so few outer halo clusters compared to M31 suggests either tidal stripping of M33's outer halo clusters by M31, or a very different, much calmer accretion history of M33.

[Cluster analysis in biomedical researches].

Science.gov (United States)

Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

2013-01-01

Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

Directory of Open Access Journals (Sweden)

Ashlock Daniel

2009-08-01

Full Text Available Abstract Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.

Science.gov (United States)

Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

2009-08-22

Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

Pupil Clustering in English Secondary Schools: One Pattern or Several?

Science.gov (United States)

Gorard, Stephen; Cheng, Shou Chen

2011-01-01

Previous international work has shown that clustering pupils with similar characteristics in particular schools yields no clear academic benefit, and can be disadvantageous both socially and personally. Understanding how and why this clustering happens, and how it may be reduced, is therefore important for policy. Yet previous work has tended to…

Application of clustering methods: Regularized Markov clustering (R-MCL) for analyzing dengue virus similarity

Science.gov (United States)

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.

Contextualizing the Cluster

DEFF Research Database (Denmark)

Giacomin, Valeria

This dissertation examines the case of the palm oil cluster in Malaysia and Indonesia, today one of the largest agricultural clusters in the world. My analysis focuses on the evolution of the cluster from the 1880s to the 1970s in order to understand how it helped these two countries to integrate...... into the global economy in both colonial and post-colonial times. The study is based on empirical material drawn from five UK archives and background research using secondary sources, interviews, and archive visits to Malaysia and Singapore. The dissertation comprises three articles, each discussing a major under...

Electrostrictive deformations in small carbon clusters, hydrocarbon molecules, and carbon nanotubes

International Nuclear Information System (INIS)

Cabria, I.; Lopez, M. J.; Alonso, J. A.; Amovilli, C.; March, N. H.

2006-01-01

The electrostrictive response of small carbon clusters, hydrocarbon molecules, and carbon nanotubes is investigated using the density functional theory. For ringlike carbon clusters, one can get insight on the deformations induced by an electric field from a simple two-dimensional model in which the positive charge of the carbon ions is smeared out in a circular homogeneous line of charge and the electronic density is calculated for a constant applied electric field within a two-dimensional Thomas-Fermi method. According to the Hellmann-Feynman theorem, this model predicts, for fields of about 1 V/A ring , only a small elongation of the ring clusters in the direction of the electric field. Full three-dimensional density functional calculations with an external electric field show similar small deformations in the ring carbon clusters compared to the simple model. The saturated benzene and phenanthrene hydrocarbon molecules do not experience any deformation, even under the action of relatively intense (1 V/A ring ) electric fields. In contrast, finite carbon nanotubes experience larger elongations (∼2.9%) induced by relatively weak (0.1 V/A ring ) applied electric fields. Both C-C bond length elongation and the deformation of the honeycomb structure contribute equally to the nanotube elongation. The effect of the electric field in hydrogen terminated nanotubes is reduced with respect to the nanotubes with dangling bonds in the edges

Extracting Aggregation Free Energies of Mixed Clusters from Simulations of Small Systems: Application to Ionic Surfactant Micelles.

Science.gov (United States)

Zhang, X; Patel, L A; Beckwith, O; Schneider, R; Weeden, C J; Kindt, J T

2017-11-14

Micelle cluster distributions from molecular dynamics simulations of a solvent-free coarse-grained model of sodium octyl sulfate (SOS) were analyzed using an improved method to extract equilibrium association constants from small-system simulations containing one or two micelle clusters at equilibrium with free surfactants and counterions. The statistical-thermodynamic and mathematical foundations of this partition-enabled analysis of cluster histograms (PEACH) approach are presented. A dramatic reduction in computational time for analysis was achieved through a strategy similar to the selector variable method to circumvent the need for exhaustive enumeration of the possible partitions of surfactants and counterions into clusters. Using statistics from a set of small-system (up to 60 SOS molecules) simulations as input, equilibrium association constants for micelle clusters were obtained as a function of both number of surfactants and number of associated counterions through a global fitting procedure. The resulting free energies were able to accurately predict micelle size and charge distributions in a large (560 molecule) system. The evolution of micelle size and charge with SOS concentration as predicted by the PEACH-derived free energies and by a phenomenological four-parameter model fit, along with the sensitivity of these predictions to variations in cluster definitions, are analyzed and discussed.

Motion of Defect Clusters and Dislocations at a Crack Tip of Irradiated Material

International Nuclear Information System (INIS)

Moon, Won Jin; Kwon, Sang Chul; Kim, Whung Whoe

2007-01-01

Effects of defect clusters on mechanical properties of irradiated materials have not been clarified until now. Two radiation hardening models have been proposed. One is a dispersed barrier hardening mechanism based on the Orowan hardening model. This explains defect clusters as barriers to a dislocation motion. Generally the dislocation would rather shear or remove the defect clusters than make so-called Orowan loops. And the other is a cascade induced source hardening mechanism, which explains defect clusters as a Cottrell atmosphere for dislocation motions. However, the above mechanisms can not explain the microstructure of deformed material after irradiation and the phenomenon of yield softening. These mechanisms are based on an immobility of clusters. But we observed defect clusters could move into a specific crystallographic direction easily. Through 3 times of High Voltage Electron Microscope analysis, defect clusters have been observed to make one dimensional motion without applied external stress. If very small defect clusters could move under a stress gradient due to interactions between clusters, we can suggest that the clusters will move more actively when a stress gradient is applied externally. In-situ tensile test at TEM, we confirmed that kind of motion. We suggest defect clusters can move into crack tip, a stress-concentrated area due to tensile stress gradient and dislocations move out from the area by shear stress. Therefore radiation hardening can be explained agglomeration of defect clusters at stress concentrated area prohibits a generation of dislocation and make an increase of yield point

Synaptic Bistability Due to Nucleation and Evaporation of Receptor Clusters

KAUST Repository

Burlakov, V. M.

2012-01-10

We introduce a bistability mechanism for long-term synaptic plasticity based on switching between two metastable states that contain significantly different numbers of synaptic receptors. One state is characterized by a two-dimensional gas of mobile interacting receptors and is stabilized against clustering by a high nucleation barrier. The other state contains a receptor gas in equilibrium with a large cluster of immobile receptors, which is stabilized by the turnover rate of receptors into and out of the synapse. Transitions between the two states can be initiated by either an increase (potentiation) or a decrease (depotentiation) of the net receptor flux into the synapse. This changes the saturation level of the receptor gas and triggers nucleation or evaporation of receptor clusters. © 2012 American Physical Society.

An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks.

Science.gov (United States)

Hosen, A S M Sanwar; Cho, Gi Hwan

2018-05-11

Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head's role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks' information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.

Cluster Matters

DEFF Research Database (Denmark)

Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha

2018-01-01

sell their products successfully in international markets, but there is also an increasingly large consumer base within India. Indeed, Indian industrial clusters have contributed to a substantial part of this growth process, and there are several hundred registered clusters within the country...... of this handbook, which focuses on the role of CSR in MSMEs. Hence we contribute to the literature on CSR in industrial clusters and specifically CSR in Indian industrial clusters by investigating the drivers of CSR in India’s industrial clusters....

Weighted Clustering

DEFF Research Database (Denmark)

Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

2012-01-01

We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

Cluster growth kinetics

International Nuclear Information System (INIS)

Dubovik, V.M.; Gal'perin, A.G.; Rikhvitskij, V.S.; Lushnikov, A.A.

2000-01-01

Processes of some traffic blocking coming into existence are considered as probabilistic ones. We study analytic solutions for models for the dynamics of both cluster growth and cluster growth with fragmentation in the systems of finite number of objects. Assuming rates constancy of both coalescence and fragmentation, the models under consideration are linear on the probability functions

Three-Dimensional Scaffold Chip with Thermosensitive Coating for Capture and Reversible Release of Individual and Cluster of Circulating Tumor Cells.

Science.gov (United States)

Cheng, Shi-Bo; Xie, Min; Chen, Yan; Xiong, Jun; Liu, Ya; Chen, Zhen; Guo, Shan; Shu, Ying; Wang, Ming; Yuan, Bi-Feng; Dong, Wei-Guo; Huang, Wei-Hua

2017-08-01

Tumor metastasis is attributed to circulating tumor cells (CTC) or CTC clusters. Many strategies have hitherto been designed to isolate CTCs, but there are few methods that can capture and gently release CTC clusters as efficient as single CTCs. Herein, we developed a three-dimensional (3D) scaffold chip with thermosensitive coating for high-efficiency capture and release of individual and cluster CTCs. The 3D scaffold chip successfully combines the specific recognition and physically obstructed effect of 3D scaffold structure to significantly improve cell clusters capture efficiency. Thermosensitive gelatin hydrogel uniformly coated on the scaffold dissolves at 37 °C quickly, and the captured cells are gently released from chip with high viability. Notably, this platform was applied to isolate CTCs from cancer patients' blood samples. This allows global DNA and RNA methylation analysis of collected single CTC and CTC clusters, indicating the great potential of this platform in cancer diagnosis and downstream analysis at the molecular level.

Three-dimensional morphological segregation in rich clusters of galaxies

International Nuclear Information System (INIS)

Salvador-Sole, E.; Sanroma, M.; Jordana, J.J.R.

1989-01-01

The implications of the observed correlation between morphological fractions and projected number density of galaxies in rich clusters are analyzed. It is found that this correlation is the result of a well-defined intrinsic correlation that depends on cluster concentration, whether the observed correlation is strictly universal or not. This dependence is in overall agreement with that expected from the action of mechanisms of environment-induced morphological evolution of galaxies. 30 references

Positron lifetime in vacancy clusters. Application to the study of vacancy-impurity interactions

International Nuclear Information System (INIS)

Corbel, C.

1986-02-01

Positron lifetime measurements are used to study the vacancy recovery (77-650 K) in 20 K electron irradiated dilute gold or iron alloys in stainless steels. Positron lifetimes in clusters of various shapes and structures are calculated to precise the information obtained by measuring the positron lifetime in a vacancy cluster of unknown size and configuration. From the calculations, we have drawn the following conclusions: - the minimal size of an unknown cluster can be deduced from the measurement of the positron lifetime in the cluster; - a decrease of the positron lifetime when the structure of the cluster evolves, means either a decrease of the size of the cluster, or, the appearance of a relaxed configuration. - The positron lifetime is very useful to discriminate between a spatial planar or relaxed configuration and a tri-dimensional one. In AuGe, AuSb, AuTn alloys, vacancy clusters decorated by solute atoms appear at 250 K. Their configurations are different from those in pure Au. Mobile vacancy-solutes complexes are involved in the clustering process in AuGe, AuSb. The clusters are probably decorated by several solute atoms in AuGe and AuSb where the resistivity evidences a clustering of solute atoms. In AuFe, vacancy-single or multi-complexes stable up to 670 K prevent cluster formation. In FeTi, FeSb, FeAu, vacancy migration is hindered by the formation of vacancy-solute complexes up to 315 K (Ti), up to 670 K (Sb), up to 700 K (Au). In FeSi, FeCu, FeAg, tri-dimensional clusters grow less easily than Fe. This is likely due to the formation of several kinds of small decorated clusters with relaxed or planar configurations. They are peculiarly stable, surviving up to 700 K at least. In Si (resp. Ti) doped 59Fe25Ni16Cr, solute atoms retain the vacancies up to 300 K (resp. 320 K) [fr

Cluster policy in Europe and Asia: A comparison using selected cluster policy characteristics

Directory of Open Access Journals (Sweden)

Martina Sopoligová

2017-10-01

Full Text Available Currently, cluster concept is one of the most important tools for governments to enhance competitiveness and innovations through sectoral specialization and cooperation. The paper focuses on applications of the cluster policy in the distinct territorial context of Europe and Asia so that to perform a comparison between different approaches to the cluster concept application in real practice. The paper introduces a comparative study of the cluster policy concepts based on the characteristics defined by the authors, such as scope, approach, targeting, autonomy, institutional coordination, policy instruments and evaluation system studied for the selected European and Asian countries such as Denmark, France, Germany, China, Japan, and South Korea. The research draws upon processing the secondary data obtained through content analysis of the related literature, government documents and strategies, and also cluster funding programmes. The findings demonstrate the diversity of cluster policies implemented in the context of European and Asian conditions at the current stage of their development.

Cluster-to-cluster transformation among Au6, Au8 and Au11 nanoclusters.

Science.gov (United States)

Ren, Xiuqing; Fu, Junhong; Lin, Xinzhang; Fu, Xuemei; Yan, Jinghui; Wu, Ren'an; Liu, Chao; Huang, Jiahui

2018-05-22

We present the cluster-to-cluster transformations among three gold nanoclusters, [Au6(dppp)4]2+ (Au6), [Au8(dppp)4Cl2]2+ (Au8) and [Au11(dppp)5]3+ (Au11). The conversion process follows a rule that states that the transformation of a small cluster to a large cluster is achieved through an oxidation process with an oxidizing agent (H2O2) or with heating, while the conversion of a large cluster to a small one occurs through a reduction process with a reducing agent (NaBH4). All the reactions were monitored using UV-Vis spectroscopy and ESI-MS. This work may provide an alternative approach to the synthesis of novel gold nanoclusters and a further understanding of the structural transformation relationship of gold nanoclusters.

The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware

Energy Technology Data Exchange (ETDEWEB)

Tierney, Brian L; Vallentin, Matthias; Sommer, Robin; Lee, Jason; Leres, Craig; Paxson, Vern; Tierney, Brian

2007-09-19

In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS's operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.

Bagged K-means clustering of metabolome data

NARCIS (Netherlands)

Hageman, J. A.; van den Berg, R. A.; Westerhuis, J. A.; Hoefsloot, H. C. J.; Smilde, A. K.

2006-01-01

Clustering of metabolomics data can be hampered by noise originating from biological variation, physical sampling error and analytical error. Using data analysis methods which are not specially suited for dealing with noisy data will yield sub optimal solutions. Bootstrap aggregating (bagging) is a

ClusterControl: a web interface for distributing and monitoring bioinformatics applications on a Linux cluster.

Science.gov (United States)

Stocker, Gernot; Rieder, Dietmar; Trajanoski, Zlatko

2004-03-22

ClusterControl is a web interface to simplify distributing and monitoring bioinformatics applications on Linux cluster systems. We have developed a modular concept that enables integration of command line oriented program into the application framework of ClusterControl. The systems facilitate integration of different applications accessed through one interface and executed on a distributed cluster system. The package is based on freely available technologies like Apache as web server, PHP as server-side scripting language and OpenPBS as queuing system and is available free of charge for academic and non-profit institutions. http://genome.tugraz.at/Software/ClusterControl

Stable Chimeras and Independently Synchronizable Clusters

Science.gov (United States)

Cho, Young Sul; Nishikawa, Takashi; Motter, Adilson E.

2017-08-01

Cluster synchronization is a phenomenon in which a network self-organizes into a pattern of synchronized sets. It has been shown that diverse patterns of stable cluster synchronization can be captured by symmetries of the network. Here, we establish a theoretical basis to divide an arbitrary pattern of symmetry clusters into independently synchronizable cluster sets, in which the synchronization stability of the individual clusters in each set is decoupled from that in all the other sets. Using this framework, we suggest a new approach to find permanently stable chimera states by capturing two or more symmetry clusters—at least one stable and one unstable—that compose the entire fully symmetric network.

Self-confinement of finite dust clusters in isotropic plasmas.

Science.gov (United States)

Miloshevsky, G V; Hassanein, A

2012-05-01

Finite two-dimensional dust clusters are systems of a small number of charged grains. The self-confinement of dust clusters in isotropic plasmas is studied using the particle-in-cell method. The energetically favorable configurations of grains in plasma are found that are due to the kinetic effects of plasma ions and electrons. The self-confinement phenomenon is attributed to the change in the plasma composition within a dust cluster resulting in grain attraction mediated by plasma ions. This is a self-consistent state of a dust cluster in which grain's repulsion is compensated by the reduced charge and floating potential on grains, overlapped ion clouds, and depleted electrons within a cluster. The common potential well is formed trapping dust clusters in the confined state. These results provide both valuable insights and a different perspective to the classical view on the formation of boundary-free dust clusters in isotropic plasmas.

Short-Term Predictive Validity of Cluster Analytic and Dimensional Classification of Child Behavioral Adjustment in School

Science.gov (United States)

Kim, Sangwon; Kamphaus, Randy W.; Baker, Jean A.

2006-01-01

A constructive debate over the classification of child psychopathology can be stimulated by investigating the validity of different classification approaches. We examined and compared the short-term predictive validity of cluster analytic and dimensional classifications of child behavioral adjustment in school using the Behavior Assessment System…

Clusters and how to make it work : toolkit for cluster strategy

NARCIS (Netherlands)

Manickam, Anu; van Berkel, Karel

2013-01-01

Clusters are the magic answer to regional economic development. Firms in clusters are more innovative; cluster policy dominates EU policy; ‘top-sectors’ and excellence are the choice of national policy makers; clusters are ‘in’. But, clusters are complex, clusters are ‘messy’; there is no clear

Ionization of nitrogen cluster beam

International Nuclear Information System (INIS)

Yano, Katsuki; Be, S.H.; Enjoji, Hiroshi; Okamoto, Kosuke

1975-01-01

A nitrogen cluster beam (neutral particle intensity of 28.6 mAsub(eq)) is ionized by electron collisions in a Bayard-Alpert gauge type ionizer. The extraction efficiency of about 65% is obtained at an electron current of 10 mA with an energy of 50 eV. The mean cluster size produced at a pressure of 663 Torr and temperature of 77.3 K is 2x10 5 molecules per cluster. By the Coulomb repulsion force, multiply ionized cluster ions are broken up into smaller fragments and the cluster ion size reduces to one-fourth at an electron current of 15 mA. Mean neutral cluster sizes depend strongly on the initial degree of saturation PHI 0 and are 2x10 5 , 7x10 4 and 3x10 4 molecules per cluster at PHI 0 's of 0.87, 0.66 and 0.39, respectively. (auth.)

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

Directory of Open Access Journals (Sweden)

Moumita Saha

2016-01-01

Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

Implementasi KD-Tree K-Means Clustering untuk Klasterisasi Dokumen

Directory of Open Access Journals (Sweden)

Eric Budiman Gosno

2013-09-01

Full Text Available Klasterisasi dokumen adalah suatu proses pengelompokan dokumen secara otomatis dan unsupervised. Klasterisasi dokumen merupakan permasalahan yang sering ditemui dalam berbagai bidang seperti text mining dan sistem temu kembali informasi. Metode klasterisasi dokumen yang memiliki akurasi dan efisiensi waktu yang tinggi sangat diperlukan untuk meningkatkan hasil pada mesin pencari web,  dan untuk proses filtering. Salah satu metode klasterisasi yang telah dikenal dan diaplikasikan dalam klasterisasi dokumen adalah K-Means Clustering. Tetapi K-Means Clustering sensitif terhadap pemilihan posisi awal dari titik tengah klaster sehingga pemilihan posisi awal dari titik tengah klaster yang buruk akan mengakibatkan K-Means Clustering terjebak dalam local optimum. KD-Tree K-Means Clustering merupakan perbaikan dari K-Means Clustering. KD-Tree K-Means Clustering menggunakan struktur data K-Dimensional Tree dan nilai kerapatan pada proses inisialisasi titik tengah klaster. Pada makalah ini diimplementasikan algoritma KD-Tree K-Means Clustering untuk permasalahan klasterisasi dokumen. Performa klasterisasi dokumen yang dihasilkan oleh metode KD-Tree K-Means Clustering pada data set 20 newsgroup memiliki nilai distorsi 3×105 lebih rendah dibandingkan dengan nilai rerata distorsi K-Means Clustering dan nilai NIG 0,09 lebih baik dibandingkan dengan nilai NIG K-Means Clustering.

Determination of atomic cluster structure with cluster fusion algorithm

DEFF Research Database (Denmark)

Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.

2005-01-01

We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....

Cluster headache

Science.gov (United States)

Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... Doctors do not know exactly what causes cluster headaches. They ... (chemical in the body released during an allergic response) or ...

Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data.

Science.gov (United States)

McParland, D; Phillips, C M; Brennan, L; Roche, H M; Gormley, I C

2017-12-10

The LIPGENE-SU.VI.MAX study, like many others, recorded high-dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account for both phenotypic and genetic factors when studying the metabolic syndrome (MetS), a complex disorder that can lead to higher risk of type 2 diabetes and cardiovascular disease. Interest lies in clustering the LIPGENE-SU.VI.MAX participants into homogeneous groups or sub-phenotypes, by jointly considering their phenotypic and genotypic data, and in determining which variables are discriminatory. A novel latent variable model that elegantly accommodates high dimensional, mixed data is developed to cluster LIPGENE-SU.VI.MAX participants using a Bayesian finite mixture model. A computationally efficient variable selection algorithm is incorporated, estimation is via a Gibbs sampling algorithm and an approximate BIC-MCMC criterion is developed to select the optimal model. Two clusters or sub-phenotypes ('healthy' and 'at risk') are uncovered. A small subset of variables is deemed discriminatory, which notably includes phenotypic and genotypic variables, highlighting the need to jointly consider both factors. Further, 7 years after the LIPGENE-SU.VI.MAX data were collected, participants underwent further analysis to diagnose presence or absence of the MetS. The two uncovered sub-phenotypes strongly correspond to the 7-year follow-up disease classification, highlighting the role of phenotypic and genotypic factors in the MetS and emphasising the potential utility of the clustering approach in early screening. Additionally, the ability of the proposed approach to define the uncertainty in sub-phenotype membership at the participant level is synonymous with the concepts of precision medicine and nutrition. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Clustering and segregation of small vacancy clusters near tungsten (0 0 1) surface

Science.gov (United States)

Duan, Guohua; Li, Xiangyan; Xu, Yichun; Zhang, Yange; Jiang, Yan; Hao, Congyu; Liu, C. S.; Fang, Q. F.; Chen, Jun-Ling; Luo, G.-N.; Wang, Zhiguang

2018-01-01

Nanoporous metals have been shown to exhibit radiation-tolerance due to the trapping of the defects by the surface. However, the behavior of vacancy clusters near the surface is not clear which involves the competition between the self-trapping and segregation of small vacancy clusters (Vn) nearby the surface. In this study, we investigated the energetic and kinetic properties of small vacancy clusters near tungsten (0 0 1) surface by combining molecular statics (MS) calculations and object Kinetic Monte Carlo (OKMC) simulations. Results show that vacancies could be clustered with the reduced formation energy and migration energy of the single vacancy around a cluster as the respective energetic and kinetic driving forces. The small cluster has a migration energy barrier comparable to that for the single vacancy; the migration energy barriers for V1-5 and V7 are 1.80, 1.94, 2.17, 2.78, 3.12 and 3.11 eV, respectively. Clusters and become unstable near surface (0 0 1) and tend to dissociate into the surface. At the operation temperature of 1000 K, the single vacancy, V2, 2 V 3 V3 and V4 were observed to segregate to the surface within a time of one hour. Meanwhile, larger clusters survived near the surface, which could serve as nucleating center for voids near the surface. Our results suggest that under a low radiation dose, surface (0 0 1) could act as a sink for small vacancy clusters, alleviating defect accumulation in the material under a low radiation dose. We also obtained several empirical expressions for the vacancy cluster formation energy, binding energy, and trapping radius as a function of the number of vacancies in the cluster.

Re-weighted Discriminatively Embedded K-Means for Multi-view Clustering.

Science.gov (United States)

Xu, Jinglin; Han, Junwei; Nie, Feiping; Li, Xuelong

2017-02-08

Recent years, more and more multi-view data are widely used in many real world applications. This kind of data (such as image data) are high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called Re-weighted Discriminatively Embedded KMeans (RDEKM), for this task. The proposed method is a multiview least-absolute residual model which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi-view clustering. Specifically, the proposed model is an unsupervised optimization scheme which utilizes Iterative Re-weighted Least Squares to solve leastabsolute residual and adaptively controls the distribution of multiple weights in a re-weighted manner only based on its own low-dimensional subspaces and a common clustering indicator matrix. Furthermore, theoretical analysis (including optimality and convergence analysis) and the optimization algorithm are also presented. Compared to several state-of-the-art multi-view clustering methods, the proposed method substantially improves the accuracy of the clustering results on widely used benchmark datasets, which demonstrates the superiority of the proposed work.

Controlled clustering of carboxylated SPIONs through polyethylenimine

Energy Technology Data Exchange (ETDEWEB)

Nesztor, Dániel; Bali, Krisztina; Tóth, Ildikó Y.; Szekeres, Márta; Tombácz, Etelka, E-mail: tombacz@chem.u-szeged.hu

2015-04-15

Clusters of magnetite nanoparticles (MNPs) were synthesized using poly(acrylic acid-co-maleic acid) coated MNPs (PAM@MNP) and branched polyethylenimine (PEI). Materials were characterized by potentiometric titration, zeta potential and dynamic light scattering (DLS) measurements. PEI and PAM@MNP are oppositely charged as characterized by zeta potential measurements (+8, −34 mV respectively) and titration (10.30 mmol −NH{sub 3}{sup +}/g PEI; 0.175 mmol −COO{sup −}/g PAM@MNP) at pH 6.5±0.2; therefore magnetic clusters are formed by electrostatic adhesion. Two different preparation methods and the effect of PEI and electrolyte (NaCl) concentration on the cluster formation was studied. Choosing an optimal concentration of PEI (charge ratio of PEI to PAM@MNP: 0.17) and electrolyte (10 mM), a concentrated (10 g MNP/L) product containing PEI–PAM@MNP nanoclusters with size of 165±10 nm was prepared. Its specific absorption rate (SAR) measured in AC magnetic field (110 kHz, 25 mT) is 12 W/g Fe. The clustered product is expected to have enhanced contrast efficiency in MRI. - Highlights: • SPION clusters of controlled size were prepared by means of electrostatic adhesion. • Nanocluster formation optimum was at 0.17 charge ratio of PEI to PAM@MNP. • Huge aggregates form at higher PEI to PAM@MNP charge ratio. • Higher ionic strength promotes the formation of clusters at lower PEI concentrations.

Configurational energies and effective cluster interactions in substitutionally disordered binary alloys

International Nuclear Information System (INIS)

Gonis, A.; Zhang, X.h.; Freeman, A.J.; Turchi, P.; Stocks, G.M.; Nicholson, D.M.

1987-01-01

The determination of configurational energies in terms of effective cluster interactions in substitutionally disordered alloys from a knowledge of the alloy electronic structure is examined within the methods of concentration waves (CW) and the generalized perturbation method (GPM), and for the first time within the embedded-cluster method (ECM). It is shown that the ECM provides the exact summation to all orders of the effective cluster interaction expansions obtained in the partially renormalized GPM. The connection between the various methods (CW, GPM, and ECM) is discussed and illustrated by means of numerical calculations for model one-dimensional tight-binding (TB) systems and for TB Hamiltonians chosen to describe Pd-V alloys. These calculations, and the formal considerations presented in the body of the paper, show the complete equivalence of converged GPM summations within specific clusters and the ECM. In addition, it is shown that an exact expansion of the configurational energy can be obtained in terms of fully renormalized effective cluster interactions. In principle, these effective cluster interactions can be used in conjunction with statistical models to determine stable ordered structures at low temperatures and alloy phase diagrams

Transport, dissociation and rotation of small self-interstitial atom clusters in tungsten

International Nuclear Information System (INIS)

Zhou, W.H.; Zhang, C.G.; Li, Y.G.; Zeng, Z.

2014-01-01

Numerical calculations have been performed to study the thermal motion of self-interstitial atom (SIA) clusters in tungsten (W). Molecular dynamics simulations show that SIA clusters exhibit a fast one-dimensional (1D) motion along the close packed 〈1 1 1〉 direction accompanied by a significant mass transport in this direction. A low frequency vibration mode is identified and considered to assist the motion of SIAs. The migration energy of SIA clusters are weakly dependent on their size in the average value of 0.019 eV, which is due to the strong interaction between SIAs revealed by calculating the potential energy curve of artificially moving the SIAs along 〈1 1 1〉 direction as well as nudged elastic band (NEB) method. The rotation process of SIA cluster is studied by activation–relaxation technique and the results show that SIA cluster presents complex rotation process. Our results on the motion SIA cluster may provide updated understanding on the performance decay of materials related to SIA defects

Exploring cosmic origins with CORE: Cluster science

Science.gov (United States)

Melin, J.-B.; Bonaldi, A.; Remazeilles, M.; Hagstotz, S.; Diego, J. M.; Hernández-Monteagudo, C.; Génova-Santos, R. T.; Luzzi, G.; Martins, C. J. A. P.; Grandis, S.; Mohr, J. J.; Bartlett, J. G.; Delabrouille, J.; Ferraro, S.; Tramonte, D.; Rubiño-Martín, J. A.; Macìas-Pérez, J. F.; Achúcarro, A.; Ade, P.; Allison, R.; Ashdown, M.; Ballardini, M.; Banday, A. J.; Banerji, R.; Bartolo, N.; Basak, S.; Basu, K.; Battye, R. A.; Baumann, D.; Bersanelli, M.; Bonato, M.; Borrill, J.; Bouchet, F.; Boulanger, F.; Brinckmann, T.; Bucher, M.; Burigana, C.; Buzzelli, A.; Cai, Z.-Y.; Calvo, M.; Carvalho, C. S.; Castellano, M. G.; Challinor, A.; Chluba, J.; Clesse, S.; Colafrancesco, S.; Colantoni, I.; Coppolecchia, A.; Crook, M.; D'Alessandro, G.; de Bernardis, P.; de Gasperis, G.; De Petris, M.; De Zotti, G.; Di Valentino, E.; Errard, J.; Feeney, S. M.; Fernández-Cobos, R.; Finelli, F.; Forastieri, F.; Galli, S.; Gerbino, M.; González-Nuevo, J.; Greenslade, J.; Hanany, S.; Handley, W.; Hervias-Caimapo, C.; Hills, M.; Hivon, E.; Kiiveri, K.; Kisner, T.; Kitching, T.; Kunz, M.; Kurki-Suonio, H.; Lamagna, L.; Lasenby, A.; Lattanzi, M.; Le Brun, A. M. C.; Lesgourgues, J.; Lewis, A.; Liguori, M.; Lindholm, V.; Lopez-Caniego, M.; Maffei, B.; Martinez-Gonzalez, E.; Masi, S.; Mazzotta, P.; McCarthy, D.; Melchiorri, A.; Molinari, D.; Monfardini, A.; Natoli, P.; Negrello, M.; Notari, A.; Paiella, A.; Paoletti, D.; Patanchon, G.; Piat, M.; Pisano, G.; Polastri, L.; Polenta, G.; Pollo, A.; Poulin, V.; Quartin, M.; Roman, M.; Salvati, L.; Tartari, A.; Tomasi, M.; Trappe, N.; Triqueneaux, S.; Trombetti, T.; Tucker, C.; Väliviita, J.; van de Weygaert, R.; Van Tent, B.; Vennin, V.; Vielva, P.; Vittorio, N.; Weller, J.; Young, K.; Zannoni, M.

2018-04-01

). Cosmological constraints from CORE cluster counts alone are competitive with other scheduled large scale structure surveys in the 2020's for measuring the dark energy equation-of-state parameters w0 and wa (σw0=0.28, σwa=0.31). In combination with primary CMB constraints, CORE cluster counts can further reduce these error bars on w0 and wa to 0.05 and 0.13 respectively, and constrain the sum of the neutrino masses, Σ mν, to 39 meV (1 sigma). The wide frequency coverage of CORE, 60–600 GHz, will enable measurement of the relativistic thermal SZE by stacking clusters. Contamination by dust emission from the clusters, however, makes constraining the temperature of the intracluster medium difficult. The kinetic SZE pairwise momentum will be extracted with 0S/N=7 in the foreground-cleaned CMB map. Measurements of TCMB(z) using CORE clusters will establish competitive constraints on the evolution of the CMB temperature: (1+z)1‑β, with an uncertainty of σβ lesssim 2.7× 10‑3 at low redshift (z lesssim 1). The wide frequency coverage also enables clean extraction of a map of the diffuse SZE signal over the sky, substantially reducing contamination by foregrounds compared to the Planck SZE map extraction. Our analysis of the one-dimensional distribution of Compton-y values in the simulated map finds an order of magnitude improvement in constraints on σ8 over the Planck result, demonstrating the potential of this cosmological probe with CORE.

Cosmology with clusters in the CMB

International Nuclear Information System (INIS)

Majumdar, Subhabrata

2008-01-01

Ever since the seminal work by Sunyaev and Zel'dovich describing the distortion of the CMB spectrum, due to photons passing through the hot inter cluster gas on its way to us from the surface of last scattering (the so called Sunyaev-Zel'dovich effect (SZE)), small scale distortions of the CMB by clusters has been used to detect clusters as well as to do cosmology with clusters. Cosmology with clusters in the CMB can be divided into three distinct regimes: a) when the clusters are completely unresolved and contribute to the secondary CMB distortions power spectrum at small angular scales; b) when we can just about resolve the clusters so as to detect the clusters through its total SZE flux such that the clusters can be tagged and counted for doing cosmology and c) when we can completely resolve the clusters so as to measure their sizes and other cluster structural properties and their evolution with redshift. In this article, we take a look at these three aspects of SZE cluster studies and their implication for using clusters as cosmological probes. We show that clusters can be used as effective probes of cosmology, when in all of these three cases, one explores the synergy between cluster physics and cosmology as well take clues about cluster physics from the latest high precision cluster observations (for example, from Chandra and XMM - Newton). As a specific case, we show how an observationally motivated cluster SZ template can explain the CBI-excess without the need for a high σ 8 . We also briefly discuss 'self-calibration' in cluster surveys and the prospect of using clusters as an ensemble of cosmic rulers to break degeneracies arising in cluster cosmology.

The impact of mobile point defect clusters in a kinetic model of pressure vessel embrittlement

International Nuclear Information System (INIS)

Stoller, R.E.

1998-05-01

The results of recent molecular dynamics simulations of displacement cascades in iron indicate that small interstitial clusters may have a very low activation energy for migration, and that their migration is 1-dimensional, rather than 3-dimensional. The mobility of these clusters can have a significant impact on the predictions of radiation damage models, particularly at the relatively low temperatures typical of commercial, light water reactor pressure vessels (RPV) and other out-of-core components. A previously-developed kinetic model used to investigate RPV embrittlement has been modified to permit an evaluation of the mobile interstitial clusters. Sink strengths appropriate to both 1- and 3-dimensional motion of the clusters were evaluated. High cluster mobility leads to a reduction in the amount of predicted embrittlement due to interstitial clusters since they are lost to sinks rather than building up in the microstructure. The sensitivity of the predictions to displacement rate also increases. The magnitude of this effect is somewhat reduced if the migration is 1-dimensional since the corresponding sink strengths are lower than those for 3-dimensional diffusion. The cluster mobility can also affect the evolution of copper-rich precipitates in the model since the radiation-enhanced diffusion coefficient increases due to the lower interstitial cluster sink strength. The overall impact of the modifications to the model is discussed in terms of the major irradiation variables and material parameter uncertainties

Exactly soluble models for surface partition of large clusters

International Nuclear Information System (INIS)

Bugaev, K.A.; Bugaev, K.A.; Elliott, J.B.

2007-01-01

The surface partition of large clusters is studied analytically within a framework of the 'Hills and Dales Model'. Three formulations are solved exactly by using the Laplace-Fourier transformation method. In the limit of small amplitude deformations, the 'Hills and Dales Model' gives the upper and lower bounds for the surface entropy coefficient of large clusters. The found surface entropy coefficients are compared with those of large clusters within the 2- and 3-dimensional Ising models

Single-cluster dynamics for the random-cluster model

NARCIS (Netherlands)

Deng, Y.; Qian, X.; Blöte, H.W.J.

2009-01-01

We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those

Cognitive Clusters in Specific Learning Disorder.

Science.gov (United States)

Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The introduction of the single overarching diagnostic category of specific learning disorder (SLD) could underemphasize interindividual clinical differences regarding intracategory cognitive functioning and learning proficiency, according to current models of multiple cognitive deficits at the basis of neurodevelopmental disorders. The characterization of specific cognitive profiles associated with an already manifest SLD could help identify possible early cognitive markers of SLD risk and distinct trajectories of atypical cognitive development leading to SLD. In this perspective, we applied a cluster analysis to identify groups of children with a Diagnostic and Statistical Manual-based diagnosis of SLD with similar cognitive profiles and to describe the association between clusters and SLD subtypes. A sample of 205 children with a diagnosis of SLD were enrolled. Cluster analyses (agglomerative hierarchical and nonhierarchical iterative clustering technique) were used successively on 10 core subtests of the Wechsler Intelligence Scale for Children-Fourth Edition. The 4-cluster solution was adopted, and external validation found differences in terms of SLD subtype frequencies and learning proficiency among clusters. Clinical implications of these findings are discussed, tracing directions for further studies.

Manipulating cluster size of polyanion-stabilized Fe3O4 magnetic nanoparticle clusters via electrostatic-mediated assembly for tunable magnetophoresis behavior

International Nuclear Information System (INIS)

Yeap, Swee Pin; Ahmad, Abdul Latif; Ooi, Boon Seng; Lim, JitKang

2015-01-01

We report in this article an approach for manipulating the size of magnetic nanoparticle clusters (MNCs) via electrostatic-mediated assembly technique using an electrolyte as a clustering agent. The clusters were surface-tethered with poly(sodium 4-styrenesulfonate) (PSS) through electrostatic compensation to enhance their colloidal stability. Dynamic light scattering was employed to trace the evolution of cluster size. Simultaneously, electrophoretic mobility and Fourier transform infrared spectroscopy analyses were conducted to investigate the possible schemes involved in both cluster formation and PSS grafting. Results showed that the average hydrodynamic cluster size of the PSS/MNCs and their corresponding size distributions were successfully shifted by means of manipulating the suspension pH, the ionic nature of the electrolyte, and the electrolyte concentration. More specifically, the electrokinetic behavior of the particles upon interaction with the electrolyte plays a profound role in the formation of the PSS/MNCs. Nonetheless, the solubility of the polymer in electrolyte solution and the purification of the particles from residual ions should not be omitted in determining the effectiveness of this clustering approach. The PSS adlayer makes the resultant entities highly water-dispersible and provides electrosteric stabilization to shield the PSS/MNCs from aggregation. In this study, the experimental observations were analyzed and discussed on the basis of existing fundamental colloidal theories. The strategy of cluster size manipulation proposed here is simple and convenient to implement. Furthermore, manipulating the size of the MNCs also facilitates the tuning of magnetophoresis kinetics on exposure to low magnetic field gradient, which makes this nano-entity useful for engineering applications, specifically in separation processes.

Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Teng Gao

2015-01-01

Full Text Available Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms.

Massive open star clusters using the VVV survey. II. Discovery of six clusters with Wolf-Rayet stars

Science.gov (United States)

Chené, A.-N.; Borissova, J.; Bonatto, C.; Majaess, D. J.; Baume, G.; Clarke, J. R. A.; Kurtev, R.; Schnurr, O.; Bouret, J.-C.; Catelan, M.; Emerson, J. P.; Feinstein, C.; Geisler, D.; de Grijs, R.; Hervé, A.; Ivanov, V. D.; Kumar, M. S. N.; Lucas, P.; Mahy, L.; Martins, F.; Mauro, F.; Minniti, D.; Moni Bidin, C.

2013-01-01

Context. The ESO Public Survey "VISTA Variables in the Vía Láctea" (VVV) provides deep multi-epoch infrared observations for an unprecedented 562 sq. degrees of the Galactic bulge, and adjacent regions of the disk. Nearly 150 new open clusters and cluster candidates have been discovered in this survey. Aims: This is the second in a series of papers about young, massive open clusters observed using the VVV survey. We present the first study of six recently discovered clusters. These clusters contain at least one newly discovered Wolf-Rayet (WR) star. Methods: Following the methodology presented in the first paper of the series, wide-field, deep JHKs VVV observations, combined with new infrared spectroscopy, are employed to constrain fundamental parameters for a subset of clusters. Results: We find that the six studied stellar groups are real young (2-7 Myr) and massive (between 0.8 and 2.2 × 103 M⊙) clusters. They are highly obscured (AV ~ 5-24 mag) and compact (1-2 pc). In addition to WR stars, two of the six clusters also contain at least one red supergiant star, and one of these two clusters also contains a blue supergiant. We claim the discovery of 8 new WR stars, and 3 stars showing WR-like emission lines which could be classified WR or OIf. Preliminary analysis provides initial masses of ~30-50 M⊙ for the WR stars. Finally, we discuss the spiral structure of the Galaxy using the six new clusters as tracers, together with the previously studied VVV clusters. Based on observations with ISAAC, VLT, ESO (programme 087.D-0341A), New Technology Telescope at ESO's La Silla Observatory (programme 087.D-0490A) and with the Clay telescope at the Las Campanas Observatory (programme CN2011A-086). Also based on data from the VVV survey (programme 172.B-2002).

Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to $k$-Clustering

OpenAIRE

Song, Zhao; Yang, Lin F.; Zhong, Peilin

2018-01-01

Sensitivity based sampling is crucial for constructing nearly-optimal coreset for $k$-means / median clustering. In this paper, we provide a novel data structure that enables sensitivity sampling over a dynamic data stream, where points from a high dimensional discrete Euclidean space can be either inserted or deleted. Based on this data structure, we provide a one-pass coreset construction for $k$-means %and M-estimator clustering using space $\\widetilde{O}(k\\mathrm{poly}(d))$ over $d$-dimen...

Three-dimensional discrete-time Lotka-Volterra models with an application to industrial clusters

Science.gov (United States)

Bischi, G. I.; Tramontana, F.

2010-10-01

We consider a three-dimensional discrete dynamical system that describes an application to economics of a generalization of the Lotka-Volterra prey-predator model. The dynamic model proposed is used to describe the interactions among industrial clusters (or districts), following a suggestion given by [23]. After studying some local and global properties and bifurcations in bidimensional Lotka-Volterra maps, by numerical explorations we show how some of them can be extended to their three-dimensional counterparts, even if their analytic and geometric characterization becomes much more difficult and challenging. We also show a global bifurcation of the three-dimensional system that has no two-dimensional analogue. Besides the particular economic application considered, the study of the discrete version of Lotka-Volterra dynamical systems turns out to be a quite rich and interesting topic by itself, i.e. from a purely mathematical point of view.

Understanding Boron through Size-Selected Clusters: Structure, Chemical Bonding, and Fluxionality

Energy Technology Data Exchange (ETDEWEB)

Sergeeva, Alina P.; Popov, Ivan A.; Piazza, Zachary A.; Li, Wei-Li; Romanescu, Constantin; Wang, Lai S.; Boldyrev, Alexander I.

2014-04-15

Conspectus Boron is an interesting element with unusual polymorphism. While three-dimensional (3D) structural motifs are prevalent in bulk boron, atomic boron clusters are found to have planar or quasi-planar structures, stabilized by localized two-center–two-electron (2c–2e) σ bonds on the periphery and delocalized multicenter–two-electron (nc–2e) bonds in both σ and π frameworks. Electron delocalization is a result of boron’s electron deficiency and leads to fluxional behavior, which has been observed in B13+ and B19–. A unique capability of the in-plane rotation of the inner atoms against the periphery of the cluster in a chosen direction by employing circularly polarized infrared radiation has been suggested. Such fluxional behaviors in boron clusters are interesting and have been proposed as molecular Wankel motors. The concepts of aromaticity and antiaromaticity have been extended beyond organic chemistry to planar boron clusters. The validity of these concepts in understanding the electronic structures of boron clusters is evident in the striking similarities of the π-systems of planar boron clusters to those of polycyclic aromatic hydrocarbons, such as benzene, naphthalene, coronene, anthracene, or phenanthrene. Chemical bonding models developed for boron clusters not only allowed the rationalization of the stability of boron clusters but also lead to the design of novel metal-centered boron wheels with a record-setting planar coordination number of 10. The unprecedented highly coordinated borometallic molecular wheels provide insights into the interactions between transition metals and boron and expand the frontier of boron chemistry. Another interesting feature discovered through cluster studies is boron transmutation. Even though it is well-known that B–, formed by adding one electron to boron, is isoelectronic to carbon, cluster studies have considerably expanded the possibilities of new structures and new materials using the B�

Horticultural cluster

OpenAIRE

SHERSTIUK S.V.; POSYLAYEVA K.I.

2013-01-01

In the article there are the theoretical and methodological approaches to the nature and existence of the cluster. The cluster differences from other kinds of cooperative and integration associations. Was develop by scientific-practical recommendations for forming a competitive horticultur cluster.

Clustering of carboxylated magnetite nanoparticles through polyethylenimine: Covalent versus electrostatic approach

Energy Technology Data Exchange (ETDEWEB)

Tóth, Ildikó Y., E-mail: Ildiko.Toth@chem.u-szeged.hu [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary); Nesztor, Dániel [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary); Novák, Levente [Department of Colloid and Environmental Chemistry, University of Debrecen, Egyetem square 1, Debrecen (Hungary); Illés, Erzsébet; Szekeres, Márta; Szabó, Tamás [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary); Tombácz, Etelka, E-mail: tombacz@chem.u-szeged.hu [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary)

2017-04-01

Carboxylated magnetite nanoparticles (MNPs) are frequently used to develop materials with enhanced properties for MRI and hyperthermia. The controlled clustering of MNPs via covalent or electrostatic approaches provides opportunity to prepare high quality materials. MNPs were prepared by co-precipitation and coated by poly(acrylic acid-co-maleic acid) (PAM@MNP). The clusters were synthesized from purified PAM@MNPs and polyethylenimine (PEI) solution via electrostatic interaction and covalent bond formation (ES-cluster and CB-cluster, respectively). The electrostatic adhesion (–NH{sub 3}{sup +} and –COO{sup –}) and the formed amide bond were confirmed by ATR-FTIR. The averaged area of CB-clusters was about twice as large as that of ES-cluster, based on TEM. The SAXS results showed that the surface of MNPs was smooth and the nanoparticles were close packed in both clusters. The pH-dependent aggregation state and zeta potential of clusters were characterized by DLS and electrophoresis measurements, the clusters were colloidally stable at pH>5. In hyperthermia experiments, the values of SAR were about two times larger for the chemically bonded cluster. The MRI studies showed exceptionally high transversion relaxivities, the r{sub 2} values are 457 mM{sup −1} s{sup −1} and 691 mM{sup −1} s{sup −1} for ES-cluster and CB-cluster, respectively. Based on these results, the chemically clustered product shows greater potential for feasible biomedical applications. - Highlights: • Chemically bonded clusters (CB-cluster) were prepared from PEI and PAM-coated MNPs. • The electrostatically clustered units (ES-cluster) are smaller and more compact. • The electrostatic adhesion and the amide bond formation were confirmed by ATR-FTIR. • CB-cluster dispersions are colloidally stable under physiological conditions. • CB-cluster shows great potential for application in MRI and hyperthermia.

TreeCluster: Massively scalable transmission clustering using phylogenetic trees

OpenAIRE

Moshiri, Alexander

2018-01-01

Background: The ability to infer transmission clusters from molecular data is critical to designing and evaluating viral control strategies. Viral sequencing datasets are growing rapidly, but standard methods of transmission cluster inference do not scale well beyond thousands of sequences. Results: I present TreeCluster, a cross-platform tool that performs transmission cluster inference on a given phylogenetic tree orders of magnitude faster than existing inference methods and supports multi...

Extracting Galaxy Cluster Gas Inhomogeneity from X-Ray Surface Brightness: A Statistical Approach and Application to Abell 3667

Science.gov (United States)

Kawahara, Hajime; Reese, Erik D.; Kitayama, Tetsu; Sasaki, Shin; Suto, Yasushi

2008-11-01

Our previous analysis indicates that small-scale fluctuations in the intracluster medium (ICM) from cosmological hydrodynamic simulations follow the lognormal probability density function. In order to test the lognormal nature of the ICM directly against X-ray observations of galaxy clusters, we develop a method of extracting statistical information about the three-dimensional properties of the fluctuations from the two-dimensional X-ray surface brightness. We first create a set of synthetic clusters with lognormal fluctuations around their mean profile given by spherical isothermal β-models, later considering polytropic temperature profiles as well. Performing mock observations of these synthetic clusters, we find that the resulting X-ray surface brightness fluctuations also follow the lognormal distribution fairly well. Systematic analysis of the synthetic clusters provides an empirical relation between the three-dimensional density fluctuations and the two-dimensional X-ray surface brightness. We analyze Chandra observations of the galaxy cluster Abell 3667, and find that its X-ray surface brightness fluctuations follow the lognormal distribution. While the lognormal model was originally motivated by cosmological hydrodynamic simulations, this is the first observational confirmation of the lognormal signature in a real cluster. Finally we check the synthetic cluster results against clusters from cosmological hydrodynamic simulations. As a result of the complex structure exhibited by simulated clusters, the empirical relation between the two- and three-dimensional fluctuation properties calibrated with synthetic clusters when applied to simulated clusters shows large scatter. Nevertheless we are able to reproduce the true value of the fluctuation amplitude of simulated clusters within a factor of 2 from their two-dimensional X-ray surface brightness alone. Our current methodology combined with existing observational data is useful in describing and inferring the

Hierarchical Star Formation in Turbulent Media: Evidence from Young Star Clusters

Energy Technology Data Exchange (ETDEWEB)

Grasha, K.; Calzetti, D. [Astronomy Department, University of Massachusetts, Amherst, MA 01003 (United States); Elmegreen, B. G. [IBM Research Division, T.J. Watson Research Center, Yorktown Heights, NY (United States); Adamo, A.; Messa, M. [Department of Astronomy, The Oskar Klein Centre, Stockholm University, Stockholm (Sweden); Aloisi, A.; Bright, S. N.; Lee, J. C.; Ryon, J. E.; Ubeda, L. [Space Telescope Science Institute, Baltimore, MD (United States); Cook, D. O. [California Institute of Technology, 1200 East California Boulevard, Pasadena, CA (United States); Dale, D. A. [Department of Physics and Astronomy, University of Wyoming, Laramie, WY (United States); Fumagalli, M. [Institute for Computational Cosmology and Centre for Extragalactic Astronomy, Department of Physics, Durham University, Durham (United Kingdom); Gallagher III, J. S. [Department of Astronomy, University of Wisconsin–Madison, Madison, WI (United States); Gouliermis, D. A. [Zentrum für Astronomie der Universität Heidelberg, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2, D-69120 Heidelberg (Germany); Grebel, E. K. [Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr. 12-14, D-69120, Heidelberg (Germany); Kahre, L. [Department of Astronomy, New Mexico State University, Las Cruces, NM (United States); Kim, H. [Gemini Observatory, La Serena (Chile); Krumholz, M. R., E-mail: kgrasha@astro.umass.edu [Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611 (Australia)

2017-06-10

We present an analysis of the positions and ages of young star clusters in eight local galaxies to investigate the connection between the age difference and separation of cluster pairs. We find that star clusters do not form uniformly but instead are distributed so that the age difference increases with the cluster pair separation to the 0.25–0.6 power, and that the maximum size over which star formation is physically correlated ranges from ∼200 pc to ∼1 kpc. The observed trends between age difference and separation suggest that cluster formation is hierarchical both in space and time: clusters that are close to each other are more similar in age than clusters born further apart. The temporal correlations between stellar aggregates have slopes that are consistent with predictions of turbulence acting as the primary driver of star formation. The velocity associated with the maximum size is proportional to the galaxy’s shear, suggesting that the galactic environment influences the maximum size of the star-forming structures.

Cluster Headache

OpenAIRE

Pearce, Iris

1985-01-01

Cluster headache is the most severe primary headache with recurrent pain attacks described as worse than giving birth. The aim of this paper was to make an overview of current knowledge on cluster headache with a focus on pathophysiology and treatment. This paper presents hypotheses of cluster headache pathophysiology, current treatment options and possible future therapy approaches. For years, the hypothalamus was regarded as the key structure in cluster headache, but is now thought to be pa...

Techniques for Representation of Regional Clusters in Geographical In-formation Systems

Directory of Open Access Journals (Sweden)

Adriana REVEIU

2011-01-01

Full Text Available This paper provides an overview of visualization techniques adapted for regional clusters presentation in Geographic Information Systems. Clusters are groups of companies and insti-tutions co-located in a specific geographic region and linked by interdependencies in providing a related group of products and services. The regional clusters can be visualized by projecting the data into two-dimensional space or using parallel coordinates. Cluster membership is usually represented by different colours or by dividing clusters into several panels of a grille display. Taking into consideration regional clusters requirements and the multilevel administrative division of the Romania’s territory, I used two cartograms: NUTS2- regions and NUTS3- counties, to illustrate the tools for regional clusters representation.

A local search for a graph clustering problem

Science.gov (United States)

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.

Cluster bomb ocular injuries.

Science.gov (United States)

Mansour, Ahmad M; Hamade, Haya; Ghaddar, Ayman; Mokadem, Ahmad Samih; El Hajj Ali, Mohamad; Awwad, Shady

2012-01-01

To present the visual outcomes and ocular sequelae of victims of cluster bombs. This retrospective, multicenter case series of ocular injury due to cluster bombs was conducted for 3 years after the war in South Lebanon (July 2006). Data were gathered from the reports to the Information Management System for Mine Action. There were 308 victims of clusters bombs; 36 individuals were killed, of which 2 received ocular lacerations and; 272 individuals were injured with 18 receiving ocular injury. These 18 surviving individuals were assessed by the authors. Ocular injury occurred in 6.5% (20/308) of cluster bomb victims. Trauma to multiple organs occurred in 12 of 18 cases (67%) with ocular injury. Ocular findings included corneal or scleral lacerations (16 eyes), corneal foreign bodies (9 eyes), corneal decompensation (2 eyes), ruptured cataract (6 eyes), and intravitreal foreign bodies (10 eyes). The corneas of one patient had extreme attenuation of the endothelium. Ocular injury occurred in 6.5% of cluster bomb victims and 67% of the patients with ocular injury sustained trauma to multiple organs. Visual morbidity in civilians is an additional reason for a global ban on the use of cluster bombs.

FPGA Implementation of one-dimensional and two-dimensional cellular automata

International Nuclear Information System (INIS)

D'Antone, I.

1999-01-01

This report describes the hardware implementation of one-dimensional and two-dimensional cellular automata (CAs). After a general introduction to the cellular automata, we consider a one-dimensional CA used to implement pseudo-random techniques in built-in self test for VLSI. Due to the increase in digital ASIC complexity, testing is becoming one of the major costs in the VLSI production. The high electronics complexity, used in particle physics experiments, demands higher reliability than in the past time. General criterions are given to evaluate the feasibility of the circuit used for testing and some quantitative parameters are underlined to optimize the architecture of the cellular automaton. Furthermore, we propose a two-dimensional CA that performs a peak finding algorithm in a matrix of cells mapping a sub-region of a calorimeter. As in a two-dimensional filtering process, the peaks of the energy clusters are found in one evolution step. This CA belongs to Wolfram class II cellular automata. Some quantitative parameters are given to optimize the architecture of the cellular automaton implemented in a commercial field programmable gate array (FPGA)

Quantum annealing for combinatorial clustering

Science.gov (United States)

Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

2018-02-01

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

Determination of hydrogen cluster velocities and comparison with numerical calculations

International Nuclear Information System (INIS)

Täschner, A.; Köhler, E.; Ortjohann, H.-W.; Khoukaz, A.

2013-01-01

The use of powerful hydrogen cluster jet targets in storage ring experiments led to the need of precise data on the mean cluster velocity as function of the stagnation temperature and pressure for the determination of the volume density of the target beams. For this purpose a large data set of hydrogen cluster velocity distributions and mean velocities was measured at a high density hydrogen cluster jet target using a trumpet shaped nozzle. The measurements have been performed at pressures above and below the critical pressure and for a broad range of temperatures relevant for target operation, e.g., at storage ring experiments. The used experimental method is described which allows for the velocity measurement of single clusters using a time-of-flight technique. Since this method is rather time-consuming and these measurements are typically interfering negatively with storage ring experiments, a method for a precise calculation of these mean velocities was needed. For this, the determined mean cluster velocities are compared with model calculations based on an isentropic one-dimensional van der Waals gas. Based on the obtained data and the presented numerical calculations, a new method has been developed which allows to predict the mean cluster velocities with an accuracy of about 5%. For this two cut-off parameters defining positions inside the nozzle are introduced, which can be determined for a given nozzle by only two velocity measurements

Computational Design of Clusters for Catalysis

Science.gov (United States)

Jimenez-Izal, Elisa; Alexandrova, Anastassia N.

2018-04-01

When small clusters are studied in chemical physics or physical chemistry, one perhaps thinks of the fundamental aspects of cluster electronic structure, or precision spectroscopy in ultracold molecular beams. However, small clusters are also of interest in catalysis, where the cold ground state or an isolated cluster may not even be the right starting point. Instead, the big question is: What happens to cluster-based catalysts under real conditions of catalysis, such as high temperature and coverage with reagents? Myriads of metastable cluster states become accessible, the entire system is dynamic, and catalysis may be driven by rare sites present only under those conditions. Activity, selectivity, and stability are highly dependent on size, composition, shape, support, and environment. To probe and master cluster catalysis, sophisticated tools are being developed for precision synthesis, operando measurements, and multiscale modeling. This review intends to tell the messy story of clusters in catalysis.

Properties of an ionised-cluster beam from a vaporised-cluster ion source

International Nuclear Information System (INIS)

Takagi, T.; Yamada, I.; Sasaki, A.

1978-01-01

A new type of ion source vaporised-metal cluster ion source, has been developed for deposition and epitaxy. A cluster consisting of 10 2 to 10 3 atoms coupled loosely together is formed by adiabatic expansion ejecting the vapour of materials into a high-vacuum region through the nozzle of a heated crucible. The clusters are ionised by electron bombardment and accelerated with neutral clusters toward a substrate. In this paper, mechanisms of cluster formation experimental results of the cluster size (atoms/cluster) and its distribution, and characteristics of the cluster ion beams are reported. The size is calculated from the kinetic equation E = (1/2)mNVsub(ej) 2 , where E is the cluster beam energy, Vsub(ej) is the ejection velocity, m is the mass of atom and N is the cluster size. The energy and the velocity of the cluster are measured by an electrostatic 127 0 energy analyser and a rotating disc system, respectively. The cluster size obtained for Ag is about 5 x 10 2 to 2 x 10 3 atoms. The retarding potential method is used to confirm the results for Ag. The same dependence on cluster size for metals such as Ag, Cu and Pb has been obtained in previous experiments. In the cluster state the cluster ion beam is easily produced by electron bombardment. About 50% of ionised clusters are obtained under typical operation conditions, because of the large ionisation cross sections of the clusters. To obtain a uniform spatial distribution, the ionising electrode system is also discussed. The new techniques are termed ionised-cluster beam deposition (ICBD) and epitaxy (ICBE). (author)

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

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.

Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

Science.gov (United States)

Muraoka, Masae; Okuda, Hiroshi

With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

A possibilistic approach to clustering

Science.gov (United States)

Krishnapuram, Raghu; Keller, James M.

1993-01-01

Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Heavy hitters via cluster-preserving clustering

DEFF Research Database (Denmark)

Larsen, Kasper Green; Nelson, Jelani; Nguyen, Huy L.

2016-01-01

In the turnstile lp heavy hitters problem with parameter ε, one must maintain a high-dimensional vector xεRn subject to updates of the form update (i,Δ) causing the change xi≤ ← xi + Δ, where iε[n], ΔεR. Upon receiving a query, the goal is to report every "heavy hitter" iε[n] with |xi| ≥ε......|x|p as part of a list L⊆[n] of size O(1/εp), i.e. proportional to the maximum possible number of heavy hitters. For any pε(0,2] the COUNTSKETCH of [CCFC04] solves lp heavy hitters using O(ε-plog n) words of space with O(log n) update time, O(nlog n) query time to output L, and whose output after any query......, providing correctness whp. In fact, a simpler version of our algorithm for p = 1 in the strict turnstile model answers queries even faster than the "dyadic trick" by roughly a log n factor, dominating it in all regards. Our main innovation is an efficient reduction from the heavy hitters to a clustering...

From bismuth oxide/hydroxide precursor clusters towards stable oxides: Proton transfer reactions and structural reorganization govern the stability of [Bi18O13(OH)10]-nitrate clusters

Science.gov (United States)

Walther, M.; Zahn, D.

2018-01-01

Structural relaxation and stability of a Bi18-cluster as obtained from association of [Bi6O4(OH)4](NO3)6 precursor clusters in DMSO solution is investigated from a combination of quantum chemical calculations and μs-scale molecular dynamics simulations using empirical interaction potentials. The Bi18-cluster undergoes a OH⋯OH proton transfer reaction, followed by considerable structural relaxation. While the aggregation of the Bi18-cluster is induced by the dissociation of a single nitrate ion leading to [Bi6O4(OH)4](NO3)5+ as an activated precursor species that can bind two more Bi6-clusters, we find the [Bi18O13(OH)10](NO3)18-x+x species (explored for x = 1-6) rather inert against either nitrate dissociation, collision with Bi6-precursors or combinations thereof.

Categorias Cluster

OpenAIRE

Queiroz, Dayane Andrade

2015-01-01

Neste trabalho apresentamos as categorias cluster, que foram introduzidas por Aslak Bakke Buan, Robert Marsh, Markus Reineke, Idun Reiten e Gordana Todorov, com o objetivo de categoriíicar as algebras cluster criadas em 2002 por Sergey Fomin e Andrei Zelevinsky. Os autores acima, em [4], mostraram que existe uma estreita relação entre algebras cluster e categorias cluster para quivers cujo grafo subjacente é um diagrama de Dynkin. Para isto desenvolveram uma teoria tilting na estrutura triang...

BRIGHTEST CLUSTER GALAXIES AND CORE GAS DENSITY IN REXCESS CLUSTERS

International Nuclear Information System (INIS)

Haarsma, Deborah B.; Leisman, Luke; Donahue, Megan; Bruch, Seth; Voit, G. Mark; Boehringer, Hans; Pratt, Gabriel W.; Pierini, Daniele; Croston, Judith H.; Arnaud, Monique

2010-01-01

We investigate the relationship between brightest cluster galaxies (BCGs) and their host clusters using a sample of nearby galaxy clusters from the Representative XMM-Newton Cluster Structure Survey. The sample was imaged with the Southern Observatory for Astrophysical Research in R band to investigate the mass of the old stellar population. Using a metric radius of 12 h -1 kpc, we found that the BCG luminosity depends weakly on overall cluster mass as L BCG ∝ M 0.18±0.07 cl , consistent with previous work. We found that 90% of the BCGs are located within 0.035 r 500 of the peak of the X-ray emission, including all of the cool core (CC) clusters. We also found an unexpected correlation between the BCG metric luminosity and the core gas density for non-cool-core (non-CC) clusters, following a power law of n e ∝ L 2.7±0.4 BCG (where n e is measured at 0.008 r 500 ). The correlation is not easily explained by star formation (which is weak in non-CC clusters) or overall cluster mass (which is not correlated with core gas density). The trend persists even when the BCG is not located near the peak of the X-ray emission, so proximity is not necessary. We suggest that, for non-CC clusters, this correlation implies that the same process that sets the central entropy of the cluster gas also determines the central stellar density of the BCG, and that this underlying physical process is likely to be mergers.

Cluster analysis of track structure

International Nuclear Information System (INIS)

Michalik, V.

1991-01-01

One of the possibilities of classifying track structures is application of conventional partition techniques of analysis of multidimensional data to the track structure. Using these cluster algorithms this paper attempts to find characteristics of radiation reflecting the spatial distribution of ionizations in the primary particle track. An absolute frequency distribution of clusters of ionizations giving the mean number of clusters produced by radiation per unit of deposited energy can serve as this characteristic. General computation techniques used as well as methods of calculations of distributions of clusters for different radiations are discussed. 8 refs.; 5 figs

Topic modeling for cluster analysis of large biological and medical datasets.

Science.gov (United States)

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

Scientific Cluster Deployment and Recovery - Using puppet to simplify cluster management

Science.gov (United States)

Hendrix, Val; Benjamin, Doug; Yao, Yushu

2012-12-01

Deployment, maintenance and recovery of a scientific cluster, which has complex, specialized services, can be a time consuming task requiring the assistance of Linux system administrators, network engineers as well as domain experts. Universities and small institutions that have a part-time FTE with limited time for and knowledge of the administration of such clusters can be strained by such maintenance tasks. This current work is the result of an effort to maintain a data analysis cluster (DAC) with minimal effort by a local system administrator. The realized benefit is the scientist, who is the local system administrator, is able to focus on the data analysis instead of the intricacies of managing a cluster. Our work provides a cluster deployment and recovery process (CDRP) based on the puppet configuration engine allowing a part-time FTE to easily deploy and recover entire clusters with minimal effort. Puppet is a configuration management system (CMS) used widely in computing centers for the automatic management of resources. Domain experts use Puppet's declarative language to define reusable modules for service configuration and deployment. Our CDRP has three actors: domain experts, a cluster designer and a cluster manager. The domain experts first write the puppet modules for the cluster services. A cluster designer would then define a cluster. This includes the creation of cluster roles, mapping the services to those roles and determining the relationships between the services. Finally, a cluster manager would acquire the resources (machines, networking), enter the cluster input parameters (hostnames, IP addresses) and automatically generate deployment scripts used by puppet to configure it to act as a designated role. In the event of a machine failure, the originally generated deployment scripts along with puppet can be used to easily reconfigure a new machine. The cluster definition produced in our CDRP is an integral part of automating cluster deployment

Enforcing conservation laws in nonequilibrium cluster perturbation theory

Science.gov (United States)

Gramsch, Christian; Potthoff, Michael

2017-05-01

Using the recently introduced time-local formulation of the nonequilibrium cluster perturbation theory (CPT), we construct a generalization of the approach such that macroscopic conservation laws are respected. This is achieved by exploiting the freedom for the choice of the starting point of the all-order perturbation theory in the intercluster hopping. The proposed conserving CPT is a self-consistent propagation scheme which respects the conservation of energy, particle number, and spin, which treats short-range correlations exactly up to the linear scale of the cluster, and which represents a mean-field-like approach on length scales beyond the cluster size. Using Green's functions, conservation laws are formulated as local constraints on the local spin-dependent particle and the doublon density. We consider them as conditional equations to self-consistently fix the time-dependent intracluster one-particle parameters. Thanks to the intrinsic causality of the CPT, this can be set up as a step-by-step time propagation scheme with a computational effort scaling linearly with the maximum propagation time and exponentially in the cluster size. As a proof of concept, we consider the dynamics of the two-dimensional, particle-hole-symmetric Hubbard model following a weak interaction quench by simply employing two-site clusters only. Conservation laws are satisfied by construction. We demonstrate that enforcing them has strong impact on the dynamics. While the doublon density is strongly oscillating within plain CPT, a monotonic relaxation is observed within the conserving CPT.

Evidence for nanoscale two-dimensional Co clusters in CoPt{sub 3} films with perpendicular magnetic anisotropy

Energy Technology Data Exchange (ETDEWEB)

Cross, J O [Department of Physics, University of Washington, Seattle, WA 98195 (United States); Newville, M [Consortium for Advanced Radiation Sources, University of Chicago, Chicago, IL 60637 (United States); Maranville, B B; Hellman, F [Department of Physics, University of California at San Diego, La Jolla, CA 92093 (United States); Bordel, C [Department of Physics, University of California at Berkeley, CA 94720 (United States); Harris, V G, E-mail: cbordel@berkeley.ed [Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115 (United States)

2010-04-14

The length scale of the local chemical anisotropy responsible for the growth-temperature-induced perpendicular magnetic anisotropy of face-centered cubic CoPt{sub 3} alloy films was investigated using polarized extended x-ray absorption fine structure (EXAFS). These x-ray measurements were performed on a series of four (111) CoPt{sub 3} films epitaxially grown on (0001) sapphire substrates. The EXAFS data show a preference for Co-Co pairs parallel to the film plane when the film exhibits magnetic anisotropy, and random chemical order otherwise. Furthermore, atomic pair correlation anisotropy was evidenced only in the EXAFS signal from the next neighbors to the absorbing Co atoms and from multiple scattering paths focused through the next neighbors. This suggests that the Co clusters are no more than a few atoms in extent in the plane and one monolayer in extent out of the plane. Our EXAFS results confirm the correlation between perpendicular magnetic anisotropy and two-dimensional Co segregation in CoPt{sub 3} alloy films, and establish a length scale on the order of 10 A for the Co clusters.

Clustering for high-dimension, low-sample size data using distance vectors

OpenAIRE

Terada, Yoshikazu

2013-01-01

In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is not the closeness, but the "values" of distance that contain information of the cluster structure in high-dimensional space. Based on this fact, we propose an efficient and simple clustering approach, called distance vector clustering, for HDLSS data. Under the assumptions given in the work of Hall et al. (2005), w...

Meaningful Clusters

Energy Technology Data Exchange (ETDEWEB)

Sanfilippo, Antonio P.; Calapristi, Augustin J.; Crow, Vernon L.; Hetzler, Elizabeth G.; Turner, Alan E.

2004-05-26

We present an approach to the disambiguation of cluster labels that capitalizes on the notion of semantic similarity to assign WordNet senses to cluster labels. The approach provides interesting insights on how document clustering can provide the basis for developing a novel approach to word sense disambiguation.

Clustering Dycom

KAUST Repository

Minku, Leandro L.

2017-10-06

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.

Feature Selection and Kernel Learning for Local Learning-Based Clustering.

Science.gov (United States)

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.

LMC clusters: young

International Nuclear Information System (INIS)

Freeman, K.C.

1980-01-01

The young globular clusters of the LMC have ages of 10 7 -10 8 y. Their masses and structure are similar to those of the smaller galactic globular clusters. Their stellar mass functions (in the mass range 6 solar masses to 1.2 solar masses) vary greatly from cluster to cluster, although the clusters are similar in total mass, age, structure and chemical composition. It would be very interesting to know why these clusters are forming now in the LMC and not in the Galaxy. The author considers the 'young globular' or 'blue populous' clusters of the LMC. The ages of these objects are 10 7 to 10 8 y, and their masses are 10 4 to 10 5 solar masses, so they are populous enough to be really useful for studying the evolution of massive stars. The author concentrates on the structure and stellar content of these young clusters. (Auth.)

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Data Analysis and Visualization (IDAV) and the Department of Computer Science, University of California, Davis, One Shields Avenue, Davis CA 95616, USA,; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,' ' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA; Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA,; Computer Science Division,University of California, Berkeley, CA, USA,; Computer Science Department, University of California, Irvine, CA, USA,; All authors are with the Berkeley Drosophila Transcription Network Project, Lawrence Berkeley National Laboratory,; Rubel, Oliver; Weber, Gunther H.; Huang, Min-Yu; Bethel, E. Wes; Biggin, Mark D.; Fowlkes, Charless C.; Hendriks, Cris L. Luengo; Keranen, Soile V. E.; Eisen, Michael B.; Knowles, David W.; Malik, Jitendra; Hagen, Hans; Hamann, Bernd

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

Quark cluster model and confinement

International Nuclear Information System (INIS)

Koike, Yuji; Yazaki, Koichi

2000-01-01

How confinement of quarks is implemented for multi-hadron systems in the quark cluster model is reviewed. In order to learn the nature of the confining interaction for fermions we first study 1+1 dimensional QED and QCD, in which the gauge field can be eliminated exactly and generates linear interaction of fermions. Then, we compare the two-body potential model, the flip-flop model and the Born-Oppenheimer approach in the strong coupling lattice QCD for the meson-meson system. Having shown how the long-range attraction between hadrons, van der Waals interaction, shows up in the two-body potential model, we discuss two distinct attempts beyond the two-body potential model: one is a many-body potential model, the flip-flop model, and the other is the Born-Oppenheimer approach in the strong coupling lattice QCD. We explain how the emergence of the long-range attraction is avoided in these attempts. Finally, we present the results of the application of the flip-flop model to the baryon-baryon scattering in the quark cluster model. (author)

Cluster evolution

International Nuclear Information System (INIS)

Schaeffer, R.

1987-01-01

The galaxy and cluster luminosity functions are constructed from a model of the mass distribution based on hierarchical clustering at an epoch where the matter distribution is non-linear. These luminosity functions are seen to reproduce the present distribution of objects as can be inferred from the observations. They can be used to deduce the redshift dependence of the cluster distribution and to extrapolate the observations towards the past. The predicted evolution of the cluster distribution is quite strong, although somewhat less rapid than predicted by the linear theory

Robust Inference with Multi-way Clustering

OpenAIRE

A. Colin Cameron; Jonah B. Gelbach; Douglas L. Miller; Doug Miller

2009-01-01

In this paper we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our...

The fragmentation of proto-globular clusters. I. Thermal instabilities

International Nuclear Information System (INIS)

Murray, S.D.; Lin, D.N.C.

1989-01-01

The metal abundances among the stars within a typical globular cluster are remarkably homogeneous. This indicates that star formation in these systems was a globally coordinated event which occurred over a time span less than or comparable to the collapse time scale of the cluster. This issue is addressed by assuming that the fragmentation of a proto-globular cluster cloud proceeded in two steps. In the first step, thermal instability led to the rapid growth of initial fluctuations. This led to a large contrast in the dynamical time scales between the perturbations and the parent cloud, and the perturbations then underwent gravitational instabilities on short time scales. This process is modeled using one-dimensional hydrodynamic simulations of clouds both with and without external heat sources and self-gravity. The models include the effects of a non-equilibrium H2 abundance. The results indicate that fragmentation can occur on time scales significantly less than the dynamical time scale of the parent cloud. 21 refs

First-principle study of silicon cluster doped with rhodium: Rh{sub 2}Si{sub n} (n = 1–11) clusters

Energy Technology Data Exchange (ETDEWEB)

Zhang, Shuai; Luo, Chang Geng; Li, Hua Yang [Department of Physics, Nanyang Normal University, Nanyang 473061 (China); Lu, Cheng, E-mail: lucheng@calypso.cn [Department of Physics, Nanyang Normal University, Nanyang 473061 (China); State Key Laboratory of Superhard Materials, Jilin University, Changchun 130012 (China); Li, Gen Quan; Lu, Zhi Wen [Department of Physics, Nanyang Normal University, Nanyang 473061 (China)

2015-06-15

The geometries, stabilities and electronic properties of rhodium-doped silicon clusters Rh{sub 2}Si{sub n} (n = 1–11) have been systematically studied by using density functional calculations at the B3LYP/GENECP level. The optimized results show that the lowest-energy isomers of Rh{sub 2}Si{sub n} clusters favor three-dimensional structures for n = 2–11. Based on the averaged binding energy, fragmentation energy, second-order energy difference and HOMO-LUMO energy gap, the stabilities of Rh{sub 2}Si{sub n} (n = 1–11) clusters have been analyzed. The calculated results suggest that the Rh{sub 2}Si{sub 6} cluster has the strongest relative stability and the doping with rhodium atoms can reduce the chemical stabilities of Si{sub n} clusters. The natural population and natural electron configuration analysis indicate that there is charge transfer from the Si atoms and 5s orbital of the Rh atoms to the 4d and 5p orbitals of Rh atoms. The analysis of electron localization function reveal that the Si–Si bonds are mainly covalent bonds and the Si–Rh bonds are almost ionic bonds. Moreover, the vertical ionization potential, vertical electron affinity, chemical hardness, chemical potential, vibrational spectrum and polarizability are also discussed. - Highlights: • The geometric structures of Rh{sub 2}Si{sub n} (n = 1–11) clusters are determined. • The stabilities and electronic properties of Rh{sub 2}Si{sub n} clusters are discussed. • The Rh{sub 2}Si{sub 6} cluster has the higher stability than other clusters. • The doped rhodium atoms can reduce the chemical stabilities of Si{sub n} clusters.

Approximate fuzzy C-means (AFCM) cluster analysis of medical magnetic resonance image (MRI) data

International Nuclear Information System (INIS)

DelaPaz, R.L.; Chang, P.J.; Bernstein, R.; Dave, J.V.

1987-01-01

The authors describe the application of an approximate fuzzy C-means (AFCM) clustering algorithm as a data dimension reduction approach to medical magnetic resonance images (MRI). Image data consisted of one T1-weighted, two T2-weighted, and one T2*-weighted (magnetic susceptibility) image for each cranial study and a matrix of 10 images generated from 10 combinations of TE and TR for each body lymphoma study. All images were obtained with a 1.5 Tesla imaging system (GE Signa). Analyses were performed on over 100 MR image sets with a variety of pathologies. The cluster analysis was operated in an unsupervised mode and computational overhead was minimized by utilizing a table look-up approach without adversely affecting accuracy. Image data were first segmented into 2 coarse clusters, each of which was then subdivided into 16 fine clusters. The final tissue classifications were presented as color-coded anatomically-mapped images and as two and three dimensional displays of cluster center data in selected feature space (minimum spanning tree). Fuzzy cluster analysis appears to be a clinically useful dimension reduction technique which results in improved diagnostic specificity of medical magnetic resonance images

On origin of stellar clusters

International Nuclear Information System (INIS)

Tovmasyan, G.M.

1977-01-01

The ratios of the gas component of the mass of young stellar clusters to their stellar mass are considered. They change by more than four orders from one cluster to another. The results are in direct contradiction with the hypothesis of formation of cluster stars from a preliminarily existing gas cloud by its condensation, and they favour the Ambartsumian hypothesis of the joint origin of stars and gas clouds from superdense protostellar matter

Asteroid clusters similar to asteroid pairs

Science.gov (United States)

Pravec, P.; Fatka, P.; Vokrouhlický, D.; Scheeres, D. J.; Kušnirák, P.; Hornoch, K.; Galád, A.; Vraštil, J.; Pray, D. P.; Krugly, Yu. N.; Gaftonyuk, N. M.; Inasaridze, R. Ya.; Ayvazian, V. R.; Kvaratskhelia, O. I.; Zhuzhunadze, V. T.; Husárik, M.; Cooney, W. R.; Gross, J.; Terrell, D.; Világi, J.; Kornoš, L.; Gajdoš, Š.; Burkhonov, O.; Ehgamberdiev, Sh. A.; Donchev, Z.; Borisov, G.; Bonev, T.; Rumyantsev, V. V.; Molotov, I. E.

2018-04-01

We studied the membership, size ratio and rotational properties of 13 asteroid clusters consisting of between 3 and 19 known members that are on similar heliocentric orbits. By backward integrations of their orbits, we confirmed their cluster membership and estimated times elapsed since separation of the secondaries (the smaller cluster members) from the primary (i.e., cluster age) that are between 105 and a few 106 years. We ran photometric observations for all the cluster primaries and a sample of secondaries and we derived their accurate absolute magnitudes and rotation periods. We found that 11 of the 13 clusters follow the same trend of primary rotation period vs mass ratio as asteroid pairs that was revealed by Pravec et al. (2010). We generalized the model of the post-fission system for asteroid pairs by Pravec et al. (2010) to a system of N components formed by rotational fission and we found excellent agreement between the data for the 11 asteroid clusters and the prediction from the theory of their formation by rotational fission. The two exceptions are the high-mass ratio (q > 0.7) clusters of (18777) Hobson and (22280) Mandragora for which a different formation mechanism is needed. Two candidate mechanisms for formation of more than one secondary by rotational fission were published: the secondary fission process proposed by Jacobson and Scheeres (2011) and a cratering collision event onto a nearly critically rotating primary proposed by Vokrouhlický et al. (2017). It will have to be revealed from future studies which of the clusters were formed by one or the other process. To that point, we found certain further interesting properties and features of the asteroid clusters that place constraints on the theories of their formation, among them the most intriguing being the possibility of a cascade disruption for some of the clusters.

Interacting star clusters in the Large Magellanic Cloud. Overmerging problem solved by cluster group formation

Science.gov (United States)

Leon, Stéphane; Bergond, Gilles; Vallenari, Antonella

1999-04-01

We present the tidal tail distributions of a sample of candidate binary clusters located in the bar of the Large Magellanic Cloud (LMC). One isolated cluster, SL 268, is presented in order to study the effect of the LMC tidal field. All the candidate binary clusters show tidal tails, confirming that the pairs are formed by physically linked objects. The stellar mass in the tails covers a large range, from 1.8x 10(3) to 3x 10(4) \\msun. We derive a total mass estimate for SL 268 and SL 356. At large radii, the projected density profiles of SL 268 and SL 356 fall off as r(-gamma ) , with gamma = 2.27 and gamma =3.44, respectively. Out of 4 pairs or multiple systems, 2 are older than the theoretical survival time of binary clusters (going from a few 10(6) years to 10(8) years). A pair shows too large age difference between the components to be consistent with classical theoretical models of binary cluster formation (Fujimoto & Kumai \\cite{fujimoto97}). We refer to this as the ``overmerging'' problem. A different scenario is proposed: the formation proceeds in large molecular complexes giving birth to groups of clusters over a few 10(7) years. In these groups the expected cluster encounter rate is larger, and tidal capture has higher probability. Cluster pairs are not born together through the splitting of the parent cloud, but formed later by tidal capture. For 3 pairs, we tentatively identify the star cluster group (SCG) memberships. The SCG formation, through the recent cluster starburst triggered by the LMC-SMC encounter, in contrast with the quiescent open cluster formation in the Milky Way can be an explanation to the paucity of binary clusters observed in our Galaxy. Based on observations collected at the European Southern Observatory, La Silla, Chile}

Full text clustering and relationship network analysis of biomedical publications.

Directory of Open Access Journals (Sweden)

Renchu Guan

Full Text Available Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

Full text clustering and relationship network analysis of biomedical publications.

Science.gov (United States)

Guan, Renchu; Yang, Chen; Marchese, Maurizio; Liang, Yanchun; Shi, Xiaohu

2014-01-01

Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

Diversity among galaxy clusters

International Nuclear Information System (INIS)

Struble, M.F.; Rood, H.J.

1988-01-01

The classification of galaxy clusters is discussed. Consideration is given to the classification scheme of Abell (1950's), Zwicky (1950's), Morgan, Matthews, and Schmidt (1964), and Morgan-Bautz (1970). Galaxies can be classified based on morphology, chemical composition, spatial distribution, and motion. The correlation between a galaxy's environment and morphology is examined. The classification scheme of Rood-Sastry (1971), which is based on clusters's morphology and galaxy population, is described. The six types of clusters they define include: (1) a cD-cluster dominated by a single large galaxy, (2) a cluster dominated by a binary, (3) a core-halo cluster, (4) a cluster dominated by several bright galaxies, (5) a cluster appearing flattened, and (6) an irregularly shaped cluster. Attention is also given to the evolution of cluster structures, which is related to initial density and cluster motion

2D Dust Clusters in Theory and Experiments

International Nuclear Information System (INIS)

Tsytovich, V.N.; Gousein-zade, N.G.; Morfill, G.E.

2005-01-01

The theory is applied for more detail analysis of existing experiments of 2D dust clusters with parabolic confinement. It is shown that the equilibrium condition and the frequency of one of the modes of the cluster determines all dimensionless parameters of the cluster allowing to predict the value of other modes and compare them with existing experimental data. This comparison depends on the shielding model, the calculations starting with N = 4 cluster breathing mode predict for Debye shielding model without attraction the frequency of the antisymmetric mode in disagreement with the observed value about 6 standard deviations, while the same calculations for the non-linear screening model gives disagreement about 1 standard deviation. Including the attraction provides an agrement with observations only for non-linear screening model showing the sensitivity of cluster structure to dust attraction. The value of the obtained attractions coefficient is in reasonable agreement with the theoretically expected value. It is shown theoretically that in absence of external parabolic confinement a weak shadow attraction can provide an existence of equilibria for 2D clusters. The equilibrium radius is rapidly decreasing with an increase of the attraction coefficient and with number of grains N in a cluster. The energies of one shell clusters with different N and the energies of N - 1 grain clusters with additional grain in the center of the shell are calculated as functions of attraction coefficient. It is demonstrated that a dissociation of cluster in several smaller clusters needs less energy than a removal of one grain from the cluster. The calculations were performed for Yukawa screening and for non-linear screening and demonstrate the sensitivity of cluster structures to the screening. Frequencies of all modes are calculated up to N = 7 for one shell structure. Stable and unstable modes as well as universal magic numbers are found