OPEN CLUSTERS AS PROBES OF THE GALACTIC MAGNETIC FIELD. I. CLUSTER PROPERTIES

Energy Technology Data Exchange (ETDEWEB)

Hoq, Sadia; Clemens, D. P., E-mail: shoq@bu.edu, E-mail: clemens@bu.edu [Institute for Astrophysical Research, 725 Commonwealth Avenue, Boston University, Boston, MA 02215 (United States)

2015-10-15

Stars in open clusters are powerful probes of the intervening Galactic magnetic field via background starlight polarimetry because they provide constraints on the magnetic field distances. We use 2MASS photometric data for a sample of 31 clusters in the outer Galaxy for which near-IR polarimetric data were obtained to determine the cluster distances, ages, and reddenings via fitting theoretical isochrones to cluster color–magnitude diagrams. The fitting approach uses an objective χ{sup 2} minimization technique to derive the cluster properties and their uncertainties. We found the ages, distances, and reddenings for 24 of the clusters, and the distances and reddenings for 6 additional clusters that were either sparse or faint in the near-IR. The derived ranges of log(age), distance, and E(B−V) were 7.25–9.63, ∼670–6160 pc, and 0.02–1.46 mag, respectively. The distance uncertainties ranged from ∼8% to 20%. The derived parameters were compared to previous studies, and most cluster parameters agree within our uncertainties. To test the accuracy of the fitting technique, synthetic clusters with 50, 100, or 200 cluster members and a wide range of ages were fit. These tests recovered the input parameters within their uncertainties for more than 90% of the individual synthetic cluster parameters. These results indicate that the fitting technique likely provides reliable estimates of cluster properties. The distances derived will be used in an upcoming study of the Galactic magnetic field in the outer Galaxy.

Minimal disease detection of B-cell lymphoproliferative disorders by flow cytometry: multidimensional cluster analysis.

Science.gov (United States)

Duque, Ricardo E

2012-04-01

Flow cytometric analysis of cell suspensions involves the sequential 'registration' of intrinsic and extrinsic parameters of thousands of cells in list mode files. Thus, it is almost irresistible to describe phenomena in numerical terms or by 'ratios' that have the appearance of 'accuracy' due to the presence of numbers obtained from thousands of cells. The concepts involved in the detection and characterization of B cell lymphoproliferative processes are revisited in this paper by identifying parameters that, when analyzed appropriately, are both necessary and sufficient. The neoplastic process (cluster) can be visualized easily because the parameters that distinguish it form a cluster in multidimensional space that is unique and distinguishable from neighboring clusters that are not of diagnostic interest but serve to provide a background. For B cell neoplasia it is operationally necessary to identify the multidimensional space occupied by a cluster whose kappa:lambda ratio is 100:0 or 0:100. Thus, the concept of kappa:lambda ratio is without meaning and would not detect B cell neoplasia in an unacceptably high number of cases.

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

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.

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 structure in Cf nuclei

International Nuclear Information System (INIS)

Singh, Shailesh K.; Biswal, S.K.; Bhuyan, M.; Patra, S.K.; Gupta, R.K.

2014-01-01

Due to the availability of advance experimental facilities, it is possible to probe the nuclei upto their nucleon level very precisely and analyzed the internal structure which will help us to resolve some mysterious problem of the decay of nuclei. Recently, the relativistic nuclear collision, confirmed the α cluster type structure in the 12 C which is the mile stone for the cluster structure in nuclei. The clustering phenomena in light and intermediate elements in nuclear chart is very interesting. There is a lot of work done by our group in the clustering behaviour of the nuclei. In this paper, the various prospectus of clustering in the isotopes of Cf nucleus including fission state is discussed. Here, 242 Cf isotope for the analysis, which is experimentally known is taken. The relativistic mean field model with well established NL3 parameter set is taken. For getting the exact ground state configuration of the isotopes, the calculation for minimizing the potential energy surface is performed by constraint method. The clustering structure of other Cf isotopes is discussed

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

Science.gov (United States)

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Structure and physical properties of silicon clusters and of vacancy clusters in bulk silicon

International Nuclear Information System (INIS)

Sieck, A.

2000-01-01

In this thesis the growth-pattern of free silicon clusters and vacancy clusters in bulk silicon is investigated. The aim is to describe and to better understand the cluster to bulk transition. Silicon structures in between clusters and solids feature new interesting physical properties. The structure and physical properties of silicon clusters can be revealed by a combination of theory and experiment, only. Low-energy clusters are determined with different optimization techniques and a density-functional based tight-binding method. Additionally, infrared and Raman spectra, and polarizabilities calculated within self-consistent field density-functional theory are provided for the smaller clusters. For clusters with 25 to 35 atoms an analysis of the shape of the clusters and the related mobilities in a buffer gas is given. Finally, the clusters observed in low-temperature experiments are identified via the best match between calculated properties and experimental data. Silicon clusters with 10 to 15 atoms have a tricapped trigonal prism as a common subunit. Clusters with up to about 25 atoms follow a prolate growth-path. In the range from 24 to 30 atoms the geometry of the clusters undergoes a transition towards compact spherical structures. Low-energy clusters with up to 240 atoms feature a bonding pattern strikingly different from the tetrahedral bonding in the solid. It follows that structures with dimensions of several Angstroem have electrical and optical properties different from the solid. The calculated stabilities and positron-lifetimes of vacancy clusters in bulk silicon indicate the positron-lifetimes of about 435 ps detected in irradiated silicon to be related to clusters of 9 or 10 vacancies. The vacancies in these clusters form neighboring hexa-rings and, therefore, minimize the number of dangling bonds. (orig.)

Server consolidation for heterogeneous computer clusters using Colored Petri Nets and CPN Tools

Directory of Open Access Journals (Sweden)

Issam Al-Azzoni

2015-10-01

Full Text Available In this paper, we present a new approach to server consolidation in heterogeneous computer clusters using Colored Petri Nets (CPNs. Server consolidation aims to reduce energy costs and improve resource utilization by reducing the number of servers necessary to run the existing virtual machines in the cluster. It exploits the emerging technology of live migration which allows migrating virtual machines between servers without stopping their provided services. Server consolidation approaches attempt to find migration plans that aim to minimize the necessary size of the cluster. Our approach finds plans which not only minimize the overall number of used servers, but also minimize the total data migration overhead. The latter objective is not taken into consideration by other approaches and heuristics. We explore the use of CPN Tools in analyzing the state spaces of the CPNs. Since the state space of the CPN model can grow exponentially with the size of the cluster, we examine different techniques to generate and analyze the state space in order to find good plans to server consolidation within acceptable time and computing power.

An Improved Cluster Richness Estimator

Energy Technology Data Exchange (ETDEWEB)

Rozo, Eduardo; /Ohio State U.; Rykoff, Eli S.; /UC, Santa Barbara; Koester, Benjamin P.; /Chicago U. /KICP, Chicago; McKay, Timothy; /Michigan U.; Hao, Jiangang; /Michigan U.; Evrard, August; /Michigan U.; Wechsler, Risa H.; /SLAC; Hansen, Sarah; /Chicago U. /KICP, Chicago; Sheldon, Erin; /New York U.; Johnston, David; /Houston U.; Becker, Matthew R.; /Chicago U. /KICP, Chicago; Annis, James T.; /Fermilab; Bleem, Lindsey; /Chicago U.; Scranton, Ryan; /Pittsburgh U.

2009-08-03

Minimizing the scatter between cluster mass and accessible observables is an important goal for cluster cosmology. In this work, we introduce a new matched filter richness estimator, and test its performance using the maxBCG cluster catalog. Our new estimator significantly reduces the variance in the L{sub X}-richness relation, from {sigma}{sub lnL{sub X}}{sup 2} = (0.86 {+-} 0.02){sup 2} to {sigma}{sub lnL{sub X}}{sup 2} = (0.69 {+-} 0.02){sup 2}. Relative to the maxBCG richness estimate, it also removes the strong redshift dependence of the richness scaling relations, and is significantly more robust to photometric and redshift errors. These improvements are largely due to our more sophisticated treatment of galaxy color data. We also demonstrate the scatter in the L{sub X}-richness relation depends on the aperture used to estimate cluster richness, and introduce a novel approach for optimizing said aperture which can be easily generalized to other mass tracers.

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.

Optimal design method to minimize users' thinking mapping load in human-machine interactions.

Science.gov (United States)

Huang, Yanqun; Li, Xu; Zhang, Jie

2015-01-01

The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today's mental work mastered society. Improving the usability of a product and minimizing user's thinking mapping and interpreting load in human-machine interactions. An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users' intentions and affordance of product interface states. By analyzing the users' thinking mapping problem, an operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets. Considering multiple factors to minimize users' thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case. The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.

Computed structure of small benzene clusters

NARCIS (Netherlands)

van de Waal, B.W.

1986-01-01

The structures of small benzene clusters (C6H6)n, n = 2–7, have been calculated employing potential-energy minimization with respect to molecular translational and rotational coordinates, using exp-6-1 non-bonded atom-atom potential functions. The influence of the adopted point-charge model is

Investigation of clustering in sets of analytical data

Energy Technology Data Exchange (ETDEWEB)

Kajfosz, J [Institute of Nuclear Physics, Cracow (Poland)

1993-04-01

Foundation of the statistical method of cluster analysis are briefly presented and its usefulness for the examination and evaluation of analytical data obtained from series of samples investigated by PIXE, PIGE or other methods is discussed. A simple program for fast examination of dissimilarities between samples within an investigated series is described. Useful information on clustering for several hundreds of samples can be obtained with minimal time and storage requirements. (author). 5 refs, 10 figs.

Investigation of clustering in sets of analytical data

International Nuclear Information System (INIS)

Kajfosz, J.

1993-04-01

Foundation of the statistical method of cluster analysis are briefly presented and its usefulness for the examination and evaluation of analytical data obtained from series of samples investigated by PIXE, PIGE or other methods is discussed. A simple program for fast examination of dissimilarities between samples within an investigated series is described. Useful information on clustering for several hundreds of samples can be obtained with minimal time and storage requirements. (author). 5 refs, 10 figs

Effects of hydrogen peroxide, modified atmosphere and their combination on quality of minimally processed cluster beans.

Science.gov (United States)

Waghmare, Roji B; Annapure, Uday S

2017-10-01

The aim of this study was to determine the potential of hydrogen peroxide (H 2 O 2 ) and modified atmosphere packaging (MAP) on quality of fresh-cut cluster beans. Fresh-cut cluster beans were dipped in a solution of 2% H 2 O 2 for 2 min, packed in an atmosphere of (5% O 2 , 10% CO 2 , 85% N 2 ) and stored in polypropylene bags at 5 °C for 35 days. Passive MAP was created by consuming O 2 and producing CO 2 by fresh-cut cluster beans. The combined effect of H 2 O 2 and MAP on physico-chemical analysis (Headspace gas, weight loss, chlorophyll, hardness and color), microbial quality (mesophilic aerobics and yeasts and molds) and sensory analysis were studied. Chemical treatment and MAP both are equally effective in extending the shelf life at 5 °C for 28 days. Hence, MAP can be an alternative for chemical treatment to achieve a shelf life of 28 days for fresh-cut cluster beans. Control samples, without chemical treatment and modified atmosphere, stored at 5 °C were spoiled after 14 days. Chemical treatment followed by MAP underwent minimum changes in weight, chlorophyll, hardness and color of fresh-cut cluster beans. Combination treatment gives a storage life of 35 days.

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

Directory of Open Access Journals (Sweden)

Qingming Zhan

2017-08-01

Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

Recent results on a non-minimal coupling between curvature and matter

International Nuclear Information System (INIS)

Páramos, Jorge

2013-01-01

This work presents a review of recent findings from the consideration of a non-minimal coupling between matter and geometry, namely the possibility of mimicking dark matter in clusters and the description of gravitational collapse — thus adding to the wide range of phenomena already covered by the theory.

A novel grain cluster-based homogenization scheme

International Nuclear Information System (INIS)

Tjahjanto, D D; Eisenlohr, P; Roters, F

2010-01-01

An efficient homogenization scheme, termed the relaxed grain cluster (RGC), for elasto-plastic deformations of polycrystals is presented. The scheme is based on a generalization of the grain cluster concept. A volume element consisting of eight (= 2 × 2 × 2) hexahedral grains is considered. The kinematics of the RGC scheme is formulated within a finite deformation framework, where the relaxation of the local deformation gradient of each individual grain is connected to the overall deformation gradient by the, so-called, interface relaxation vectors. The set of relaxation vectors is determined by the minimization of the constitutive energy (or work) density of the overall cluster. An additional energy density associated with the mismatch at the grain boundaries due to relaxations is incorporated as a penalty term into the energy minimization formulation. Effectively, this penalty term represents the kinematical condition of deformation compatibility at the grain boundaries. Simulations have been performed for a dual-phase grain cluster loaded in uniaxial tension. The results of the simulations are presented and discussed in terms of the effective stress–strain response and the overall deformation anisotropy as functions of the penalty energy parameters. In addition, the prediction of the RGC scheme is compared with predictions using other averaging schemes, as well as to the result of direct finite element (FE) simulation. The comparison indicates that the present RGC scheme is able to approximate FE simulation results of relatively fine discretization at about three orders of magnitude lower computational cost

Balanced Cluster Head Selection Based on Modified k-Means in a Distributed Wireless Sensor Network

OpenAIRE

Periyasamy, Sasikumar; Khara, Sibaram; Thangavelu, Shankar

2016-01-01

A major problem with Wireless Sensor Networks (WSNs) is the maximization of effective network lifetime through minimization of energy usage in the network nodes. A modified k-means (Mk-means) algorithm for clustering was proposed which includes three cluster heads (simultaneously chosen) for each cluster. These cluster heads (CHs) use a load sharing mechanism to rotate as the active cluster head, which conserves residual energy of the nodes, thereby extending network lifetime. Moreover, it re...

Performance Analysis of Entropy Methods on K Means in Clustering Process

Science.gov (United States)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

Modification of MSDR algorithm and ITS implementation on graph clustering

Science.gov (United States)

Prastiwi, D.; Sugeng, K. A.; Siswantining, T.

2017-07-01

Maximum Standard Deviation Reduction (MSDR) is a graph clustering algorithm to minimize the distance variation within a cluster. In this paper we propose a modified MSDR by replacing one technical step in MSDR which uses polynomial regression, with a new and simpler step. This leads to our new algorithm called Modified MSDR (MMSDR). We implement the new algorithm to separate a domestic flight network of an Indonesian airline into two large clusters. Further analysis allows us to discover a weak link in the network, which should be improved by adding more flights.

Efficient similarity-based data clustering by optimal object to cluster reallocation.

Science.gov (United States)

Rossignol, Mathias; Lagrange, Mathieu; Cont, Arshia

2018-01-01

We present an iterative flat hard clustering algorithm designed to operate on arbitrary similarity matrices, with the only constraint that these matrices be symmetrical. Although functionally very close to kernel k-means, our proposal performs a maximization of average intra-class similarity, instead of a squared distance minimization, in order to remain closer to the semantics of similarities. We show that this approach permits the relaxing of some conditions on usable affinity matrices like semi-positiveness, as well as opening possibilities for computational optimization required for large datasets. Systematic evaluation on a variety of data sets shows that compared with kernel k-means and the spectral clustering methods, the proposed approach gives equivalent or better performance, while running much faster. Most notably, it significantly reduces memory access, which makes it a good choice for large data collections. Material enabling the reproducibility of the results is made available online.

Modeling the formation of globular cluster systems in the Virgo cluster

International Nuclear Information System (INIS)

Li, Hui; Gnedin, Oleg Y.

2014-01-01

The mass distribution and chemical composition of globular cluster (GC) systems preserve fossil record of the early stages of galaxy formation. The observed distribution of GC colors within massive early-type galaxies in the ACS Virgo Cluster Survey (ACSVCS) reveals a multi-modal shape, which likely corresponds to a multi-modal metallicity distribution. We present a simple model for the formation and disruption of GCs that aims to match the ACSVCS data. This model tests the hypothesis that GCs are formed during major mergers of gas-rich galaxies and inherit the metallicity of their hosts. To trace merger events, we use halo merger trees extracted from a large cosmological N-body simulation. We select 20 halos in the mass range of 2 × 10 12 to 7 × 10 13 M ☉ and match them to 19 Virgo galaxies with K-band luminosity between 3 × 10 10 and 3 × 10 11 L ☉ . To set the [Fe/H] abundances, we use an empirical galaxy mass-metallicity relation. We find that a minimal merger ratio of 1:3 best matches the observed cluster metallicity distribution. A characteristic bimodal shape appears because metal-rich GCs are produced by late mergers between massive halos, while metal-poor GCs are produced by collective merger activities of less massive hosts at early times. The model outcome is robust to alternative prescriptions for cluster formation rate throughout cosmic time, but a gradual evolution of the mass-metallicity relation with redshift appears to be necessary to match the observed cluster metallicities. We also affirm the age-metallicity relation, predicted by an earlier model, in which metal-rich clusters are systematically several billion younger than their metal-poor counterparts.

Evolution of homeobox genes.

Science.gov (United States)

Holland, Peter W H

2013-01-01

Many homeobox genes encode transcription factors with regulatory roles in animal and plant development. Homeobox genes are found in almost all eukaryotes, and have diversified into 11 gene classes and over 100 gene families in animal evolution, and 10 to 14 gene classes in plants. The largest group in animals is the ANTP class which includes the well-known Hox genes, plus other genes implicated in development including ParaHox (Cdx, Xlox, Gsx), Evx, Dlx, En, NK4, NK3, Msx, and Nanog. Genomic data suggest that the ANTP class diversified by extensive tandem duplication to generate a large array of genes, including an NK gene cluster and a hypothetical ProtoHox gene cluster that duplicated to generate Hox and ParaHox genes. Expression and functional data suggest that NK, Hox, and ParaHox gene clusters acquired distinct roles in patterning the mesoderm, nervous system, and gut. The PRD class is also diverse and includes Pax2/5/8, Pax3/7, Pax4/6, Gsc, Hesx, Otx, Otp, and Pitx genes. PRD genes are not generally arranged in ancient genomic clusters, although the Dux, Obox, and Rhox gene clusters arose in mammalian evolution as did several non-clustered PRD genes. Tandem duplication and genome duplication expanded the number of homeobox genes, possibly contributing to the evolution of developmental complexity, but homeobox gene loss must not be ignored. Evolutionary changes to homeobox gene expression have also been documented, including Hox gene expression patterns shifting in concert with segmental diversification in vertebrates and crustaceans, and deletion of a Pitx1 gene enhancer in pelvic-reduced sticklebacks. WIREs Dev Biol 2013, 2:31-45. doi: 10.1002/wdev.78 For further resources related to this article, please visit the WIREs website. The author declares that he has no conflicts of interest. Copyright © 2012 Wiley Periodicals, Inc.

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)

Hot-spots of radio sources in clusters of galaxies

International Nuclear Information System (INIS)

Saikia, D.J.

1979-01-01

A sample of extragalactic double radio sources is examined to test for a correlation between the prominence of compact hot-spots located at their outer edges and membership of clusters of galaxies. To minimize the effects of incompleteness in published catalogues of clusters, cluster classification is based on the number of galaxies in the neighbourhood of each source. After eliminating possible selection effects, it is found that sources in regions of high galactic density tend to have less prominent hot-spots. It is argued that the result is consistent with the 'continuous-flow' models of radio sources, but poses problems for the gravitational slingshot model. (author)

Sequential unconstrained minimization algorithms for constrained optimization

International Nuclear Information System (INIS)

Byrne, Charles

2008-01-01

The problem of minimizing a function f(x):R J → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G k (x)=f(x)+g k (x), to obtain x k . The auxiliary functions g k (x):D subset of R J → R + are nonnegative on the set D, each x k is assumed to lie within D, and the objective is to minimize the continuous function f:R J → R over x in the set C = D-bar, the closure of D. We assume that such minimizers exist, and denote one such by x-circumflex. We assume that the functions g k (x) satisfy the inequalities 0≤g k (x)≤G k-1 (x)-G k-1 (x k-1 ), for k = 2, 3, .... Using this assumption, we show that the sequence {(x k )} is decreasing and converges to f(x-circumflex). If the restriction of f(x) to D has bounded level sets, which happens if x-circumflex is unique and f(x) is closed, proper and convex, then the sequence {x k } is bounded, and f(x*)=f(x-circumflex), for any cluster point x*. Therefore, if x-circumflex is unique, x* = x-circumflex and {x k } → x-circumflex. When x-circumflex is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton–Raphson method. The proof techniques used for SUMMA can be extended to obtain related results

A strategy to find minimal energy nanocluster structures.

Science.gov (United States)

Rogan, José; Varas, Alejandro; Valdivia, Juan Alejandro; Kiwi, Miguel

2013-11-05

An unbiased strategy to search for the global and local minimal energy structures of free standing nanoclusters is presented. Our objectives are twofold: to find a diverse set of low lying local minima, as well as the global minimum. To do so, we use massively the fast inertial relaxation engine algorithm as an efficient local minimizer. This procedure turns out to be quite efficient to reach the global minimum, and also most of the local minima. We test the method with the Lennard-Jones (LJ) potential, for which an abundant literature does exist, and obtain novel results, which include a new local minimum for LJ13 , 10 new local minima for LJ14 , and thousands of new local minima for 15≤N≤65. Insights on how to choose the initial configurations, analyzing the effectiveness of the method in reaching low-energy structures, including the global minimum, are developed as a function of the number of atoms of the cluster. Also, a novel characterization of the potential energy surface, analyzing properties of the local minima basins, is provided. The procedure constitutes a promising tool to generate a diverse set of cluster conformations, both two- and three-dimensional, that can be used as an input for refinement by means of ab initio methods. Copyright © 2013 Wiley Periodicals, Inc.

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

Pixel-Cluster Counting Luminosity Measurement in ATLAS

CERN Document Server

McCormack, William Patrick; The ATLAS collaboration

2016-01-01

A precision measurement of the delivered luminosity is a key component of the ATLAS physics program at the Large Hadron Collider (LHC). A fundamental ingredient of the strategy to control the systematic uncertainties affecting the absolute luminosity has been to compare the measurements of several luminometers, most of which use more than one counting technique. The level of consistency across the various methods provides valuable cross-checks as well as an estimate of the detector-related systematic uncertainties. This poster describes the development of a luminosity algorithm based on pixel-cluster counting in the recently installed ATLAS inner b-layer (IBL), using data recorded during the 2015 pp run at the LHC. The noise and background contamination of the luminosity-associated cluster count is minimized by a multi-component fit to the measured cluster-size distribution in the forward pixel modules of the IBL. The linearity, long-term stability and statistical precision of the cluster-counting method are ...

Pixel-Cluster Counting Luminosity Measurement In ATLAS

CERN Document Server

AUTHOR|(SzGeCERN)782710; The ATLAS collaboration

2017-01-01

A precision measurement of the delivered luminosity is a key component of the ATLAS physics program at the Large Hadron Collider (LHC). A fundamental ingredient of the strategy to control the systematic uncertainties affecting the absolute luminosity has been to compare the measure- ments of several luminometers, most of which use more than one counting technique. The level of consistency across the various methods provides valuable cross-checks as well as an estimate of the detector-related systematic uncertainties. This poster describes the development of a luminosity algorithm based on pixel-cluster counting in the recently installed ATLAS inner b-layer (IBL), using data recorded during the 2015 pp run at the LHC. The noise and background contamination of the luminosity-associated cluster count is minimized by a multi-component fit to the measured cluster-size distribution in the forward pixel modules of the IBL. The linearity, long-term stability and statistical precision of the cluster- counting method a...

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.

Non-negative matrix factorization by maximizing correntropy for cancer clustering

KAUST Repository

Wang, Jim Jing-Yan; Wang, Xiaolei; Gao, Xin

2013-01-01

Background: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.

Non-negative matrix factorization by maximizing correntropy for cancer clustering

KAUST Repository

Wang, Jim Jing-Yan

2013-03-24

Background: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.

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

Significance of calculated cluster conformations of benzene: comment on a publication by D. E. Williams

NARCIS (Netherlands)

van de Waal, B.W.

1981-01-01

Results of potential-energy minimization, applied to clusters of benzene molecules, have been reported recently by Williams [Acta Cryst. (1980), A36, 715-723]. Two stable tridecamer clusters were found and compared with a 13-molecule fragment from crystalline orthorhombic benzene. In this comment

Cluster-based service discovery for heterogeneous wireless sensor networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

2007-01-01

We propose an energy-efficient service discovery protocol for heterogeneous wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the

Brain tumor segmentation based on a hybrid clustering technique

Directory of Open Access Journals (Sweden)

Eman Abdel-Maksoud

2015-03-01

This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

Development of a small-scale computer cluster

Science.gov (United States)

Wilhelm, Jay; Smith, Justin T.; Smith, James E.

2008-04-01

An increase in demand for computing power in academia has necessitated the need for high performance machines. Computing power of a single processor has been steadily increasing, but lags behind the demand for fast simulations. Since a single processor has hard limits to its performance, a cluster of computers can have the ability to multiply the performance of a single computer with the proper software. Cluster computing has therefore become a much sought after technology. Typical desktop computers could be used for cluster computing, but are not intended for constant full speed operation and take up more space than rack mount servers. Specialty computers that are designed to be used in clusters meet high availability and space requirements, but can be costly. A market segment exists where custom built desktop computers can be arranged in a rack mount situation, gaining the space saving of traditional rack mount computers while remaining cost effective. To explore these possibilities, an experiment was performed to develop a computing cluster using desktop components for the purpose of decreasing computation time of advanced simulations. This study indicates that small-scale cluster can be built from off-the-shelf components which multiplies the performance of a single desktop machine, while minimizing occupied space and still remaining cost effective.

A pilot cluster randomized controlled trial of structured goal-setting following stroke.

Science.gov (United States)

Taylor, William J; Brown, Melanie; William, Levack; McPherson, Kathryn M; Reed, Kirk; Dean, Sarah G; Weatherall, Mark

2012-04-01

To determine the feasibility, the cluster design effect and the variance and minimal clinical importance difference in the primary outcome in a pilot study of a structured approach to goal-setting. A cluster randomized controlled trial. Inpatient rehabilitation facilities. People who were admitted to inpatient rehabilitation following stroke who had sufficient cognition to engage in structured goal-setting and complete the primary outcome measure. Structured goal elicitation using the Canadian Occupational Performance Measure. Quality of life at 12 weeks using the Schedule for Individualised Quality of Life (SEIQOL-DW), Functional Independence Measure, Short Form 36 and Patient Perception of Rehabilitation (measuring satisfaction with rehabilitation). Assessors were blinded to the intervention. Four rehabilitation services and 41 patients were randomized. We found high values of the intraclass correlation for the outcome measures (ranging from 0.03 to 0.40) and high variance of the SEIQOL-DW (SD 19.6) in relation to the minimally importance difference of 2.1, leading to impractically large sample size requirements for a cluster randomized design. A cluster randomized design is not a practical means of avoiding contamination effects in studies of inpatient rehabilitation goal-setting. Other techniques for coping with contamination effects are necessary.

Artificial immune kernel clustering network for unsupervised image segmentation

Institute of Scientific and Technical Information of China (English)

Wenlong Huang; Licheng Jiao

2008-01-01

An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the support vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coefficient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algorithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predefined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

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.

Minimization of PWR reactor control rods wear

International Nuclear Information System (INIS)

Ponzoni Filho, Pedro; Moura Angelkorte, Gunther de

1995-01-01

The Rod Cluster Control Assemblies (RCCA's) of Pressurized Water Reactors (PWR's) have experienced a continuously wall cladding wear when Reactor Coolant Pumps (RCP's) are running. Fretting wear is a result of vibrational contact between RCCA rodlets and the guide cards which provide lateral support for the rodlets when RCCA's are withdrawn from the core. A procedure is developed to minimize the rodlets wear, by the shuffling and axial reposition of RCCA's every operating cycle. These shuffling and repositions are based on measurement of the rodlet cladding thickness of all RCCA's. (author). 3 refs, 2 figs, 2 tabs

AGE AND MASS FOR 920 LARGE MAGELLANIC CLOUD CLUSTERS DERIVED FROM 100 MILLION MONTE CARLO SIMULATIONS

International Nuclear Information System (INIS)

Popescu, Bogdan; Hanson, M. M.; Elmegreen, Bruce G.

2012-01-01

We present new age and mass estimates for 920 stellar clusters in the Large Magellanic Cloud (LMC) based on previously published broadband photometry and the stellar cluster analysis package, MASSCLEANage. Expressed in the generic fitting formula, d 2 N/dMdt∝M α t β , the distribution of observed clusters is described by α = –1.5 to –1.6 and β = –2.1 to –2.2. For 288 of these clusters, ages have recently been determined based on stellar photometric color-magnitude diagrams, allowing us to gauge the confidence of our ages. The results look very promising, opening up the possibility that this sample of 920 clusters, with reliable and consistent age, mass, and photometric measures, might be used to constrain important characteristics about the stellar cluster population in the LMC. We also investigate a traditional age determination method that uses a χ 2 minimization routine to fit observed cluster colors to standard infinite-mass limit simple stellar population models. This reveals serious defects in the derived cluster age distribution using this method. The traditional χ 2 minimization method, due to the variation of U, B, V, R colors, will always produce an overdensity of younger and older clusters, with an underdensity of clusters in the log (age/yr) = [7.0, 7.5] range. Finally, we present a unique simulation aimed at illustrating and constraining the fading limit in observed cluster distributions that includes the complex effects of stochastic variations in the observed properties of stellar clusters.

Science from a glimpse: Hubble SNAPshot observations of massive galaxy clusters

Science.gov (United States)

Repp, A.; Ebeling, H.

2018-06-01

Hubble Space Telescope SNAPshot surveys of 86 X-ray selected galaxy clusters at 0.3 0.3. Examining the evolution of the slope of the cluster red sequence, we observe at best a slight decrease with redshift, indicating minimal age contribution since z ˜ 1. Congruent to previous studies' findings, we note that the two BCGs which are significantly bluer (≥5σ) than their clusters' red sequences reside in relaxed clusters and exhibit pronounced internal structure. Thanks to our targets' high X-ray luminosity, the subset of our sample observed with Chandra adds valuable leverage to the X-ray luminosity-optical richness relation, which, albeit with substantial scatter, is now clearly established from groups to extremely massive clusters of galaxies. We conclude that SNAPshot observations of MACS clusters stand to continue to play a vital pathfinder role for astrophysical investigations across the entire electromagnetic spectrum.

Data Clustering

Science.gov (United States)

Wagstaff, Kiri L.

2012-03-01

clustering, in which some partial information about item assignments or other components of the resulting output are already known and must be accommodated by the solution. Some algorithms seek a partition of the data set into distinct clusters, while others build a hierarchy of nested clusters that can capture taxonomic relationships. Some produce a single optimal solution, while others construct a probabilistic model of cluster membership. More formally, clustering algorithms operate on a data set X composed of items represented by one or more features (dimensions). These could include physical location, such as right ascension and declination, as well as other properties such as brightness, color, temporal change, size, texture, and so on. Let D be the number of dimensions used to represent each item, xi ∈ RD. The clustering goal is to produce an organization P of the items in X that optimizes an objective function f : P -> R, which quantifies the quality of solution P. Often f is defined so as to maximize similarity within a cluster and minimize similarity between clusters. To that end, many algorithms make use of a measure d : X x X -> R of the distance between two items. A partitioning algorithm produces a set of clusters P = {c1, . . . , ck} such that the clusters are nonoverlapping (c_i intersected with c_j = empty set, i != j) subsets of the data set (Union_i c_i=X). Hierarchical algorithms produce a series of partitions P = {p1, . . . , pn }. For a complete hierarchy, the number of partitions n’= n, the number of items in the data set; the top partition is a single cluster containing all items, and the bottom partition contains n clusters, each containing a single item. For model-based clustering, each cluster c_j is represented by a model m_j , such as the cluster center or a Gaussian distribution. The wide array of available clustering algorithms may seem bewildering, and covering all of them is beyond the scope of this chapter. Choosing among them for a

A New Soft Computing Method for K-Harmonic Means Clustering.

Science.gov (United States)

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

The Cost of Troubleshooting Cost Clusters with Inside Information

DEFF Research Database (Denmark)

Ottosen, Thorsten Jørgen; Jensen, Finn V.

2010-01-01

Decision theoretical troubleshooting is about minimizing the expected cost of solving a certain problem like repairing a complicated man-made device. In this paper we consider situations where you have to take apart some of the device to get access to certain clusters and actions. Specifically, w...

The influence of nanoscale morphology on the resistivity of cluster-assembled nanostructured metallic thin films

International Nuclear Information System (INIS)

Barborini, E; Bertolini, G; Repetto, P; Leccardi, M; Vinati, S; Corbelli, G; Milani, P

2010-01-01

We have studied in situ the evolution of the electrical resistivity of Fe, Pd, Nb, W and Mo cluster-assembled films during their growth by supersonic cluster beam deposition. We observed resistivity of cluster-assembled films several orders of magnitude larger than the bulk, as well as an increase in resistivity by increasing the film thickness in contrast to what was observed for atom-assembled metallic films. This suggests that the nanoscale morphological features typical of ballistic films growth, such as the minimal cluster-cluster interconnection and the evolution of surface roughness with thickness, are responsible for the observed behaviour.

Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey

Directory of Open Access Journals (Sweden)

Bilal Jan

2017-01-01

Full Text Available Wireless sensor networks (WSN are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

Energy-Efficient Cluster-Based Service Discovery in Wireless Sensor Networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

We propose an energy-efficient service discovery protocol for wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the communication

Energy-Efficient Cluster-Based Service Discovery in Wireless Sensor Networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

2006-01-01

We propose an energy-efficient service discovery protocol for wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the communication

Robustness in cluster analysis in the presence of anomalous observations

NARCIS (Netherlands)

Zhuk, EE

Cluster analysis of multivariate observations in the presence of "outliers" (anomalous observations) in a sample is studied. The expected (mean) fraction of erroneous decisions for the decision rule is computed analytically by minimizing the intraclass scatter. A robust decision rule (stable to

Minimizing off-Target Mutagenesis Risks Caused by Programmable Nucleases.

Science.gov (United States)

Ishida, Kentaro; Gee, Peter; Hotta, Akitsu

2015-10-16

Programmable nucleases, such as zinc finger nucleases (ZFNs), transcription activator like effector nucleases (TALENs), and clustered regularly interspersed short palindromic repeats associated protein-9 (CRISPR-Cas9), hold tremendous potential for applications in the clinical setting to treat genetic diseases or prevent infectious diseases. However, because the accuracy of DNA recognition by these nucleases is not always perfect, off-target mutagenesis may result in undesirable adverse events in treated patients such as cellular toxicity or tumorigenesis. Therefore, designing nucleases and analyzing their activity must be carefully evaluated to minimize off-target mutagenesis. Furthermore, rigorous genomic testing will be important to ensure the integrity of nuclease modified cells. In this review, we provide an overview of available nuclease designing platforms, nuclease engineering approaches to minimize off-target activity, and methods to evaluate both on- and off-target cleavage of CRISPR-Cas9.

The minimally tuned minimal supersymmetric standard model

International Nuclear Information System (INIS)

Essig, Rouven; Fortin, Jean-Francois

2008-01-01

The regions in the Minimal Supersymmetric Standard Model with the minimal amount of fine-tuning of electroweak symmetry breaking are presented for general messenger scale. No a priori relations among the soft supersymmetry breaking parameters are assumed and fine-tuning is minimized with respect to all the important parameters which affect electroweak symmetry breaking. The superpartner spectra in the minimally tuned region of parameter space are quite distinctive with large stop mixing at the low scale and negative squark soft masses at the high scale. The minimal amount of tuning increases enormously for a Higgs mass beyond roughly 120 GeV

Active Semisupervised Clustering Algorithm with Label Propagation for Imbalanced and Multidensity Datasets

Directory of Open Access Journals (Sweden)

Mingwei Leng

2013-01-01

Full Text Available The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced.

An alternating minimization method for blind deconvolution from Poisson data

International Nuclear Information System (INIS)

Prato, Marco; La Camera, Andrea; Bonettini, Silvia

2014-01-01

Blind deconvolution is a particularly challenging inverse problem since information on both the desired target and the acquisition system have to be inferred from the measured data. When the collected data are affected by Poisson noise, this problem is typically addressed by the minimization of the Kullback-Leibler divergence, in which the unknowns are sought in particular feasible sets depending on the a priori information provided by the specific application. If these sets are separated, then the resulting constrained minimization problem can be addressed with an inexact alternating strategy. In this paper we apply this optimization tool to the problem of reconstructing astronomical images from adaptive optics systems, and we show that the proposed approach succeeds in providing very good results in the blind deconvolution of nondense stellar clusters

The minimal non-minimal standard model

International Nuclear Information System (INIS)

Bij, J.J. van der

2006-01-01

In this Letter I discuss a class of extensions of the standard model that have a minimal number of possible parameters, but can in principle explain dark matter and inflation. It is pointed out that the so-called new minimal standard model contains a large number of parameters that can be put to zero, without affecting the renormalizability of the model. With the extra restrictions one might call it the minimal (new) non-minimal standard model (MNMSM). A few hidden discrete variables are present. It is argued that the inflaton should be higher-dimensional. Experimental consequences for the LHC and the ILC are discussed

Restoration ecology: two-sex dynamics and cost minimization.

Directory of Open Access Journals (Sweden)

Ferenc Molnár

Full Text Available We model a spatially detailed, two-sex population dynamics, to study the cost of ecological restoration. We assume that cost is proportional to the number of individuals introduced into a large habitat. We treat dispersal as homogeneous diffusion in a one-dimensional reaction-diffusion system. The local population dynamics depends on sex ratio at birth, and allows mortality rates to differ between sexes. Furthermore, local density dependence induces a strong Allee effect, implying that the initial population must be sufficiently large to avert rapid extinction. We address three different initial spatial distributions for the introduced individuals; for each we minimize the associated cost, constrained by the requirement that the species must be restored throughout the habitat. First, we consider spatially inhomogeneous, unstable stationary solutions of the model's equations as plausible candidates for small restoration cost. Second, we use numerical simulations to find the smallest rectangular cluster, enclosing a spatially homogeneous population density, that minimizes the cost of assured restoration. Finally, by employing simulated annealing, we minimize restoration cost among all possible initial spatial distributions of females and males. For biased sex ratios, or for a significant between-sex difference in mortality, we find that sex-specific spatial distributions minimize the cost. But as long as the sex ratio maximizes the local equilibrium density for given mortality rates, a common homogeneous distribution for both sexes that spans a critical distance yields a similarly low cost.

Restoration ecology: two-sex dynamics and cost minimization.

Science.gov (United States)

Molnár, Ferenc; Caragine, Christina; Caraco, Thomas; Korniss, Gyorgy

2013-01-01

We model a spatially detailed, two-sex population dynamics, to study the cost of ecological restoration. We assume that cost is proportional to the number of individuals introduced into a large habitat. We treat dispersal as homogeneous diffusion in a one-dimensional reaction-diffusion system. The local population dynamics depends on sex ratio at birth, and allows mortality rates to differ between sexes. Furthermore, local density dependence induces a strong Allee effect, implying that the initial population must be sufficiently large to avert rapid extinction. We address three different initial spatial distributions for the introduced individuals; for each we minimize the associated cost, constrained by the requirement that the species must be restored throughout the habitat. First, we consider spatially inhomogeneous, unstable stationary solutions of the model's equations as plausible candidates for small restoration cost. Second, we use numerical simulations to find the smallest rectangular cluster, enclosing a spatially homogeneous population density, that minimizes the cost of assured restoration. Finally, by employing simulated annealing, we minimize restoration cost among all possible initial spatial distributions of females and males. For biased sex ratios, or for a significant between-sex difference in mortality, we find that sex-specific spatial distributions minimize the cost. But as long as the sex ratio maximizes the local equilibrium density for given mortality rates, a common homogeneous distribution for both sexes that spans a critical distance yields a similarly low cost.

K-AP: Generating specified K clusters by efficient Affinity Propagation

KAUST Repository

Zhang, Xiangliang

2010-12-01

The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a data set. However, it suffers two major shortcomings: i) the number of clusters is vague with the user-defined parameter called self-confidence, and ii) the quadratic computational complexity. When aiming at a given number of clusters due to prior knowledge, AP has to be launched many times until an appropriate setting of self-confidence is found. The re-launched AP increases the computational cost by one order of magnitude. In this paper, we propose an algorithm, called K-AP, to exploit the immediate results of K clusters by introducing a constraint in the process of message passing. Through theoretical analysis and experimental validation, K-AP was shown to be able to directly generate K clusters as user defined, with a negligible increase of computational cost compared to AP. In the meanwhile, K-AP preserves the clustering quality as AP in terms of the distortion. K-AP is more effective than k-medoids w.r.t. the distortion minimization and higher clustering purity. © 2010 IEEE.

Portuguese Lexical Clusters and CVC Sequences in Speech Perception and Production.

Science.gov (United States)

Cunha, Conceição

2015-01-01

This paper investigates similarities between lexical consonant clusters and CVC sequences differing in the presence or absence of a lexical vowel in speech perception and production in two Portuguese varieties. The frequent high vowel deletion in the European variety (EP) and the realization of intervening vocalic elements between lexical clusters in Brazilian Portuguese (BP) may minimize the contrast between lexical clusters and CVC sequences in the two Portuguese varieties. In order to test this hypothesis we present a perception experiment with 72 participants and a physiological analysis of 3-dimensional movement data from 5 EP and 4 BP speakers. The perceptual results confirmed a gradual confusion of lexical clusters and CVC sequences in EP, which corresponded roughly to the gradient consonantal overlap found in production. © 2015 S. Karger AG, Basel.

IMPROVEMENT OF THE RICHNESS ESTIMATES OF maxBCG CLUSTERS

International Nuclear Information System (INIS)

Rozo, Eduardo; Rykoff, Eli S.; Koester, Benjamin P.; Hansen, Sarah; Becker, Matthew; Bleem, Lindsey; McKay, Timothy; Hao Jiangang; Evrard, August; Wechsler, Risa H.; Sheldon, Erin; Johnston, David; Annis, James; Scranton, Ryan

2009-01-01

Minimizing the scatter between cluster mass and accessible observables is an important goal for cluster cosmology. In this work, we introduce a new matched filter richness estimator, and test its performance using the maxBCG cluster catalog. Our new estimator significantly reduces the variance in the L X -richness relation, from σ lnLx 2 = (0.86±0.02) 2 to σ lnLx 2 = (0.69±0.02) 2 . Relative to the maxBCG richness estimate, it also removes the strong redshift dependence of the L X -richness scaling relations, and is significantly more robust to photometric and redshift errors. These improvements are largely due to the better treatment of galaxy color data. We also demonstrate the scatter in the L X -richness relation depends on the aperture used to estimate cluster richness, and introduce a novel approach for optimizing said aperture which can easily be generalized to other mass tracers.

A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization.

Science.gov (United States)

He, Xiaofei; Ji, Ming; Zhang, Chiyuan; Bao, Hujun

2011-10-01

In many information processing tasks, one is often confronted with very high-dimensional data. Feature selection techniques are designed to find the meaningful feature subset of the original features which can facilitate clustering, classification, and retrieval. In this paper, we consider the feature selection problem in unsupervised learning scenarios, which is particularly difficult due to the absence of class labels that would guide the search for relevant information. Based on Laplacian regularized least squares, which finds a smooth function on the data manifold and minimizes the empirical loss, we propose two novel feature selection algorithms which aim to minimize the expected prediction error of the regularized regression model. Specifically, we select those features such that the size of the parameter covariance matrix of the regularized regression model is minimized. Motivated from experimental design, we use trace and determinant operators to measure the size of the covariance matrix. Efficient computational schemes are also introduced to solve the corresponding optimization problems. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithms.

Minimalism

CERN Document Server

Obendorf, Hartmut

2009-01-01

The notion of Minimalism is proposed as a theoretical tool supporting a more differentiated understanding of reduction and thus forms a standpoint that allows definition of aspects of simplicity. This book traces the development of minimalism, defines the four types of minimalism in interaction design, and looks at how to apply it.

Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering.

Science.gov (United States)

He, Zhaoshui; Xie, Shengli; Zdunek, Rafal; Zhou, Guoxu; Cichocki, Andrzej

2011-12-01

Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.

Palladium clusters deposited on the heterogeneous substrates

Energy Technology Data Exchange (ETDEWEB)

Wang, Kun, E-mail: cqdxwk@126.com [College of Power Engineering, Chongqing University, Chongqing 400044 (China); Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of PRC, Chongqing 400044 (China); Liu, Juanfang, E-mail: juanfang@cqu.edu.cn [College of Power Engineering, Chongqing University, Chongqing 400044 (China); Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of PRC, Chongqing 400044 (China); Chen, Qinghua, E-mail: qhchen@cqu.edu.cn [College of Power Engineering, Chongqing University, Chongqing 400044 (China); Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of PRC, Chongqing 400044 (China)

2016-07-15

Graphical abstract: The site-exchange between the substrate and cluster atoms can result in the formation of the surface alloys and the reconstruction of the cluster structure before the collision system approaching the thermal equilibrium. The deposited cluster adjusted the atom arrangement as possibly as to match the substrate lattice arrangement from bottom to up. The structural reconstruction is accompanied by the system potential energy minimization. - Highlights: • The deposition process can divide explicitly into three stages: adsorption, collision, relaxation. • The local melt does not emerge inside the substrate during the deposition process. • Surface alloys are formed by the site-exchange between the cluster and substrate atoms. • The cluster reconstructs the atom arrangement following as the substrate lattice arrangement from bottom to up. • The structural reconstruction ability and scope depend on the cluster size and incident energy. - Abstract: To improve the performance of the Pd composite membrane prepared by the cold spraying technology, it is extremely essential to give insights into the deposition process of the cluster and the heterogeneous deposition of the big Pd cluster at the different incident velocities on the atomic level. The deposition behavior, morphologies, energetic and interfacial configuration were examined by the molecular dynamic simulation and characterized by the cluster flattening ratio, the substrate maximum local temperature, the atom-embedded layer number and the surface-alloy formation. According to the morphology evolution, three deposition stages and the corresponding structural and energy evolution were clearly identified. The cluster deformation and penetrating depth increased with the enhancement of the incident velocity, but the increase degree also depended on the substrate hardness. The interfacial interaction between the cluster and the substrate can be improved by the higher substrate local temperature

Fitting Latent Cluster Models for Networks with latentnet

Directory of Open Access Journals (Sweden)

Pavel N. Krivitsky

2007-12-01

Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.

Oxidation of ligand-protected aluminum clusters: An ab initio molecular dynamics study

International Nuclear Information System (INIS)

Alnemrat, Sufian; Hooper, Joseph P.

2014-01-01

We report Car-Parrinello molecular dynamics simulations of the oxidation of ligand-protected aluminum clusters that form a prototypical cluster-assembled material. These clusters contain a small aluminum core surrounded by a monolayer of organic ligand. The aromatic cyclopentadienyl ligands form a strong bond with surface Al atoms, giving rise to an organometallic cluster that crystallizes into a low-symmetry solid and is briefly stable in air before oxidizing. Our calculations of isolated aluminum/cyclopentadienyl clusters reacting with oxygen show minimal reaction between the ligand and O 2 molecules at simulation temperatures of 500 and 1000 K. In all cases, the reaction pathway involves O 2 diffusing through the ligand barrier, splitting into atomic oxygen upon contact with the aluminum, and forming an oxide cluster with aluminum/ligand bonds still largely intact. Loss of individual aluminum-ligand units, as expected from unimolecular decomposition calculations, is not observed except following significant oxidation. These calculations highlight the role of the ligand in providing a steric barrier against oxidizers and in maintaining the large aluminum surface area of the solid-state cluster material

Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change

Directory of Open Access Journals (Sweden)

Knol Dirk L

2006-08-01

Full Text Available Abstract Changes in scores on health status questionnaires are difficult to interpret. Several methods to determine minimally important changes (MICs have been proposed which can broadly be divided in distribution-based and anchor-based methods. Comparisons of these methods have led to insight into essential differences between these approaches. Some authors have tried to come to a uniform measure for the MIC, such as 0.5 standard deviation and the value of one standard error of measurement (SEM. Others have emphasized the diversity of MIC values, depending on the type of anchor, the definition of minimal importance on the anchor, and characteristics of the disease under study. A closer look makes clear that some distribution-based methods have been merely focused on minimally detectable changes. For assessing minimally important changes, anchor-based methods are preferred, as they include a definition of what is minimally important. Acknowledging the distinction between minimally detectable and minimally important changes is useful, not only to avoid confusion among MIC methods, but also to gain information on two important benchmarks on the scale of a health status measurement instrument. Appreciating the distinction, it becomes possible to judge whether the minimally detectable change of a measurement instrument is sufficiently small to detect minimally important changes.

Centroid based clustering of high throughput sequencing reads based on n-mer counts.

Science.gov (United States)

Solovyov, Alexander; Lipkin, W Ian

2013-09-08

Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering. We study several centroid based algorithms for clustering sequences based on word counts. Study of their performance shows that using k-means algorithm with or without the data whitening is efficient from the computational point of view. A higher clustering accuracy can be achieved using the soft expectation maximization method, whereby each sequence is attributed to each cluster with a specific probability. We implement an open source tool for alignment-free clustering. It is publicly available from github: https://github.com/luscinius/afcluster. We show the utility of alignment-free sequence clustering for high throughput sequencing analysis despite its limitations. In particular, it allows one to perform assembly with reduced resources and a minimal loss of quality. The major factor affecting performance of alignment-free read clustering is the length of the read.

A Multi-Hop Clustering Mechanism for Scalable IoT Networks.

Science.gov (United States)

Sung, Yoonyoung; Lee, Sookyoung; Lee, Meejeong

2018-03-23

It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63-87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6-89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.

Communication: A Jastrow factor coupled cluster theory for weak and strong electron correlation

International Nuclear Information System (INIS)

Neuscamman, Eric

2013-01-01

We present a Jastrow-factor-inspired variant of coupled cluster theory that accurately describes both weak and strong electron correlation. Compatibility with quantum Monte Carlo allows for variational energy evaluations and an antisymmetric geminal power reference, two features not present in traditional coupled cluster that facilitate a nearly exact description of the strong electron correlations in minimal-basis N 2 bond breaking. In double-ζ treatments of the HF and H 2 O bond dissociations, where both weak and strong correlations are important, this polynomial cost method proves more accurate than either traditional coupled cluster or complete active space perturbation theory. These preliminary successes suggest a deep connection between the ways in which cluster operators and Jastrow factors encode correlation

Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks.

Science.gov (United States)

Arunraja, Muruganantham; Malathi, Veluchamy; Sakthivel, Erulappan

2015-11-01

Wireless sensor networks are engaged in various data gathering applications. The major bottleneck in wireless data gathering systems is the finite energy of sensor nodes. By conserving the on board energy, the life span of wireless sensor network can be well extended. Data communication being the dominant energy consuming activity of wireless sensor network, data reduction can serve better in conserving the nodal energy. Spatial and temporal correlation among the sensor data is exploited to reduce the data communications. Data similar cluster formation is an effective way to exploit spatial correlation among the neighboring sensors. By sending only a subset of data and estimate the rest using this subset is the contemporary way of exploiting temporal correlation. In Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks, we construct data similar iso-clusters with minimal communication overhead. The intra-cluster communication is reduced using adaptive-normalized least mean squares based dual prediction framework. The cluster head reduces the inter-cluster data payload using a lossless compressive forwarding technique. The proposed work achieves significant data reduction in both the intra-cluster and the inter-cluster communications, with the optimal data accuracy of collected data. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Minimal surfaces

CERN Document Server

Dierkes, Ulrich; Sauvigny, Friedrich; Jakob, Ruben; Kuster, Albrecht

2010-01-01

Minimal Surfaces is the first volume of a three volume treatise on minimal surfaces (Grundlehren Nr. 339-341). Each volume can be read and studied independently of the others. The central theme is boundary value problems for minimal surfaces. The treatise is a substantially revised and extended version of the monograph Minimal Surfaces I, II (Grundlehren Nr. 295 & 296). The first volume begins with an exposition of basic ideas of the theory of surfaces in three-dimensional Euclidean space, followed by an introduction of minimal surfaces as stationary points of area, or equivalently

A density-dependent switch drives stochastic clustering and polarization of signaling molecules.

Directory of Open Access Journals (Sweden)

Alexandra Jilkine

2011-11-01

Full Text Available Positive feedback plays a key role in the ability of signaling molecules to form highly localized clusters in the membrane or cytosol of cells. Such clustering can occur in the absence of localizing mechanisms such as pre-existing spatial cues, diffusional barriers, or molecular cross-linking. What prevents positive feedback from amplifying inevitable biological noise when an un-clustered "off" state is desired? And, what limits the spread of clusters when an "on" state is desired? Here, we show that a minimal positive feedback circuit provides the general principle for both suppressing and amplifying noise: below a critical density of signaling molecules, clustering switches off; above this threshold, highly localized clusters are recurrently generated. Clustering occurs only in the stochastic regime, suggesting that finite sizes of molecular populations cannot be ignored in signal transduction networks. The emergence of a dominant cluster for finite numbers of molecules is partly a phenomenon of random sampling, analogous to the fixation or loss of neutral mutations in finite populations. We refer to our model as the "neutral drift polarity model." Regulating the density of signaling molecules provides a simple mechanism for a positive feedback circuit to robustly switch between clustered and un-clustered states. The intrinsic ability of positive feedback both to create and suppress clustering is a general mechanism that could operate within diverse biological networks to create dynamic spatial organization.

Satellite cluster flight using on-off cyclic control

Science.gov (United States)

Zhang, Hao; Gurfil, Pini

2015-01-01

Nano-satellite clusters and disaggregated satellites are new concepts in the realm of distributed satellite systems, which require complex cluster management - mainly regulating the maximal and minimal inter-satellite distances on time scales of years - while utilizing simple on-off propulsion systems. The simple actuators and long time scales require judicious astrodynamical modeling coupled with specialized orbit control. This paper offers a satellite cluster orbit control law which works for long time scales in a perturbed environment while utilizing fixed-magnitude thrusters. The main idea is to design a distributed controller which balances the fuel consumption among the satellites, thus mitigating the effect of differential drag perturbations. The underlying methodology utilizes a cyclic control algorithm based on a mean orbital elements feedback. Stability properties of the closed-loop cyclic control system do not adhere to the classical Lyapunov stability theory, so an effort is made to define and implement a suitable stability theory of noncompact equilibria sets. A state selection scheme is proposed for efficiently establishing a low Earth orbit cluster. Several simulations, including a real mission study, and several comparative investigations, are performed to show the strengths of the proposed control law.

Clinical assessment using an algorithm based on clustering Fuzzy c-means

NARCIS (Netherlands)

Guijarro-Rodriguez, A.; Cevallos-Torres, L.; Yepez-Holguin, J.; Botto-Tobar, M.; Valencia-García, R.; Lagos-Ortiz, K.; Alcaraz-Mármol, G.; Del Cioppo, J.; Vera-Lucio, N.; Bucaram-Leverone, M.

2017-01-01

The Fuzzy c-means (FCM) algorithms dene a grouping criterion from a function, which seeks to minimize iteratively the function up to an optimal fuzzy partition is obtained. In the execution of this algorithm relates each element to the clusters that were determined in the same n-dimensional space,

TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

Directory of Open Access Journals (Sweden)

C. Jehan

2016-06-01

Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images

Science.gov (United States)

Yang, Zhengfan; Fang, Jia; Chittuluru, Johnathan; Asturias, Francisco J.; Penczek, Pawel A.

2012-01-01

SUMMARY Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis. The task is handled by iterative algorithms, whose performance is compromised by the compounded limitations of image alignment and K-means clustering. Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images. ISAC requires only a small number of simple parameters and, with minimal human intervention, can eliminate bias from two-dimensional image clustering and maximize the quality of group averages that can be used for ab initio three-dimensional structural determination and analysis of macromolecular conformational variability. Repeated testing of the stability and reproducibility of a solution within ISAC eliminates heterogeneous or incorrect classes and introduces critical validation to the process of EM image clustering. PMID:22325773

THE PANCHROMATIC HUBBLE ANDROMEDA TREASURY. III. MEASURING AGES AND MASSES OF PARTIALLY RESOLVED STELLAR CLUSTERS

Energy Technology Data Exchange (ETDEWEB)

Beerman, Lori C.; Johnson, L. Clifton; Fouesneau, Morgan; Dalcanton, Julianne J.; Weisz, Daniel R.; Williams, Ben F. [Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195 (United States); Seth, Anil C. [Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112 (United States); Bell, Eric F. [Department of Astronomy, University of Michigan, 500 Church Street, Ann Arbor, MI 48109 (United States); Bianchi, Luciana C. [Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Caldwell, Nelson [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Dolphin, Andrew E. [Raytheon Company, 1151 East Hermans Road, Tucson, AZ 85756 (United States); Gouliermis, Dimitrios A. [Zentrum fuer Astronomie, Institut fuer Theoretische Astrophysik, Universitaet Heidelberg, Albert-Ueberle-Strasse 2, D-69120 Heidelberg (Germany); Kalirai, Jason S. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Larsen, Soren S. [Department of Astrophysics, IMAPP, Radboud University Nijmegen, P.O. Box 9010, NL-6500 GL Nijmegen (Netherlands); Melbourne, Jason L. [Caltech Optical Observatories, Division of Physics, Mathematics and Astronomy, Mail Stop 301-17, California Institute of Technology, Pasadena, CA 91125 (United States); Rix, Hans-Walter [Max-Planck-Institut fuer Astronomie, Koenigstuhl 17, D-69117 Heidelberg (Germany); Skillman, Evan D., E-mail: beermalc@astro.washington.edu [Department of Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455 (United States)

2012-12-01

The apparent age and mass of a stellar cluster can be strongly affected by stochastic sampling of the stellar initial mass function (IMF), when inferred from the integrated color of low-mass clusters ({approx}<10{sup 4} M {sub Sun }). We use simulated star clusters to show that these effects are minimized when the brightest, rapidly evolving stars in a cluster can be resolved, and the light of the fainter, more numerous unresolved stars can be analyzed separately. When comparing the light from the less luminous cluster members to models of unresolved light, more accurate age estimates can be obtained than when analyzing the integrated light from the entire cluster under the assumption that the IMF is fully populated. We show the success of this technique first using simulated clusters, and then with a stellar cluster in M31. This method represents one way of accounting for the discrete, stochastic sampling of the stellar IMF in less massive clusters and can be leveraged in studies of clusters throughout the Local Group and other nearby galaxies.

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Science.gov (United States)

Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

2018-01-01

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

Directory of Open Access Journals (Sweden)

Tingquan Deng

2016-01-01

Full Text Available There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

KAUST Repository

Celik, Abdulkadir; Kamal, Ahmed E.

2016-01-01

) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3

The Observational and Theoretical Tidal Radii of Globular Clusters in M87

Science.gov (United States)

Webb, Jeremy J.; Sills, Alison; Harris, William E.

2012-02-01

Globular clusters have linear sizes (tidal radii) which theory tells us are determined by their masses and by the gravitational potential of their host galaxy. To explore the relationship between observed and expected radii, we utilize the globular cluster population of the Virgo giant M87. Unusually deep, high signal-to-noise images of M87 are used to measure the effective and limiting radii of approximately 2000 globular clusters. To compare with these observations, we simulate a globular cluster population that has the same characteristics as the observed M87 cluster population. Placing these simulated clusters in the well-studied tidal field of M87, the orbit of each cluster is solved and the theoretical tidal radius of each cluster is determined. We compare the predicted relationship between cluster size and projected galactocentric distance to observations. We find that for an isotropic distribution of cluster velocities, theoretical tidal radii are approximately equal to observed limiting radii for R gc < 10 kpc. However, the isotropic simulation predicts a steep increase in cluster size at larger radii, which is not observed in large galaxies beyond the Milky Way. To minimize the discrepancy between theory and observations, we explore the effects of orbital anisotropy on cluster sizes, and suggest a possible orbital anisotropy profile for M87 which yields a better match between theory and observations. Finally, we suggest future studies which will establish a stronger link between theoretical tidal radii and observed radii.

THE OBSERVATIONAL AND THEORETICAL TIDAL RADII OF GLOBULAR CLUSTERS IN M87

International Nuclear Information System (INIS)

Webb, Jeremy J.; Sills, Alison; Harris, William E.

2012-01-01

Globular clusters have linear sizes (tidal radii) which theory tells us are determined by their masses and by the gravitational potential of their host galaxy. To explore the relationship between observed and expected radii, we utilize the globular cluster population of the Virgo giant M87. Unusually deep, high signal-to-noise images of M87 are used to measure the effective and limiting radii of approximately 2000 globular clusters. To compare with these observations, we simulate a globular cluster population that has the same characteristics as the observed M87 cluster population. Placing these simulated clusters in the well-studied tidal field of M87, the orbit of each cluster is solved and the theoretical tidal radius of each cluster is determined. We compare the predicted relationship between cluster size and projected galactocentric distance to observations. We find that for an isotropic distribution of cluster velocities, theoretical tidal radii are approximately equal to observed limiting radii for R gc < 10 kpc. However, the isotropic simulation predicts a steep increase in cluster size at larger radii, which is not observed in large galaxies beyond the Milky Way. To minimize the discrepancy between theory and observations, we explore the effects of orbital anisotropy on cluster sizes, and suggest a possible orbital anisotropy profile for M87 which yields a better match between theory and observations. Finally, we suggest future studies which will establish a stronger link between theoretical tidal radii and observed radii.

Physisorption of helium on a TiO{sub 2}(110) surface: Periodic and finite cluster approaches

Energy Technology Data Exchange (ETDEWEB)

Lara-Castells, Maria Pilar de, E-mail: Pilar.deLara.Castells@csic.es [Instituto de Fisica Fundamental (C.S.I.C.), Serrano 123, E-28006 Madrid (Spain); Aguirre, Nestor F. [Instituto de Fisica Fundamental (C.S.I.C.), Serrano 123, E-28006 Madrid (Spain); Mitrushchenkov, Alexander O. [Universite Paris-Est, Laboratoire Modelisation et Simulation Multi Echelle, MSME UMR 8208 CNRS, 5 bd Descartes, 77454 Marne-la-Vallee (France)

2012-05-03

Graphical abstract: The physisorption of helium on the TiO{sub 2}(110) surface is explored by using finite cluster and periodic approaches (see left panel). Once the basis set is specifically tailored to minimize the BSSE (rigth panel), DFT periodic calculations using the PBE functional (left panel) yield interaction potentials in good agreement with those obtained using post-HF methods as the LMP2 treatment (see left panel). Highlights: Black-Right-Pointing-Pointer He/TiO{sub 2}(110) is a simplest example of physisorption on transition-metal oxide surfaces. Black-Right-Pointing-Pointer Optimized basis sets that minimize the BSSE are better suited for physisorption problems. Black-Right-Pointing-Pointer FCI benchmarks on the He{sub 2} bound-state assess the Counterpoise scheme reliability. Black-Right-Pointing-Pointer Periodic DFT-PBE and post-HF results on H-saturated clusters compare satisfactorily. Black-Right-Pointing-Pointer Correlation energies by using embedded and H-saturated clusters agree well. - Abstract: As a proto-typical case of physisorption on an extended transition-metal oxide surface, the interaction of a helium atom with a TiO{sub 2}(110) - (1 Multiplication-Sign 1) surface is studied here by using finite cluster and periodic approaches and both wave-function-based (post-Hartree-Fock) quantum chemistry methods and density functional theory. Both classical and advanced finite cluster approaches, based on localized Wannier orbitals combined with one-particle embedding potentials, are applied to provide (reference) coupled-cluster and second-order Moeller-Plesset interaction energies. It is shown that, once the basis set is specifically tailored to minimize the basis set superposition error, periodic calculations using the Perdew-Burke-Ernzerhof functional yield short and medium-range interaction potentials in very reasonable agreement with those obtained using the correlated wave-function-based methods, while small long-range dispersion corrections

CLUSTERING PENENTUAN POTENSI KEJAHATAN DAERAH DI KOTA BANJARBARU DENGAN METODE K-MEANS

Directory of Open Access Journals (Sweden)

Sri Rahayu

2016-09-01

Full Text Available Abstract Within the scope of the police, the data held in the database can be used to make a crime report, the presumption of evil to come, and so on. With the data mining based on the amount of data stored so much, these data can be processed to find the useful knowledge for police. One technique that is known in the data mining clustering techniques. The purpose of the job grouping (clustering the data can be divided into two, namely grouping for understanding and grouping to use. Methods K-Means clustering is a method for engineering the most simple and common. KMeans clustering is one method of data non-hierarchy (partition which seeks to partition the existing data in the form of two or more groups. This method of partitioning data into groups so that the same characteristic of data put into the same group and a different characteristic data are grouped into another group. The purpose of this grouping is to minimize the objective function is set in the grouping process, which generally seek to minimize the variation within a group and maximize the variation between groups. The data mined to determine the potential clustering of crime in the city area of crime data Banjarbaru is owned by the city police in the Police Banjarbaru. Thus this study aims to assess the stage of clustering techniques and build clustering determination of potential crime areas in the city Banjarbaru. Keywords:Clustering, Data mining, K-Means, K-Means Clustering ABSTRAK Dalam ruang lingkup kepolisian, data-data yang dimiliki pada basis data dapat dimanfaatkan untuk pembuatan laporan kejahatan, praduga kejahatan yang akan datang, dan sebagainya.Dengan adanya data mining yang didasarkan pada jumlah data yang tersimpan begitu banyak, data-data tersebut dapat diproses untuk menemukan suatu pengetahuan yang berguna bagi pihak kepolisian.Salah satu teknik yang dikenal dalam data mining yaitu teknik clustering.Tujuan pekerjaan pengelompokan (clustering data dapat dibedakan

Clustering analysis for muon tomography data elaboration in the Muon Portal project

Science.gov (United States)

Bandieramonte, M.; Antonuccio-Delogu, V.; Becciani, U.; Costa, A.; La Rocca, P.; Massimino, P.; Petta, C.; Pistagna, C.; Riggi, F.; Riggi, S.; Sciacca, E.; Vitello, F.

2015-05-01

Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data.

Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data

Energy Technology Data Exchange (ETDEWEB)

Getman, Dan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Lopez, Anthony [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Dyson, Mark [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2015-09-01

In this report, we introduce a methodology to achieve multiple levels of spatial resolution reduction of solar resource data, with minimal impact on data variability, for use in energy systems modeling. The selection of an appropriate clustering algorithm, parameter selection including cluster size, methods of temporal data segmentation, and methods of cluster evaluation are explored in the context of a repeatable process. In describing this process, we illustrate the steps in creating a reduced resolution, but still viable, dataset to support energy systems modeling, e.g. capacity expansion or production cost modeling. This process is demonstrated through the use of a solar resource dataset; however, the methods are applicable to other resource data represented through spatiotemporal grids, including wind data. In addition to energy modeling, the techniques demonstrated in this paper can be used in a novel top-down approach to assess renewable resources within many other contexts that leverage variability in resource data but require reduction in spatial resolution to accommodate modeling or computing constraints.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

Science.gov (United States)

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Sungyoung Lee

2011-12-01

Full Text Available In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO clustering scheme and traditional multihop Single-Input-Single-Output (SISO routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.

Cooperative relay-based multicasting for energy and delay minimization

KAUST Repository

Atat, Rachad

2012-08-01

Relay-based multicasting for the purpose of cooperative content distribution is studied. Optimized relay selection is performed with the objective of minimizing the energy consumption or the content distribution delay within a cluster of cooperating mobiles. Two schemes are investigated. The first consists of the BS sending the data only to the relay, and the second scheme considers the scenario of threshold-based multicasting by the BS, where a relay is selected to transmit the data to the mobiles that were not able to receive the multicast data. Both schemes show significant superiority compared to the non-cooperative scenarios, in terms of energy consumption and delay reduction. © 2012 IEEE.

Optimized data fusion for K-means Laplacian clustering

Science.gov (United States)

Yu, Shi; Liu, Xinhai; Tranchevent, Léon-Charles; Glänzel, Wolfgang; Suykens, Johan A. K.; De Moor, Bart; Moreau, Yves

2011-01-01

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20980271

A simplified density matrix minimization for linear scaling self-consistent field theory

International Nuclear Information System (INIS)

Challacombe, M.

1999-01-01

A simplified version of the Li, Nunes and Vanderbilt [Phys. Rev. B 47, 10891 (1993)] and Daw [Phys. Rev. B 47, 10895 (1993)] density matrix minimization is introduced that requires four fewer matrix multiplies per minimization step relative to previous formulations. The simplified method also exhibits superior convergence properties, such that the bulk of the work may be shifted to the quadratically convergent McWeeny purification, which brings the density matrix to idempotency. Both orthogonal and nonorthogonal versions are derived. The AINV algorithm of Benzi, Meyer, and Tuma [SIAM J. Sci. Comp. 17, 1135 (1996)] is introduced to linear scaling electronic structure theory, and found to be essential in transformations between orthogonal and nonorthogonal representations. These methods have been developed with an atom-blocked sparse matrix algebra that achieves sustained megafloating point operations per second rates as high as 50% of theoretical, and implemented in the MondoSCF suite of linear scaling SCF programs. For the first time, linear scaling Hartree - Fock theory is demonstrated with three-dimensional systems, including water clusters and estane polymers. The nonorthogonal minimization is shown to be uncompetitive with minimization in an orthonormal representation. An early onset of linear scaling is found for both minimal and double zeta basis sets, and crossovers with a highly optimized eigensolver are achieved. Calculations with up to 6000 basis functions are reported. The scaling of errors with system size is investigated for various levels of approximation. copyright 1999 American Institute of Physics

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.

Indirect photometric detection of boron cluster anions electrophoretically separated in methanol.

Science.gov (United States)

Vítová, Lada; Fojt, Lukáš; Vespalec, Radim

2014-04-18

3,5-Dinitrobenzoate and picrate are light absorbing anions pertinent to indirect photometric detection of boron cluster anions in buffered methanolic background electrolytes (BGEs). Tris(hydroxymethyl)aminomethane and morpholine have been used as buffering bases, which eliminated baseline steps, and minimized the baseline noise. In methanolic BGEs, mobilities of boron cluster anions depend on both ionic constituents of the BGE buffer. This dependence can be explained by ion pair interaction of detected anions with BGE cations, which are not bonded into ion pairs with the BGE anions. The former ion pair interaction decreases sensitivity of the indirect photometric detection. Copyright © 2014 Elsevier B.V. All rights reserved.

A Parallel FPGA Implementation for Real-Time 2D Pixel Clustering for the ATLAS Fast TracKer Processor

CERN Document Server

Sotiropoulou, C-L; The ATLAS collaboration; Annovi, A; Beretta, M; Kordas, K; Nikolaidis, S; Petridou, C; Volpi, G

2014-01-01

The parallel 2D pixel clustering FPGA implementation used for the input system of the ATLAS Fast TracKer (FTK) processor is presented. The input system for the FTK processor will receive data from the Pixel and micro-strip detectors from inner ATLAS read out drivers (RODs) at full rate, for total of 760Gbs, as sent by the RODs after level-1 triggers. Clustering serves two purposes, the first is to reduce the high rate of the received data before further processing, the second is to determine the cluster centroid to obtain the best spatial measurement. For the pixel detectors the clustering is implemented by using a 2D-clustering algorithm that takes advantage of a moving window technique to minimize the logic required for cluster identification. The cluster detection window size can be adjusted for optimizing the cluster identification process. Additionally, the implementation can be parallelized by instantiating multiple cores to identify different clusters independently thus exploiting more FPGA resources. ...

A Parallel FPGA Implementation for Real-Time 2D Pixel Clustering for the ATLAS Fast TracKer Processor

CERN Document Server

Sotiropoulou, C-L; The ATLAS collaboration; Annovi, A; Beretta, M; Kordas, K; Nikolaidis, S; Petridou, C; Volpi, G

2014-01-01

The parallel 2D pixel clustering FPGA implementation used for the input system of the ATLAS Fast TracKer (FTK) processor is presented. The input system for the FTK processor will receive data from the Pixel and micro-strip detectors from inner ATLAS read out drivers (RODs) at full rate, for total of 760Gbs, as sent by the RODs after level1 triggers. Clustering serves two purposes, the first is to reduce the high rate of the received data before further processing, the second is to determine the cluster centroid to obtain the best spatial measurement. For the pixel detectors the clustering is implemented by using a 2D-clustering algorithm that takes advantage of a moving window technique to minimize the logic required for cluster identification. The cluster detection window size can be adjusted for optimizing the cluster identification process. Additionally, the implementation can be parallelized by instantiating multiple cores to identify different clusters independently thus exploiting more FPGA resources. T...

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network

Directory of Open Access Journals (Sweden)

C. Vimalarani

2016-01-01

Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Arabic web pages clustering and annotation using semantic class features

Directory of Open Access Journals (Sweden)

Hanan M. Alghamdi

2014-12-01

Full Text Available To effectively manage the great amount of data on Arabic web pages and to enable the classification of relevant information are very important research problems. Studies on sentiment text mining have been very limited in the Arabic language because they need to involve deep semantic processing. Therefore, in this paper, we aim to retrieve machine-understandable data with the help of a Web content mining technique to detect covert knowledge within these data. We propose an approach to achieve clustering with semantic similarities. This approach comprises integrating k-means document clustering with semantic feature extraction and document vectorization to group Arabic web pages according to semantic similarities and then show the semantic annotation. The document vectorization helps to transform text documents into a semantic class probability distribution or semantic class density. To reach semantic similarities, the approach extracts the semantic class features and integrates them into the similarity weighting schema. The quality of the clustering result has evaluated the use of the purity and the mean intra-cluster distance (MICD evaluation measures. We have evaluated the proposed approach on a set of common Arabic news web pages. We have acquired favorable clustering results that are effective in minimizing the MICD, expanding the purity and lowering the runtime.

Alcohol Prevention and School Students: Findings from an Australian 2-Year Trial of Integrated Harm Minimization School Drug Education

Science.gov (United States)

Midford, Richard; Ramsden, Robyn; Lester, Leanne; Cahill, Helen; Mitchell, Johanna; Foxcroft, David R.; Venning, Lynne

2014-01-01

The Drug Education in Victorian Schools program provided integrated education about licit and illicit drugs, employed a harm minimization approach that incorporated participatory, critical thinking and skill-based teaching methods, and engaged parental influence through home activities. A cluster-randomized, controlled trial of the program was…

Enhancing multiphoton upconversion through energy clustering at sublattice level

Science.gov (United States)

Wang, Juan; Deng, Renren; MacDonald, Mark A.; Chen, Bolei; Yuan, Jikang; Wang, Feng; Chi, Dongzhi; Andy Hor, Tzi Sum; Zhang, Peng; Liu, Guokui; Han, Yu; Liu, Xiaogang

2014-02-01

The applications of lanthanide-doped upconversionnanocrystals in biological imaging, photonics, photovoltaics and therapeutics have fuelled a growing demand for rational control over the emission profiles of the nanocrystals. A common strategy for tuning upconversion luminescence is to control the doping concentration of lanthanide ions. However, the phenomenon of concentration quenching of the excited state at high doping levels poses a significant constraint. Thus, the lanthanide ions have to be stringently kept at relatively low concentrations to minimize luminescence quenching. Here we describe a new class of upconversion nanocrystals adopting an orthorhombic crystallographic structure in which the lanthanide ions are distributed in arrays of tetrad clusters. Importantly, this unique arrangement enables the preservation of excitation energy within the sublattice domain and effectively minimizes the migration of excitation energy to defects, even in stoichiometric compounds with a high Yb3+ content (calculated as 98 mol%). This allows us to generate an unusual four-photon-promoted violet upconversion emission from Er3+ with an intensity that is more than eight times higher than previously reported. Our results highlight that the approach to enhancing upconversion through energy clustering at the sublattice level may provide new opportunities for light-triggered biological reactions and photodynamic therapy.

Enhancing multiphoton upconversion through energy clustering at sublattice level

KAUST Repository

Wang, Juan

2013-11-24

The applications of lanthanide-doped upconversionnanocrystals in biological imaging, photonics, photovoltaics and therapeutics have fuelled a growing demand for rational control over the emission profiles of the nanocrystals. A common strategy for tuning upconversion luminescence is to control the doping concentration of lanthanide ions. However, the phenomenon of concentration quenching of the excited state at high doping levels poses a significant constraint. Thus, the lanthanide ions have to be stringently kept at relatively low concentrations to minimize luminescence quenching. Here we describe a new class of upconversion nanocrystals adopting an orthorhombic crystallographic structure in which the lanthanide ions are distributed in arrays of tetrad clusters. Importantly, this unique arrangement enables the preservation of excitation energy within the sublattice domain and effectively minimizes the migration of excitation energy to defects, even in stoichiometric compounds with a high Yb 3+ content (calculated as 98 mol%). This allows us to generate an unusual four-photon-promoted violet upconversion emission from Er 3+ with an intensity that is more than eight times higher than previously reported. Our results highlight that the approach to enhancing upconversion through energy clustering at the sublattice level may provide new opportunities for light-triggered biological reactions and photodynamic therapy. © 2014 Macmillan Publishers Limited. All rights reserved.

Entropy Minimizing Curves with Application to Flight Path Design and Clustering

Directory of Open Access Journals (Sweden)

Stéphane Puechmorel

2016-09-01

Full Text Available Air traffic management (ATM aims at providing companies with a safe and ideally optimal aircraft trajectory planning. Air traffic controllers act on flight paths in such a way that no pair of aircraft come closer than the regulatory separation norms. With the increase of traffic, it is expected that the system will reach its limits in the near future: a paradigm change in ATM is planned with the introduction of trajectory-based operations. In this context, sets of well-separated flight paths are computed in advance, tremendously reducing the number of unsafe situations that must be dealt with by controllers. Unfortunately, automated tools used to generate such planning generally issue trajectories not complying with operational practices or even flight dynamics. In this paper, a means of producing realistic air routes from the output of an automated trajectory design tool is investigated. For that purpose, the entropy of a system of curves is first defined, and a mean of iteratively minimizing it is presented. The resulting curves form a route network that is suitable for use in a semi-automated ATM system with human in the loop. The tool introduced in this work is quite versatile and may be applied also to unsupervised classification of curves: an example is given for French traffic.

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)

FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

Directory of Open Access Journals (Sweden)

Taegwon Jeong

2011-05-01

Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

Science.gov (United States)

Lee, Chongdeuk; Jeong, Taegwon

2011-01-01

Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

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

Galaxy clusters in simulations of the local Universe: a matter of constraints

Science.gov (United States)

Sorce, Jenny G.; Tempel, Elmo

2018-06-01

To study the full formation and evolution history of galaxy clusters and their population, high-resolution simulations of the latter are flourishing. However, comparing observed clusters to the simulated ones on a one-to-one basis to refine the models and theories down to the details is non-trivial. The large variety of clusters limits the comparisons between observed and numerical clusters. Simulations resembling the local Universe down to the cluster scales permit pushing the limit. Simulated and observed clusters can be matched on a one-to-one basis for direct comparisons provided that clusters are well reproduced besides being in the proper large-scale environment. Comparing random and local Universe-like simulations obtained with differently grouped observational catalogues of peculiar velocities, this paper shows that the grouping scheme used to remove non-linear motions in the catalogues that constrain the simulations affects the quality of the numerical clusters. With a less aggressive grouping scheme - galaxies still falling on to clusters are preserved - combined with a bias minimization scheme, the mass of the dark matter haloes, simulacra for five local clusters - Virgo, Centaurus, Coma, Hydra, and Perseus - is increased by 39 per cent closing the gap with observational mass estimates. Simulacra are found on average in 89 per cent of the simulations, an increase of 5 per cent with respect to the previous grouping scheme. The only exception is Perseus. Since the Perseus-Pisces region is not well covered by the used peculiar velocity catalogue, the latest release lets us foresee a better simulacrum for Perseus in a near future.

Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

KAUST Repository

Celik, Abdulkadir

2016-06-27

In this paper, we address energy efficient (EE) cooperative spectrum sensing policies for large scale heterogeneous cognitive radio networks (CRNs) which consist of multiple primary channels and large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3) inter-cluster energy and throughput fairness. We adopt and demonstrate how to solve these using the nondominated sorting genetic algorithm-II. The micro perspective, on the other hand, operates as a sub-procedure on cluster formations decided by the macro perspective. For the micro perspectives, we first propose a procedure to select the cluster head (CH) which yields: 1) the best CH which gives the minimum total multi-hop error rate and 2) the optimal routing paths from SUs to the CH. Exploiting Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different local detection performances. Then, a convex optimization framework is established to minimize the intra-cluster energy cost by jointly obtaining the optimal sensing durations and thresholds of feature detectors for the proposed voting rule. Likewise, instead of a common fixed sample size test, we developed a weighted sample size test for quantized soft decision fusion to obtain a more EE regime under heterogeneity. We have shown that the combination of proposed CH selection and cooperation schemes gives a superior performance in terms of energy efficiency and robustness against reporting error wall.

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

A HIGH FIDELITY SAMPLE OF COLD FRONT CLUSTERS FROM THE CHANDRA ARCHIVE

International Nuclear Information System (INIS)

Owers, Matt S.; Nulsen, Paul E. J.; Markevitch, Maxim; Couch, Warrick J.

2009-01-01

This paper presents a sample of 'cold front' clusters selected from the Chandra archive. The clusters are selected based purely on the existence of surface brightness edges in their Chandra images which are modeled as density jumps. A combination of the derived density and temperature jumps across the fronts is used to select nine robust examples of cold front clusters: 1ES0657 - 558, Abell 1201, Abell 1758N, MS1455.0+2232, Abell 2069, Abell 2142, Abell 2163, RXJ1720.1+2638, and Abell 3667. This sample is the subject of an ongoing study aimed at relating cold fronts to cluster merger activity, and understanding how the merging environment affects the cluster constituents. Here, temperature maps are presented along with the Chandra X-ray images. A dichotomy is found in the sample in that there exists a subsample of cold front clusters which are clearly mergers based on their X-ray morphologies, and a second subsample of clusters which harbor cold fronts, but have surprisingly relaxed X-ray morphologies, and minimal evidence for merger activity at other wavelengths. For this second subsample, the existence of a cold front provides the sole evidence for merger activity at X-ray wavelengths. We discuss how cold fronts can provide additional information which may be used to constrain merger histories, and also the possibility of using cold fronts to distinguish major and minor mergers.

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.

Context-sensitive intra-class clustering

KAUST Repository

Yu, Yingwei

2014-02-01

This paper describes a new semi-supervised learning algorithm for intra-class clustering (ICC). ICC partitions each class into sub-classes in order to minimize overlap across clusters from different classes. This is achieved by allowing partitioning of a certain class to be assisted by data points from other classes in a context-dependent fashion. The result is that overlap across sub-classes (both within- and across class) is greatly reduced. ICC is particularly useful when combined with algorithms that assume that each class has a unimodal Gaussian distribution (e.g., Linear Discriminant Analysis (LDA), quadratic classifiers), an assumption that is not always true in many real-world situations. ICC can help partition non-Gaussian, multimodal distributions to overcome such a problem. In this sense, ICC works as a preprocessor. Experiments with our ICC algorithm on synthetic data sets and real-world data sets indicated that it can significantly improve the performance of LDA and quadratic classifiers. We expect our approach to be applicable to a broader class of pattern recognition problems where class-conditional densities are significantly non-Gaussian or multi-modal. © 2013 Elsevier Ltd. All rights reserved.

Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification.

Science.gov (United States)

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA) is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE). The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

Directory of Open Access Journals (Sweden)

Qing Ye

2015-01-01

Full Text Available A novel semisupervised extreme learning machine (ELM with clustering discrimination manifold regularization (CDMR framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE. The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

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

Parallel clustering algorithm for large-scale biological data sets.

Science.gov (United States)

Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang

2014-01-01

Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.

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...

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

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.

Multiscale deep drawing analysis of dual-phase steels using grain cluster-based RGC scheme

International Nuclear Information System (INIS)

Tjahjanto, D D; Eisenlohr, P; Roters, F

2015-01-01

Multiscale modelling and simulation play an important role in sheet metal forming analysis, since the overall material responses at macroscopic engineering scales, e.g. formability and anisotropy, are strongly influenced by microstructural properties, such as grain size and crystal orientations (texture). In the present report, multiscale analysis on deep drawing of dual-phase steels is performed using an efficient grain cluster-based homogenization scheme.The homogenization scheme, called relaxed grain cluster (RGC), is based on a generalization of the grain cluster concept, where a (representative) volume element consists of p  ×  q  ×  r (hexahedral) grains. In this scheme, variation of the strain or deformation of individual grains is taken into account through the, so-called, interface relaxation, which is formulated within an energy minimization framework. An interfacial penalty term is introduced into the energy minimization framework in order to account for the effects of grain boundaries.The grain cluster-based homogenization scheme has been implemented and incorporated into the advanced material simulation platform DAMASK, which purposes to bridge the macroscale boundary value problems associated with deep drawing analysis to the micromechanical constitutive law, e.g. crystal plasticity model. Standard Lankford anisotropy tests are performed to validate the model parameters prior to the deep drawing analysis. Model predictions for the deep drawing simulations are analyzed and compared to the corresponding experimental data. The result shows that the predictions of the model are in a very good agreement with the experimental measurement. (paper)

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

A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering.

Science.gov (United States)

Yang, Shangming; Yi, Zhang; He, Xiaofei; Li, Xuelong

2015-12-01

Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.

Unit commitment solution using agglomerative and divisive cluster algorithm : an effective new methodology

Energy Technology Data Exchange (ETDEWEB)

Reddy, N.M.; Reddy, K.R. [G. Narayanamma Inst. of Technology and Science, Hyderabad (India). Dept. of Electrical Engineering; Ramana, N.V. [JNTU College of Engineering, Jagityala (India). Dept. of Electrical Engineering

2008-07-01

Thermal power plants consist of several generating units with different generating capacities, fuel cost per MWH generated, minimum up/down times, and start-up or shut-down costs. The Unit Commitment (UC) problem in power systems involves determining the start-up and shut-down schedules of thermal generating units to meet forecasted load over a future short term for a period of one to seven days. This paper presented a new approach for the most complex UC problem using agglomerative and divisive hierarchical clustering. Euclidean costs, which are a measure of differences in fuel cost and start-up costs of any two units, were first calculated. Then, depending on the value of Euclidean costs, similar type of units were placed in a cluster. The proposed methodology has 2 individual algorithms. An agglomerative cluster algorithm is used while the load is increasing, and a divisive cluster algorithm is used when the load is decreasing. A search was conducted for an optimal solution for a minimal number of clusters and cluster data points. A standard ten-unit thermal unit power system was used to test and evaluate the performance of the method for a period of 24 hours. The new approach proved to be quite effective and satisfactory. 15 refs., 9 tabs., 5 figs.

The Trade-off Between Housing Density and Sprawl Area: Minimizing Impacts to Carabid Beetles (Coleoptera: Carabidae

Directory of Open Access Journals (Sweden)

Sara A. Gagné

2010-12-01

Full Text Available Increasing housing density has negative effects on native biodiversity. This implies that we should build at low density to conserve native species. However, for a given human population, low-density development must cover a large area, resulting in sprawl. A pertinent question is then, at what housing density are the impacts of a given human population on native biodiversity minimized? We addressed this question with carabid beetles in Ottawa and Gatineau, Canada. First, we collected beetles at 22 sites representing a range of housing densities. We then used these data to estimate beetle abundance and species richness in hypothetical development scenarios representing the housing density/sprawl area trade-off. Our results suggest that clustering development at a high housing density minimizes the impacts of a given human population on carabid beetles. If these results are general across all forest taxa, then planning that favors densification rather than sprawl would minimize urbanization effects on forest biodiversity.

Development of a cluster-jet target for PANDA

International Nuclear Information System (INIS)

Gruber, A.; Marton, J.; Widmann, E.; Zmeskal, J.; PANDA Cluster Jet Target Group

2006-01-01

Full text: The Stefan Meyer Institute (SMI) is part of the international PANDA collaboration. The universal detector will be constructed for the future high-energy antiproton storage ring HESR at FAIR (Facility for Antiproton and Ion Research, GSI/Darmstadt). PANDA will use antiproton beams (1.5 to 15 GeV/c) for hadron physics in the charmonium region. The physics program of PANDA will comprehend charmonium spectroscopy below and above open charm threshold, search for exotics (glueballs, hybrids), lambda and double-lambda hypernuclei studies and the investigation of in-medium modifications of charmed mesons - an experimentally unexplored field. SMI contributes to major parts of the PANDA detector like the hydrogen cluster-jet target and the antiproton - cluster jet interaction zone: in order to reach the desired target density, an optimization of the nozzle and the skimmer arrangement is essential. A density-profile monitor for the cluster-jet was designed and built at SMI. Several nozzle types have been studied using different gases, temperatures and inlet pressures. To ensure low background the residual gas load in the interaction zone has to be minimized. The installation of NEG (non-evaporative-getter) coated beam pipes is planned. A prototype of the interaction zone has been set up at SMI. The pumping capacity of NEG and the reactivation cycles were tested. The status of the development of the cluster-jet target and studies of the interaction region will be presented (author)

A High Performance Multi-Core FPGA Implementation for 2D Pixel Clustering for the ATLAS Fast TracKer (FTK) Processor

CERN Document Server

Sotiropoulou, C-L; The ATLAS collaboration; Beretta, M; Gkaitatzis, S; Kordas, K; Nikolaidis, S; Petridou, C; Volpi, G

2014-01-01

The high performance multi-core 2D pixel clustering FPGA implementation used for the input system of the ATLAS Fast TracKer (FTK) processor is presented. The input system for the FTK processor will receive data from the Pixel and micro-strip detectors read out drivers (RODs) at 760Gbps, the full rate of level 1 triggers. Clustering is required as a method to reduce the high rate of the received data before further processing, as well as to determine the cluster centroid for obtaining obtain the best spatial measurement. Our implementation targets the pixel detectors and uses a 2D-clustering algorithm that takes advantage of a moving window technique to minimize the logic required for cluster identification. The design is fully generic and the cluster detection window size can be adjusted for optimizing the cluster identification process. Τhe implementation can be parallelized by instantiating multiple cores to identify different clusters independently thus exploiting more FPGA resources. This flexibility mak...

Hydration of Atmospheric Molecular Clusters: Systematic Configurational Sampling.

Science.gov (United States)

Kildgaard, Jens; Mikkelsen, Kurt V; Bilde, Merete; Elm, Jonas

2018-05-09

We present a new systematic configurational sampling algorithm for investigating the potential energy surface of hydrated atmospheric molecular clusters. The algo- rithm is based on creating a Fibonacci sphere around each atom in the cluster and adding water molecules to each point in 9 different orientations. To allow the sam- pling of water molecules to existing hydrogen bonds, the cluster is displaced along the hydrogen bond and a water molecule is placed in between in three different ori- entations. Generated redundant structures are eliminated based on minimizing the root mean square distance (RMSD) of different conformers. Initially, the clusters are sampled using the semiempirical PM6 method and subsequently using density func- tional theory (M06-2X and ωB97X-D) with the 6-31++G(d,p) basis set. Applying the developed algorithm we study the hydration of sulfuric acid with up to 15 water molecules. We find that the additions of the first four water molecules "saturate" the sulfuric acid molecule and are more thermodynamically favourable than the addition of water molecule 5-15. Using the large generated set of conformers, we assess the performance of approximate methods (ωB97X-D, M06-2X, PW91 and PW6B95-D3) in calculating the binding energies and assigning the global minimum conformation compared to high level CCSD(T)-F12a/VDZ-F12 reference calculations. The tested DFT functionals systematically overestimates the binding energies compared to cou- pled cluster calculations, and we find that this deficiency can be corrected by a simple scaling factor.

Simultaneous falsification of ΛCDM and quintessence with massive, distant clusters

International Nuclear Information System (INIS)

Mortonson, Michael J.; Hu, Wayne; Huterer, Dragan

2011-01-01

Observation of even a single massive cluster, especially at high redshift, can falsify the standard cosmological framework consisting of a cosmological constant and cold dark matter (ΛCDM) with Gaussian initial conditions by exposing an inconsistency between the well-measured expansion history and the growth of structure it predicts. Through a likelihood analysis of current cosmological data that constrain the expansion history, we show that the ΛCDM upper limits on the expected number of massive, distant clusters are nearly identical to limits predicted by all quintessence models where dark energy is a minimally coupled scalar field with a canonical kinetic term. We provide convenient fitting formulas for the confidence level at which the observation of a cluster of mass M at redshift z can falsify ΛCDM and quintessence given cosmological parameter uncertainties and sample variance, as well as for the expected number of such clusters in the light cone and the Eddington bias factor that must be applied to observed masses. By our conservative confidence criteria, which equivalently require masses 3 times larger than typically expected in surveys of a few hundred square degrees, none of the presently known clusters falsify these models. Various systematic errors, including uncertainties in the form of the mass function and differences between supernova light curve fitters, typically shift the exclusion curves by less than 10% in mass, making current statistical and systematic uncertainties in cluster mass determination the most critical factor in assessing falsification of ΛCDM and quintessence.

Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

Directory of Open Access Journals (Sweden)

Youngmin Lee

2016-08-01

Full Text Available Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.

Power Minimization techniques for Networked Data Centers

International Nuclear Information System (INIS)

Low, Steven; Tang, Kevin

2011-01-01

Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

Comments on "The multisynapse neural network and its application to fuzzy clustering".

Science.gov (United States)

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

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.

A semi-supervised method to detect seismic random noise with fuzzy GK clustering

International Nuclear Information System (INIS)

Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert

2008-01-01

We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise

Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling.

Science.gov (United States)

Sefuba, Maria; Walingo, Tom; Takawira, Fambirai

2015-09-18

This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols.

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.

Application of cloud computing in power routing for clusters of microgrids using oblivious network routing algorithm

DEFF Research Database (Denmark)

Amini, M. Hadi; Broojeni, Kianoosh G.; Dragicevic, Tomislav

2017-01-01

of microgrid while preventing congestion as well as minimizing the power loss. Then, we present a two-layer simulation platform which considers both communication layer and physical layer of the microgrids' cluster. In order to improve the security of communication network, we perform the computations...... regarding the oblivious power routing via a cloud-based network. The proposed framework can be used for further studies that deal with the real-time simulation of the clusters of microgrids. In order to validate the effectiveness of the proposed framework, we implement our proposed oblivious routing scheme...

The evolution of early-type galaxies in distant clusters

International Nuclear Information System (INIS)

Stanford, S.A.; Eisenhardt, P.R.; Dickinson, M.

1998-01-01

We present results from an optical-infrared photometric study of early-type (E+S0) galaxies in 19 galaxy clusters out to z=0.9. The galaxy sample is selected on the basis of morphologies determined from Hubble Space Telescope (HST) WFPC2 images and is photometrically defined in the K band in order to minimize redshift-dependent selection biases. Using new ground-based photometry in five optical and infrared bands for each cluster, we examine the evolution of the color-magnitude relation for early-type cluster galaxies, considering its slope, intercept, and color scatter around the mean relation. New multiwavelength photometry of galaxies in the Coma Cluster is used to provide a baseline sample at z∼0 with which to compare the distant clusters. The optical - IR colors of the early-type cluster galaxies become bluer with increasing redshift in a manner consistent with the passive evolution of an old stellar population formed at an early cosmic epoch. The degree of color evolution is similar for clusters at similar redshift and does not depend strongly on the optical richness or X-ray luminosity of the cluster, which suggests that the history of early-type galaxies is relatively insensitive to environment, at least above a certain density threshold. The slope of the color-magnitude relationship shows no significant change out to z=0.9, which provides evidence that it arises from a correlation between galaxy mass and metallicity, not age. Finally, the intrinsic scatter in the optical - IR colors of the galaxies is small and nearly constant with redshift, which indicates that the majority of giant, early-type galaxies in clusters share a common star formation history, with little perturbation due to uncorrelated episodes of later star formation. Taken together, our results are consistent with models in which most early-type galaxies in rich clusters are old, formed the majority of their stars at high redshift in a well-synchronized fashion, and evolved quiescently

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.

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

Minimally invasive orthognathic surgery.

Science.gov (United States)

Resnick, Cory M; Kaban, Leonard B; Troulis, Maria J

2009-02-01

Minimally invasive surgery is defined as the discipline in which operative procedures are performed in novel ways to diminish the sequelae of standard surgical dissections. The goals of minimally invasive surgery are to reduce tissue trauma and to minimize bleeding, edema, and injury, thereby improving the rate and quality of healing. In orthognathic surgery, there are two minimally invasive techniques that can be used separately or in combination: (1) endoscopic exposure and (2) distraction osteogenesis. This article describes the historical developments of the fields of orthognathic surgery and minimally invasive surgery, as well as the integration of the two disciplines. Indications, techniques, and the most current outcome data for specific minimally invasive orthognathic surgical procedures are presented.

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...

Regularity of Minimal Surfaces

CERN Document Server

Dierkes, Ulrich; Tromba, Anthony J; Kuster, Albrecht

2010-01-01

"Regularity of Minimal Surfaces" begins with a survey of minimal surfaces with free boundaries. Following this, the basic results concerning the boundary behaviour of minimal surfaces and H-surfaces with fixed or free boundaries are studied. In particular, the asymptotic expansions at interior and boundary branch points are derived, leading to general Gauss-Bonnet formulas. Furthermore, gradient estimates and asymptotic expansions for minimal surfaces with only piecewise smooth boundaries are obtained. One of the main features of free boundary value problems for minimal surfaces is t

Cluster Physics with Merging Galaxy Clusters

Directory of Open Access Journals (Sweden)

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.

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

Science.gov (United States)

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

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.

Minimal Poems Written in 1979 Minimal Poems Written in 1979

Directory of Open Access Journals (Sweden)

Sandra Sirangelo Maggio

2008-04-01

Full Text Available The reading of M. van der Slice's Minimal Poems Written in 1979 (the work, actually, has no title reminded me of a book I have seen a long time ago. called Truth, which had not even a single word printed inside. In either case we have a sample of how often excentricities can prove efficient means of artistic creativity, in this new literary trend known as Minimalism. The reading of M. van der Slice's Minimal Poems Written in 1979 (the work, actually, has no title reminded me of a book I have seen a long time ago. called Truth, which had not even a single word printed inside. In either case we have a sample of how often excentricities can prove efficient means of artistic creativity, in this new literary trend known as Minimalism.

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

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects

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Dreyhaupt, Jens

2017-05-01

Full Text Available An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called “cluster randomization”. Compared with studies with individual randomization, studies with cluster randomization normally require (significantly larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies.Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects.

Science.gov (United States)

Dreyhaupt, Jens; Mayer, Benjamin; Keis, Oliver; Öchsner, Wolfgang; Muche, Rainer

2017-01-01

An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called "cluster randomization"). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies. Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

Prediction of strontium bromide laser efficiency using cluster and decision tree analysis

Directory of Open Access Journals (Sweden)

Iliev Iliycho

2018-01-01

Full Text Available Subject of investigation is a new high-powered strontium bromide (SrBr2 vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.

Text Clustering Algorithm Based on Random Cluster Core

Directory of Open Access Journals (Sweden)

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.

Correlates of minimal dating.

Science.gov (United States)

Leck, Kira

2006-10-01

Researchers have associated minimal dating with numerous factors. The present author tested shyness, introversion, physical attractiveness, performance evaluation, anxiety, social skill, social self-esteem, and loneliness to determine the nature of their relationships with 2 measures of self-reported minimal dating in a sample of 175 college students. For women, shyness, introversion, physical attractiveness, self-rated anxiety, social self-esteem, and loneliness correlated with 1 or both measures of minimal dating. For men, physical attractiveness, observer-rated social skill, social self-esteem, and loneliness correlated with 1 or both measures of minimal dating. The patterns of relationships were not identical for the 2 indicators of minimal dating, indicating the possibility that minimal dating is not a single construct as researchers previously believed. The present author discussed implications and suggestions for future researchers.

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

Comparative Investigation of Guided Fuzzy Clustering and Mean Shift Clustering for Edge Detection in Electrical Resistivity Tomography Images of Mineral Deposits

Science.gov (United States)

Ward, Wil; Wilkinson, Paul; Chambers, Jon; Bai, Li

2014-05-01

Geophysical surveying using electrical resistivity tomography (ERT) can be used as a rapid non-intrusive method to investigate mineral deposits [1]. One of the key challenges with this approach is to find a robust automated method to assess and characterise deposits on the basis of an ERT image. Recent research applying edge detection techniques has yielded a framework that can successfully locate geological interfaces in ERT images using a minimal assumption data clustering technique, the guided fuzzy clustering method (gfcm) [2]. Non-parametric clustering techniques are statistically grounded methods of image segmentation that do not require any assumptions about the distribution of data under investigation. This study is a comparison of two such methods to assess geological structure based on the resistivity images. In addition to gfcm, a method called mean-shift clustering [3] is investigated with comparisons directed at accuracy, computational expense, and degree of user interaction. Neither approach requires the number of clusters as input (a common parameter and often impractical), rather they are based on a similar theory that data can be clustered based on peaks in the probability density function (pdf) of the data. Each local maximum in these functions represents the modal value of a particular population corresponding to a cluster and as such the data are assigned based on their relationships to these model values. The two methods differ in that gfcm approximates the pdf using kernel density estimation and identifies population means, assigning cluster membership probabilities to each resistivity value in the model based on its distance from the distribution averages. Whereas, in mean-shift clustering, the density function is not calculated, but a gradient ascent method creates a vector that leads each datum towards high density distributions iteratively using weighted kernels to calculate locally dense regions. The only parameter needed in both methods

Minimal Super Technicolor

DEFF Research Database (Denmark)

Antola, M.; Di Chiara, S.; Sannino, F.

2011-01-01

We introduce novel extensions of the Standard Model featuring a supersymmetric technicolor sector (supertechnicolor). As the first minimal conformal supertechnicolor model we consider N=4 Super Yang-Mills which breaks to N=1 via the electroweak interactions. This is a well defined, economical......, between unparticle physics and Minimal Walking Technicolor. We consider also other N =1 extensions of the Minimal Walking Technicolor model. The new models allow all the standard model matter fields to acquire a mass....

Performance of clustering techniques for solving multi depot vehicle routing problem

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Eliana M. Toro-Ocampo

2016-01-01

Full Text Available The vehicle routing problem considering multiple depots is classified as NP-hard. MDVRP determines simultaneously the routes of a set of vehicles and aims to meet a set of clients with a known demand. The objective function of the problem is to minimize the total distance traveled by the routes given that all customers must be served considering capacity constraints in depots and vehicles. This paper presents a hybrid methodology that combines agglomerative clustering techniques to generate initial solutions with an iterated local search algorithm (ILS to solve the problem. Although previous studies clustering methods have been proposed like strategies to generate initial solutions, in this work the search is intensified on the information generated after applying the clustering technique. Besides an extensive analysis on the performance of techniques, and their effect in the final solution is performed. The operation of the proposed methodology is feasible and effective to solve the problem regarding the quality of the answers and computational times obtained on request evaluated literature

Are Hox genes ancestrally involved in axial patterning? Evidence from the hydrozoan Clytia hemisphaerica (Cnidaria.

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Roxane Chiori

Full Text Available BACKGROUND: The early evolution and diversification of Hox-related genes in eumetazoans has been the subject of conflicting hypotheses concerning the evolutionary conservation of their role in axial patterning and the pre-bilaterian origin of the Hox and ParaHox clusters. The diversification of Hox/ParaHox genes clearly predates the origin of bilaterians. However, the existence of a "Hox code" predating the cnidarian-bilaterian ancestor and supporting the deep homology of axes is more controversial. This assumption was mainly based on the interpretation of Hox expression data from the sea anemone, but growing evidence from other cnidarian taxa puts into question this hypothesis. METHODOLOGY/PRINCIPAL FINDINGS: Hox, ParaHox and Hox-related genes have been investigated here by phylogenetic analysis and in situ hybridisation in Clytia hemisphaerica, an hydrozoan species with medusa and polyp stages alternating in the life cycle. Our phylogenetic analyses do not support an origin of ParaHox and Hox genes by duplication of an ancestral ProtoHox cluster, and reveal a diversification of the cnidarian HOX9-14 genes into three groups called A, B, C. Among the 7 examined genes, only those belonging to the HOX9-14 and the CDX groups exhibit a restricted expression along the oral-aboral axis during development and in the planula larva, while the others are expressed in very specialised areas at the medusa stage. CONCLUSIONS/SIGNIFICANCE: Cross species comparison reveals a strong variability of gene expression along the oral-aboral axis and during the life cycle among cnidarian lineages. The most parsimonious interpretation is that the Hox code, collinearity and conservative role along the antero-posterior axis are bilaterian innovations.

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

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....

Source selection for cluster weak lensing measurements in the Hyper Suprime-Cam survey

Science.gov (United States)

Medezinski, Elinor; Oguri, Masamune; Nishizawa, Atsushi J.; Speagle, Joshua S.; Miyatake, Hironao; Umetsu, Keiichi; Leauthaud, Alexie; Murata, Ryoma; Mandelbaum, Rachel; Sifón, Cristóbal; Strauss, Michael A.; Huang, Song; Simet, Melanie; Okabe, Nobuhiro; Tanaka, Masayuki; Komiyama, Yutaka

2018-03-01

We present optimized source galaxy selection schemes for measuring cluster weak lensing (WL) mass profiles unaffected by cluster member dilution from the Subaru Hyper Suprime-Cam Strategic Survey Program (HSC-SSP). The ongoing HSC-SSP survey will uncover thousands of galaxy clusters to z ≲ 1.5. In deriving cluster masses via WL, a critical source of systematics is contamination and dilution of the lensing signal by cluster members, and by foreground galaxies whose photometric redshifts are biased. Using the first-year CAMIRA catalog of ˜900 clusters with richness larger than 20 found in ˜140 deg2 of HSC-SSP data, we devise and compare several source selection methods, including selection in color-color space (CC-cut), and selection of robust photometric redshifts by applying constraints on their cumulative probability distribution function (P-cut). We examine the dependence of the contamination on the chosen limits adopted for each method. Using the proper limits, these methods give mass profiles with minimal dilution in agreement with one another. We find that not adopting either the CC-cut or P-cut methods results in an underestimation of the total cluster mass (13% ± 4%) and the concentration of the profile (24% ± 11%). The level of cluster contamination can reach as high as ˜10% at R ≈ 0.24 Mpc/h for low-z clusters without cuts, while employing either the P-cut or CC-cut results in cluster contamination consistent with zero to within the 0.5% uncertainties. Our robust methods yield a ˜60 σ detection of the stacked CAMIRA surface mass density profile, with a mean mass of M200c = [1.67 ± 0.05(stat)] × 1014 M⊙/h.

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.

Partitional clustering algorithms

CERN Document Server

2015-01-01

This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Dao-Wei Bi

2007-07-01

Full Text Available A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement.

Role of shell corrections in the phenomenon of cluster radioactivity

Science.gov (United States)

Kaur, Mandeep; Singh, Bir Bikram; Sharma, Manoj K.

2018-05-01

The detailed investigation has been carried out to explore the role of shell corrections in the decay of various radioactive parent nuclei in trans-lead region, specifically, which lead to doubly magic 208Pb daughter nucleus through emission of clusters such as 14C, 18,20O, 22,24,26Ne, 28,30 Mg and 34S i. The fragmentation potential comprises of binding energies (BE), Coulomb potential (Vc) and nuclear or proximity potential (VP) of the decaying fragments (or clusters). It is relevant to mention here that the contributions of VLDM (T=0) and δU (T=0) in the BE have been analysed within the Strutinsky renormanlization procedure. In the framework of quantum mechanical fragmentation theory (QMFT), we have investigated the above mentioned cluster decays with and without inclusion of shell corrections in the fragmentation potential for spherical as well as non-compact oriented nuclei. We find that the experimentally observed clusters 14C, 18,20O, 22,24,26 Ne, 28,30 Mg and 34Si having doubly magic 208 Pb daughter nucleus are not strongly minimized, they do so only after the inclusion of shell corrections in the fragmentation potential. The nuclear structure information carried by the shell corrections have been explored via these calculations, within the collective clusterisation process of QMFT, in the study of ground state decay of radioactive nuclei. The role of different parts of fragmentation potentials such as VLDM, δU, Vc and Vp is dually analysed for better understanding of radioactive cluster decay.

REVISED MASS-TO-LIGHT RATIOS FOR NEARBY GALAXY GROUPS AND CLUSTERS

International Nuclear Information System (INIS)

Shan, Yutong; Courteau, Stéphane; McDonald, Michael

2015-01-01

We present a detailed investigation of the cluster stellar mass-to-light (M*/L) ratio and cumulative stellar masses, derived on a galaxy-by-galaxy basis, for 12 massive (M 500 ∼ 10 14 -10 15 M ☉ ), nearby clusters with available optical imaging data from the Sloan Digital Sky Survey Data Release 10 and X-ray data from the Chandra X-ray Observatory. Our method involves a statistical cluster membership using both photometric and spectroscopic redshifts when available to maximize completeness while minimizing contamination effects. We show that different methods of estimating the stellar mass-to-light ratio from observed photometry result in systematic discrepancies in the total stellar masses and average mass-to-light ratios of cluster galaxies. Nonetheless, all conversion methodologies point to a lack of correlation between M*/L i and total cluster mass, even though low-mass groups contain relatively more blue galaxies. We also find no statistically significant correlation between M*/L i and the fraction of blue galaxies (g – i < 0.85). For the mass range covered by our sample, the assumption of a Chabrier initial mass function (IMF) yields an integrated M*/L i ≅ 1.7 ± 0.2 M ☉ /L i, ☉ , a lower value than used in most similar studies, though consistent with the study of low-mass galaxy groups by Leauthaud et al. A light (diet) Salpeter IMF would imply a ∼60% increase in M*/L i

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...

Measuring incident light on grape clusters using photosensitive paper and image analysis

International Nuclear Information System (INIS)

Price, S.F.; Schuette, M.L.; Tassie, E.

1995-01-01

Digital imaging and analysis was used to quantify and characterize the light exposure patterns of photosensitive paper tubes placed in representative cluster positions in two grape (Vitis vinifera L.) canopies: a minimally pruned and a vertically trained canopy. Blue pixel values of the captured images had a negative correlation with the log of irradiance from an integrating quantum sensor (r2 = 0.9308). The spectral response of the photosensitive paper was not measured. Histograms of incident light distribution on individual paper tubes were developed using imaging software. Histograms were able to quantify the distribution of incident light on individual tubes and were clearly related to the tube's exposure in the canopy. Average population curves of pixel light distribution of 20 tubes in each canopy were able to differentiate the typical cluster light environment in the two canopies. Tubes in the minimally pruned canopy had a larger proportion of their surface exposed to irradiances > 50 micromoles.s-1 m-2 and 65% higher average irradiance than the vertical canopy. Image analysis of photosensitive paper appears to be a workable method to record the distribution of incident light in plant canopies and may have utility in a range of ecological studies

High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

Directory of Open Access Journals (Sweden)

Dieter Hendricks

2016-02-01

Full Text Available We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

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.

MCBT: Multi-Hop Cluster Based Stable Backbone Trees for Data Collection and Dissemination in WSNs

Directory of Open Access Journals (Sweden)

Tae-Jin Lee

2009-07-01

Full Text Available We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs. The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD and Multicluster, Mobile, Multimedia radio network (MMM, consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime.

Discriminative clustering on manifold for adaptive transductive classification.

Science.gov (United States)

Zhang, Zhao; Jia, Lei; Zhang, Min; Li, Bing; Zhang, Li; Li, Fanzhang

2017-10-01

In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manifold learning, discriminative clustering and adaptive classification into a unified model. Also, our method incorporates the adaptive graph weight construction with label propagation. Specifically, our method is capable of propagating label information using adaptive weights over low-dimensional manifold features, which is different from most existing studies that usually predict the labels and construct the weights in the original Euclidean space. For transductive classification by our formulation, we first perform the joint discriminative K-means clustering and manifold learning to capture the low-dimensional nonlinear manifolds. Then, we construct the adaptive weights over the learnt manifold features, where the adaptive weights are calculated through performing the joint minimization of the reconstruction errors over features and soft labels so that the graph weights can be joint-optimal for data representation and classification. Using the adaptive weights, we can easily estimate the unknown labels of samples. After that, our method returns the updated weights for further updating the manifold features. Extensive simulations on image classification and segmentation show that our proposed algorithm can deliver the state-of-the-art performance on several public datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of a continuous-variable quadripartite cluster state from a single optical parametric oscillator

International Nuclear Information System (INIS)

Midgley, S. L. W.; Olsen, M. K.; Bradley, A. S.; Pfister, O.

2010-01-01

We examine the feasibility of generating continuous-variable multipartite entanglement in an intracavity concurrent downconversion scheme that has been proposed for the generation of cluster states by Menicucci et al. [Phys. Rev. Lett. 101, 130501 (2008)]. By calculating optimized versions of the van Loock-Furusawa correlations we demonstrate genuine quadripartite entanglement and investigate the degree of entanglement present. Above the oscillation threshold the basic cluster state geometry under consideration suffers from phase diffusion. We alleviate this problem by incorporating a small injected signal into our analysis. Finally, we investigate squeezed joint operators. While the squeezed joint operators approach zero in the undepleted regime, we find that this is not the case when we consider the full interaction Hamiltonian and the presence of a cavity. In fact, we find that the decay of these operators is minimal in a cavity, and even depletion alone inhibits cluster state formation.

Long-Ranged Oppositely Charged Interactions for Designing New Types of Colloidal Clusters

Directory of Open Access Journals (Sweden)

Ahmet Faik Demirörs

2015-04-01

Full Text Available Getting control over the valency of colloids is not trivial and has been a long-desired goal for the colloidal domain. Typically, tuning the preferred number of neighbors for colloidal particles requires directional bonding, as in the case of patchy particles, which is difficult to realize experimentally. Here, we demonstrate a general method for creating the colloidal analogs of molecules and other new regular colloidal clusters without using patchiness or complex bonding schemes (e.g., DNA coating by using a combination of long-ranged attractive and repulsive interactions between oppositely charged particles that also enable regular clusters of particles not all in close contact. We show that, due to the interplay between their attractions and repulsions, oppositely charged particles dispersed in an intermediate dielectric constant (4<ϵ<10 provide a viable approach for the formation of binary colloidal clusters. Tuning the size ratio and interactions of the particles enables control of the type and shape of the resulting regular colloidal clusters. Finally, we present an example of clusters made up of negatively charged large and positively charged small satellite particles, for which the electrostatic properties and interactions can be changed with an electric field. It appears that for sufficiently strong fields the satellite particles can move over the surface of the host particles and polarize the clusters. For even stronger fields, the satellite particles can be completely pulled off, reversing the net charge on the cluster. With computer simulations, we investigate how charged particles distribute on an oppositely charged sphere to minimize their energy and compare the results with the solutions to the well-known Thomson problem. We also use the simulations to explore the dependence of such clusters on Debye screening length κ^{−1} and the ratio of charges on the particles, showing good agreement with experimental observations.

Supercomputer and cluster performance modeling and analysis efforts:2004-2006.

Energy Technology Data Exchange (ETDEWEB)

Sturtevant, Judith E.; Ganti, Anand; Meyer, Harold (Hal) Edward; Stevenson, Joel O.; Benner, Robert E., Jr. (.,; .); Goudy, Susan Phelps; Doerfler, Douglas W.; Domino, Stefan Paul; Taylor, Mark A.; Malins, Robert Joseph; Scott, Ryan T.; Barnette, Daniel Wayne; Rajan, Mahesh; Ang, James Alfred; Black, Amalia Rebecca; Laub, Thomas William; Vaughan, Courtenay Thomas; Franke, Brian Claude

2007-02-01

This report describes efforts by the Performance Modeling and Analysis Team to investigate performance characteristics of Sandia's engineering and scientific applications on the ASC capability and advanced architecture supercomputers, and Sandia's capacity Linux clusters. Efforts to model various aspects of these computers are also discussed. The goals of these efforts are to quantify and compare Sandia's supercomputer and cluster performance characteristics; to reveal strengths and weaknesses in such systems; and to predict performance characteristics of, and provide guidelines for, future acquisitions and follow-on systems. Described herein are the results obtained from running benchmarks and applications to extract performance characteristics and comparisons, as well as modeling efforts, obtained during the time period 2004-2006. The format of the report, with hypertext links to numerous additional documents, purposefully minimizes the document size needed to disseminate the extensive results from our research.

Effects of Discrete Charge Clustering in Simulations of Charged Interfaces.

Science.gov (United States)

Grime, John M A; Khan, Malek O

2010-10-12

A system of counterions between charged surfaces is investigated, with the surfaces represented by uniform charged planes and three different arrangements of discrete surface charges - an equispaced grid and two different clustered arrangements. The behaviors of a series of systems with identical net surface charge density are examined, with particular emphasis placed on the long ranged corrections via the method of "charged slabs" and the effects of the simulation cell size. Marked differences are observed in counterion distributions and the osmotic pressure dependent on the particular representation of the charged surfaces; the uniformly charged surfaces and equispaced grids of discrete charge behave in a broadly similar manner, but the clustered systems display a pronounced decrease in osmotic pressure as the simulation size is increased. The influence of the long ranged correction is shown to be minimal for all but the very smallest of system sizes.

Minimizing Mutual Couping

DEFF Research Database (Denmark)

2010-01-01

Disclosed herein are techniques, systems, and methods relating to minimizing mutual coupling between a first antenna and a second antenna.......Disclosed herein are techniques, systems, and methods relating to minimizing mutual coupling between a first antenna and a second antenna....

Effectiveness of a Minimal Intervention for Stress-related mental disorders with Sick leave (MISS; study protocol of a cluster randomised controlled trial in general practice [ISRCTN43779641

Directory of Open Access Journals (Sweden)

van Marwijk Harm WJ

2006-05-01

Full Text Available Abstract Background The main aims of this paper are to describe the setting and design of a Minimal Intervention in general practice for Stress-related mental disorders in patients on Sick leave (MISS, as well as to ascertain the study complies with the requirements for a cluster randomised controlled trial (RCT. The potential adverse consequences of sick leave due to Stress-related Mental Disorders (SMDs are extensive, but often not recognised. Since most people having SMDs with sick leave consult their general practitioner (GP at an early stage, a tailored intervention given by GPs is justified. We provide a detailed description of the MISS; that is more accurate assessment, education, advice and monitoring to treat SMDs in patients on sick leave. Our hypothesis is that the MISS will be more effective compared to the usual care, in reducing days of sick leave of these patients. Methods The design is a pragmatic RCT. Randomisation is at the level of GPs. They received the MISS-training versus no training, in order to compare the MISS vs. usual care at patient level. Enrolment of patients took place after screening in the source population, that comprised 20–60 year old primary care attendees. Inclusion criteria were: moderately elevated distress levels, having a paid job and sick leave for no longer than three months. There is a one year follow up. The primary outcome measure is lasting full return to work. Reduction of SMD- symptoms is one of the secondary outcome measures. Forty-six GPs and 433 patients agreed to participate. Discussion In our study design, attention is given to the practical application of the requirements for a pragmatic trial. The results of this cluster RCT will add to the evidence about treatment options in general practice for SMDs in patients on sick leave, and might contribute to a new and appropriate guideline. These results will be available at the end of 2006.

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

Genetic search for an optimal power flow solution from a high density cluster

Energy Technology Data Exchange (ETDEWEB)

Amarnath, R.V. [Hi-Tech College of Engineering and Technology, Hyderabad (India); Ramana, N.V. [JNTU College of Engineering, Jagityala (India)

2008-07-01

This paper proposed a novel method to solve optimal power flow (OPF) problems. The method is based on a genetic algorithm (GA) search from a High Density Cluster (GAHDC). The algorithm of the proposed method includes 3 stages, notably (1) a suboptimal solution is obtained via a conventional analytical method, (2) a high density cluster, which consists of other suboptimal data points from the first stage, is formed using a density-based cluster algorithm, and (3) a genetic algorithm based search is carried out for the exact optimal solution from a low population sized, high density cluster. The final optimal solution thoroughly satisfies the well defined fitness function. A standard IEEE 30-bus test system was considered for the simulation study. Numerical results were presented and compared with the results of other approaches. It was concluded that although there is not much difference in numerical values, the proposed method has the advantage of minimal computational effort and reduced CPU time. As such, the method would be suitable for online applications such as the present Optimal Power Flow problem. 24 refs., 2 tabs., 4 figs.

Minimal improvement of nurses' motivational interviewing skills in routine diabetes care one year after training: a cluster randomized trial.

Science.gov (United States)

Jansink, Renate; Braspenning, Jozé; Laurant, Miranda; Keizer, Ellen; Elwyn, Glyn; Weijden, Trudy van der; Grol, Richard

2013-03-28

The effectiveness of nurse-led motivational interviewing (MI) in routine diabetes care in general practice is inconclusive. Knowledge about the extent to which nurses apply MI skills and the factors that affect the usage can help to understand the black box of this intervention. The current study compared MI skills of trained versus non-trained general practice nurses in diabetes consultations. The nurses participated in a cluster randomized trial in which a comprehensive program (including MI training) was tested on improving clinical parameters, lifestyle, patients' readiness to change lifestyle, and quality of life. Fifty-eight general practices were randomly assigned to usual care (35 nurses) or the intervention (30 nurses). The ratings of applying 24 MI skills (primary outcome) were based on five consultation recordings per nurse at baseline and 14 months later. Two judges evaluated independently the MI skills and the consultation characteristics time, amount of nurse communication, amount of lifestyle discussion and patients' readiness to change. The effect of the training on the MI skills was analysed with a multilevel linear regression by comparing baseline and the one-year follow-up between the interventions with usual care group. The overall effect of the consultation characteristics on the MI skills was studied in a multilevel regression analyses. At one year follow up, it was demonstrated that the nurses improved on 2 of the 24 MI skills, namely, "inviting the patient to talk about behaviour change" (mean difference=0.39, p=0.009), and "assessing patient's confidence in changing their lifestyle" (mean difference=0.28, p=0.037). Consultation time and the amount of lifestyle discussion as well as the patients' readiness to change health behaviour was associated positively with applying MI skills. The maintenance of the MI skills one year after the training program was minimal. The question is whether the success of MI to change unhealthy behaviour must be

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....

Large-Scale Multi-Dimensional Document Clustering on GPU Clusters

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Mueller, Frank [North Carolina State University; Zhang, Yongpeng [ORNL; Potok, Thomas E [ORNL

2010-01-01

Document clustering plays an important role in data mining systems. Recently, a flocking-based document clustering algorithm has been proposed to solve the problem through simulation resembling the flocking behavior of birds in nature. This method is superior to other clustering algorithms, including k-means, in the sense that the outcome is not sensitive to the initial state. One limitation of this approach is that the algorithmic complexity is inherently quadratic in the number of documents. As a result, execution time becomes a bottleneck with large number of documents. In this paper, we assess the benefits of exploiting the computational power of Beowulf-like clusters equipped with contemporary Graphics Processing Units (GPUs) as a means to significantly reduce the runtime of flocking-based document clustering. Our framework scales up to over one million documents processed simultaneously in a sixteennode GPU cluster. Results are also compared to a four-node cluster with higher-end GPUs. On these clusters, we observe 30X-50X speedups, which demonstrates the potential of GPU clusters to efficiently solve massive data mining problems. Such speedups combined with the scalability potential and accelerator-based parallelization are unique in the domain of document-based data mining, to the best of our knowledge.

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.

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 ...

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

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...

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

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....

Legal incentives for minimizing waste

International Nuclear Information System (INIS)

Clearwater, S.W.; Scanlon, J.M.

1991-01-01

Waste minimization, or pollution prevention, has become an integral component of federal and state environmental regulation. Minimizing waste offers many economic and public relations benefits. In addition, waste minimization efforts can also dramatically reduce potential criminal requirements. This paper addresses the legal incentives for minimizing waste under current and proposed environmental laws and regulations

Determination of Arctic sea ice variability modes on interannual timescales via nonhierarchical clustering

Science.gov (United States)

Fučkar, Neven-Stjepan; Guemas, Virginie; Massonnet, François; Doblas-Reyes, Francisco

2015-04-01

Over the modern observational era, the northern hemisphere sea ice concentration, age and thickness have experienced a sharp long-term decline superimposed with strong internal variability. Hence, there is a crucial need to identify robust patterns of Arctic sea ice variability on interannual timescales and disentangle them from the long-term trend in noisy datasets. The principal component analysis (PCA) is a versatile and broadly used method for the study of climate variability. However, the PCA has several limiting aspects because it assumes that all modes of variability have symmetry between positive and negative phases, and suppresses nonlinearities by using a linear covariance matrix. Clustering methods offer an alternative set of dimension reduction tools that are more robust and capable of taking into account possible nonlinear characteristics of a climate field. Cluster analysis aggregates data into groups or clusters based on their distance, to simultaneously minimize the distance between data points in a given cluster and maximize the distance between the centers of the clusters. We extract modes of Arctic interannual sea-ice variability with nonhierarchical K-means cluster analysis and investigate the mechanisms leading to these modes. Our focus is on the sea ice thickness (SIT) as the base variable for clustering because SIT holds most of the climate memory for variability and predictability on interannual timescales. We primarily use global reconstructions of sea ice fields with a state-of-the-art ocean-sea-ice model, but we also verify the robustness of determined clusters in other Arctic sea ice datasets. Applied cluster analysis over the 1958-2013 period shows that the optimal number of detrended SIT clusters is K=3. Determined SIT cluster patterns and their time series of occurrence are rather similar between different seasons and months. Two opposite thermodynamic modes are characterized with prevailing negative or positive SIT anomalies over the

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.

MOCUS, Minimal Cut Sets and Minimal Path Sets from Fault Tree Analysis

International Nuclear Information System (INIS)

Fussell, J.B.; Henry, E.B.; Marshall, N.H.

1976-01-01

1 - Description of problem or function: From a description of the Boolean failure logic of a system, called a fault tree, and control parameters specifying the minimal cut set length to be obtained MOCUS determines the system failure modes, or minimal cut sets, and the system success modes, or minimal path sets. 2 - Method of solution: MOCUS uses direct resolution of the fault tree into the cut and path sets. The algorithm used starts with the main failure of interest, the top event, and proceeds to basic independent component failures, called primary events, to resolve the fault tree to obtain the minimal sets. A key point of the algorithm is that an and gate alone always increases the number of path sets; an or gate alone always increases the number of cut sets and increases the size of path sets. Other types of logic gates must be described in terms of and and or logic gates. 3 - Restrictions on the complexity of the problem: Output from MOCUS can include minimal cut and path sets for up to 20 gates

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...

Observed damage during Argon gas cluster depth profiles of compound semiconductors

Energy Technology Data Exchange (ETDEWEB)

Barlow, Anders J., E-mail: anders.barlow@ncl.ac.uk; Portoles, Jose F.; Cumpson, Peter J. [National EPSRC XPS Users' Service (NEXUS), School of Mechanical and Systems Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU (United Kingdom)

2014-08-07

Argon Gas Cluster Ion Beam (GCIB) sources have become very popular in XPS and SIMS in recent years, due to the minimal chemical damage they introduce in the depth-profiling of polymer and other organic materials. These GCIB sources are therefore particularly useful for depth-profiling polymer and organic materials, but also (though more slowly) the surfaces of inorganic materials such as semiconductors, due to the lower roughness expected in cluster ion sputtering compared to that introduced by monatomic ions. We have examined experimentally a set of five compound semiconductors, cadmium telluride (CdTe), gallium arsenide (GaAs), gallium phosphide (GaP), indium arsenide (InAs), and zinc selenide (ZnSe) and a high-κ dielectric material, hafnium oxide (HfO), in their response to argon cluster profiling. An experimentally determined HfO etch rate of 0.025 nm/min (3.95 × 10{sup −2} amu/atom in ion) for 6 keV Ar gas clusters is used in the depth scale conversion for the profiles of the semiconductor materials. The assumption has been that, since the damage introduced into polymer materials is low, even though sputter yields are high, then there is little likelihood of damaging inorganic materials at all with cluster ions. This seems true in most cases; however, in this work, we report for the first time that this damage can in fact be very significant in the case of InAs, causing the formation of metallic indium that is readily visible even to the naked eye.

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.

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.

An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer

Science.gov (United States)

Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein

2018-05-01

Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.

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...

Is non-minimal inflation eternal?

International Nuclear Information System (INIS)

Feng, Chao-Jun; Li, Xin-Zhou

2010-01-01

The possibility that the non-minimal coupling inflation could be eternal is investigated. We calculate the quantum fluctuation of the inflaton in a Hubble time and find that it has the same value as that in the minimal case in the slow-roll limit. Armed with this result, we have studied some concrete non-minimal inflationary models including the chaotic inflation and the natural inflation, in which the inflaton is non-minimally coupled to the gravity. We find that the non-minimal coupling inflation could be eternal in some parameter spaces.

Parameter-free Network Sparsification and Data Reduction by Minimal Algorithmic Information Loss

KAUST Repository

Zenil, Hector

2018-02-16

The study of large and complex datasets, or big data, organized as networks has emerged as one of the central challenges in most areas of science and technology. Cellular and molecular networks in biology is one of the prime examples. Henceforth, a number of techniques for data dimensionality reduction, especially in the context of networks, have been developed. Yet, current techniques require a predefined metric upon which to minimize the data size. Here we introduce a family of parameter-free algorithms based on (algorithmic) information theory that are designed to minimize the loss of any (enumerable computable) property contributing to the object\\'s algorithmic content and thus important to preserve in a process of data dimension reduction when forcing the algorithm to delete first the least important features. Being independent of any particular criterion, they are universal in a fundamental mathematical sense. Using suboptimal approximations of efficient (polynomial) estimations we demonstrate how to preserve network properties outperforming other (leading) algorithms for network dimension reduction. Our method preserves all graph-theoretic indices measured, ranging from degree distribution, clustering-coefficient, edge betweenness, and degree and eigenvector centralities. We conclude and demonstrate numerically that our parameter-free, Minimal Information Loss Sparsification (MILS) method is robust, has the potential to maximize the preservation of all recursively enumerable features in data and networks, and achieves equal to significantly better results than other data reduction and network sparsification methods.

Analytical network process based optimum cluster head selection in wireless sensor network.

Science.gov (United States)

Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

Balancing collective and individual interests in transactive energy management of interconnected micro-grid clusters

International Nuclear Information System (INIS)

Chen, Yang; Hu, Mengqi

2016-01-01

The emerging technology, transactive energy network, can allow multiple interconnected micro-grids (a.k.a. micro-grid clusters) to exchange energy for greater energy efficiency. Existing research has demonstrated that the micro-grid clusters can achieve some collective interests (e.g., minimizing total energy cost). However, some micro-grids may have to make sacrifices of their individual interests (e.g., increasing cost) for collective interests of the clusters. To bridge these research gaps, we propose four different transactive energy management models for micro-grid clusters where each micro-grid is allowed to have energy transactions with others. The first model focuses on maximizing collective interests, both the collective and individual interests are considered in the second model, and the last two models aim to maximize both the collective and individual interests. The performances of the proposed models are evaluated using a cluster of sixteen micro-grids with different energy profiles. It is demonstrated that 1) all of the four models can maximize the collective interests, 2) the third model can maximize the relative individual interests where each micro-grid can achieve the same percentage of cost savings as the clusters, and 3) the fourth model can maximize the absolute individual interests where each micro-grid can achieve the same amount of cost savings. - Highlights: • A modeling framework is developed for transactive energy management of the micro-grid clusters. • Four operation decision models are developed to balance the collective and individual interests. • The prices of local energy transaction are modeled. • The micro-grid clusters can achieve 15.34% energy cost savings.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

Science.gov (United States)

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

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)

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.

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”

AUTOMATIC EXTRACTION OF ROCK JOINTS FROM LASER SCANNED DATA BY MOVING LEAST SQUARES METHOD AND FUZZY K-MEANS CLUSTERING

Directory of Open Access Journals (Sweden)

S. Oh

2012-09-01

Full Text Available Recent development of laser scanning device increased the capability of representing rock outcrop in a very high resolution. Accurate 3D point cloud model with rock joint information can help geologist to estimate stability of rock slope on-site or off-site. An automatic plane extraction method was developed by computing normal directions and grouping them in similar direction. Point normal was calculated by moving least squares (MLS method considering every point within a given distance to minimize error to the fitting plane. Normal directions were classified into a number of dominating clusters by fuzzy K-means clustering. Region growing approach was exploited to discriminate joints in a point cloud. Overall procedure was applied to point cloud with about 120,000 points, and successfully extracted joints with joint information. The extraction procedure was implemented to minimize number of input parameters and to construct plane information into the existing point cloud for less redundancy and high usability of the point cloud itself.

Minimal families of curves on surfaces

KAUST Repository

Lubbes, Niels

2014-11-01

A minimal family of curves on an embedded surface is defined as a 1-dimensional family of rational curves of minimal degree, which cover the surface. We classify such minimal families using constructive methods. This allows us to compute the minimal families of a given surface.The classification of minimal families of curves can be reduced to the classification of minimal families which cover weak Del Pezzo surfaces. We classify the minimal families of weak Del Pezzo surfaces and present a table with the number of minimal families of each weak Del Pezzo surface up to Weyl equivalence.As an application of this classification we generalize some results of Schicho. We classify algebraic surfaces that carry a family of conics. We determine the minimal lexicographic degree for the parametrization of a surface that carries at least 2 minimal families. © 2014 Elsevier B.V.

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

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Jean Marie Vianney Kinani

2017-01-01

Full Text Available We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR and fluid-attenuated inversion recovery (FLAIR images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

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...

Hexavalent Chromium Minimization Strategy

Science.gov (United States)

2011-05-01

Logistics 4 Initiative - DoD Hexavalent Chromium Minimization Non- Chrome Primer IIEXAVAJ ENT CHRO:M I~UMI CHROMIUM (VII Oil CrfVli.J CANCEfl HAnRD CD...Management Office of the Secretary of Defense Hexavalent Chromium Minimization Strategy Report Documentation Page Form ApprovedOMB No. 0704-0188...00-2011 4. TITLE AND SUBTITLE Hexavalent Chromium Minimization Strategy 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6

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.

Ab initio study of the structural, magnetic, and electronic properties of copper and silver clusters and their alloys with one palladium atom

Directory of Open Access Journals (Sweden)

S. J Hashemifar

2015-01-01

Full Text Available In this paper, the structural, magnetic, and electronic properties of two- to nine-atom copper and silver clusters and their alloys with one palladium atom are investigated by using full-potential all-electron density functional computations. After calculating minimized energy of several structural isomers of every nanocluster, it is argued that the small size nanoclusters (up to size of 6, ‎ prefer planar structures, while by increasing size a 2D-3D structural transformation is observed. The structural transformation of pure and copper-palladium clusters occurs in the size of seven and that of silver-palladium cluster in happens at the size of six. The calculated second difference and dissociation energies confirm that the two- and eight- atom pure clusters and three- and seven- atom alloyed clusters are magic clusters. The electronic and magnetic properties of stable isomers are calculated and considered after applying many body based GW correction.

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

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.

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.

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.

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.)

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

Minimal and non-minimal standard models: Universality of radiative corrections

International Nuclear Information System (INIS)

Passarino, G.

1991-01-01

The possibility of describing electroweak processes by means of models with a non-minimal Higgs sector is analyzed. The renormalization procedure which leads to a set of fitting equations for the bare parameters of the lagrangian is first reviewed for the minimal standard model. A solution of the fitting equations is obtained, which correctly includes large higher-order corrections. Predictions for physical observables, notably the W boson mass and the Z O partial widths, are discussed in detail. Finally the extension to non-minimal models is described under the assumption that new physics will appear only inside the vector boson self-energies and the concept of universality of radiative corrections is introduced, showing that to a large extent they are insensitive to the details of the enlarged Higgs sector. Consequences for the bounds on the top quark mass are also discussed. (orig.)

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

Cluster-cluster aggregation of Ising dipolar particles under thermal noise

KAUST Repository

Suzuki, Masaru

2009-08-14

The cluster-cluster aggregation processes of Ising dipolar particles under thermal noise are investigated in the dilute condition. As the temperature increases, changes in the typical structures of clusters are observed from chainlike (D1) to crystalline (D2) through fractal structures (D1.45), where D is the fractal dimension. By calculating the bending energy of the chainlike structure, it is found that the transition temperature is associated with the energy gap between the chainlike and crystalline configurations. The aggregation dynamics changes from being dominated by attraction to diffusion involving changes in the dynamic exponent z=0.2 to 0.5. In the region of temperature where the fractal clusters grow, different growth rates are observed between charged and neutral clusters. Using the Smoluchowski equation with a twofold kernel, this hetero-aggregation process is found to result from two types of dynamics: the diffusive motion of neutral clusters and the weak attractive motion between charged clusters. The fact that changes in structures and dynamics take place at the same time suggests that transitions in the structure of clusters involve marked changes in the dynamics of the aggregation processes. © 2009 The American Physical Society.

New Theoretical Developments in Exploring Electronically Excited States: Including Localized Configuration Interaction Singles and Application to Large Helium Clusters

Science.gov (United States)

Closser, Kristina Danielle

This thesis presents new developments in excited state electronic structure theory. Contrasted with the ground state, the electronically excited states of atoms and molecules often are unstable and have short lifetimes, exhibit a greater diversity of character and are generally less well understood. The very unusual excited states of helium clusters motivated much of this work. These clusters consist of large numbers of atoms (experimentally 103--109 atoms) and bands of nearly degenerate excited states. For an isolated atom the lowest energy excitation energies are from 1s → 2s and 1s → 2 p transitions, and in clusters describing the lowest energy band minimally requires four states per atom. In the ground state the clusters are weakly bound by van der Waals interactions, however in the excited state they can form well-defined covalent bonds. The computational cost of quantum chemical calculations rapidly becomes prohibitive as the size of the systems increase. Standard excited-state methods such as configuration interaction singles (CIS) and time-dependent density functional theory (TD-DFT) can be used with ≈100 atoms, and are optimized to treat only a few states. Thus, one of our primary aims is to develop a method which can treat these large systems with large numbers of nearly degenerate excited states. Additionally, excited states are generally formed far from their equilibrium structures. Vertical excitations from the ground state induce dynamics in the excited states. Thus, another focus of this work is to explore the results of these forces and the fate of the excited states. Very little was known about helium cluster excited states when this work began, thus we first investigated the excitations in small helium clusters consisting of 7 or 25 atoms using CIS. The character of these excited states was determined using attachment/detachment density analysis and we found that in the n = 2 manifold the excitations could generally be interpreted as

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

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

Science.gov (United States)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

Barriers to energy efficiency in small industry clusters: Multi-criteria-based prioritization using the analytic hierarchy process

International Nuclear Information System (INIS)

Nagesha, N.; Balachandra, P.

2006-01-01

The small scale industry (SSI) is an important component of Indian economy and a majority of SSI units tend to exist in geographical clusters. Energy efficiency is crucial for the survival and growth of energy intensive SSI clusters, not only to improve their competitiveness through cost reduction but also to minimize adverse environmental impacts. However, this is easier said than done due to the presence of a variety of barriers. The identification of relevant barriers and their appropriate prioritization in such clusters is a prerequisite to effectively tackle them. This paper identifies relevant barriers to energy efficiency and their dimensions in SSI clusters. Further, the barriers are prioritized based on the perceptions and experiences of entrepreneurs, the main stakeholders of SSIs, using the analytic hierarchy process (AHP). The field data from two energy intensive clusters of foundry and brick and tile in Karnataka (a state in India) reveal that the prioritization remained the same despite differences in the relative weights of barrier groups. The financial and economic barrier (FEB) and behavioural and personal barrier (BPB) have emerged as the top two impediments to energy efficiency improvements

Distributed cluster management techniques for unattended ground sensor networks

Science.gov (United States)

Essawy, Magdi A.; Stelzig, Chad A.; Bevington, James E.; Minor, Sharon

2005-05-01

Smart Sensor Networks are becoming important target detection and tracking tools. The challenging problems in such networks include the sensor fusion, data management and communication schemes. This work discusses techniques used to distribute sensor management and multi-target tracking responsibilities across an ad hoc, self-healing cluster of sensor nodes. Although miniaturized computing resources possess the ability to host complex tracking and data fusion algorithms, there still exist inherent bandwidth constraints on the RF channel. Therefore, special attention is placed on the reduction of node-to-node communications within the cluster by minimizing unsolicited messaging, and distributing the sensor fusion and tracking tasks onto local portions of the network. Several challenging problems are addressed in this work including track initialization and conflict resolution, track ownership handling, and communication control optimization. Emphasis is also placed on increasing the overall robustness of the sensor cluster through independent decision capabilities on all sensor nodes. Track initiation is performed using collaborative sensing within a neighborhood of sensor nodes, allowing each node to independently determine if initial track ownership should be assumed. This autonomous track initiation prevents the formation of duplicate tracks while eliminating the need for a central "management" node to assign tracking responsibilities. Track update is performed as an ownership node requests sensor reports from neighboring nodes based on track error covariance and the neighboring nodes geo-positional location. Track ownership is periodically recomputed using propagated track states to determine which sensing node provides the desired coverage characteristics. High fidelity multi-target simulation results are presented, indicating the distribution of sensor management and tracking capabilities to not only reduce communication bandwidth consumption, but to also

Dark matter searches with Cherenkov telescopes: nearby dwarf galaxies or local galaxy clusters?

Energy Technology Data Exchange (ETDEWEB)

Sánchez-Conde, Miguel A. [SLAC National Laboratory and Kavli Institute for Particle Astrophysics and Cosmology, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Cannoni, Mirco; Gómez, Mario E. [Dpto. Física Aplicada, Facultad de Ciencias Experimentales, Universidad de Huelva, 21071 Huelva (Spain); Zandanel, Fabio; Prada, Francisco, E-mail: masc@stanford.edu, E-mail: mirco.cannoni@dfa.uhu.es, E-mail: fabio@iaa.es, E-mail: mario.gomez@dfa.uhu.es, E-mail: fprada@iaa.es [Instituto de Astrofísica de Andalucía (CSIC), E-18008, Granada (Spain)

2011-12-01

In this paper, we compare dwarf galaxies and galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies (Draco, Ursa Minor, Wilman 1 and Segue 1) and local galaxy clusters (Perseus, Coma, Ophiuchus, Virgo, Fornax, NGC 5813 and NGC 5846), and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman 1 appears as the best candidate in the sample. However, its mass modeling is still rather uncertain, so probably other candidates with less uncertainties and quite similar fluxes, namely Ursa Minor and Segue 1, might be better options. As for galaxy clusters, Virgo represents the one with the highest flux. However, its large spatial extension can be a serious handicap for IACT observations and posterior data analysis. Yet, other local galaxy cluster candidates with more moderate emission regions, such as Perseus, may represent good alternatives. After comparing dwarfs and clusters, we found that the former exhibit annihilation flux profiles that, at the center, are roughly one order of magnitude higher than those of clusters, although galaxy clusters can yield similar, or even higher, integrated fluxes for the whole object once substructure is taken into account. Even when any of these objects are strictly point-like according to the properties of their annihilation signals, we conclude that dwarf galaxies are best suited for observational strategies based on the search of point-like sources, while galaxy clusters represent best targets for analyses that can deal with rather extended emissions. Finally, we study the detection prospects for present and future IACTs in the framework of the constrained minimal supersymmetric standard model. We

Dark Matter Searches with Cherenkov Telescopes: Nearby Dwarf Galaxies or Local Galaxy Clusters?

Energy Technology Data Exchange (ETDEWEB)

Sanchez-Conde, Miguel A.; /KIPAC, Menlo Park /SLAC /IAC, La Laguna /Laguna U., Tenerife; Cannoni, Mirco; /Huelva U.; Zandanel, Fabio; /IAA, Granada; Gomez, Mario E.; /Huelva U.; Prada, Francisco; /IAA, Granada

2012-06-06

In this paper, we compare dwarf galaxies and galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies (Draco, Ursa Minor, Wilman 1 and Segue 1) and local galaxy clusters (Perseus, Coma, Ophiuchus, Virgo, Fornax, NGC 5813 and NGC 5846), and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman 1 appears as the best candidate in the sample. However, its mass modeling is still rather uncertain, so probably other candidates with less uncertainties and quite similar fluxes, namely Ursa Minor and Segue 1, might be better options. As for galaxy clusters, Virgo represents the one with the highest flux. However, its large spatial extension can be a serious handicap for IACT observations and posterior data analysis. Yet, other local galaxy cluster candidates with more moderate emission regions, such as Perseus, may represent good alternatives. After comparing dwarfs and clusters, we found that the former exhibit annihilation flux profiles that, at the center, are roughly one order of magnitude higher than those of clusters, although galaxy clusters can yield similar, or even higher, integrated fluxes for the whole object once substructure is taken into account. Even when any of these objects are strictly point-like according to the properties of their annihilation signals, we conclude that dwarf galaxies are best suited for observational strategies based on the search of point-like sources, while galaxy clusters represent best targets for analyses that can deal with rather extended emissions. Finally, we study the detection prospects for present and future IACTs in the framework of the constrained minimal supersymmetric standard model. We

Minimal improvement of nurses’ motivational interviewing skills in routine diabetes care one year after training: a cluster randomized trial

Science.gov (United States)

2013-01-01

Background The effectiveness of nurse-led motivational interviewing (MI) in routine diabetes care in general practice is inconclusive. Knowledge about the extent to which nurses apply MI skills and the factors that affect the usage can help to understand the black box of this intervention. The current study compared MI skills of trained versus non-trained general practice nurses in diabetes consultations. The nurses participated in a cluster randomized trial in which a comprehensive program (including MI training) was tested on improving clinical parameters, lifestyle, patients’ readiness to change lifestyle, and quality of life. Methods Fifty-eight general practices were randomly assigned to usual care (35 nurses) or the intervention (30 nurses). The ratings of applying 24 MI skills (primary outcome) were based on five consultation recordings per nurse at baseline and 14 months later. Two judges evaluated independently the MI skills and the consultation characteristics time, amount of nurse communication, amount of lifestyle discussion and patients’ readiness to change. The effect of the training on the MI skills was analysed with a multilevel linear regression by comparing baseline and the one-year follow-up between the interventions with usual care group. The overall effect of the consultation characteristics on the MI skills was studied in a multilevel regression analyses. Results At one year follow up, it was demonstrated that the nurses improved on 2 of the 24 MI skills, namely, “inviting the patient to talk about behaviour change” (mean difference=0.39, p=0.009), and “assessing patient’s confidence in changing their lifestyle” (mean difference=0.28, p=0.037). Consultation time and the amount of lifestyle discussion as well as the patients’ readiness to change health behaviour was associated positively with applying MI skills. Conclusions The maintenance of the MI skills one year after the training program was minimal. The question is whether

Cluster-cluster aggregation of Ising dipolar particles under thermal noise

KAUST Repository

Suzuki, Masaru; Kun, Ferenc; Ito, Nobuyasu

2009-01-01

The cluster-cluster aggregation processes of Ising dipolar particles under thermal noise are investigated in the dilute condition. As the temperature increases, changes in the typical structures of clusters are observed from chainlike (D1

Re-estimating sample size in cluster randomized trials with active recruitment within clusters

NARCIS (Netherlands)

van Schie, Sander; Moerbeek, Mirjam

2014-01-01

Often only a limited number of clusters can be obtained in cluster randomised trials, although many potential participants can be recruited within each cluster. Thus, active recruitment is feasible within the clusters. To obtain an efficient sample size in a cluster randomised trial, the cluster

Minimal Gromov-Witten rings

International Nuclear Information System (INIS)

Przyjalkowski, V V

2008-01-01

We construct an abstract theory of Gromov-Witten invariants of genus 0 for quantum minimal Fano varieties (a minimal class of varieties which is natural from the quantum cohomological viewpoint). Namely, we consider the minimal Gromov-Witten ring: a commutative algebra whose generators and relations are of the form used in the Gromov-Witten theory of Fano varieties (of unspecified dimension). The Gromov-Witten theory of any quantum minimal variety is a homomorphism from this ring to C. We prove an abstract reconstruction theorem which says that this ring is isomorphic to the free commutative ring generated by 'prime two-pointed invariants'. We also find solutions of the differential equation of type DN for a Fano variety of dimension N in terms of the generating series of one-pointed Gromov-Witten invariants

LOD-based clustering techniques for efficient large-scale terrain storage and visualization

Science.gov (United States)

Bao, Xiaohong; Pajarola, Renato

2003-05-01

Large multi-resolution terrain data sets are usually stored out-of-core. To visualize terrain data at interactive frame rates, the data needs to be organized on disk, loaded into main memory part by part, then rendered efficiently. Many main-memory algorithms have been proposed for efficient vertex selection and mesh construction. Organization of terrain data on disk is quite difficult because the error, the triangulation dependency and the spatial location of each vertex all need to be considered. Previous terrain clustering algorithms did not consider the per-vertex approximation error of individual terrain data sets. Therefore, the vertex sequences on disk are exactly the same for any terrain. In this paper, we propose a novel clustering algorithm which introduces the level-of-detail (LOD) information to terrain data organization to map multi-resolution terrain data to external memory. In our approach the LOD parameters of the terrain elevation points are reflected during clustering. The experiments show that dynamic loading and paging of terrain data at varying LOD is very efficient and minimizes page faults. Additionally, the preprocessing of this algorithm is very fast and works from out-of-core.

Minimal Marking: A Success Story

Science.gov (United States)

McNeilly, Anne

2014-01-01

The minimal-marking project conducted in Ryerson's School of Journalism throughout 2012 and early 2013 resulted in significantly higher grammar scores in two first-year classes of minimally marked university students when compared to two traditionally marked classes. The "minimal-marking" concept (Haswell, 1983), which requires…

Minimal families of curves on surfaces

KAUST Repository

Lubbes, Niels

2014-01-01

A minimal family of curves on an embedded surface is defined as a 1-dimensional family of rational curves of minimal degree, which cover the surface. We classify such minimal families using constructive methods. This allows us to compute the minimal

Multi-Optimisation Consensus Clustering

Science.gov (United States)

Li, Jian; Swift, Stephen; Liu, Xiaohui

Ensemble Clustering has been developed to provide an alternative way of obtaining more stable and accurate clustering results. It aims to avoid the biases of individual clustering algorithms. However, it is still a challenge to develop an efficient and robust method for Ensemble Clustering. Based on an existing ensemble clustering method, Consensus Clustering (CC), this paper introduces an advanced Consensus Clustering algorithm called Multi-Optimisation Consensus Clustering (MOCC), which utilises an optimised Agreement Separation criterion and a Multi-Optimisation framework to improve the performance of CC. Fifteen different data sets are used for evaluating the performance of MOCC. The results reveal that MOCC can generate more accurate clustering results than the original CC algorithm.

Electron: Cluster interactions

International Nuclear Information System (INIS)

Scheidemann, A.A.; Knight, W.D.

1994-02-01

Beam depletion spectroscopy has been used to measure absolute total inelastic electron-sodium cluster collision cross sections in the energy range from E ∼ 0.1 to E ∼ 6 eV. The investigation focused on the closed shell clusters Na 8 , Na 20 , Na 40 . The measured cross sections show an increase for the lowest collision energies where electron attachment is the primary scattering channel. The electron attachment cross section can be understood in terms of Langevin scattering, connecting this measurement with the polarizability of the cluster. For energies above the dissociation energy the measured electron-cluster cross section is energy independent, thus defining an electron-cluster interaction range. This interaction range increases with the cluster size

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)

The clustered nucleus-cluster structures in stable and unstable nuclei

International Nuclear Information System (INIS)

Freer, Martin

2007-01-01

The subject of clustering has a lineage which runs throughout the history of nuclear physics. Its attraction is the simplification of the often uncorrelated behaviour of independent particles to organized and coherent quasi-crystalline structures. In this review the ideas behind the development of clustering in light nuclei are investigated, mostly from the stand-point of the harmonic oscillator framework. This allows a unifying description of alpha-conjugate and neutron-rich nuclei, alike. More sophisticated models of clusters are explored, such as antisymmetrized molecular dynamics. A number of contemporary topics in clustering are touched upon; the 3α-cluster state in 12 C, nuclear molecules and clustering at the drip-line. Finally, an understanding of the 12 C+ 12 C resonances in 24 Mg, within the framework of the theoretical ideas developed in the review, is presented

Regional Innovation Clusters

Data.gov (United States)

Small Business Administration — The Regional Innovation Clusters serve a diverse group of sectors and geographies. Three of the initial pilot clusters, termed Advanced Defense Technology clusters,...

Choosing the Number of Clusters in K-Means Clustering

Science.gov (United States)

Steinley, Douglas; Brusco, Michael J.

2011-01-01

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple…

Personalized PageRank Clustering: A graph clustering algorithm based on random walks

Science.gov (United States)

A. Tabrizi, Shayan; Shakery, Azadeh; Asadpour, Masoud; Abbasi, Maziar; Tavallaie, Mohammad Ali

2013-11-01

Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.

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 ...

Using self-consistent Gibbs free energy surfaces to calculate size distributions of neutral and charged clusters for the sulfuric acid-water binary system

Science.gov (United States)

Smith, J. A.; Froyd, K. D.; Toon, O. B.

2012-12-01

We construct tables of reaction enthalpies and entropies for the association reactions involving sulfuric acid vapor, water vapor, and the bisulfate ion. These tables are created from experimental measurements and quantum chemical calculations for molecular clusters and a classical thermodynamic model for larger clusters. These initial tables are not thermodynamically consistent. For example, the Gibbs free energy of associating a cluster consisting of one acid molecule and two water molecules depends on the order in which the cluster was assembled: add two waters and then the acid or add an acid and a water and then the second water. We adjust the values within the tables using the method of Lagrange multipliers to minimize the adjustments and produce self-consistent Gibbs free energy surfaces for the neutral clusters and the charged clusters. With the self-consistent Gibbs free energy surfaces, we calculate size distributions of neutral and charged clusters for a variety of atmospheric conditions. Depending on the conditions, nucleation can be dominated by growth along the neutral channel or growth along the ion channel followed by ion-ion recombination.

Herd Clustering: A synergistic data clustering approach using collective intelligence

KAUST Repository

Wong, Kachun; Peng, Chengbin; Li, Yue; Chan, Takming

2014-01-01

, this principle is used to develop a new clustering algorithm. Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances

Waste minimization assessment procedure

International Nuclear Information System (INIS)

Kellythorne, L.L.

1993-01-01

Perry Nuclear Power Plant began developing a waste minimization plan early in 1991. In March of 1991 the plan was documented following a similar format to that described in the EPA Waste Minimization Opportunity Assessment Manual. Initial implementation involved obtaining management's commitment to support a waste minimization effort. The primary assessment goal was to identify all hazardous waste streams and to evaluate those streams for minimization opportunities. As implementation of the plan proceeded, non-hazardous waste streams routinely generated in large volumes were also evaluated for minimization opportunities. The next step included collection of process and facility data which would be useful in helping the facility accomplish its assessment goals. This paper describes the resources that were used and which were most valuable in identifying both the hazardous and non-hazardous waste streams that existed on site. For each material identified as a waste stream, additional information regarding the materials use, manufacturer, EPA hazardous waste number and DOT hazard class was also gathered. Once waste streams were evaluated for potential source reduction, recycling, re-use, re-sale, or burning for heat recovery, with disposal as the last viable alternative

Westinghouse Hanford Company waste minimization actions

International Nuclear Information System (INIS)

Greenhalgh, W.O.

1988-09-01

Companies that generate hazardous waste materials are now required by national regulations to establish a waste minimization program. Accordingly, in FY88 the Westinghouse Hanford Company formed a waste minimization team organization. The purpose of the team is to assist the company in its efforts to minimize the generation of waste, train personnel on waste minimization techniques, document successful waste minimization effects, track dollar savings realized, and to publicize and administer an employee incentive program. A number of significant actions have been successful, resulting in the savings of materials and dollars. The team itself has been successful in establishing some worthwhile minimization projects. This document briefly describes the waste minimization actions that have been successful to date. 2 refs., 26 figs., 3 tabs

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

Coordinated Voltage Control in Offshore HVDC Connected Cluster of Wind Power Plants

DEFF Research Database (Denmark)

Sakamuri, Jayachandra N.; Rather, Zakir Hussain; Rimez, Johan

2016-01-01

This paper presents a coordinated voltage control scheme (CVCS) for a cluster of offshore wind power plants (OWPPs) connected to a VSC HVDC system. The primary control point of the proposed voltage control scheme is the introduced Pilot bus, which is having the highest short circuit capacity...... in the offshore AC grid. The developed CVCS comprehends an optimization algorithm, aiming for minimum active power losses in the offshore grid, to generate voltage reference to the Pilot bus. During steady state operation, the Pilot bus voltage is controlled by dispatching reactive power references to each wind...... turbine (WT) in the WPP cluster based on their available reactive power margin and network sensitivity based participation factors, which are derived from the dV/dQ sensitivity of a WT bus w.r.t the Pilot bus. This method leads to minimization of the risk of undesired effects, particularly overvoltage...

Cluster consensus in discrete-time networks of multiagents with inter-cluster nonidentical inputs.

Science.gov (United States)

Han, Yujuan; Lu, Wenlian; Chen, Tianping

2013-04-01

In this paper, cluster consensus of multiagent systems is studied via inter-cluster nonidentical inputs. Here, we consider general graph topologies, which might be time-varying. The cluster consensus is defined by two aspects: intracluster synchronization, the state at which differences between each pair of agents in the same cluster converge to zero, and inter-cluster separation, the state at which agents in different clusters are separated. For intra-cluster synchronization, the concepts and theories of consensus, including the spanning trees, scramblingness, infinite stochastic matrix product, and Hajnal inequality, are extended. As a result, it is proved that if the graph has cluster spanning trees and all vertices self-linked, then the static linear system can realize intra-cluster synchronization. For the time-varying coupling cases, it is proved that if there exists T > 0 such that the union graph across any T-length time interval has cluster spanning trees and all graphs has all vertices self-linked, then the time-varying linear system can also realize intra-cluster synchronization. Under the assumption of common inter-cluster influence, a sort of inter-cluster nonidentical inputs are utilized to realize inter-cluster separation, such that each agent in the same cluster receives the same inputs and agents in different clusters have different inputs. In addition, the boundedness of the infinite sum of the inputs can guarantee the boundedness of the trajectory. As an application, we employ a modified non-Bayesian social learning model to illustrate the effectiveness of our results.

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

Minimal but non-minimal inflation and electroweak symmetry breaking

Energy Technology Data Exchange (ETDEWEB)

Marzola, Luca [National Institute of Chemical Physics and Biophysics,Rävala 10, 10143 Tallinn (Estonia); Institute of Physics, University of Tartu,Ravila 14c, 50411 Tartu (Estonia); Racioppi, Antonio [National Institute of Chemical Physics and Biophysics,Rävala 10, 10143 Tallinn (Estonia)

2016-10-07

We consider the most minimal scale invariant extension of the standard model that allows for successful radiative electroweak symmetry breaking and inflation. The framework involves an extra scalar singlet, that plays the rôle of the inflaton, and is compatibile with current experimental bounds owing to the non-minimal coupling of the latter to gravity. This inflationary scenario predicts a very low tensor-to-scalar ratio r≈10{sup −3}, typical of Higgs-inflation models, but in contrast yields a scalar spectral index n{sub s}≃0.97 which departs from the Starobinsky limit. We briefly discuss the collider phenomenology of the framework.

GALAXY CLUSTERS AT HIGH REDSHIFT AND EVOLUTION OF BRIGHTEST CLUSTER GALAXIES

International Nuclear Information System (INIS)

Wen, Z. L.; Han, J. L.

2011-01-01

Identification of high-redshift clusters is important for studies of cosmology and cluster evolution. Using photometric redshifts of galaxies, we identify 631 clusters from the Canada-France-Hawaii Telescope (CFHT) wide field, 202 clusters from the CFHT deep field, 187 clusters from the Cosmic Evolution Survey (COSMOS) field, and 737 clusters from the Spitzer Wide-area InfraRed Extragalactic Survey (SWIRE) field. The redshifts of these clusters are in the range 0.1 ∼ + - m 3.6 μ m colors of the BCGs are consistent with a stellar population synthesis model in which the BCGs are formed at redshift z f ≥ 2 and evolved passively. The g' - z' and B - m 3.6μm colors of the BCGs at redshifts z > 0.8 are systematically bluer than the passive evolution model for galaxies formed at z f ∼ 2, indicating star formation in high-redshift BCGs.

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review.

Science.gov (United States)

Kristunas, Caroline; Morris, Tom; Gray, Laura

2017-11-15

To investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of these trials. Any, not limited to healthcare settings. Any taking part in an SW-CRT published up to March 2016. The primary outcome is the variability in cluster sizes, measured by the coefficient of variation (CV) in cluster size. Secondary outcomes include the difference between the cluster sizes assumed during the sample size calculation and those observed during the trial, any reported variability in cluster sizes and whether the methods of sample size calculation and methods of analysis accounted for any variability in cluster sizes. Of the 101 included SW-CRTs, 48% mentioned that the included clusters were known to vary in size, yet only 13% of these accounted for this during the calculation of the sample size. However, 69% of the trials did use a method of analysis appropriate for when clusters vary in size. Full trial reports were available for 53 trials. The CV was calculated for 23 of these: the median CV was 0.41 (IQR: 0.22-0.52). Actual cluster sizes could be compared with those assumed during the sample size calculation for 14 (26%) of the trial reports; the cluster sizes were between 29% and 480% of that which had been assumed. Cluster sizes often vary in SW-CRTs. Reporting of SW-CRTs also remains suboptimal. The effect of unequal cluster sizes on the statistical power of SW-CRTs needs further exploration and methods appropriate to studies with unequal cluster sizes need to be employed. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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.

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

Directory of Open Access Journals (Sweden)

Deepa Devasenapathy

2015-01-01

Full Text Available The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

Science.gov (United States)

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

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.

Ruled Laguerre minimal surfaces

KAUST Repository

Skopenkov, Mikhail

2011-10-30

A Laguerre minimal surface is an immersed surface in ℝ 3 being an extremal of the functional ∫ (H 2/K-1)dA. In the present paper, we prove that the only ruled Laguerre minimal surfaces are up to isometry the surfaces ℝ (φλ) = (Aφ, Bφ, Cφ + D cos 2φ) + λ(sin φ, cos φ, 0), where A,B,C,D ε ℝ are fixed. To achieve invariance under Laguerre transformations, we also derive all Laguerre minimal surfaces that are enveloped by a family of cones. The methodology is based on the isotropic model of Laguerre geometry. In this model a Laguerre minimal surface enveloped by a family of cones corresponds to a graph of a biharmonic function carrying a family of isotropic circles. We classify such functions by showing that the top view of the family of circles is a pencil. © 2011 Springer-Verlag.

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

Directory of Open Access Journals (Sweden)

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.

Management of cluster headache

DEFF Research Database (Denmark)

Tfelt-Hansen, Peer C; Jensen, Rigmor H

2012-01-01

The prevalence of cluster headache is 0.1% and cluster headache is often not diagnosed or misdiagnosed as migraine or sinusitis. In cluster headache there is often a considerable diagnostic delay - an average of 7 years in a population-based survey. Cluster headache is characterized by very severe...... or severe orbital or periorbital pain with a duration of 15-180 minutes. The cluster headache attacks are accompanied by characteristic associated unilateral symptoms such as tearing, nasal congestion and/or rhinorrhoea, eyelid oedema, miosis and/or ptosis. In addition, there is a sense of restlessness...... and agitation. Patients may have up to eight attacks per day. Episodic cluster headache (ECH) occurs in clusters of weeks to months duration, whereas chronic cluster headache (CCH) attacks occur for more than 1 year without remissions. Management of cluster headache is divided into acute attack treatment...

Global Analysis of Minimal Surfaces

CERN Document Server

Dierkes, Ulrich; Tromba, Anthony J

2010-01-01

Many properties of minimal surfaces are of a global nature, and this is already true for the results treated in the first two volumes of the treatise. Part I of the present book can be viewed as an extension of these results. For instance, the first two chapters deal with existence, regularity and uniqueness theorems for minimal surfaces with partially free boundaries. Here one of the main features is the possibility of 'edge-crawling' along free parts of the boundary. The third chapter deals with a priori estimates for minimal surfaces in higher dimensions and for minimizers of singular integ

Minimal Surfaces for Hitchin Representations

DEFF Research Database (Denmark)

Li, Qiongling; Dai, Song

2018-01-01

. In this paper, we investigate the properties of immersed minimal surfaces inside symmetric space associated to a subloci of Hitchin component: $q_n$ and $q_{n-1}$ case. First, we show that the pullback metric of the minimal surface dominates a constant multiple of the hyperbolic metric in the same conformal...... class and has a strong rigidity property. Secondly, we show that the immersed minimal surface is never tangential to any flat inside the symmetric space. As a direct corollary, the pullback metric of the minimal surface is always strictly negatively curved. In the end, we find a fully decoupled system...

Cluster Dynamics: Laying the Foundation for Tailoring the Design of Cluster ASSE

Science.gov (United States)

2016-02-25

AFRL-AFOSR-VA-TR-2016-0081 CLUSTER DYNAMICS: LAYING THE FOUNDATION FOR TAILORING THE DESIGN OF CLUSTER ASSE Albert Castleman PENNSYLVANIA STATE...15-10-2015 4. TITLE AND SUBTITLE CLUSTER DYNAMICS: LAYING THE FOUNDATION FOR TAILORING THE DESIGN OF CLUSTER ASSEMBLED NANOSCALE MATERIALS 5a... clusters as the building blocks of new materials with tailored properties that are beneficial to the AFOSR. Our continuing program is composed of two

Minimal Webs in Riemannian Manifolds

DEFF Research Database (Denmark)

Markvorsen, Steen

2008-01-01

For a given combinatorial graph $G$ a {\\it geometrization} $(G, g)$ of the graph is obtained by considering each edge of the graph as a $1-$dimensional manifold with an associated metric $g$. In this paper we are concerned with {\\it minimal isometric immersions} of geometrized graphs $(G, g......)$ into Riemannian manifolds $(N^{n}, h)$. Such immersions we call {\\em{minimal webs}}. They admit a natural 'geometric' extension of the intrinsic combinatorial discrete Laplacian. The geometric Laplacian on minimal webs enjoys standard properties such as the maximum principle and the divergence theorems, which...... are of instrumental importance for the applications. We apply these properties to show that minimal webs in ambient Riemannian spaces share several analytic and geometric properties with their smooth (minimal submanifold) counterparts in such spaces. In particular we use appropriate versions of the divergence...

Waste minimization handbook, Volume 1

Energy Technology Data Exchange (ETDEWEB)

Boing, L.E.; Coffey, M.J.

1995-12-01

This technical guide presents various methods used by industry to minimize low-level radioactive waste (LLW) generated during decommissioning and decontamination (D and D) activities. Such activities generate significant amounts of LLW during their operations. Waste minimization refers to any measure, procedure, or technique that reduces the amount of waste generated during a specific operation or project. Preventive waste minimization techniques implemented when a project is initiated can significantly reduce waste. Techniques implemented during decontamination activities reduce the cost of decommissioning. The application of waste minimization techniques is not limited to D and D activities; it is also useful during any phase of a facility`s life cycle. This compendium will be supplemented with a second volume of abstracts of hundreds of papers related to minimizing low-level nuclear waste. This second volume is expected to be released in late 1996.

Waste minimization handbook, Volume 1

International Nuclear Information System (INIS)

Boing, L.E.; Coffey, M.J.

1995-12-01

This technical guide presents various methods used by industry to minimize low-level radioactive waste (LLW) generated during decommissioning and decontamination (D and D) activities. Such activities generate significant amounts of LLW during their operations. Waste minimization refers to any measure, procedure, or technique that reduces the amount of waste generated during a specific operation or project. Preventive waste minimization techniques implemented when a project is initiated can significantly reduce waste. Techniques implemented during decontamination activities reduce the cost of decommissioning. The application of waste minimization techniques is not limited to D and D activities; it is also useful during any phase of a facility's life cycle. This compendium will be supplemented with a second volume of abstracts of hundreds of papers related to minimizing low-level nuclear waste. This second volume is expected to be released in late 1996

Cluster cosmological analysis with X ray instrumental observables: introduction and testing of AsPIX method

International Nuclear Information System (INIS)

Valotti, Andrea

2016-01-01

Cosmology is one of the fundamental pillars of astrophysics, as such it contains many unsolved puzzles. To investigate some of those puzzles, we analyze X-ray surveys of galaxy clusters. These surveys are possible thanks to the bremsstrahlung emission of the intra-cluster medium. The simultaneous fit of cluster counts as a function of mass and distance provides an independent measure of cosmological parameters such as Ω m , σ s , and the dark energy equation of state w0. A novel approach to cosmological analysis using galaxy cluster data, called top-down, was developed in N. Clerc et al. (2012). This top-down approach is based purely on instrumental observables that are considered in a two-dimensional X-ray color-magnitude diagram. The method self-consistently includes selection effects and scaling relationships. It also provides a means of bypassing the computation of individual cluster masses. My work presents an extension of the top-down method by introducing the apparent size of the cluster, creating a three-dimensional X-ray cluster diagram. The size of a cluster is sensitive to both the cluster mass and its angular diameter, so it must also be included in the assessment of selection effects. The performance of this new method is investigated using a Fisher analysis. In parallel, I have studied the effects of the intrinsic scatter in the cluster size scaling relation on the sample selection as well as on the obtained cosmological parameters. To validate the method, I estimate uncertainties of cosmological parameters with MCMC method Amoeba minimization routine and using two simulated XMM surveys that have an increasing level of complexity. The first simulated survey is a set of toy catalogues of 100 and 10000 deg 2 , whereas the second is a 1000 deg 2 catalogue that was generated using an Aardvark semi-analytical N-body simulation. This comparison corroborates the conclusions of the Fisher analysis. In conclusion, I find that a cluster diagram that accounts

Determining characteristic principal clusters in the “cluster-plus-glue-atom” model

International Nuclear Information System (INIS)

Du, Jinglian; Wen, Bin; 2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" data-affiliation=" (M2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" >Melnik, Roderick; Kawazoe, Yoshiyuki

2014-01-01

The “cluster-plus-glue-atom” model can easily describe the structure of complex metallic alloy phases. However, the biggest obstacle limiting the application of this model is that it is difficult to determine the characteristic principal cluster. In the case when interatomic force constants (IFCs) inside the cluster lead to stronger interaction than the interaction between the clusters, a new rule for determining the characteristic principal cluster in the “cluster-plus-glue-atom” model has been proposed on the basis of IFCs. To verify this new rule, the alloy phases in Cu–Zr and Al–Ni–Zr systems have been tested, and our results indicate that the present new rule for determining characteristic principal clusters is effective and reliable

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.

T-cell triggering thresholds are modulated by the number of antigen within individual T-cell receptor clusters

Energy Technology Data Exchange (ETDEWEB)

Manz, Boryana N. [Howard Hughes Medical Inst., Chevy Chase, MD (United States); Univ. of California, Berkeley, CA (United States); Jackson, Bryan L. [Howard Hughes Medical Inst., Chevy Chase, MD (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Petit, Rebecca S. [Howard Hughes Medical Inst., Chevy Chase, MD (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Dustin, Michael L. [New York School of Medicine, New York, NY (United States); Groves, Jay [Howard Hughes Medical Inst., Chevy Chase, MD (United States); Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2011-05-31

T cells react to extremely small numbers of activating agonist peptides. Spatial organization of T-cell receptors (TCR) and their peptide-major histocompatibility complex (pMHC) ligands into microclusters is correlated with T-cell activation. In this study, we have designed an experimental strategy that enables control over the number of agonist peptides per TCR cluster, without altering the total number engaged by the cell. Supported membranes, partitioned with grids of barriers to lateral mobility, provide an effective way of limiting the total number of pMHC ligands that may be assembled within a single TCR cluster. Observations directly reveal that restriction of pMHC content within individual TCR clusters can decrease T-cell sensitivity for triggering initial calcium flux at fixed total pMHC density. Further analysis suggests that triggering thresholds are determined by the number of activating ligands available to individual TCR clusters, not by the total number encountered by the cell. Results from a series of experiments in which the overall agonist density and the maximum number of agonist per TCR cluster are independently varied in primary T cells indicate that the most probable minimal triggering unit for calcium signaling is at least four pMHC in a single cluster for this system. In conclusion, this threshold is unchanged by inclusion of coagonist pMHC, but costimulation of CD28 by CD80 can modulate the threshold lower.

Symmetries of cluster configurations

International Nuclear Information System (INIS)

Kramer, P.

1975-01-01

A deeper understanding of clustering phenomena in nuclei must encompass at least two interrelated aspects of the subject: (A) Given a system of A nucleons with two-body interactions, what are the relevant and persistent modes of clustering involved. What is the nature of the correlated nucleon groups which form the clusters, and what is their mutual interaction. (B) Given the cluster modes and their interaction, what systematic patterns of nuclear structure and reactions emerge from it. Are there, for example, families of states which share the same ''cluster parents''. Which cluster modes are compatible or exclude each other. What quantum numbers could characterize cluster configurations. There is no doubt that we can learn a good deal from the experimentalists who have discovered many of the features relevant to aspect (B). Symmetries specific to cluster configurations which can throw some light on both aspects of clustering are discussed

Open source clustering software.

Science.gov (United States)

de Hoon, M J L; Imoto, S; Nolan, J; Miyano, S

2004-06-12

We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code. The complete source code is available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster. Alternatively, Algorithm::Cluster can be downloaded from CPAN, while Pycluster is also available as part of the Biopython distribution.

Electronic structure and properties of designer clusters and cluster-assemblies

International Nuclear Information System (INIS)

Khanna, S.N.; Jena, P.

1995-01-01

Using self-consistent calculations based on density functional theory, we demonstrate that electronic shell filling and close atomic packing criteria can be used to design ultra-stable clusters. Interaction of these clusters with each other and with gas atoms is found to be weak confirming their chemical inertness. A crystal composed of these inert clusters is expected to have electronic properties that are markedly different from crystals where atoms are the building blocks. The recent observation of ferromagnetism in potassium clusters assembled in zeolite cages is discussed. (orig.)

Cluster Headache

Science.gov (United States)

... a role. Unlike migraine and tension headache, cluster headache generally isn't associated with triggers, such as foods, hormonal changes or stress. Once a cluster period begins, however, drinking alcohol ...

Performance Evaluation of Spectral Clustering Algorithm using Various Clustering Validity Indices

OpenAIRE

M. T. Somashekara; D. Manjunatha

2014-01-01

In spite of the popularity of spectral clustering algorithm, the evaluation procedures are still in developmental stage. In this article, we have taken benchmarking IRIS dataset for performing comparative study of twelve indices for evaluating spectral clustering algorithm. The results of the spectral clustering technique were also compared with k-mean algorithm. The validity of the indices was also verified with accuracy and (Normalized Mutual Information) NMI score. Spectral clustering algo...

Self-selection in size and structure in argon clusters formed on amorphous carbon

Energy Technology Data Exchange (ETDEWEB)

Krainyukova, Nina V.; Waal, Benjamin W. van de

2004-07-01

Argon clusters formed on an amorphous carbon substrate as deposited from the vapor phase were studied by means of transmission high energy electron diffraction using the liquid helium cryostat. Electron diffractograms were analysed on the basis of assumption that there exist a cluster size distribution in samples formed on substrate and multi-shell structures such as icosahedra, decahedra, fcc and hcp were probed for different sizes up to {approx}15 000 atoms. The experimental data were considered as a result of a superposition of diffracted intensities from clusters of different sizes and structures. The comparative analysis was based on the R-factor minimization that was found to be equal to 0.014 for the best fit between experiment and modelling. The total size and structure distribution function shows the presence of 'non-crystallographic' structures such as icosahedra and decahedra with five-fold symmetry that was found to prevail and a smaller amount of fcc and hcp structures. Possible growth mechanisms as well as observed general tendency to self-selection in sizes and structures are presumably governed by confined pore-like geometry in an amorphous carbon substrate.

Sparse subspace clustering for data with missing entries and high-rank matrix completion.

Science.gov (United States)

Fan, Jicong; Chow, Tommy W S

2017-09-01

Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Substructure in clusters of galaxies

International Nuclear Information System (INIS)

Fitchett, M.J.

1988-01-01

Optical observations suggesting the existence of substructure in clusters of galaxies are examined. Models of cluster formation and methods used to detect substructure in clusters are reviewed. Consideration is given to classification schemes based on a departure of bright cluster galaxies from a spherically symmetric distribution, evidence for statistically significant substructure, and various types of substructure, including velocity, spatial, and spatial-velocity substructure. The substructure observed in the galaxy distribution in clusters is discussed, focusing on observations from general cluster samples, the Virgo cluster, the Hydra cluster, Centaurus, the Coma cluster, and the Cancer cluster. 88 refs

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

Directory of Open Access Journals (Sweden)

Musaab Riyadh

2017-01-01

Full Text Available The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

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.

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.

The correlation functions for the clustering of galaxies and Abell clusters

International Nuclear Information System (INIS)

Jones, B.J.T.; Jones, J.E.; Copenhagen Univ.

1985-01-01

The difference in amplitudes between the galaxy-galaxy correlation function and the correlation function between Abell clusters is a consequence of two facts. Firstly, most Abell clusters with z<0.08 lie in a relatively small volume of the sampled space, and secondly, the fraction of galaxies lying in Abell clusters differs considerably inside and outside of this volume. (The Abell clusters are confined to a smaller volume of space than are the galaxies.) We discuss the implications of this interpretation of the clustering correlation functions and present a simple model showing how such a situation may arise quite naturally in standard theories for galaxy formation. (orig.)

Protein Charge and Mass Contribute to the Spatio-temporal Dynamics of Protein-Protein Interactions in a Minimal Proteome

Science.gov (United States)

Xu, Yu; Wang, Hong; Nussinov, Ruth; Ma, Buyong

2013-01-01

We constructed and simulated a ‘minimal proteome’ model using Langevin dynamics. It contains 206 essential protein types which were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins which tend to have larger sizes can provide large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that ‘proper’ populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of E. Coli may have a larger protein-protein interaction network than that based on the lower organism M. pneumoniae. PMID:23420643

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).

Cosmological constraints with clustering-based redshifts

Science.gov (United States)

Kovetz, Ely D.; Raccanelli, Alvise; Rahman, Mubdi

2017-07-01

We demonstrate that observations lacking reliable redshift information, such as photometric and radio continuum surveys, can produce robust measurements of cosmological parameters when empowered by clustering-based redshift estimation. This method infers the redshift distribution based on the spatial clustering of sources, using cross-correlation with a reference data set with known redshifts. Applying this method to the existing Sloan Digital Sky Survey (SDSS) photometric galaxies, and projecting to future radio continuum surveys, we show that sources can be efficiently divided into several redshift bins, increasing their ability to constrain cosmological parameters. We forecast constraints on the dark-energy equation of state and on local non-Gaussianity parameters. We explore several pertinent issues, including the trade-off between including more sources and minimizing the overlap between bins, the shot-noise limitations on binning and the predicted performance of the method at high redshifts, and most importantly pay special attention to possible degeneracies with the galaxy bias. Remarkably, we find that once this technique is implemented, constraints on dynamical dark energy from the SDSS imaging catalogue can be competitive with, or better than, those from the spectroscopic BOSS survey and even future planned experiments. Further, constraints on primordial non-Gaussianity from future large-sky radio-continuum surveys can outperform those from the Planck cosmic microwave background experiment and rival those from future spectroscopic galaxy surveys. The application of this method thus holds tremendous promise for cosmology.

Globular clusters and galaxy halos

International Nuclear Information System (INIS)

Van Den Bergh, S.

1984-01-01

Using semipartial correlation coefficients and bootstrap techniques, a study is made of the important features of globular clusters with respect to the total number of galaxy clusters and dependence of specific galaxy cluster on parent galaxy type, cluster radii, luminosity functions and cluster ellipticity. It is shown that the ellipticity of LMC clusters correlates significantly with cluster luminosity functions, but not with cluster age. The cluter luminosity value above which globulars are noticeably flattened may differ by a factor of about 100 from galaxy to galaxy. Both in the Galaxy and in M31 globulars with small core radii have a Gaussian distribution over luminosity, whereas clusters with large core radii do not. In the cluster systems surrounding the Galaxy, M31 and NGC 5128 the mean radii of globular clusters was found to increase with the distance from the nucleus. Central galaxies in rich clusters have much higher values for specific globular cluster frequency than do other cluster ellipticals, suggesting that such central galaxies must already have been different from normal ellipticals at the time they were formed

Ruled Laguerre minimal surfaces

KAUST Repository

Skopenkov, Mikhail; Pottmann, Helmut; Grohs, Philipp

2011-01-01

A Laguerre minimal surface is an immersed surface in ℝ 3 being an extremal of the functional ∫ (H 2/K-1)dA. In the present paper, we prove that the only ruled Laguerre minimal surfaces are up to isometry the surfaces ℝ (φλ) = (Aφ, Bφ, Cφ + D cos 2φ

Isotopic clusters

International Nuclear Information System (INIS)

Geraedts, J.M.P.

1983-01-01

Spectra of isotopically mixed clusters (dimers of SF 6 ) are calculated as well as transition frequencies. The result leads to speculations about the suitability of the laser-cluster fragmentation process for isotope separation. (Auth.)

Simulating The Dynamical Evolution Of Galaxies In Group And Cluster Environments

Science.gov (United States)

Vijayaraghavan, Rukmani

2015-07-01

Galaxy clusters are harsh environments for their constituent galaxies. A variety of physical processes effective in these dense environments transform gas-rich, spiral, star-forming galaxies to elliptical or spheroidal galaxies with very little gas and therefore minimal star formation. The consequences of these processes are well understood observationally. Galaxies in progressively denser environments have systematically declining star formation rates and gas content. However, a theoretical understanding of of where, when, and how these processes act, and the interplay between the various galaxy transformation mechanisms in clusters remains elusive. In this dissertation, I use numerical simulations of cluster mergers as well as galaxies evolving in quiescent environments to develop a theoretical framework to understand some of the physics of galaxy transformation in cluster environments. Galaxies can be transformed in smaller groups before they are accreted by their eventual massive cluster environments, an effect termed `pre-processing'. Galaxy cluster mergers themselves can accelerate many galaxy transformation mechanisms, including tidal and ram pressure stripping of galaxies and galaxy-galaxy collisions and mergers that result in reassemblies of galaxies' stars and gas. Observationally, cluster mergers have distinct velocity and phase-space signatures depending on the observer's line of sight with respect to the merger direction. Using dark matter only as well as hydrodynamic simulations of cluster mergers with random ensembles of particles tagged with galaxy models, I quantify the effects of cluster mergers on galaxy evolution before, during, and after the mergers. Based on my theoretical predictions of the dynamical signatures of these mergers in combination with galaxy transformation signatures, one can observationally identify remnants of mergers and quantify the effect of the environment on galaxies in dense group and cluster environments. The presence of

THE DYNAMICAL STATE OF BRIGHTEST CLUSTER GALAXIES AND THE FORMATION OF CLUSTERS

International Nuclear Information System (INIS)

Coziol, R.; Andernach, H.; Caretta, C. A.; Alamo-MartInez, K. A.; Tago, E.

2009-01-01

A large sample of Abell clusters of galaxies, selected for the likely presence of a dominant galaxy, is used to study the dynamical properties of the brightest cluster members (BCMs). From visual inspection of Digitized Sky Survey images combined with redshift information we identify 1426 candidate BCMs located in 1221 different redshift components associated with 1169 different Abell clusters. This is the largest sample published so far of such galaxies. From our own morphological classification we find that ∼92% of the BCMs in our sample are early-type galaxies and 48% are of cD type. We confirm what was previously observed based on much smaller samples, namely, that a large fraction of BCMs have significant peculiar velocities. From a subsample of 452 clusters having at least 10 measured radial velocities, we estimate a median BCM peculiar velocity of 32% of their host clusters' radial velocity dispersion. This suggests that most BCMs are not at rest in the potential well of their clusters. This phenomenon is common to galaxy clusters in our sample, and not a special trait of clusters hosting cD galaxies. We show that the peculiar velocity of the BCM is independent of cluster richness and only slightly dependent on the Bautz-Morgan type. We also find a weak trend for the peculiar velocity to rise with the cluster velocity dispersion. The strongest dependence is with the morphological type of the BCM: cD galaxies tend to have lower relative peculiar velocities than elliptical galaxies. This result points to a connection between the formation of the BCMs and that of their clusters. Our data are qualitatively consistent with the merging-groups scenario, where BCMs in clusters formed first in smaller subsystems comparable to compact groups of galaxies. In this scenario, clusters would have formed recently from the mergers of many such groups and would still be in a dynamically unrelaxed state.

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

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.

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

Science.gov (United States)

Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

Clusters of Antibiotic Resistance Genes Enriched Together Stay Together in Swine Agriculture.

Science.gov (United States)

Johnson, Timothy A; Stedtfeld, Robert D; Wang, Qiong; Cole, James R; Hashsham, Syed A; Looft, Torey; Zhu, Yong-Guan; Tiedje, James M

2016-04-12

Antibiotic resistance is a worldwide health risk, but the influence of animal agriculture on the genetic context and enrichment of individual antibiotic resistance alleles remains unclear. Using quantitative PCR followed by amplicon sequencing, we quantified and sequenced 44 genes related to antibiotic resistance, mobile genetic elements, and bacterial phylogeny in microbiomes from U.S. laboratory swine and from swine farms from three Chinese regions. We identified highly abundant resistance clusters: groups of resistance and mobile genetic element alleles that cooccur. For example, the abundance of genes conferring resistance to six classes of antibiotics together with class 1 integrase and the abundance of IS6100-type transposons in three Chinese regions are directly correlated. These resistance cluster genes likely colocalize in microbial genomes in the farms. Resistance cluster alleles were dramatically enriched (up to 1 to 10% as abundant as 16S rRNA) and indicate that multidrug-resistant bacteria are likely the norm rather than an exception in these communities. This enrichment largely occurred independently of phylogenetic composition; thus, resistance clusters are likely present in many bacterial taxa. Furthermore, resistance clusters contain resistance genes that confer resistance to antibiotics independently of their particular use on the farms. Selection for these clusters is likely due to the use of only a subset of the broad range of chemicals to which the clusters confer resistance. The scale of animal agriculture and its wastes, the enrichment and horizontal gene transfer potential of the clusters, and the vicinity of large human populations suggest that managing this resistance reservoir is important for minimizing human risk. Agricultural antibiotic use results in clusters of cooccurring resistance genes that together confer resistance to multiple antibiotics. The use of a single antibiotic could select for an entire suite of resistance genes if

Y-12 Plant waste minimization strategy

International Nuclear Information System (INIS)

Kane, M.A.

1987-01-01

The 1984 Amendments to the Resource Conservation and Recovery Act (RCRA) mandate that waste minimization be a major element of hazardous waste management. In response to this mandate and the increasing costs for waste treatment, storage, and disposal, the Oak Ridge Y-12 Plant developed a waste minimization program to encompass all types of wastes. Thus, waste minimization has become an integral part of the overall waste management program. Unlike traditional approaches, waste minimization focuses on controlling waste at the beginning of production instead of the end. This approach includes: (1) substituting nonhazardous process materials for hazardous ones, (2) recycling or reusing waste effluents, (3) segregating nonhazardous waste from hazardous and radioactive waste, and (4) modifying processes to generate less waste or less toxic waste. An effective waste minimization program must provide the appropriate incentives for generators to reduce their waste and provide the necessary support mechanisms to identify opportunities for waste minimization. This presentation focuses on the Y-12 Plant's strategy to implement a comprehensive waste minimization program. This approach consists of four major program elements: (1) promotional campaign, (2) process evaluation for waste minimization opportunities, (3) waste generation tracking system, and (4) information exchange network. The presentation also examines some of the accomplishments of the program and issues which need to be resolved

Cluster Decline and Resilience

DEFF Research Database (Denmark)

Østergaard, Christian Richter; Park, Eun Kyung

Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark, 1963......-2011. Our longitudinal study reveals that technological lock-in and exit of key firms have contributed to impairment of the cluster’s resilience in adapting to disruptions. Entrepreneurship has a positive effect on cluster resilience, while multinational companies have contradicting effects by bringing...... in new resources to the cluster but being quick to withdraw in times of crisis....

The rotation of galaxy clusters

International Nuclear Information System (INIS)

Tovmassian, H.M.

2015-01-01

The method for detection of the galaxy cluster rotation based on the study of distribution of member galaxies with velocities lower and higher of the cluster mean velocity over the cluster image is proposed. The search for rotation is made for flat clusters with a/b> 1.8 and BMI type clusters which are expected to be rotating. For comparison there were studied also round clusters and clusters of NBMI type, the second by brightness galaxy in which does not differ significantly from the cluster cD galaxy. Seventeen out of studied 65 clusters are found to be rotating. It was found that the detection rate is sufficiently high for flat clusters, over 60 per cent, and clusters of BMI type with dominant cD galaxy, ≈ 35 per cent. The obtained results show that clusters were formed from the huge primordial gas clouds and preserved the rotation of the primordial clouds, unless they did not have mergings with other clusters and groups of galaxies, in the result of which the rotation has been prevented

Agricultural Clusters in the Netherlands

NARCIS (Netherlands)

Schouten, M.A.; Heijman, W.J.M.

2012-01-01

Michael Porter was the first to use the term cluster in an economic context. He introduced the term in The Competitive Advantage of Nations (1990). The term cluster is also known as business cluster, industry cluster, competitive cluster or Porterian cluster. This article aims at determining and

Minimal open strings

International Nuclear Information System (INIS)

Hosomichi, Kazuo

2008-01-01

We study FZZT-branes and open string amplitudes in (p, q) minimal string theory. We focus on the simplest boundary changing operators in two-matrix models, and identify the corresponding operators in worldsheet theory through the comparison of amplitudes. Along the way, we find a novel linear relation among FZZT boundary states in minimal string theory. We also show that the boundary ground ring is realized on physical open string operators in a very simple manner, and discuss its use for perturbative computation of higher open string amplitudes.

Minimal Composite Inflation

DEFF Research Database (Denmark)

Channuie, Phongpichit; Jark Joergensen, Jakob; Sannino, Francesco

2011-01-01

We investigate models in which the inflaton emerges as a composite field of a four dimensional, strongly interacting and nonsupersymmetric gauge theory featuring purely fermionic matter. We show that it is possible to obtain successful inflation via non-minimal coupling to gravity, and that the u......We investigate models in which the inflaton emerges as a composite field of a four dimensional, strongly interacting and nonsupersymmetric gauge theory featuring purely fermionic matter. We show that it is possible to obtain successful inflation via non-minimal coupling to gravity...

Minimal abdominal incisions

Directory of Open Access Journals (Sweden)

João Carlos Magi

2017-04-01

Full Text Available Minimally invasive procedures aim to resolve the disease with minimal trauma to the body, resulting in a rapid return to activities and in reductions of infection, complications, costs and pain. Minimally incised laparotomy, sometimes referred to as minilaparotomy, is an example of such minimally invasive procedures. The aim of this study is to demonstrate the feasibility and utility of laparotomy with minimal incision based on the literature and exemplifying with a case. The case in question describes reconstruction of the intestinal transit with the use of this incision. Male, young, HIV-positive patient in a late postoperative of ileotiflectomy, terminal ileostomy and closing of the ascending colon by an acute perforating abdomen, due to ileocolonic tuberculosis. The barium enema showed a proximal stump of the right colon near the ileostomy. The access to the cavity was made through the orifice resulting from the release of the stoma, with a lateral-lateral ileo-colonic anastomosis with a 25 mm circular stapler and manual closure of the ileal stump. These surgeries require their own tactics, such as rigor in the lysis of adhesions, tissue traction, and hemostasis, in addition to requiring surgeon dexterity – but without the need for investments in technology; moreover, the learning curve is reported as being lower than that for videolaparoscopy. Laparotomy with minimal incision should be considered as a valid and viable option in the treatment of surgical conditions. Resumo: Procedimentos minimamente invasivos visam resolver a doença com o mínimo de trauma ao organismo, resultando em retorno rápido às atividades, reduções nas infecções, complicações, custos e na dor. A laparotomia com incisão mínima, algumas vezes referida como minilaparotomia, é um exemplo desses procedimentos minimamente invasivos. O objetivo deste trabalho é demonstrar a viabilidade e utilidade das laparotomias com incisão mínima com base na literatura e

Cluster analysis as a method for determining size ranges for spinal implants: disc lumbar replacement prosthesis dimensions from magnetic resonance images.

Science.gov (United States)

Lei, Dang; Holder, Roger L; Smith, Francis W; Wardlaw, Douglas; Hukins, David W L

2006-12-01

Statistical analysis of clinical radiologic data. To develop an objective method for finding the number of sizes for a lumbar disc replacement. Cluster analysis is a well-established technique for sorting observations into clusters so that the "similarity level" is maximal if they belong to the same cluster and minimal otherwise. Magnetic resonance scans from 69 patients, with no abnormal discs, yielded 206 sagittal and transverse images of 206 discs (levels L3-L4-L5-S1). Anteroposterior and lateral dimensions were measured from vertebral margins on transverse images; disc heights were measured from sagittal images. Hierarchical cluster analysis was performed to determine the number of clusters followed by nonhierarchical (K-means) cluster analysis. Discriminant analysis was used to determine how well the clusters could be used to classify an observation. The most successful method of clustering the data involved the following parameters: anteroposterior dimension; lateral dimension (both were the mean of results from the superior and inferior margins of a vertebral body, measured on transverse images); and maximum disc height (from a midsagittal image). These were grouped into 7 clusters so that a discriminant analysis was capable of correctly classifying 97.1% of the observations. The mean and standard deviations for the parameter values in each cluster were determined. Cluster analysis has been successfully used to find the dimensions of the minimum number of prosthesis sizes required to replace L3-L4 to L5-S1 discs; the range of sizes would enable them to be used at higher lumbar levels in some patients.

A GMBCG GALAXY CLUSTER CATALOG OF 55,424 RICH CLUSTERS FROM SDSS DR7

International Nuclear Information System (INIS)

Hao Jiangang; Annis, James; Johnston, David E.; McKay, Timothy A.; Evrard, August; Siegel, Seth R.; Gerdes, David; Koester, Benjamin P.; Rykoff, Eli S.; Rozo, Eduardo; Wechsler, Risa H.; Busha, Michael; Becker, Matthew; Sheldon, Erin

2010-01-01

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red-sequence plus brightest cluster galaxy (BCG) feature, which is unique for galaxy clusters and does not exist among field galaxies. Red-sequence clustering in color space is detected using an Error Corrected Gaussian Mixture Model. We run GMBCG on 8240 deg 2 of photometric data from SDSS DR7 to assemble the largest ever optical galaxy cluster catalog, consisting of over 55,000 rich clusters across the redshift range from 0.1 < z < 0.55. We present Monte Carlo tests of completeness and purity and perform cross-matching with X-ray clusters and with the maxBCG sample at low redshift. These tests indicate high completeness and purity across the full redshift range for clusters with 15 or more members.

A GMBCG galaxy cluster catalog of 55,880 rich clusters from SDSS DR7

Energy Technology Data Exchange (ETDEWEB)

Hao, Jiangang; McKay, Timothy A.; Koester, Benjamin P.; Rykoff, Eli S.; Rozo, Eduardo; Annis, James; Wechsler, Risa H.; Evrard, August; Siegel, Seth R.; Becker, Matthew; Busha, Michael; /Fermilab /Michigan U. /Chicago U., Astron. Astrophys. Ctr. /UC, Santa Barbara /KICP, Chicago /KIPAC, Menlo Park /SLAC /Caltech /Brookhaven

2010-08-01

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy clusters and does not exist among field galaxies. Red sequence clustering in color space is detected using an Error Corrected Gaussian Mixture Model. We run GMBCG on 8240 square degrees of photometric data from SDSS DR7 to assemble the largest ever optical galaxy cluster catalog, consisting of over 55,000 rich clusters across the redshift range from 0.1 < z < 0.55. We present Monte Carlo tests of completeness and purity and perform cross-matching with X-ray clusters and with the maxBCG sample at low redshift. These tests indicate high completeness and purity across the full redshift range for clusters with 15 or more members.

Subspace K-means clustering

NARCIS (Netherlands)

Timmerman, Marieke E.; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

2013-01-01

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

Clustering analysis

International Nuclear Information System (INIS)

Romli

1997-01-01

Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

Cluster analysis

CERN Document Server

Everitt, Brian S; Leese, Morven; Stahl, Daniel

2011-01-01

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

Cluster editing

DEFF Research Database (Denmark)

Böcker, S.; Baumbach, Jan

2013-01-01

. The problem has been the inspiration for numerous algorithms in bioinformatics, aiming at clustering entities such as genes, proteins, phenotypes, or patients. In this paper, we review exact and heuristic methods that have been proposed for the Cluster Editing problem, and also applications......The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side...

Ethical implications of excessive cluster sizes in cluster randomised trials.

Science.gov (United States)

Hemming, Karla; Taljaard, Monica; Forbes, Gordon; Eldridge, Sandra M; Weijer, Charles

2018-02-20

The cluster randomised trial (CRT) is commonly used in healthcare research. It is the gold-standard study design for evaluating healthcare policy interventions. A key characteristic of this design is that as more participants are included, in a fixed number of clusters, the increase in achievable power will level off. CRTs with cluster sizes that exceed the point of levelling-off will have excessive numbers of participants, even if they do not achieve nominal levels of power. Excessively large cluster sizes may have ethical implications due to exposing trial participants unnecessarily to the burdens of both participating in the trial and the potential risks of harm associated with the intervention. We explore these issues through the use of two case studies. Where data are routinely collected, available at minimum cost and the intervention poses low risk, the ethical implications of excessively large cluster sizes are likely to be low (case study 1). However, to maximise the social benefit of the study, identification of excessive cluster sizes can allow for prespecified and fully powered secondary analyses. In the second case study, while there is no burden through trial participation (because the outcome data are routinely collected and non-identifiable), the intervention might be considered to pose some indirect risk to patients and risks to the healthcare workers. In this case study it is therefore important that the inclusion of excessively large cluster sizes is justifiable on other grounds (perhaps to show sustainability). In any randomised controlled trial, including evaluations of health policy interventions, it is important to minimise the burdens and risks to participants. Funders, researchers and research ethics committees should be aware of the ethical issues of excessively large cluster sizes in cluster trials. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is

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

Cluster ion beam facilities

International Nuclear Information System (INIS)

Popok, V.N.; Prasalovich, S.V.; Odzhaev, V.B.; Campbell, E.E.B.

2001-01-01

A brief state-of-the-art review in the field of cluster-surface interactions is presented. Ionised cluster beams could become a powerful and versatile tool for the modification and processing of surfaces as an alternative to ion implantation and ion assisted deposition. The main effects of cluster-surface collisions and possible applications of cluster ion beams are discussed. The outlooks of the Cluster Implantation and Deposition Apparatus (CIDA) being developed in Guteborg University are shown

Altered dynamic functional connectivity in the default mode network in patients with cirrhosis and minimal hepatic encephalopathy

International Nuclear Information System (INIS)

Chen, Hua-Jun; Lin, Hai-Long; Chen, Qiu-Feng; Liu, Peng-Fei

2017-01-01

Abnormal brain intrinsic functional connectivity (FC) has been documented in minimal hepatic encephalopathy (MHE) by static connectivity analysis. However, changes in dynamic FC (dFC) remain unknown. We aimed to identify altered dFC within the default mode network (DMN) associated with MHE. Resting-state functional MRI data were acquired from 20 cirrhotic patients with MHE and 24 healthy controls. DMN seed regions were defined using seed-based FC analysis (centered on the posterior cingulate cortex (PCC)). Dynamic FC architecture was calculated using a sliding time-window method. K-means clustering (number of clusters = 2-4) was applied to estimate FC states. When the number of clusters was 2, MHE patients presented weaker connectivity strengths compared with controls in states 1 and 2. In state 1, decreased FC strength was found between the PCC/precuneus (PCUN) and right medial temporal lobe (MTL)/bilateral lateral temporal cortex (LTC); left inferior parietal lobule (IPL) and right MTL/left LTC; right IPL and right MTL/bilateral LTC; right MTL and right LTC; and medial prefrontal cortex (MPFC) and right MTL/bilateral LTC. In state 2, reduced FC strength was observed between the PCC/PCUN and bilateral MTL/bilateral LTC; left IPL and left MTL/bilateral LTC/MPFC; and left LTC and right LTC. Altered connectivities from state 1 were correlated with patient cognitive performance. Similar findings were observed when the number of clusters was set to 3 or 4. Aberrant dynamic DMN connectivity is an additional characteristic of MHE. Dynamic connectivity analysis offers a novel paradigm for understanding MHE-related mechanisms. (orig.)

Altered dynamic functional connectivity in the default mode network in patients with cirrhosis and minimal hepatic encephalopathy

Energy Technology Data Exchange (ETDEWEB)

Chen, Hua-Jun; Lin, Hai-Long [Fujian Medical University Union Hospital, Department of Radiology, Fuzhou (China); Chen, Qiu-Feng; Liu, Peng-Fei [Central South University, School of Information Science and Engineering, Changsha (China)

2017-09-15

Abnormal brain intrinsic functional connectivity (FC) has been documented in minimal hepatic encephalopathy (MHE) by static connectivity analysis. However, changes in dynamic FC (dFC) remain unknown. We aimed to identify altered dFC within the default mode network (DMN) associated with MHE. Resting-state functional MRI data were acquired from 20 cirrhotic patients with MHE and 24 healthy controls. DMN seed regions were defined using seed-based FC analysis (centered on the posterior cingulate cortex (PCC)). Dynamic FC architecture was calculated using a sliding time-window method. K-means clustering (number of clusters = 2-4) was applied to estimate FC states. When the number of clusters was 2, MHE patients presented weaker connectivity strengths compared with controls in states 1 and 2. In state 1, decreased FC strength was found between the PCC/precuneus (PCUN) and right medial temporal lobe (MTL)/bilateral lateral temporal cortex (LTC); left inferior parietal lobule (IPL) and right MTL/left LTC; right IPL and right MTL/bilateral LTC; right MTL and right LTC; and medial prefrontal cortex (MPFC) and right MTL/bilateral LTC. In state 2, reduced FC strength was observed between the PCC/PCUN and bilateral MTL/bilateral LTC; left IPL and left MTL/bilateral LTC/MPFC; and left LTC and right LTC. Altered connectivities from state 1 were correlated with patient cognitive performance. Similar findings were observed when the number of clusters was set to 3 or 4. Aberrant dynamic DMN connectivity is an additional characteristic of MHE. Dynamic connectivity analysis offers a novel paradigm for understanding MHE-related mechanisms. (orig.)

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

What Makes Clusters Decline?

DEFF Research Database (Denmark)

Østergaard, Christian Richter; Park, Eun Kyung

2015-01-01

Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark. The longit...... but being quick to withdraw in times of crisis....

Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

Directory of Open Access Journals (Sweden)

Jocelyn H Bolin

2014-04-01

Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

Science.gov (United States)

Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Defining functioning levels in patients with schizophrenia: A combination of a novel clustering method and brain SPECT analysis.

Science.gov (United States)

Catherine, Faget-Agius; Aurélie, Vincenti; Eric, Guedj; Pierre, Michel; Raphaëlle, Richieri; Marine, Alessandrini; Pascal, Auquier; Christophe, Lançon; Laurent, Boyer

2017-12-30

This study aims to define functioning levels of patients with schizophrenia by using a method of interpretable clustering based on a specific functioning scale, the Functional Remission Of General Schizophrenia (FROGS) scale, and to test their validity regarding clinical and neuroimaging characterization. In this observational study, patients with schizophrenia have been classified using a hierarchical top-down method called clustering using unsupervised binary trees (CUBT). Socio-demographic, clinical, and neuroimaging SPECT perfusion data were compared between the different clusters to ensure their clinical relevance. A total of 242 patients were analyzed. A four-group functioning level structure has been identified: 54 are classified as "minimal", 81 as "low", 64 as "moderate", and 43 as "high". The clustering shows satisfactory statistical properties, including reproducibility and discriminancy. The 4 clusters consistently differentiate patients. "High" functioning level patients reported significantly the lowest scores on the PANSS and the CDSS, and the highest scores on the GAF, the MARS and S-QoL 18. Functioning levels were significantly associated with cerebral perfusion of two relevant areas: the left inferior parietal cortex and the anterior cingulate. Our study provides relevant functioning levels in schizophrenia, and may enhance the use of functioning scale. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

The HectoMAP Cluster Survey. I. redMaPPer Clusters

Science.gov (United States)

Sohn, Jubee; Geller, Margaret J.; Rines, Kenneth J.; Hwang, Ho Seong; Utsumi, Yousuke; Diaferio, Antonaldo

2018-04-01

We use the dense HectoMAP redshift survey to explore the properties of 104 redMaPPer cluster candidates. The redMaPPer systems in HectoMAP cover the full range of richness and redshift (0.08 systems included in the Subaru/Hyper Suprime-Cam public data release are bona fide clusters. The median number of spectroscopic members per cluster is ∼20. We include redshifts of 3547 member candidates listed in the redMaPPer catalog whether they are cluster members or not. We evaluate the redMaPPer membership probability spectroscopically. The purity (number of real systems) in redMaPPer exceeds 90% even at the lowest richness. Three massive galaxy clusters (M ∼ 2 × 1013 M ⊙) associated with X-ray emission in the HectoMAP region are not included in the public redMaPPer catalog with λ rich > 20, because they lie outside the cuts for this catalog.

Herd Clustering: A synergistic data clustering approach using collective intelligence

KAUST Repository

Wong, Kachun

2014-10-01

Traditional data mining methods emphasize on analytical abilities to decipher data, assuming that data are static during a mining process. We challenge this assumption, arguing that we can improve the analysis by vitalizing data. In this paper, this principle is used to develop a new clustering algorithm. Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances are represented by moving particles. Particles attract each other locally and form clusters by themselves as shown in the case studies reported. To demonstrate its effectiveness, the performance of HC is compared to other state-of-the art clustering methods on more than thirty datasets using four performance metrics. An application for DNA motif discovery is also conducted. The results support the effectiveness of HC and thus the underlying philosophy. © 2014 Elsevier B.V.

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

Innovation performance and clusters: a dynamic capability perspective on regional technology clusters

OpenAIRE

Röttmer, Nicole

2009-01-01

This research provides a novel, empirically tested, actionable theory of cluster innovativeness. Cluster innovativeness has for long been subject of research and resulting policy efforts. The cluster's endowment with assets, such as specialized labor, firms, research institutes, existing regional networks and a specific culture are, among others, recognized as sources of innovativeness. While the asset structure of clusters as been subject to a variety of research efforts, the evidence on the...

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...

Elucidation of the Pattern of the Onset of Male Lower Urinary Tract Symptoms Using Cluster Analysis: Efficacy of Tamsulosin in Each Symptom Group.

Science.gov (United States)

Aikawa, Ken; Kataoka, Masao; Ogawa, Soichiro; Akaihata, Hidenori; Sato, Yuichi; Yabe, Michihiro; Hata, Junya; Koguchi, Tomoyuki; Kojima, Yoshiyuki; Shiragasawa, Chihaya; Kobayashi, Toshimitsu; Yamaguchi, Osamu

2015-08-01

To present a new grouping of male patients with lower urinary tract symptoms (LUTS) based on symptom patterns and clarify whether the therapeutic effect of α1-blocker differs among the groups. We performed secondary analysis of anonymous data from 4815 patients enrolled in a postmarketing surveillance study of tamsulosin in Japan. Data on 7 International Prostate Symptom Score (IPSS) items at the initial visit were used in the cluster analysis. IPSS and quality of life (QOL) scores before and after tamsulosin treatment for 12 weeks were assessed in each cluster. Partial correlation coefficients were also obtained for IPSS and QOL scores based on changes before and after treatment. Five symptom groups were identified by cluster analysis of IPSS. On their symptom profile, each cluster was labeled as minimal type (cluster 1), multiple severe type (cluster 2), weak stream type (cluster 3), storage type (cluster 4), and voiding type (cluster 5). Prevalence and the mean symptom score were significantly improved in almost all symptoms in all clusters by tamsulosin treatment. Nocturia and weak stream had the strongest effect on QOL in clusters 1, 2, and 4 and clusters 3 and 5, respectively. The study clarified that 5 characteristic symptom patterns exist by cluster analysis of IPSS in male patients with LUTS. Tamsulosin improved various symptoms and QOL in each symptom group. The study reports many male patients with LUTS being satisfied with monotherapy using tamsulosin and suggests the usefulness of α1-blockers as a drug of first choice. Copyright © 2015 Elsevier Inc. All rights reserved.

Photochemistry in rare gas clusters

International Nuclear Information System (INIS)

Moeller, T.; Haeften, K. von; Pietrowski, R. von

1999-01-01

In this contribution photochemical processes in pure rare gas clusters will be discussed. The relaxation dynamics of electronically excited He clusters is investigated with luminescence spectroscopy. After electronic excitation of He clusters many sharp lines are observed in the visible and infrared spectral range which can be attributed to He atoms and molecules desorbing from the cluster. It turns out that the desorption of electronically excited He atoms and molecules is an important decay channel. The findings for He clusters are compared with results for Ar clusters. While desorption of electronically excited He atoms is observed for all clusters containing up to several thousand atoms a corresponding process in Ar clusters is only observed for very small clusters (N<10). (orig.)

Photochemistry in rare gas clusters

Energy Technology Data Exchange (ETDEWEB)

Moeller, T.; Haeften, K. von; Pietrowski, R. von [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany). Hamburger Synchrotronstrahlungslabor; Laarman, T. [Universitaet Hamburg, II. Institut fuer Experimentalphysik, Luruper Chaussee 149, D-22761 Hamburg (Germany)

1999-12-01

In this contribution photochemical processes in pure rare gas clusters will be discussed. The relaxation dynamics of electronically excited He clusters is investigated with luminescence spectroscopy. After electronic excitation of He clusters many sharp lines are observed in the visible and infrared spectral range which can be attributed to He atoms and molecules desorbing from the cluster. It turns out that the desorption of electronically excited He atoms and molecules is an important decay channel. The findings for He clusters are compared with results for Ar clusters. While desorption of electronically excited He atoms is observed for all clusters containing up to several thousand atoms a corresponding process in Ar clusters is only observed for very small clusters (N<10). (orig.)

Stochastic coupled cluster theory: Efficient sampling of the coupled cluster expansion

Science.gov (United States)

Scott, Charles J. C.; Thom, Alex J. W.

2017-09-01

We consider the sampling of the coupled cluster expansion within stochastic coupled cluster theory. Observing the limitations of previous approaches due to the inherently non-linear behavior of a coupled cluster wavefunction representation, we propose new approaches based on an intuitive, well-defined condition for sampling weights and on sampling the expansion in cluster operators of different excitation levels. We term these modifications even and truncated selections, respectively. Utilising both approaches demonstrates dramatically improved calculation stability as well as reduced computational and memory costs. These modifications are particularly effective at higher truncation levels owing to the large number of terms within the cluster expansion that can be neglected, as demonstrated by the reduction of the number of terms to be sampled when truncating at triple excitations by 77% and hextuple excitations by 98%.

Cluster analysis for applications

CERN Document Server

Anderberg, Michael R

1973-01-01

Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

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.

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...

Low-energy electron collisions with metal clusters: Electron capture and cluster fragmentation

International Nuclear Information System (INIS)

Kresin, V.V.; Scheidemann, A.; Knight, W.D.

1993-01-01

The authors have carried out the first measurement of absolute cross sections for the interaction between electrons and size-resolved free metal clusters. Integral inelastic scattering cross sections have been determined for electron-Na n cluster collisions in the energy range from 0.1 eV to 30 eV. At energies ≤1 eV, cross sections increase with decreasing impact energies, while at higher energies they remain essentially constant. The dominant processes are electron attachment in the low-energy range, and collision-induced fragmentation at higher energies. The magnitude of electron capture cross sections can be quantitatively explained by the effect of the strong polarization field induced in the cluster by the incident electron. The cross sections are very large, reaching values of hundreds of angstrom 2 ; this is due to the highly polarizable nature of metal clusters. The inelastic interaction range for fragmentation collisions is also found to considerably exceed the cluster radius, again reflecting the long-range character of electron-cluster interactions. The important role played by the polarization interaction represents a bridge between the study of collision processes and the extensive research on cluster response properties. Furthermore, insight into the mechanisms of electron scattering is important for understanding production and detection of cluster ions in mass spectrometry and related processes

THE ASSEMBLY OF GALAXY CLUSTERS

International Nuclear Information System (INIS)

Berrier, Joel C.; Stewart, Kyle R.; Bullock, James S.; Purcell, Chris W.; Barton, Elizabeth J.; Wechsler, Risa H.

2009-01-01

We study the formation of 53 galaxy cluster-size dark matter halos (M = 10 14.0-14.76 M sun ) formed within a pair of cosmological Λ cold dark matter N-body simulations, and track the accretion histories of cluster subhalos with masses large enough to host ∼0.3 L * galaxies. By associating subhalos with cluster galaxies, we find the majority of galaxies in clusters experience no 'preprocessing' in the group environment prior to their accretion into the cluster. On average, 70% of cluster galaxies fall into the cluster potential directly from the field, with no luminous companions in their host halos at the time of accretion; less than 12% are accreted as members of groups with five or more galaxies. Moreover, we find that cluster galaxies are significantly less likely to have experienced a merger in the recent past (∼<6 Gyr) than a field halo of the same mass. These results suggest that local cluster processes such as ram pressure stripping, galaxy harassment, or strangulation play the dominant role in explaining the difference between cluster and field populations at a fixed stellar mass, and that pre-evolution or past merging in the group environment is of secondary importance for setting cluster galaxy properties for most clusters. The accretion times for z = 0 cluster members are quite extended, with ∼20% incorporated into the cluster halo more than 7 Gyr ago and ∼20% within the last 2 Gyr. By comparing the observed morphological fractions in cluster and field populations, we estimate an approximate timescale for late-type to early-type transformation within the cluster environment to be ∼6 Gyr.

Globular Clusters - Guides to Galaxies

CERN Document Server

Richtler, Tom; Joint ESO-FONDAP Workshop on Globular Clusters

2009-01-01

The principal question of whether and how globular clusters can contribute to a better understanding of galaxy formation and evolution is perhaps the main driving force behind the overall endeavour of studying globular cluster systems. Naturally, this splits up into many individual problems. The objective of the Joint ESO-FONDAP Workshop on Globular Clusters - Guides to Galaxies was to bring together researchers, both observational and theoretical, to present and discuss the most recent results. Topics covered in these proceedings are: internal dynamics of globular clusters and interaction with host galaxies (tidal tails, evolution of cluster masses), accretion of globular clusters, detailed descriptions of nearby cluster systems, ultracompact dwarfs, formations of massive clusters in mergers and elsewhere, the ACS Virgo survey, galaxy formation and globular clusters, dynamics and kinematics of globular cluster systems and dark matter-related problems. With its wide coverage of the topic, this book constitute...

Innovation performance and clusters : a dynamic capability perspective on regional technology clusters

NARCIS (Netherlands)

Röttmer, Nicole

2009-01-01

This research provides a novel, empirically tested, actionable theory of cluster innovativeness. Cluster innovativeness has for long been subject of research and resulting policy efforts. The cluster's endowment with assets, such as specialized labor, firms, research institutes, existing regional

Minimal Flavour Violation and Beyond

CERN Document Server

Isidori, Gino

2012-01-01

We review the formulation of the Minimal Flavour Violation (MFV) hypothesis in the quark sector, as well as some "variations on a theme" based on smaller flavour symmetry groups and/or less minimal breaking terms. We also review how these hypotheses can be tested in B decays and by means of other flavour-physics observables. The phenomenological consequences of MFV are discussed both in general terms, employing a general effective theory approach, and in the specific context of the Minimal Supersymmetric extension of the SM.

Mixed waste and waste minimization: The effect of regulations and waste minimization on the laboratory

International Nuclear Information System (INIS)

Dagan, E.B.; Selby, K.B.

1993-08-01

The Hanford Site is located in the State of Washington and is subject to state and federal environmental regulations that hamper waste minimization efforts. This paper addresses the negative effect of these regulations on waste minimization and mixed waste issues related to the Hanford Site. Also, issues are addressed concerning the regulations becoming more lenient. In addition to field operations, the Hanford Site is home to the Pacific Northwest Laboratory which has many ongoing waste minimization activities of particular interest to laboratories

The Innovation Clusters in the Developments by the Scandinavian School of Cluster Theory

Directory of Open Access Journals (Sweden)

Onipko Tetiana A.

2017-08-01

Full Text Available The article generalizes and analyzes the developments by the Scandinavian School of cluster theory (scientists from Sweden, Norway and Denmark on innovative clusters. It has been found that the Scandinavian scientists considered innovative clusters as an integral component of both the regional and the national innovation systems. It has been clarified that the efficiency of an innovative cluster depends largely on the «knowledge base». It was emphasized that innovative clusters, by facilitating interactive training and generating new ideas, stimulate the development of the «economy of training». It has been determined that the coordinating structures of innovative clusters are the institutions of cooperation that facilitate interaction between enterprises, scientific centres, and authorities. It has been specified that innovative clusters contribute to the emerging of benefits for participants, including the growing opportunities for innovation, improved conditions for establishing a business, and increased productivity. It has been concluded that the development of the inner environment of an innovative cluster depends largely on its relationships to the external environment.

Clusters of atoms and molecules theory, experiment, and clusters of atoms

CERN Document Server

1994-01-01

Clusters of Atoms and Molecules is devoted to theoretical concepts and experimental techniques important in the rapidly expanding field of cluster science. Cluster properties are dicussed for clusteres composed of alkali metals, semiconductors, transition metals, carbon, oxides and halides of alkali metals, rare gases, and neutral molecules. The book is composed of several well-integrated treatments all prepared by experts. Each contribution starts out as simple as possible and ends with the latest results so that the book can serve as a text for a course, an introduction into the field, or as a reference book for the expert.

Trimming and clustering sugarcane ESTs

Directory of Open Access Journals (Sweden)

Guilherme P. Telles

2001-12-01

Full Text Available The original clustering procedure adopted in the Sugarcane Expressed Sequence Tag project (SUCEST had many problems, for instance too many clusters, the presence of ribosomal sequences, etc. We therefore redesigned the clustering procedure entirely, including a much more careful initial trimming of the reads. In this paper the new trimming and clustering strategies are described in detail and we give the new official figures for the project, 237,954 expressed sequence tags and 43,141 clusters.O método de clustering adotado no Projeto SUCEST (Sugarcane EST Project tinha vários problemas (muitos clusters, presença de seqüências de ribossomo etc. Nós assumimos a tarefa de reprojetar todo o processo de clustering, propondo uma "limpeza" inicial mais cuidadosa das seqüências. Neste artigo as estratégias de limpeza das seqüências e de clustering são descritas em detalhe, incluindo os números oficiais do projeto (237,954 ESTs e 43,141 clusters.

Interpretation of custom designed Illumina genotype cluster plots for targeted association studies and next-generation sequence validation

Directory of Open Access Journals (Sweden)

Tindall Elizabeth A

2010-02-01

Full Text Available Abstract Background High-throughput custom designed genotyping arrays are a valuable resource for biologically focused research studies and increasingly for validation of variation predicted by next-generation sequencing (NGS technologies. We investigate the Illumina GoldenGate chemistry using custom designed VeraCode and sentrix array matrix (SAM assays for each of these applications, respectively. We highlight applications for interpretation of Illumina generated genotype cluster plots to maximise data inclusion and reduce genotyping errors. Findings We illustrate the dramatic effect of outliers in genotype calling and data interpretation, as well as suggest simple means to avoid genotyping errors. Furthermore we present this platform as a successful method for two-cluster rare or non-autosomal variant calling. The success of high-throughput technologies to accurately call rare variants will become an essential feature for future association studies. Finally, we highlight additional advantages of the Illumina GoldenGate chemistry in generating unusually segregated cluster plots that identify potential NGS generated sequencing error resulting from minimal coverage. Conclusions We demonstrate the importance of visually inspecting genotype cluster plots generated by the Illumina software and issue warnings regarding commonly accepted quality control parameters. In addition to suggesting applications to minimise data exclusion, we propose that the Illumina cluster plots may be helpful in identifying potential in-put sequence errors, particularly important for studies to validate NGS generated variation.

In vivo UVB irradiation induces clustering of Fas (CD95) on human epidermal cells

DEFF Research Database (Denmark)

Bang, Bo; Gniadecki, Robert; Larsen, Jørgen K

2003-01-01

In vitro studies with human cell lines have demonstrated that the death receptor Fas plays a role in ultraviolet (UV)-induced apoptosis. The purpose of the present study was to investigate the relation between Fas expression and apoptosis as well as clustering of Fas in human epidermis after...... a single dose of UVB irradiation. Normal healthy individuals were irradiated with three minimal erythema doses (MED) of UVB on forearm or buttock skin. Suction blisters from unirradiated and irradiated skin were raised, and Fas, FasL, and apoptosis of epidermal cells quantified by flow cytometry....... Clustering of Fas was from skin biopsied. Soluble FasL in suction blister fluid was quantified by ELISA. Flow cytometric analysis demonstrated increased expression intensity of Fas after irradiation, with 1.6-,2.2- and 2.7-fold increased median expression at 24, 48 and 72 h after irradiation, respectively (n...

Evolution of the spherical clusters

International Nuclear Information System (INIS)

Surdin, V.G.

1978-01-01

The possible processes of the Galaxy spherical clusters formation and evolution are described on a popular level. The orbits of spherical cluster motion and their spatial velocities are determined. Given are the distrbutions of spherical cluster stars according to their velocities and the observed distribution of spherical clusters in the area of the Galaxy slow evolution. The dissipation and dynamic friction processes destructing clusters with the mass less than 10 4 of solar mass and bringing about the reduction of clusters in the Galaxy are considered. The paradox of forming mainly X-ray sources in spherical clusters is explained. The schematic image of possible ways of forming X-ray sources in spherical clusters is given

Taxonomic minimalism.

Science.gov (United States)

Beattle, A J; Oliver, I

1994-12-01

Biological surveys are in increasing demand while taxonomic resources continue to decline. How much formal taxonomy is required to get the job done? The answer depends on the kind of job but it is possible that taxonomic minimalism, especially (1) the use of higher taxonomic ranks, (2) the use of morphospecies rather than species (as identified by Latin binomials), and (3) the involvement of taxonomic specialists only for training and verification, may offer advantages for biodiversity assessment, environmental monitoring and ecological research. As such, formal taxonomy remains central to the process of biological inventory and survey but resources may be allocated more efficiently. For example, if formal Identification is not required, resources may be concentrated on replication and increasing sample sizes. Taxonomic minimalism may also facilitate the inclusion in these activities of important but neglected groups, especially among the invertebrates, and perhaps even microorganisms. Copyright © 1994. Published by Elsevier Ltd.

Minimizing waste in environmental restoration

International Nuclear Information System (INIS)

Thuot, J.R.; Moos, L.

1996-01-01

Environmental restoration, decontamination and decommissioning, and facility dismantlement projects are not typically known for their waste minimization and pollution prevention efforts. Typical projects are driven by schedules and milestones with little attention given to cost or waste minimization. Conventional wisdom in these projects is that the waste already exists and cannot be reduced or minimized; however, there are significant areas where waste and cost can be reduced by careful planning and execution. Waste reduction can occur in three ways: beneficial reuse or recycling, segregation of waste types, and reducing generation of secondary waste

Nuclear cluster states

International Nuclear Information System (INIS)

Rae, W.D.M.; Merchant, A.C.

1993-01-01

We review clustering in light nuclei including molecular resonances in heavy ion reactions. In particular we study the systematics, paying special attention to the relationships between cluster states and superdeformed configurations. We emphasise the selection rules which govern the formation and decay of cluster states. We review some recent experimental results from Daresbury and elsewhere. In particular we report on the evidence for a 7-α chain state in 28 Si in experiments recently performed at the NSF, Daresbury. Finally we begin to address theoretically the important question of the lifetimes of cluster states as deduced from the experimental energy widths of the resonances. (Author)

Cluster size selectivity in the product distribution of ethene dehydrogenation on niobium clusters.

Science.gov (United States)

Parnis, J Mark; Escobar-Cabrera, Eric; Thompson, Matthew G K; Jacula, J Paul; Lafleur, Rick D; Guevara-García, Alfredo; Martínez, Ana; Rayner, David M

2005-08-18

Ethene reactions with niobium atoms and clusters containing up to 25 constituent atoms have been studied in a fast-flow metal cluster reactor. The clusters react with ethene at about the gas-kinetic collision rate, indicating a barrierless association process as the cluster removal step. Exceptions are Nb8 and Nb10, for which a significantly diminished rate is observed, reflecting some cluster size selectivity. Analysis of the experimental primary product masses indicates dehydrogenation of ethene for all clusters save Nb10, yielding either Nb(n)C2H2 or Nb(n)C2. Over the range Nb-Nb6, the extent of dehydrogenation increases with cluster size, then decreases for larger clusters. For many clusters, secondary and tertiary product masses are also observed, showing varying degrees of dehydrogenation corresponding to net addition of C2H4, C2H2, or C2. With Nb atoms and several small clusters, formal addition of at least six ethene molecules is observed, suggesting a polymerization process may be active. Kinetic analysis of the Nb atom and several Nb(n) cluster reactions with ethene shows that the process is consistent with sequential addition of ethene units at rates corresponding approximately to the gas-kinetic collision frequency for several consecutive reacting ethene molecules. Some variation in the rate of ethene pick up is found, which likely reflects small energy barriers or steric constraints associated with individual mechanistic steps. Density functional calculations of structures of Nb clusters up to Nb(6), and the reaction products Nb(n)C2H2 and Nb(n)C2 (n = 1...6) are presented. Investigation of the thermochemistry for the dehydrogenation of ethene to form molecular hydrogen, for the Nb atom and clusters up to Nb6, demonstrates that the exergonicity of the formation of Nb(n)C2 species increases with cluster size over this range, which supports the proposal that the extent of dehydrogenation is determined primarily by thermodynamic constraints. Analysis of

A parallel FPGA implementation for real-time 2D pixel clustering for the ATLAS Fast Tracker Processor

International Nuclear Information System (INIS)

Sotiropoulou, C L; Gkaitatzis, S; Kordas, K; Nikolaidis, S; Petridou, C; Annovi, A; Beretta, M; Volpi, G

2014-01-01

The parallel 2D pixel clustering FPGA implementation used for the input system of the ATLAS Fast TracKer (FTK) processor is presented. The input system for the FTK processor will receive data from the Pixel and micro-strip detectors from inner ATLAS read out drivers (RODs) at full rate, for total of 760Gbs, as sent by the RODs after level-1 triggers. Clustering serves two purposes, the first is to reduce the high rate of the received data before further processing, the second is to determine the cluster centroid to obtain the best spatial measurement. For the pixel detectors the clustering is implemented by using a 2D-clustering algorithm that takes advantage of a moving window technique to minimize the logic required for cluster identification. The cluster detection window size can be adjusted for optimizing the cluster identification process. Additionally, the implementation can be parallelized by instantiating multiple cores to identify different clusters independently thus exploiting more FPGA resources. This flexibility makes the implementation suitable for a variety of demanding image processing applications. The implementation is robust against bit errors in the input data stream and drops all data that cannot be identified. In the unlikely event of missing control words, the implementation will ensure stable data processing by inserting the missing control words in the data stream. The 2D pixel clustering implementation is developed and tested in both single flow and parallel versions. The first parallel version with 16 parallel cluster identification engines is presented. The input data from the RODs are received through S-Links and the processing units that follow the clustering implementation also require a single data stream, therefore data parallelizing (demultiplexing) and serializing (multiplexing) modules are introduced in order to accommodate the parallelized version and restore the data stream afterwards. The results of the first hardware tests of

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

Quantization of the minimal and non-minimal vector field in curved space

OpenAIRE

Toms, David J.

2015-01-01

The local momentum space method is used to study the quantized massive vector field (the Proca field) with the possible addition of non-minimal terms. Heat kernel coefficients are calculated and used to evaluate the divergent part of the one-loop effective action. It is shown that the naive expression for the effective action that one would write down based on the minimal coupling case needs modification. We adopt a Faddeev-Jackiw method of quantization and consider the case of an ultrastatic...

Massively parallel Monte Carlo. Experiences running nuclear simulations on a large condor cluster

International Nuclear Information System (INIS)

Tickner, James; O'Dwyer, Joel; Roach, Greg; Uher, Josef; Hitchen, Greg

2010-01-01

The trivially-parallel nature of Monte Carlo (MC) simulations make them ideally suited for running on a distributed, heterogeneous computing environment. We report on the setup and operation of a large, cycle-harvesting Condor computer cluster, used to run MC simulations of nuclear instruments ('jobs') on approximately 4,500 desktop PCs. Successful operation must balance the competing goals of maximizing the availability of machines for running jobs whilst minimizing the impact on users' PC performance. This requires classification of jobs according to anticipated run-time and priority and careful optimization of the parameters used to control job allocation to host machines. To maximize use of a large Condor cluster, we have created a powerful suite of tools to handle job submission and analysis, as the manual creation, submission and evaluation of large numbers (hundred to thousands) of jobs would be too arduous. We describe some of the key aspects of this suite, which has been interfaced to the well-known MCNP and EGSnrc nuclear codes and our in-house PHOTON optical MC code. We report on our practical experiences of operating our Condor cluster and present examples of several large-scale instrument design problems that have been solved using this tool. (author)

Fundamental physics from future weak-lensing calibrated Sunyaev-Zel'dovich galaxy cluster counts

Science.gov (United States)

Madhavacheril, Mathew S.; Battaglia, Nicholas; Miyatake, Hironao

2017-11-01

Future high-resolution measurements of the cosmic microwave background (CMB) will produce catalogs of tens of thousands of galaxy clusters through the thermal Sunyaev-Zel'dovich (tSZ) effect. We forecast how well different configurations of a CMB Stage-4 experiment can constrain cosmological parameters, in particular, the amplitude of structure as a function of redshift σ8(z ) , the sum of neutrino masses Σ mν, and the dark energy equation of state w (z ). A key element of this effort is calibrating the tSZ scaling relation by measuring the lensing signal around clusters. We examine how the mass calibration from future optical surveys like the Large Synoptic Survey Telescope (LSST) compares with a purely internal calibration using lensing of the CMB itself. We find that, due to its high-redshift leverage, internal calibration gives constraints on cosmological parameters comparable to the optical calibration, and can be used as a cross-check of systematics in the optical measurement. We also show that in contrast to the constraints using the CMB lensing power spectrum, lensing-calibrated tSZ cluster counts can detect a minimal Σ mν at the 3 - 5 σ level even when the dark energy equation of state is freed up.

Clustering at high redshifts

International Nuclear Information System (INIS)

Shaver, P.A.

1986-01-01

Evidence for clustering of and with high-redshift QSOs is discussed. QSOs of different redshifts show no clustering, but QSOs of similar redshifts appear to be clustered on a scale comparable to that of galaxies at the present epoch. In addition, spectroscopic studies of close pairs of QSOs indicate that QSOs are surrounded by a relatively high density of absorbing matter, possibly clusters of galaxies

Clustering high dimensional data

DEFF Research Database (Denmark)

Assent, Ira

2012-01-01

High-dimensional data, i.e., data described by a large number of attributes, pose specific challenges to clustering. The so-called ‘curse of dimensionality’, coined originally to describe the general increase in complexity of various computational problems as dimensionality increases, is known...... to render traditional clustering algorithms ineffective. The curse of dimensionality, among other effects, means that with increasing number of dimensions, a loss of meaningful differentiation between similar and dissimilar objects is observed. As high-dimensional objects appear almost alike, new approaches...... for clustering are required. Consequently, recent research has focused on developing techniques and clustering algorithms specifically for high-dimensional data. Still, open research issues remain. Clustering is a data mining task devoted to the automatic grouping of data based on mutual similarity. Each cluster...

Performance criteria for graph clustering and Markov cluster experiments

NARCIS (Netherlands)

S. van Dongen

2000-01-01

textabstractIn~[1] a cluster algorithm for graphs was introduced called the Markov cluster algorithm or MCL~algorithm. The algorithm is based on simulation of (stochastic) flow in graphs by means of alternation of two operators, expansion and inflation. The results in~[2] establish an intrinsic

Electronic structure of metal clusters

International Nuclear Information System (INIS)

Wertheim, G.K.

1989-01-01

Photoemission spectra of valence electrons in metal clusters, together with threshold ionization potential measurements, provide a coherent picture of the development of the electronic structure from the isolated atom to the large metallic cluster. An insulator-metal transition occurs at an intermediate cluster size, which serves to define the boundary between small and large clusters. Although the outer electrons may be delocalized over the entire cluster, a small cluster remains insulating until the density of states near the Fermi level exceeds 1/kT. In large clusters, with increasing cluster size, the band structure approaches that of the bulk metal. However, the bands remain significantly narrowed even in a 1000-atom cluster, giving an indication of the importance of long-range order. The core-electron binding-energy shifts of supported metal clusters depend on changes in the band structure in the initial state, as well as on various final-state effects, including changes in core hole screening and the coulomb energy of the final-state charge. For cluster supported on amorphous carbon, this macroscopic coulomb shift is often dominant, as evidenced by the parallel shifts of the core-electron binding energy and the Fermi edge. Auger data confirm that final-state effects dominate in cluster of Sn and some other metals. Surface atom core-level shifts provide a valuable guide to the contributions of initial-state changes in band structure to cluster core-electron binding energy shifts, especially for Au and Pt. The available data indicate that the shift observed in supported, metallic clusters arise largely from the charge left on the cluster by photoemission. As the metal-insulator transition is approached from above, metallic screening is suppressed and the shift is determined by the local environment. (orig.)

Globular clusters - Fads and fallacies

International Nuclear Information System (INIS)

White, R.E.

1991-01-01

The types of globular clusters observed in the Milky Way Galaxy are described together with their known characteristics, with special attention given to correcting the erroneous statements made earlier about globular clusters. Among these are the following statements: the Galaxy is surrounded by many hundreds of globular clusters; all globular clusters are located toward the Galactic center, all globular clusters are metal poor and move about the Galaxy in highly elliptical paths; all globular clusters contain RR Lyrae-type variable stars, and the RR Lyrae stars found outside of globulars have come from cluster dissolution or ejection; all of the stars in a given cluster were born at the same time and have the same chemical composition; X-ray globulars are powered by central black holes; and the luminosity functions for globular clusters are well defined and well determined. Consideration is given to the fact that globular clusters in the Magellanic Clouds differ from those in the Milky Way by their age distribution and that the globulars of the SMC differ from those of the LMC

Sludge minimization technologies - an overview

Energy Technology Data Exchange (ETDEWEB)

Oedegaard, Hallvard

2003-07-01

The management of wastewater sludge from wastewater treatment plants represents one of the major challenges in wastewater treatment today. The cost of the sludge treatment amounts to more that the cost of the liquid in many cases. Therefore the focus on and interest in sludge minimization is steadily increasing. In the paper an overview is given for sludge minimization (sludge mass reduction) options. It is demonstrated that sludge minimization may be a result of reduced production of sludge and/or disintegration processes that may take place both in the wastewater treatment stage and in the sludge stage. Various sludge disintegration technologies for sludge minimization are discussed, including mechanical methods (focusing on stirred ball-mill, high-pressure homogenizer, ultrasonic disintegrator), chemical methods (focusing on the use of ozone), physical methods (focusing on thermal and thermal/chemical hydrolysis) and biological methods (focusing on enzymatic processes). (author)

Seizure clusters: characteristics and treatment.

Science.gov (United States)

Haut, Sheryl R

2015-04-01

Many patients with epilepsy experience 'clusters' or flurries of seizures, also termed acute repetitive seizures (ARS). Seizure clustering has a significant impact on health and quality of life. This review summarizes recent advances in the definition and neurophysiologic understanding of clustering, the epidemiology and risk factors for clustering and both inpatient and outpatient clinical implications. New treatments for seizure clustering/ARS are perhaps the area of greatest recent progress. Efforts have focused on creating a uniform definition of a seizure cluster. In neurophysiologic studies of refractory epilepsy, seizures within a cluster appear to be self-triggering. Clinical progress has been achieved towards a more precise prevalence of clustering, and consensus guidelines for epilepsy monitoring unit safety. The greatest recent advances are in the study of nonintravenous route of benzodiazepines as rescue medications for seizure clusters/ARS. Rectal benzodiazepines have been very effective but barriers to use exist. New data on buccal, intramuscular and intranasal preparations are anticipated to lead to a greater number of approved treatments. Progesterone may be effective for women who experience catamenial clusters. Seizure clustering is common, particularly in the setting of medically refractory epilepsy. Clustering worsens health and quality of life, and the field requires greater focus on clarifying of definition and clinical implications. Progress towards the development of nonintravenous routes of benzodiazepines has the potential to improve care in this area.

On the kinematic separation of field and cluster stars across the bulge globular NGC 6528

Energy Technology Data Exchange (ETDEWEB)

Lagioia, E. P.; Bono, G.; Buonanno, R. [Dipartimento di Fisica, Università degli Studi di Roma-Tor Vergata, via della Ricerca Scientifica 1, I-00133 Roma (Italy); Milone, A. P. [Research School of Astronomy and Astrophysics, The Australian National University, Cotter Road, Weston, ACT 2611 (Australia); Stetson, P. B. [Dominion Astrophysical Observatory, Herzberg Institute of Astrophysics, National Research Council, 5071 West Saanich Road, Victoria, BC V9E 2E7 (Canada); Prada Moroni, P. G. [Dipartimento di Fisica, Università di Pisa, I-56127 Pisa (Italy); Dall' Ora, M. [INAF-Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli (Italy); Aparicio, A.; Monelli, M. [Instituto de Astrofìsica de Canarias, E-38200 La Laguna, Tenerife, Canary Islands (Spain); Calamida, A.; Ferraro, I.; Iannicola, G. [INAF-Osservatorio Astronomico di Roma, Via Frascati 33, I-00044 Monte Porzio Catone (Italy); Gilmozzi, R. [European Southern Observatory, Karl-Schwarzschild-Straße 2, D-85748 Garching (Germany); Matsunaga, N. [Kiso Observatory, Institute of Astronomy, School of Science, The University of Tokyo, 10762-30, Mitake, Kiso-machi, Kiso-gun, 3 Nagano 97-0101 (Japan); Walker, A., E-mail: eplagioia@roma2.infn.it [Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena (Chile)

2014-02-10

We present deep and precise multi-band photometry of the Galactic bulge globular cluster NGC 6528. The current data set includes optical and near-infrared images collected with ACS/WFC, WFC3/UVIS, and WFC3/IR on board the Hubble Space Telescope. The images cover a time interval of almost 10 yr, and we have been able to carry out a proper-motion separation between cluster and field stars. We performed a detailed comparison in the m {sub F814W}, m {sub F606W} – m {sub F814W} color-magnitude diagram with two empirical calibrators observed in the same bands. We found that NGC 6528 is coeval with and more metal-rich than 47 Tuc. Moreover, it appears older and more metal-poor than the super-metal-rich open cluster NGC 6791. The current evidence is supported by several diagnostics (red horizontal branch, red giant branch bump, shape of the sub-giant branch, slope of the main sequence) that are minimally affected by uncertainties in reddening and distance. We fit the optical observations with theoretical isochrones based on a scaled-solar chemical mixture and found an age of 11 ± 1 Gyr and an iron abundance slightly above solar ([Fe/H] = +0.20). The iron abundance and the old cluster age further support the recent spectroscopic findings suggesting a rapid chemical enrichment of the Galactic bulge.

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.

Minimal and careful processing

OpenAIRE

Nielsen, Thorkild

2004-01-01

In several standards, guidelines and publications, organic food processing is strongly associated with "minimal processing" and "careful processing". The term "minimal processing" is nowadays often used in the general food processing industry and described in literature. The term "careful processing" is used more specifically within organic food processing but is not yet clearly defined. The concept of carefulness seems to fit very well with the processing of organic foods, especially if it i...

Clustering on Membranes

DEFF Research Database (Denmark)

Johannes, Ludger; Pezeshkian, Weria; Ipsen, John H

2018-01-01

Clustering of extracellular ligands and proteins on the plasma membrane is required to perform specific cellular functions, such as signaling and endocytosis. Attractive forces that originate in perturbations of the membrane's physical properties contribute to this clustering, in addition to direct...... protein-protein interactions. However, these membrane-mediated forces have not all been equally considered, despite their importance. In this review, we describe how line tension, lipid depletion, and membrane curvature contribute to membrane-mediated clustering. Additional attractive forces that arise...... from protein-induced perturbation of a membrane's fluctuations are also described. This review aims to provide a survey of the current understanding of membrane-mediated clustering and how this supports precise biological functions....

Non-minimal Wu-Yang monopole

International Nuclear Information System (INIS)

Balakin, A.B.; Zayats, A.E.

2007-01-01

We discuss new exact spherically symmetric static solutions to non-minimally extended Einstein-Yang-Mills equations. The obtained solution to the Yang-Mills subsystem is interpreted as a non-minimal Wu-Yang monopole solution. We focus on the analysis of two classes of the exact solutions to the gravitational field equations. Solutions of the first class belong to the Reissner-Nordstroem type, i.e., they are characterized by horizons and by the singularity at the point of origin. The solutions of the second class are regular ones. The horizons and singularities of a new type, the non-minimal ones, are indicated

Global survey of star clusters in the Milky Way. VI. Age distribution and cluster formation history

Science.gov (United States)

Piskunov, A. E.; Just, A.; Kharchenko, N. V.; Berczik, P.; Scholz, R.-D.; Reffert, S.; Yen, S. X.

2018-06-01

Context. The all-sky Milky Way Star Clusters (MWSC) survey provides uniform and precise ages, along with other relevant parameters, for a wide variety of clusters in the extended solar neighbourhood. Aims: In this study we aim to construct the cluster age distribution, investigate its spatial variations, and discuss constraints on cluster formation scenarios of the Galactic disk during the last 5 Gyrs. Methods: Due to the spatial extent of the MWSC, we have considered spatial variations of the age distribution along galactocentric radius RG, and along Z-axis. For the analysis of the age distribution we used 2242 clusters, which all lie within roughly 2.5 kpc of the Sun. To connect the observed age distribution to the cluster formation history we built an analytical model based on simple assumptions on the cluster initial mass function and on the cluster mass-lifetime relation, fit it to the observations, and determined the parameters of the cluster formation law. Results: Comparison with the literature shows that earlier results strongly underestimated the number of evolved clusters with ages t ≳ 100 Myr. Recent studies based on all-sky catalogues agree better with our data, but still lack the oldest clusters with ages t ≳ 1 Gyr. We do not observe a strong variation in the age distribution along RG, though we find an enhanced fraction of older clusters (t > 1 Gyr) in the inner disk. In contrast, the distribution strongly varies along Z. The high altitude distribution practically does not contain clusters with t < 1 Gyr. With simple assumptions on the cluster formation history, the cluster initial mass function and the cluster lifetime we can reproduce the observations. The cluster formation rate and the cluster lifetime are strongly degenerate, which does not allow us to disentangle different formation scenarios. In all cases the cluster formation rate is strongly declining with time, and the cluster initial mass function is very shallow at the high mass end.

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

Science.gov (United States)

Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.

2018-04-01

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

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.

Feasibility of an iPad application for studying menopause-related symptom clusters and women's heuristics.

Science.gov (United States)

Ismail, Rita; Linder, Lauri A; MacPherson, Catherine Fiona; Fugate Woods, Nancy

2016-01-01

To evaluate feasibility, including usability and utility, of the Computerized Symptom Capture Tool for Menopause (C-SCAT-M), a symptom heuristics application (app) for the iPad, with midlife women. Thirty midlife women aged 40-60 and experiencing symptoms they associated with menopause were recruited through flyers posted on a university campus, primary care and women's health clinics. The C-SCAT-M guided participants to identify symptoms they experienced, draw temporal and causal relationships between symptoms and identify symptom clusters. Women were encouraged to think aloud as they encountered questions or problems and their comments were audio recorded. After completing the C-SCAT-M, they completed a 22-item acceptability survey and a demographic survey. Data were downloaded from catalyst website and analyzed using SPSS. Women completed the C-SCAT-M with minimal difficulty, with most indicating that using the app was very/extremely easy and most (57%) preferred using the iPad app to paper. Most women stated that the final diagrams were very/extremely accurate depictions of their symptom clusters and relationships (77%). The C-SCAT-M demonstrated initial feasibility, including usability and utility, for collecting data about symptom clusters experienced by midlife women.

Wilson loops in minimal surfaces

International Nuclear Information System (INIS)

Drukker, Nadav; Gross, David J.; Ooguri, Hirosi

1999-01-01

The AdS/CFT correspondence suggests that the Wilson loop of the large N gauge theory with N = 4 supersymmetry in 4 dimensions is described by a minimal surface in AdS 5 x S 5 . The authors examine various aspects of this proposal, comparing gauge theory expectations with computations of minimal surfaces. There is a distinguished class of loops, which the authors call BPS loops, whose expectation values are free from ultra-violet divergence. They formulate the loop equation for such loops. To the extent that they have checked, the minimal surface in AdS 5 x S 5 gives a solution of the equation. The authors also discuss the zig-zag symmetry of the loop operator. In the N = 4 gauge theory, they expect the zig-zag symmetry to hold when the loop does not couple the scalar fields in the supermultiplet. They will show how this is realized for the minimal surface

Wilson loops and minimal surfaces

International Nuclear Information System (INIS)

Drukker, Nadav; Gross, David J.; Ooguri, Hirosi

1999-01-01

The AdS-CFT correspondence suggests that the Wilson loop of the large N gauge theory with N=4 supersymmetry in four dimensions is described by a minimal surface in AdS 5 xS 5 . We examine various aspects of this proposal, comparing gauge theory expectations with computations of minimal surfaces. There is a distinguished class of loops, which we call BPS loops, whose expectation values are free from ultraviolet divergence. We formulate the loop equation for such loops. To the extent that we have checked, the minimal surface in AdS 5 xS 5 gives a solution of the equation. We also discuss the zigzag symmetry of the loop operator. In the N=4 gauge theory, we expect the zigzag symmetry to hold when the loop does not couple the scalar fields in the supermultiplet. We will show how this is realized for the minimal surface. (c) 1999 The American Physical Society

K-means Clustering: Lloyd's algorithm

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. K-means Clustering: Lloyd's algorithm. Refines clusters iteratively. Cluster points using Voronoi partitioning of the centers; Centroids of the clusters determine the new centers. Bad example k = 3, n =4.

Structure and bonding in clusters

International Nuclear Information System (INIS)

Kumar, V.

1991-10-01

We review here the recent progress made in the understanding of the electronic and atomic structure of small clusters of s-p bonded materials using the density functional molecular dynamics technique within the local density approximation. Starting with a brief description of the method, results are presented for alkali metal clusters, clusters of divalent metals such as Mg and Be which show a transition from van der Waals or weak chemical bonding to metallic behaviour as the cluster size grows and clusters of Al, Sn and Sb. In the case of semiconductors, we discuss results for Si, Ge and GaAs clusters. Clusters of other materials such as P, C, S, and Se are also briefly discussed. From these and other available results we suggest the possibility of unique structures for the magic clusters. (author). 69 refs, 7 figs, 1 tab

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....

Cluster structures in light nuclei

International Nuclear Information System (INIS)

Horiuchi, H.

2000-01-01

Complete text of publication follows. Clustering in neutron-rich nuclei is discussed. To understand the novel features (1,2,3) of the clustering in neutron-rich nuclei, the basic features of the clustering in stable nuclei (4) are briefly reviewed. In neutron-rich nuclei, the requirement of the stability of clusters is questioned and the threshold rule is no more obeyed. Examples of clustering in Be and B isotopes (4,5) are discussed in some detail. Possible existence of novel type of clustering near neutron dripline is suggested (1). (author)

Data clustering algorithms and applications

CERN Document Server

Aggarwal, Charu C

2013-01-01

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea

BioCluster: Tool for Identification and Clustering of Enterobacteriaceae Based on Biochemical Data

Directory of Open Access Journals (Sweden)

Ahmed Abdullah

2015-06-01

Full Text Available Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC and the Improved Hierarchical Clustering (IHC, a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1–47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.

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.

Reduct Driven Pattern Extraction from Clusters

Directory of Open Access Journals (Sweden)

Shuchita Upadhyaya

2009-03-01

Full Text Available Clustering algorithms give general description of clusters, listing number of clusters and member entities in those clusters. However, these algorithms lack in generating cluster description in the form of pattern. From data mining perspective, pattern learning from clusters is as important as cluster finding. In the proposed approach, reduct derived from rough set theory is employed for pattern formulation. Further, reduct are the set of attributes which distinguishes the entities in a homogenous cluster, hence these can be clear cut removed from the same. Remaining attributes are then ranked for their contribution in the cluster. Pattern is formulated with the conjunction of most contributing attributes such that pattern distinctively describes the cluster with minimum error.

The interaction of super-intense ultra-short laser pulse and micro-clusters with large atomic clusters

International Nuclear Information System (INIS)

Miao Jingwei; Yang Chaowen; An Zhu; Yuan Xuedong; Sun Weiguo; Luo Xiaobing; Wang Hu; Bai Lixing; Shi Miangong; Miao Lei; Zhen Zhijian; Gu Yuqin; Liu Hongjie; Zhu Zhouseng; Sun Liwei; Liao Xuehua

2007-01-01

The fusion mechanism of large deuterium clusters (100-1000 Atoms/per cluster) in super-intense ultra-short laser pulse field, Coulomb explosions of micro-cluster in solids, gases and Large-size clusters have been studied using the interaction of a high-intensity femtosecond laser pulses with large deuterium clusters, collision of high-quality beam of micro-cluster from 2.5 MV van de Graaff accelerator with solids, gases and large clusters. The experimental advance of the project is reported. (authors)

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

Science.gov (United States)

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Star clusters in evolving galaxies

Science.gov (United States)

Renaud, Florent

2018-04-01

Their ubiquity and extreme densities make star clusters probes of prime importance of galaxy evolution. Old globular clusters keep imprints of the physical conditions of their assembly in the early Universe, and younger stellar objects, observationally resolved, tell us about the mechanisms at stake in their formation. Yet, we still do not understand the diversity involved: why is star cluster formation limited to 105M⊙ objects in the Milky Way, while some dwarf galaxies like NGC 1705 are able to produce clusters 10 times more massive? Why do dwarfs generally host a higher specific frequency of clusters than larger galaxies? How to connect the present-day, often resolved, stellar systems to the formation of globular clusters at high redshift? And how do these links depend on the galactic and cosmological environments of these clusters? In this review, I present recent advances on star cluster formation and evolution, in galactic and cosmological context. The emphasis is put on the theory, formation scenarios and the effects of the environment on the evolution of the global properties of clusters. A few open questions are identified.

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

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.

Coma cluster of galaxies

Science.gov (United States)

1999-01-01

Atlas Image mosaic, covering 34' x 34' on the sky, of the Coma cluster, aka Abell 1656. This is a particularly rich cluster of individual galaxies (over 1000 members), most prominently the two giant ellipticals, NGC 4874 (right) and NGC 4889 (left). The remaining members are mostly smaller ellipticals, but spiral galaxies are also evident in the 2MASS image. The cluster is seen toward the constellation Coma Berenices, but is actually at a distance of about 100 Mpc (330 million light years, or a redshift of 0.023) from us. At this distance, the cluster is in what is known as the 'Hubble flow,' or the overall expansion of the Universe. As such, astronomers can measure the Hubble Constant, or the universal expansion rate, based on the distance to this cluster. Large, rich clusters, such as Coma, allow astronomers to measure the 'missing mass,' i.e., the matter in the cluster that we cannot see, since it gravitationally influences the motions of the member galaxies within the cluster. The near-infrared maps the overall luminous mass content of the member galaxies, since the light at these wavelengths is dominated by the more numerous older stellar populations. Galaxies, as seen by 2MASS, look fairly smooth and homogeneous, as can be seen from the Hubble 'tuning fork' diagram of near-infrared galaxy morphology. Image mosaic by S. Van Dyk (IPAC).