Science.gov

Sample records for agglomerative hierarchical clustering

  1. Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering

    PubMed Central

    2015-01-01

    Agglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments. The algorithm is based on an iterative divide-and-conquer strategy. The data is first split into independent subsets, each of which is clustered separately. Thus reduces the storage required for sequential implementations, and allows concurrent computation on parallel computing hardware. The resultant clusters are merged and subsequently re-divided into subsets, which are passed to the following iteration. We show that MAHC can match and even surpass the performance of the exact implementation when applied to datasets of speech segments. PMID:26517376

  2. Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images

    PubMed Central

    Medina, Ollantay; Manian, Vidya; Chinea, J. Danilo

    2013-01-01

    Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agglomerative clustering is our primary tool to retrieve clusters whose Shannon entropy should reflect species richness on a given zone. However, in a high spectral mixing scenario, an additional unmixing step, just before entropy computation, is required; cluster centroids are enough for the unmixing process. Entropies computed using the proposed method correlate well with the ones calculated directly from synthetic and field data. PMID:24132230

  3. An agglomerative hierarchical clustering approach to visualisation in Bayesian clustering problems

    PubMed Central

    Dawson, Kevin J.; Belkhir, Khalid

    2009-01-01

    Clustering problems (including the clustering of individuals into outcrossing populations, hybrid generations, full-sib families and selfing lines) have recently received much attention in population genetics. In these clustering problems, the parameter of interest is a partition of the set of sampled individuals, - the sample partition. In a fully Bayesian approach to clustering problems of this type, our knowledge about the sample partition is represented by a probability distribution on the space of possible sample partitions. Since the number of possible partitions grows very rapidly with the sample size, we can not visualise this probability distribution in its entirety, unless the sample is very small. As a solution to this visualisation problem, we recommend using an agglomerative hierarchical clustering algorithm, which we call the exact linkage algorithm. This algorithm is a special case of the maximin clustering algorithm that we introduced previously. The exact linkage algorithm is now implemented in our software package Partition View. The exact linkage algorithm takes the posterior co-assignment probabilities as input, and yields as output a rooted binary tree, - or more generally, a forest of such trees. Each node of this forest defines a set of individuals, and the node height is the posterior co-assignment probability of this set. This provides a useful visual representation of the uncertainty associated with the assignment of individuals to categories. It is also a useful starting point for a more detailed exploration of the posterior distribution in terms of the co-assignment probabilities. PMID:19337306

  4. Hierarchic Agglomerative Clustering Methods for Automatic Document Classification.

    ERIC Educational Resources Information Center

    Griffiths, Alan; And Others

    1984-01-01

    Considers classifications produced by application of single linkage, complete linkage, group average, and word clustering methods to Keen and Cranfield document test collections, and studies structure of hierarchies produced, extent to which methods distort input similarity matrices during classification generation, and retrieval effectiveness…

  5. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    NASA Astrophysics Data System (ADS)

    Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.

    2015-11-01

    In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs) by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen-Rocky Mountain Biogenic Aerosol Study) ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of

  6. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    NASA Astrophysics Data System (ADS)

    Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.

    2015-07-01

    In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP) by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution due to poor

  7. Combining Analytical Hierarchy Process and Agglomerative Hierarchical Clustering in Search of Expert Consensus in Green Corridors Development Management

    NASA Astrophysics Data System (ADS)

    Shapira, Aviad; Shoshany, Maxim; Nir-Goldenberg, Sigal

    2013-07-01

    Environmental management and planning are instrumental in resolving conflicts arising between societal needs for economic development on the one hand and for open green landscapes on the other hand. Allocating green corridors between fragmented core green areas may provide a partial solution to these conflicts. Decisions regarding green corridor development require the assessment of alternative allocations based on multiple criteria evaluations. Analytical Hierarchy Process provides a methodology for both a structured and consistent extraction of such evaluations and for the search for consensus among experts regarding weights assigned to the different criteria. Implementing this methodology using 15 Israeli experts—landscape architects, regional planners, and geographers—revealed inherent differences in expert opinions in this field beyond professional divisions. The use of Agglomerative Hierarchical Clustering allowed to identify clusters representing common decisions regarding criterion weights. Aggregating the evaluations of these clusters revealed an important dichotomy between a pragmatist approach that emphasizes the weight of statutory criteria and an ecological approach that emphasizes the role of the natural conditions in allocating green landscape corridors.

  8. Hierarchical agglomerative sub-clustering technique for particles management in PIC simulations

    NASA Astrophysics Data System (ADS)

    Grasso, Giacomo; Frignani, Michele; Rocchi, Federico; Sumini, Marco

    2010-08-01

    The effectiveness of Particle-In-Cell (PIC) codes lies mainly in the robustness of the methods implemented, under the fundamental assumption that a sufficient number of pseudo-particles is concerned for a correct representation of the system. The consequent drawback is the huge increase of computational time required to run a simulation, to what concerns the particles charge assignment to the grid and the motion of the former through the latter. Moreover the coupling of such methods with Monte-Carlo-Collisional (MCC) modules causes another expensive computational cost to simulate particle multiple collisions with background gas and domain boundaries. Particles management techniques are therefore often introduced in PIC-MCC codes in order to improve the distribution of pseudo-particles in the simulation domain: as a matter of facts, the aim at managing the number of samples according to the importance of the considered region is a main question for codes simulating a local phenomenon in a larger domain or a strongly collisional system (e.g.: a ionizing plasma, where the number of particles increases exponentially). A clustering procedure based on the distribution function sampling applied to the 5D phase space (2D in space, 3D in velocity) is here proposed, representing the leading criterion for particles merging and splitting procedures guaranteeing the second order charge moments conservation. Applied to the study of the electrical breakdown in the early discharge phase of a Plasma Focus device, this technique is shown to increase performances of both PIC kernel and MCC module preserving the solution of the electric field and increasing samples representativeness in stochastic calculations (with respect to more traditional merging and splitting procedures).

  9. Value-balanced agglomerative connectivity clustering

    NASA Astrophysics Data System (ADS)

    Gupta, Gunjan K.; Ghosh, Joydeep

    2001-03-01

    In this paper we propose a new clustering framework for transactional data-sets involving large numbers of customers and products. Such transactional data pose particular issues such as very high dimensionality (greater than 10,000), and sparse categorical entries, that have been dealt with more effectively using a graph-based approach to clustering such as ROCK. But large transactional data raises certain other issues such as how to compare diverse products (e.g. milk vs. cars) cluster balancing and outlier removal, that need to be addressed. We first propose a new similarity measure that takes the value of the goods purchased into account, and form a value-based graph representation based on this similarity measure. A novel value-based balancing criterion that allows the user to control the balancing of clusters, is then defined. This balancing criterion is integrated with a value-based goodness measure for merging two clusters in an agglomerative clustering routine. Since graph-based clustering algorithms are very sensitive to outliers, we also propose a fast, effective and simple outlier detection and removal method based on under-clustering or over- partitioning. The performance of the proposed clustering framework is compared with leading graph-theoretic approaches such as ROCK and METIS.

  10. Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching

    PubMed Central

    Huh, Yong; Yu, Kiyun; Park, Woojin

    2016-01-01

    This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48. PMID:27348229

  11. Agglomerative clustering-based approach for two-dimensional phase unwrapping.

    PubMed

    Herráez, Miguel Arevalillo; Boticario, Jesús G; Lalor, Michael J; Burton, David R

    2005-03-01

    We describe a novel algorithm for two-dimensional phase unwrapping. The technique combines the principles of agglomerative clustering and use of heuristics to construct a discontinuous quality-guided path. Unlike other quality-guided algorithms, which establish the path at the start of the unwrapping process, our technique constructs the path as the unwrapping process evolves. This makes the technique less prone to error propagation, although it presents higher execution times than other existing algorithms. The algorithm reacts satisfactorily to random noise and breaks in the phase distribution. A variation of the algorithm is also presented that considerably reduces the execution time without affecting the results significantly. PMID:15765690

  12. Instability of Hierarchical Cluster Analysis Due to Input Order of the Data: The PermuCLUSTER Solution

    ERIC Educational Resources Information Center

    van der Kloot, Willem A.; Spaans, Alexander M. J.; Heiser, Willem J.

    2005-01-01

    Hierarchical agglomerative cluster analysis (HACA) may yield different solutions under permutations of the input order of the data. This instability is caused by ties, either in the initial proximity matrix or arising during agglomeration. The authors recommend to repeat the analysis on a large number of random permutations of the rows and columns…

  13. Detection of Significant Groups in Hierarchical Clustering by Resampling

    PubMed Central

    Sebastiani, Paola; Perls, Thomas T.

    2016-01-01

    Hierarchical clustering is a simple and reproducible technique to rearrange data of multiple variables and sample units and visualize possible groups in the data. Despite the name, hierarchical clustering does not provide clusters automatically, and “tree-cutting” procedures are often used to identify subgroups in the data by cutting the dendrogram that represents the similarities among groups used in the agglomerative procedure. We introduce a resampling-based technique that can be used to identify cut-points of a dendrogram with a significance level based on a reference distribution for the heights of the branch points. The evaluation on synthetic data shows that the technique is robust in a variety of situations. An example with real biomarker data from the Long Life Family Study shows the usefulness of the method. PMID:27551289

  14. Detection of Significant Groups in Hierarchical Clustering by Resampling.

    PubMed

    Sebastiani, Paola; Perls, Thomas T

    2016-01-01

    Hierarchical clustering is a simple and reproducible technique to rearrange data of multiple variables and sample units and visualize possible groups in the data. Despite the name, hierarchical clustering does not provide clusters automatically, and "tree-cutting" procedures are often used to identify subgroups in the data by cutting the dendrogram that represents the similarities among groups used in the agglomerative procedure. We introduce a resampling-based technique that can be used to identify cut-points of a dendrogram with a significance level based on a reference distribution for the heights of the branch points. The evaluation on synthetic data shows that the technique is robust in a variety of situations. An example with real biomarker data from the Long Life Family Study shows the usefulness of the method. PMID:27551289

  15. Hierarchical clustering using mutual information

    NASA Astrophysics Data System (ADS)

    Kraskov, A.; Stögbauer, H.; Andrzejak, R. G.; Grassberger, P.

    2005-04-01

    We present a conceptually simple method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X, Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use this both in the Shannon (probabilistic) version of information theory and in the Kolmogorov (algorithmic) version. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and to the output of independent components analysis (ICA) as illustrated with the ECG of a pregnant woman.

  16. Cluster assembly of hierarchical nanostructures

    SciTech Connect

    Siegel, R.W.

    1992-02-01

    In the past few years, atom clusters with diameters in the range of 2--20 nm of a variety of materials, including both metals and ceramics, have been synthesized by evaporation and condensation in high-purity gases and subsequently consolidated in situ under ultrahigh vacuum conditions to create nanophase materials. These new utlrafine-grained materials have properties that are often significantly different and considerably improved relative to those of their coarser-grained counterparts owing to both their small grain-size scale and the large percentage of their atoms in grain boundary environments. Since their properties can be engineered during the synthesis and processing steps, cluster-assembled materials appear to have significant potential for the introduction of a hierarchy of both structure and properties. Some of the recent research on nanophase materials related to properties and scale are reviewed and some of the possibilities for synthesizing hierarchical nanostructures via cluster assembly are considered.

  17. Multiscale hierarchical support vector clustering

    NASA Astrophysics Data System (ADS)

    Hansen, Michael Saas; Holm, David Alberg; Sjöstrand, Karl; Ley, Carsten Dan; Rowland, Ian John; Larsen, Rasmus

    2008-03-01

    Clustering is the preferred choice of method in many applications, and support vector clustering (SVC) has proven efficient for clustering noisy and high-dimensional data sets. A method for multiscale support vector clustering is demonstrated, using the recently emerged method for fast calculation of the entire regularization path of the support vector domain description. The method is illustrated on artificially generated examples, and applied for detecting blood vessels from high resolution time series of magnetic resonance imaging data. The obtained results are robust while the need for parameter estimation is reduced, compared to support vector clustering.

  18. Managing Clustered Data Using Hierarchical Linear Modeling

    ERIC Educational Resources Information Center

    Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.

    2012-01-01

    Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…

  19. Convex Clustering: An Attractive Alternative to Hierarchical Clustering

    PubMed Central

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

    2015-01-01

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

  20. Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

    PubMed Central

    Yin, Jiandong; Yang, Jiawen; Guo, Qiyong

    2014-01-01

    During dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI), it has been demonstrated that the arterial input function (AIF) can be obtained using fuzzy c-means (FCM) and k-means clustering methods. However, due to the dependence on the initial centers of clusters, both clustering methods have poor reproducibility between the calculation and recalculation steps. To address this problem, the present study developed an alternative clustering technique based on the agglomerative hierarchy (AH) method for AIF determination. The performance of AH method was evaluated using simulated data and clinical data based on comparisons with the two previously demonstrated clustering-based methods in terms of the detection accuracy, calculation reproducibility, and computational complexity. The statistical analysis demonstrated that, at the cost of a significantly longer execution time, AH method obtained AIFs more in line with the expected AIF, and it was perfectly reproducible at different time points. In our opinion, the disadvantage of AH method in terms of the execution time can be alleviated by introducing a professional high-performance workstation. The findings of this study support the feasibility of using AH clustering method for detecting the AIF automatically. PMID:24932638

  1. Hierarchical link clustering algorithm in networks

    NASA Astrophysics Data System (ADS)

    Bodlaj, Jernej; Batagelj, Vladimir

    2015-06-01

    Hierarchical network clustering is an approach to find tightly and internally connected clusters (groups or communities) of nodes in a network based on its structure. Instead of nodes, it is possible to cluster links of the network. The sets of nodes belonging to clusters of links can overlap. While overlapping clusters of nodes are not always expected, they are natural in many applications. Using appropriate dissimilarity measures, we can complement the clustering strategy to consider, for example, the semantic meaning of links or nodes based on their properties. We propose a new hierarchical link clustering algorithm which in comparison to existing algorithms considers node and/or link properties (descriptions, attributes) of the input network alongside its structure using monotonic dissimilarity measures. The algorithm determines communities that form connected subnetworks (relational constraint) containing locally similar nodes with respect to their description. It is only implicitly based on the corresponding line graph of the input network, thus reducing its space and time complexities. We investigate both complexities analytically and statistically. Using provided dissimilarity measures, our algorithm can, in addition to the general overlapping community structure of input networks, uncover also related subregions inside these communities in a form of hierarchy. We demonstrate this ability on real-world and artificial network examples.

  2. Hierarchical link clustering algorithm in networks.

    PubMed

    Bodlaj, Jernej; Batagelj, Vladimir

    2015-06-01

    Hierarchical network clustering is an approach to find tightly and internally connected clusters (groups or communities) of nodes in a network based on its structure. Instead of nodes, it is possible to cluster links of the network. The sets of nodes belonging to clusters of links can overlap. While overlapping clusters of nodes are not always expected, they are natural in many applications. Using appropriate dissimilarity measures, we can complement the clustering strategy to consider, for example, the semantic meaning of links or nodes based on their properties. We propose a new hierarchical link clustering algorithm which in comparison to existing algorithms considers node and/or link properties (descriptions, attributes) of the input network alongside its structure using monotonic dissimilarity measures. The algorithm determines communities that form connected subnetworks (relational constraint) containing locally similar nodes with respect to their description. It is only implicitly based on the corresponding line graph of the input network, thus reducing its space and time complexities. We investigate both complexities analytically and statistically. Using provided dissimilarity measures, our algorithm can, in addition to the general overlapping community structure of input networks, uncover also related subregions inside these communities in a form of hierarchy. We demonstrate this ability on real-world and artificial network examples. PMID:26172761

  3. A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables

    PubMed Central

    Marrelec, Guillaume; Messé, Arnaud; Bellec, Pierre

    2015-01-01

    The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering. PMID:26406245

  4. A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables.

    PubMed

    Marrelec, Guillaume; Messé, Arnaud; Bellec, Pierre

    2015-01-01

    The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering. PMID:26406245

  5. Hierarchical Clustering of Shotgun Proteomics Data

    PubMed Central

    Koskinen, Ville R.; Emery, Patrick A.; Creasy, David M.; Cottrell, John S.

    2011-01-01

    A new result report for Mascot search results is described. A greedy set cover algorithm is used to create a minimal set of proteins, which is then grouped into families on the basis of shared peptide matches. Protein families with multiple members are represented by dendrograms, generated by hierarchical clustering using the score of the nonshared peptide matches as a distance metric. The peptide matches to the proteins in a family can be compared side by side to assess the experimental evidence for each protein. If the evidence for a particular family member is considered inadequate, the dendrogram can be cut to reduce the number of distinct family members. PMID:21447708

  6. Technique for fast and efficient hierarchical clustering

    DOEpatents

    Stork, Christopher

    2013-10-08

    A fast and efficient technique for hierarchical clustering of samples in a dataset includes compressing the dataset to reduce a number of variables within each of the samples of the dataset. A nearest neighbor matrix is generated to identify nearest neighbor pairs between the samples based on differences between the variables of the samples. The samples are arranged into a hierarchy that groups the samples based on the nearest neighbor matrix. The hierarchy is rendered to a display to graphically illustrate similarities or differences between the samples.

  7. Hierarchical Image Segmentation Using Correlation Clustering.

    PubMed

    Alush, Amir; Goldberger, Jacob

    2016-06-01

    In this paper, we apply efficient implementations of integer linear programming to the problem of image segmentation. The image is first grouped into superpixels and then local information is extracted for each pair of spatially adjacent superpixels. Given local scores on a map of several hundred superpixels, we use correlation clustering to find the global segmentation that is most consistent with the local evidence. We show that, although correlation clustering is known to be NP-hard, finding the exact global solution is still feasible by breaking the segmentation problem down into subproblems. Each such sub-problem can be viewed as an automatically detected image part. We can further accelerate the process by using the cutting-plane method, which provides a hierarchical structure of the segmentations. The efficiency and improved performance of the proposed method is compared to several state-of-the-art methods and demonstrated on several standard segmentation data sets. PMID:26701901

  8. A Hierarchical Clustering Methodology for the Estimation of Toxicity

    EPA Science Inventory

    A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

  9. Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images

    PubMed Central

    Nunez-Iglesias, Juan; Kennedy, Ryan; Parag, Toufiq; Shi, Jianbo; Chklovskii, Dmitri B.

    2013-01-01

    We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images. PMID:23977123

  10. Hierarchical clustering using correlation metric and spatial continuity constraint

    DOEpatents

    Stork, Christopher L.; Brewer, Luke N.

    2012-10-02

    Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

  11. Hierarchical clustering of 54 races and strains of the mulberry silkworm, Bombyx mori L: Significance of biochemical parameters.

    PubMed

    Chatterjee, S N; Datta, R K

    1992-12-01

    A detailed analysis was undertaken to test the efficacy of hierarchical agglomerative clustering (UPGMA method) in grouping the races and strains of the mulberry silkworm, Bombyx moti L., and to ascertain the importance of biochemical parameters in the clustering process. The analysis was based on data from two rearing seasons with 54 selected races/strains of different geographic origin and varying yield potentials. The results indicate that seven clusters can be realised with yield parameters alone, whereas the inclusion of biochemical parameters in clustering resulted into two broad groups: one having all the breeds with high cocoon weight and shell weight, the other having all the low-yielding silkworm strains both from India and from other countries. Further sub-grouping under these two groups highlights genetical differences associated with the differentiation of various groups of races in temperate and tropical areas as well as their significance for silkworm breeding. Estimates of all ten variables were further subjected to 'quick clustering' and the results showed that cluster 5, constituted by 38 lowyielding strains of India, China and Europe, had the highest values of the final cluster centre for amylase and the effective rate of rearing (ERR), while clusters 1 and 4 had the highest values for invertase and alkaline phosphatase. The evolutionary aspect of the genetic channelisation of silkworm races from various countries is discussed against the background of differences in the biochemical parameters and yield variables. PMID:24197452

  12. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

    EPA Science Inventory

    Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

  13. Estimation of Carcinogenicity using Hierarchical Clustering and Nearest Neighbor Methodologies

    EPA Science Inventory

    Previously a hierarchical clustering (HC) approach and a nearest neighbor (NN) approach were developed to model acute aquatic toxicity end points. These approaches were developed to correlate the toxicity for large, noncongeneric data sets. In this study these approaches applie...

  14. Graphical Evaluation of Hierarchical Clustering Schemes. Technical Report No. 1.

    ERIC Educational Resources Information Center

    Halff, Henry M.

    Graphical methods for evaluating the fit of Johnson's hierarchical clustering schemes are presented together with an example. These evaluation methods examine the extent to which the clustering algorithm can minimize the overlap of the distributions of intracluster and intercluster distances. (Author)

  15. Hierarchical Clustering and the Concept of Space Distortion.

    ERIC Educational Resources Information Center

    Hubert, Lawrence; Schultz, James

    An empirical assesssment of the space distortion properties of two prototypic hierarchical clustering procedures is given in terms of an occupancy model developed from combinatorics. Using one simple example, the single-link and complete-link clustering strategies now in common use in the behavioral sciences are empirically shown to be space…

  16. Hierarchical modeling of cluster size in wildlife surveys

    USGS Publications Warehouse

    Royle, J. Andrew

    2008-01-01

    Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

  17. An agglomerative approach for shot summarization based on content homogeneity

    NASA Astrophysics Data System (ADS)

    Ioannidis, Antonis; Chasanis, Vasileios; Likas, Aristidis

    2015-02-01

    An efficient shot summarization method is presented based on agglomerative clustering of the shot frames. Unlike other agglomerative methods, our approach relies on a cluster merging criterion that computes the content homogeneity of a merged cluster. An important feature of the proposed approach is the automatic estimation of the number of a shot's most representative frames, called keyframes. The method starts by splitting each video sequence into small, equal sized clusters (segments). Then, agglomerative clustering is performed, where from the current set of clusters, a pair of clusters is selected and merged to form a larger unimodal (homogeneous) cluster. The algorithm proceeds until no further cluster merging is possible. At the end, the medoid of each of the final clusters is selected as keyframe and the set of keyframes constitutes the summary of the shot. Numerical experiments demonstrate that our method reasonable estimates the number of ground-truth keyframes, while extracting non-repetitive keyframes that efficiently summarize the content of each shot.

  18. Modified distance in average linkage based on M-estimator and MADn criteria in hierarchical cluster analysis

    NASA Astrophysics Data System (ADS)

    Muda, Nora; Othman, Abdul Rahman

    2015-10-01

    The process of grouping a set of objects into classes of similar objects is called clustering. It divides a large group of observations into smaller groups so that the observations within each group are relatively similar and the observations in different groups are relatively dissimilar. In this study, an agglomerative method in hierarchical cluster analysis is chosen and clusters were constructed by using an average linkage technique. An average linkage technique requires distance between clusters, which is calculated based on the average distance between all pairs of points, one group with another group. In calculating the average distance, the distance will not be robust when there is an outlier. Therefore, the average distance in average linkage needs to be modified in order to overcome the problem of outlier. Therefore, the criteria of outlier detection based on MADn criteria is used and the average distance is recalculated without the outlier. Next, the distance in average linkage is calculated based on a modified one step M-estimator (MOM). The groups of cluster are presented in dendrogram graph. To evaluate the goodness of a modified distance in the average linkage clustering, the bootstrap analysis is conducted on the dendrogram graph and the bootstrap value (BP) are assessed for each branch in dendrogram that formed the group, to ensure the reliability of the branches constructed. This study found that the average linkage technique with modified distance is significantly superior than the usual average linkage technique, if there is an outlier. Both of these techniques are said to be similar if there is no outlier.

  19. Hierarchically clustered adaptive quantization CMAC and its learning convergence.

    PubMed

    Teddy, S D; Lai, E M K; Quek, C

    2007-11-01

    The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically

  20. Hierarchical spike clustering analysis for investigation of interneuron heterogeneity.

    PubMed

    Boehlen, Anne; Heinemann, Uwe; Henneberger, Christian

    2016-04-21

    Action potentials represent the output of a neuron. Especially interneurons display a variety of discharge patterns ranging from regular action potential firing to prominent spike clustering or stuttering. The mechanisms underlying this heterogeneity remain incompletely understood. We established hierarchical cluster analysis of spike trains as a measure of spike clustering. A clustering index was calculated from action potential trains recorded in the whole-cell patch clamp configuration from hippocampal (CA1, stratum radiatum) and entorhinal (medial entorhinal cortex, layer 2) interneurons in acute slices and simulated data. Prominent, region-dependent, but also variable spike clustering was detected using this measure. Further analysis revealed a strong positive correlation between spike clustering and membrane potentials oscillations but an inverse correlation with neuronal resonance. Furthermore, clustering was more pronounced when the balance between fast-activating K(+) currents, assessed by the spike repolarisation time, and hyperpolarization-activated currents, gauged by the size of the sag potential, was shifted in favour of fast K(+) currents. Simulations of spike clustering confirmed that variable ratios of fast K(+) and hyperpolarization-activated currents could underlie different degrees of spike clustering and could thus be crucial for temporally structuring interneuron spike output. PMID:26987719

  1. Biologically supervised hierarchical clustering algorithms for gene expression data.

    PubMed

    Boratyn, Grzegorz M; Datta, Susmita; Datta, Somnath

    2006-01-01

    Cluster analysis has become a standard part of gene expression analysis. In this paper, we propose a novel semi-supervised approach that offers the same flexibility as that of a hierarchical clustering. Yet it utilizes, along with the experimental gene expression data, common biological information about different genes that is being complied at various public, Web accessible databases. We argue that such an approach is inherently superior than the standard unsupervised approach of grouping genes based on expression data alone. It is shown that our biologically supervised methods produce better clustering results than the corresponding unsupervised methods as judged by the distance from the model temporal profiles. R-codes of the clustering algorithm are available from the authors upon request. PMID:17947147

  2. KSC-N: Clustering of Hierarchical Time Profile Data.

    PubMed

    Heylen, Joke; Van Mechelen, Iven; Verduyn, Philippe; Ceulemans, Eva

    2016-06-01

    Quite a few studies in the behavioral sciences result in hierarchical time profile data, with a number of time profiles being measured for each person under study. Associated research questions often focus on individual differences in profile repertoire, that is, differences between persons in the number and the nature of profile shapes that show up for each person. In this paper, we introduce a new method, called KSC-N, that parsimoniously captures such differences while neatly disentangling variability in shape and amplitude. KSC-N induces a few person clusters from the data and derives for each person cluster the types of profile shape that occur most for the persons in that cluster. An algorithm for fitting KSC-N is proposed and evaluated in a simulation study. Finally, the new method is applied to emotional intensity profile data. PMID:25491164

  3. Formation of Cool Cores in Galaxy Clusters via Hierarchical Mergers

    NASA Astrophysics Data System (ADS)

    Motl, Patrick M.; Burns, Jack O.; Loken, Chris; Norman, Michael L.; Bryan, Greg

    2004-05-01

    We present a new scenario for the formation of cool cores in rich galaxy clusters, based on results from recent high spatial dynamic range, adaptive mesh Eulerian hydrodynamic simulations of large-scale structure formation. We find that cores of cool gas, material that would be identified as a classical cooling flow on the basis of its X-ray luminosity excess and temperature profile, are built from the accretion of discrete stable subclusters. Any ``cooling flow'' present is overwhelmed by the velocity field within the cluster; the bulk flow of gas through the cluster typically has speeds up to about 2000 km s-1, and significant rotation is frequently present in the cluster core. The inclusion of consistent initial cosmological conditions for the cluster within its surrounding supercluster environment is crucial when the evolution of cool cores in rich galaxy clusters is simulated. This new model for the hierarchical assembly of cool gas naturally explains the high frequency of cool cores in rich galaxy clusters, despite the fact that a majority of these clusters show evidence of substructure that is believed to arise from recent merger activity. Furthermore, our simulations generate complex cluster cores in concordance with recent X-ray observations of cool fronts, cool ``bullets,'' and filaments in a number of galaxy clusters. Our simulations were computed with a coupled N-body, Eulerian, adaptive mesh refinement, hydrodynamics cosmology code that properly treats the effects of shocks and radiative cooling by the gas. We employ up to seven levels of refinement to attain a peak resolution of 15.6 kpc within a volume 256 Mpc on a side and assume a standard ΛCDM cosmology.

  4. Hierarchical clustering in chameleon f(R) gravity

    NASA Astrophysics Data System (ADS)

    Hellwing, Wojciech A.; Li, Baojiu; Frenk, Carlos S.; Cole, Shaun

    2013-11-01

    We use a suite of high-resolution state-of-the-art N-body dark matter simulations of chameleon f(R) gravity to study the higher order volume-averaged correlation functions overline{ξ _n} together with the hierarchical nth-order correlation amplitudes S_n=overline{ξ }_n/overline{ξ }_2^{n-1} and density distribution functions (PDF). We show that under the non-linear modifications of gravity the hierarchical scaling of the reduced cumulants is preserved. This is however characterized by significant changes in the values of both overline{ξ _n} and Sn and their scale dependence with respect to General Relativity gravity (GR). In addition, we measure a significant increase of the non-linear σ8 parameter reaching 14, 5 and 0.5 per cent in excess of the GR value for the three flavours of our f(R) models. We further note that the values of the reduced cumulants up to order n = 9 are significantly increased in f(R) gravity for all our models at small scales R ≲ 30 h-1 Mpc. In contrast, the values of the hierarchical amplitudes, Sn, are smaller in f(R) indicating that the modified gravity density distribution functions are deviating from the GR case. Furthermore, we find that the redshift evolution of relative deviations of the f(R) hierarchical correlation amplitudes is fastest at high and moderate redshifts 1 ≤ z ≤ 4. The growth of these deviations significantly slows down in the low-redshift universe. We also compute the PDFs and show that for scales below ˜20 h-1 Mpc, they are significantly shifted in f(R) gravity towards the low densities. Finally, we discuss the implications of our theoretical predictions for measurements of the hierarchical clustering in galaxy redshift surveys, including the important problems of the galaxy biasing and redshift space distortions.

  5. The Effectiveness of Query-Specific Hierarchic Clustering in Information Retrieval.

    ERIC Educational Resources Information Center

    Tombros, Anastasios; Villa, Robert; Van Rijsbergen, C. J.

    2002-01-01

    Discusses hierarchic document clustering in information retrieval and its potential improved effectiveness over inverted file search (IFS). Describes experiments conducted to determine whether hierarchic clustering can increase the retrieval effectiveness compared to both static clustering and of conventional IFS when it is applied to search…

  6. Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.

    PubMed

    Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne A; Ferber, Reed

    2015-11-01

    Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners. PMID:26456422

  7. Directed Assembly of Hierarchically Ordered Clusters from Anisotropic Microparticles

    NASA Astrophysics Data System (ADS)

    Han, Koohee; Bharti, Bhuvnesh; Shields, C. Wyatt, IV; Lopez, Gabriel P.; Velev, Orlin D.

    The directed assembly of colloidal particles with specific connectivity, symmetry, and directional response requires controlled interactions and means of programmable binding force. We will show how patchy microparticles can be hierarchically assembled into ordered clusters, resulting from directional interactions between metal-coated facets. First, we introduce lipid mediated capillary bridging as a new class of binding force for directed assembly of metallo-dielectric patchy microspheres. Iron oxide surface patches on latex microspheres were selectively wetted with liquid lipids, guiding the particle assembly into well-defined 2D and 3D clusters. The temperature driven fluid-to-gel phase transition of the fatty acids acts as a thermal switch for cluster assembly and disassembly. Secondly, we used external fields to bind patchy microcubes based on their polarization configuration and interparticle interaction. We present assembled clusters of cobalt-coated patchy microcubes that can be dynamically reconfigured using external magnetic field. The residual polarization of ferromagnetic cobalt patches allows for preserving the assembled sequence even in the absence of the field and drives dynamic reconfiguration of assembled clusters. NSF Grant #DMR-1121107.

  8. Shape Retrieval Using Hierarchical Total Bregman Soft Clustering

    PubMed Central

    Liu, Meizhu; Vemuri, Baba C.; Amari, Shun-ichi; Nielsen, Frank

    2012-01-01

    In this paper, we consider the family of total Bregman divergences (tBDs) as an efficient and robust “distance” measure to quantify the dissimilarity between shapes. We use the tBD based ℓ1-norm center as the representative of a set of shapes, and call it the t-center. First, we briefly present and analyze the properties of the tBDs and t-centers following our previous work in [1]. Then, we prove that for any tBD, there exists a distribution which belongs to the lifted exponential family of statistical distributions. Further, we show that finding the maximum a posteriori estimate of the parameters of the lifted exponential family distribution is equivalent to minimizing the tBD to find the t-centers. This leads to a new clustering technique namely, the total Bregman soft clustering algorithm. We evaluate the tBD, t-center and the soft clustering algorithm on shape retrieval applications. Our shape retrieval framework is composed of three steps: (1) extraction of the shape boundary points (2) affine alignment of the shapes and use of a Gaussian mixture model (GMM) [2], [3], [4] to represent the aligned boundaries, and (3) comparison of the GMMs using tBD to find the best matches given a query shape. To further speed up the shape retrieval algorithm, we perform hierarchical clustering of the shapes using our total Bregman soft clustering algorithm. This enables us to compare the query with a small subset of shapes which are chosen to be the cluster t-centers. We evaluate our method on various public domain 2D and 3D databases, and demonstrate comparable or better results than state-of-the-art retrieval techniques. PMID:22331859

  9. Dynamic Categorization of Clinical Research Eligibility Criteria by Hierarchical Clustering

    PubMed Central

    Luo, Zhihui; Yetisgen-Yildiz, Meliha; Weng, Chunhua

    2011-01-01

    Objective To semi-automatically induce semantic categories of eligibility criteria from text and to automatically classify eligibility criteria based on their semantic similarity. Design The UMLS semantic types and a set of previously developed semantic preference rules were utilized to create an unambiguous semantic feature representation to induce eligibility criteria categories through hierarchical clustering and to train supervised classifiers. Measurements We induced 27 categories and measured the prevalence of the categories in 27,278 eligibility criteria from 1,578 clinical trials and compared the classification performance (i.e., precision, recall, and F1-score) between the UMLS-based feature representation and the “bag of words” feature representation among five common classifiers in Weka, including J48, Bayesian Network, Naïve Bayesian, Nearest Neighbor, and Instance-based Learning Classifier. Results The UMLS semantic feature representation outperforms the “bag of words” feature representation in 89% of the criteria categories. Using the semantically induced categories, machine-learning classifiers required only 2,000 instances to stabilize classification performance. The J48 classifier yielded the best F1-score and the Bayesian Network classifier achieved the best learning efficiency. Conclusion The UMLS is an effective knowledge source and can enable an efficient feature representation for semi-automated semantic category induction and automatic categorization for clinical research eligibility criteria and possibly other clinical text. PMID:21689783

  10. Hierarchical clusters in families with type 2 diabetes

    PubMed Central

    García-Solano, Beatriz; Gallegos-Cabriales, Esther C; Gómez-Meza, Marco V; García-Madrid, Guillermina; Flores-Merlo, Marcela; García-Solano, Mauro

    2015-01-01

    Families represent more than a set of individuals; family is more than a sum of its individual members. With this classification, nurses can identify the family health-illness beliefs obey family as a unit concept, and plan family inclusion into the type 2 diabetes treatment, whom is not considered in public policy, despite families share diet, exercise, and self-monitoring with a member who suffers type 2 diabetes. The aim of this study was to determine whether the characteristics, functionality, routines, and family and individual health in type 2 diabetes describes the differences and similarities between families to consider them as a unit. We performed an exploratory, descriptive hierarchical cluster analysis of 61 families using three instruments and a questionnaire, in addition to weight, height, body fat percentage, hemoglobin A1c, total cholesterol, triglycerides, low-density lipoprotein and high-density lipoprotein. The analysis produced three groups of families. Wilk’s lambda demonstrated statistically significant differences provided by age (Λ = 0.778, F = 2.098, p = 0.010) and family health (Λ = 0.813, F = 2.650, p = 0.023). A post hoc Tukey test coincided with the three subsets. Families with type 2 diabetes have common elements that make them similar, while sharing differences that make them unique. PMID:27347419

  11. The Evolution of Brightest Cluster Galaxies in a Hierarchical Universe

    NASA Astrophysics Data System (ADS)

    Tonini, Chiara; Bernyk, Maksym; Croton, Darren; Maraston, Claudia; Thomas, Daniel

    2012-11-01

    We investigate the evolution of brightest cluster galaxies (BCGs) from redshift z ~ 1.6 to z = 0. We upgrade the hierarchical semi-analytic model of Croton et al. with a new spectro-photometric model that produces realistic galaxy spectra, making use of the Maraston stellar populations and a new recipe for the dust extinction. We compare the model predictions of the K-band luminosity evolution and the J - K, V - I, and I - K color evolution with a series of data sets, including those of Collins et al. who argued that semi-analytic models based on the Millennium simulation cannot reproduce the red colors and high luminosity of BCGs at z > 1. We show instead that the model is well in range of the observed luminosity and correctly reproduces the color evolution of BCGs in the whole redshift range up to z ~ 1.6. We argue that the success of the semi-analytic model is in large part due to the implementation of a more sophisticated spectro-photometric model. An analysis of the model BCGs shows an increase in mass by a factor of 2-3 since z ~ 1, and star formation activity down to low redshifts. While the consensus regarding BCGs is that they are passively evolving, we argue that this conclusion is affected by the degeneracy between star formation history and stellar population models used in spectral energy distribution fitting, and by the inefficacy of toy models of passive evolution to capture the complexity of real galaxies, especially those with rich merger histories like BCGs. Following this argument, we also show that in the semi-analytic model the BCGs show a realistic mix of stellar populations, and that these stellar populations are mostly old. In addition, the age-redshift relation of the model BCGs follows that of the universe, meaning that given their merger history and star formation history, the ageing of BCGs is always dominated by the ageing of their stellar populations. In a ΛCDM universe, we define such evolution as "passive in the hierarchical sense."

  12. THE EVOLUTION OF BRIGHTEST CLUSTER GALAXIES IN A HIERARCHICAL UNIVERSE

    SciTech Connect

    Tonini, Chiara; Bernyk, Maksym; Croton, Darren; Maraston, Claudia; Thomas, Daniel

    2012-11-01

    We investigate the evolution of brightest cluster galaxies (BCGs) from redshift z {approx} 1.6 to z = 0. We upgrade the hierarchical semi-analytic model of Croton et al. with a new spectro-photometric model that produces realistic galaxy spectra, making use of the Maraston stellar populations and a new recipe for the dust extinction. We compare the model predictions of the K-band luminosity evolution and the J - K, V - I, and I - K color evolution with a series of data sets, including those of Collins et al. who argued that semi-analytic models based on the Millennium simulation cannot reproduce the red colors and high luminosity of BCGs at z > 1. We show instead that the model is well in range of the observed luminosity and correctly reproduces the color evolution of BCGs in the whole redshift range up to z {approx} 1.6. We argue that the success of the semi-analytic model is in large part due to the implementation of a more sophisticated spectro-photometric model. An analysis of the model BCGs shows an increase in mass by a factor of 2-3 since z {approx} 1, and star formation activity down to low redshifts. While the consensus regarding BCGs is that they are passively evolving, we argue that this conclusion is affected by the degeneracy between star formation history and stellar population models used in spectral energy distribution fitting, and by the inefficacy of toy models of passive evolution to capture the complexity of real galaxies, especially those with rich merger histories like BCGs. Following this argument, we also show that in the semi-analytic model the BCGs show a realistic mix of stellar populations, and that these stellar populations are mostly old. In addition, the age-redshift relation of the model BCGs follows that of the universe, meaning that given their merger history and star formation history, the ageing of BCGs is always dominated by the ageing of their stellar populations. In a {Lambda}CDM universe, we define such evolution as 'passive

  13. D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

    NASA Astrophysics Data System (ADS)

    Suhaibah, A.; Uznir, U.; Anton, F.; Mioc, D.; Rahman, A. A.

    2016-06-01

    Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

  14. Growing self-organizing trees for autonomous hierarchical clustering.

    PubMed

    Doan, Nhat-Quang; Azzag, Hanane; Lebbah, Mustapha

    2013-05-01

    This paper presents a new unsupervised learning method based on growing processes and autonomous self-assembly rules. This method, called Growing Self-organizing Trees (GSoT), can grow both network size and tree topology to represent the topological and hierarchical dataset organization, allowing a rapid and interactive visualization. Tree construction rules draw inspiration from elusive properties of biological organization to build hierarchical structures. Experiments conducted on real datasets demonstrate good GSoT performance and provide visual results that are generated during the training process. PMID:23041056

  15. A Hierarchical Bayesian Procedure for Two-Mode Cluster Analysis

    ERIC Educational Resources Information Center

    DeSarbo, Wayne S.; Fong, Duncan K. H.; Liechty, John; Saxton, M. Kim

    2004-01-01

    This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters.…

  16. Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

    PubMed

    Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

    2014-09-30

    This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. PMID:25035965

  17. The Common Prescription Patterns Based on the Hierarchical Clustering of Herb-Pairs Efficacies

    PubMed Central

    2016-01-01

    Prescription patterns are rules or regularities used to generate, recognize, or judge a prescription. Most of existing studies focused on the specific prescription patterns for diverse diseases or syndromes, while little attention was paid to the common patterns, which reflect the global view of the regularities of prescriptions. In this paper, we designed a method CPPM to find the common prescription patterns. The CPPM is based on the hierarchical clustering of herb-pair efficacies (HPEs). Firstly, HPEs were hierarchically clustered; secondly, the individual herbs are labeled by the HPEC (the clusters of HPEs); and then the prescription patterns were extracted from the combinations of HPEC; finally the common patterns are recognized statistically. The results showed that HPEs have hierarchical clustering structure. When the clustering level is 2 and the HPEs were classified into two clusters, the common prescription patterns are obvious. Among 332 candidate prescriptions, 319 prescriptions follow the common patterns. The description of the patterns is that if a prescription contains the herbs of the cluster (C 1), it is very likely to have other herbs of another cluster (C 2); while a prescription has the herbs of C 2, it may have no herbs of C 1. Finally, we discussed that the common patterns are mathematically coincident with the Blood-Qi theory. PMID:27190534

  18. Security clustering algorithm based on reputation in hierarchical peer-to-peer network

    NASA Astrophysics Data System (ADS)

    Chen, Mei; Luo, Xin; Wu, Guowen; Tan, Yang; Kita, Kenji

    2013-03-01

    For the security problems of the hierarchical P2P network (HPN), the paper presents a security clustering algorithm based on reputation (CABR). In the algorithm, we take the reputation mechanism for ensuring the security of transaction and use cluster for managing the reputation mechanism. In order to improve security, reduce cost of network brought by management of reputation and enhance stability of cluster, we select reputation, the historical average online time, and the network bandwidth as the basic factors of the comprehensive performance of node. Simulation results showed that the proposed algorithm improved the security, reduced the network overhead, and enhanced stability of cluster.

  19. Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning.

    PubMed

    Collins, Anne Gabrielle Eva; Frank, Michael Joshua

    2016-07-01

    Often the world is structured such that distinct sensory contexts signify the same abstract rule set. Learning from feedback thus informs us not only about the value of stimulus-action associations but also about which rule set applies. Hierarchical clustering models suggest that learners discover structure in the environment, clustering distinct sensory events into a single latent rule set. Such structure enables a learner to transfer any newly acquired information to other contexts linked to the same rule set, and facilitates re-use of learned knowledge in novel contexts. Here, we show that humans exhibit this transfer, generalization and clustering during learning. Trial-by-trial model-based analysis of EEG signals revealed that subjects' reward expectations incorporated this hierarchical structure; these structured neural signals were predictive of behavioral transfer and clustering. These results further our understanding of how humans learn and generalize flexibly by building abstract, behaviorally relevant representations of the complex, high-dimensional sensory environment. PMID:27082659

  20. Fuzzy hierarchical cross-clustering of data from abandoned mine site contaminated with heavy metals

    NASA Astrophysics Data System (ADS)

    Pourjabbar, A.; Sârbu, C.; Kostarelos, K.; Einax, J. W.; Büchel, G.

    2014-11-01

    The characteristics of pore water and slate samples are critically analyzed using fuzzy hierarchical cross-clustering statistical techniques. The main aim of this study was to investigate the source of contamination near an abandoned uranium mine in Germany. The mining activities were abandoned in 1990 the site was closed, and the surrounding area was remediated. However, heavy metal contamination is still detectable in water, soil and plants today. Hence, investigating the source of the current contamination is an important task. In order to achieve the goal, results from chemical analysis of both pore water samples and leachates from slate samples were initially analyzed using hard (classical) hierarchical clustering algorithms that did not provide meaningful results. By using two fuzzy clustering algorithms, Fuzzy Divisive Hierarchical Clustering (FDHC) and Fuzzy Hierarchical Cross-Clustering (FHCC), a relationship between the leachate from Ordovician-Silurian slate samples (10 samples collected from the test site and the surrounding area) and pore water samples (53 samples collected from 3 locations within the test site at 3 depths over the course of 4 years) was identified. The leachate data formed a cluster which was statistically similar to the cluster formed by the pore water samples collected from two of three locations. In addition, the fuzzy cross-clustering approach allowed for the identification of the characteristics (qualitative and quantitative) responsible for the observed similarities between all the samples. We conclude that the fuzzy algorithms were a better tool for the analysis and interpretation of geological/hydrogeological data where the data sets have an inherent vagueness/uncertainty.

  1. Studying Sub-Dendrograms of Resting-State Functional Networks with Voxel-Wise Hierarchical Clustering

    PubMed Central

    Wang, Yanlu; Msghina, Mussie; Li, Tie-Qiang

    2016-01-01

    Hierarchical clustering is a useful data-driven approach to classify complex data and has been used to analyze resting-state functional magnetic resonance imaging (fMRI) data and derive functional networks of the human brain at very large scale, such as the entire visual or sensory-motor cortex. In this study, we developed a voxel-wise, whole-brain hierarchical clustering framework to perform multi-stage analysis of group-averaged resting-state fMRI data in different levels of detail. With the framework we analyzed particularly the somatosensory motor and visual systems in fine details and constructed the corresponding sub-dendrograms, which corroborate consistently with the known modular organizations from previous clinical and experimental studies. The framework provides a useful tool for data-driven analysis of resting-state fMRI data to gain insight into the hierarchical organization and degree of functional modulation among the sub-units. PMID:27014015

  2. Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

    NASA Astrophysics Data System (ADS)

    Wu, Xin-Ye; Zheng, Zhi-Gang

    2013-08-01

    A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank's database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be "colored" and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

  3. Applying Social Networking and Clustering Algorithms to Galaxy Groups in ALFALFA

    NASA Astrophysics Data System (ADS)

    Bramson, Ali; Wilcots, E. M.

    2012-01-01

    Because most galaxies live in groups, and the environment in which it resides affects the evolution of a galaxy, it is crucial to develop tools to understand how galaxies are distributed within groups. At the same time we must understand how groups are distributed and connected in the larger scale structure of the Universe. I have applied a variety of networking techniques to assess the substructure of galaxy groups, including distance matrices, agglomerative hierarchical clustering algorithms and dendrograms. We use distance matrices to locate groupings spatially in 3-D. Dendrograms created from agglomerative hierarchical clustering results allow us to quantify connections between galaxies and galaxy groups. The shape of the dendrogram reveals if the group is spatially homogenous or clumpy. These techniques are giving us new insight into the structure and dynamical state of galaxy groups and large scale structure. We specifically apply these techniques to the ALFALFA survey of the Coma-Abell 1367 supercluster and its resident galaxy groups.

  4. A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis.

    ERIC Educational Resources Information Center

    Milligan, Glenn W.; Cooper, Martha C.

    1986-01-01

    Five external criteria were used to evaluate the extent of recovery of the true structure in a hierarchical clustering solution. The results of the study indicated that the Hubert and Arabie adjusted Rank index was best suited to the task of comparison across hierarchy levels. (Author/LMO)

  5. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

    EPA Science Inventory

    In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER bindi...

  6. Hierarchical Clustering and Visualization of Aggregate Cyber Data

    SciTech Connect

    Patton, Robert M; Beaver, Justin M; Steed, Chad A; Potok, Thomas E; Treadwell, Jim N

    2011-01-01

    Most commercial intrusion detections systems (IDS) can produce a very high volume of alerts, and are typically plagued by a high false positive rate. The approach described here uses Splunk to aggregate IDS alerts. The aggregated IDS alerts are retrieved from Splunk programmatically and are then clustered using text analysis and visualized using a sunburst diagram to provide an additional understanding of the data. The equivalent of what the cluster analysis and visualization provides would require numerous detailed queries using Splunk and considerable manual effort.

  7. Correlation Based Hierarchical Clustering in Financial Time Series

    NASA Astrophysics Data System (ADS)

    Micciche', S.; Lillo, F.; Mantegna, R. N.

    2005-09-01

    We review a correlation based clustering procedure applied to a portfolio of assets synchronously traded in a financial market. The portfolio considered consists of the set of 500 highly capitalized stocks traded at the New York Stock Exchange during the time period 1987-1998. We show that meaningful economic information can be extracted from correlation matrices.

  8. Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering

    PubMed Central

    de Brevern, Alexandre G; Hazout, Serge; Malpertuy, Alain

    2004-01-01

    Background Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray datasets often contain missing values (MVs) representing a major drawback for the use of the clustering methods. Usually the MVs are not treated, or replaced by zero or estimated by the k-Nearest Neighbor (kNN) approach. The topic of the paper is to study the stability of gene clusters, defined by various hierarchical clustering algorithms, of microarrays experiments including or not MVs. Results In this study, we show that the MVs have important effects on the stability of the gene clusters. Moreover, the magnitude of the gene misallocations is depending on the aggregation algorithm. The most appropriate aggregation methods (e.g. complete-linkage and Ward) are highly sensitive to MVs, and surprisingly, for a very tiny proportion of MVs (e.g. 1%). In most of the case, the MVs must be replaced by expected values. The MVs replacement by the kNN approach clearly improves the identification of co-expressed gene clusters. Nevertheless, we observe that kNN approach is less suitable for the extreme values of gene expression. Conclusion The presence of MVs (even at a low rate) is a major factor of gene cluster instability. In addition, the impact depends on the hierarchical clustering algorithm used. Some methods should be used carefully. Nevertheless, the kNN approach constitutes one efficient method for restoring the missing expression gene values, with a low error level. Our study highlights the need of statistical treatments in microarray data to avoid misinterpretation. PMID:15324460

  9. A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis.

    PubMed

    Milligan, G W; Cooper, M C

    1986-10-01

    Five external criteria were used to evaluate the extent of recovery of the true structure in a hierarchical clustering solution. This was accomplished by comparing the partitions produced by the clustering algorithm with the partition that indicates the true cluster structure known to exist in the data. The five criteria examined were the Rand, the Morey and Agresti adjusted Rand, the Hubert and Arabie adjusted Rand, the Jaccard, and the Fowlkes and Mallows measures. The results of the study indicated that the Hubert and Arabie adjusted Rank index was best suited to the task of comparison across hierarchy levels. Deficiencies with the other measures are noted. PMID:26828221

  10. On Comparison of Clustering Methods for Pharmacoepidemiological Data.

    PubMed

    Feuillet, Fanny; Bellanger, Lise; Hardouin, Jean-Benoit; Victorri-Vigneau, Caroline; Sébille, Véronique

    2015-01-01

    The high consumption of psychotropic drugs is a public health problem. Rigorous statistical methods are needed to identify consumption characteristics in post-marketing phase. Agglomerative hierarchical clustering (AHC) and latent class analysis (LCA) can both provide clusters of subjects with similar characteristics. The objective of this study was to compare these two methods in pharmacoepidemiology, on several criteria: number of clusters, concordance, interpretation, and stability over time. From a dataset on bromazepam consumption, the two methods present a good concordance. AHC is a very stable method and it provides homogeneous classes. LCA is an inferential approach and seems to allow identifying more accurately extreme deviant behavior. PMID:24905478

  11. Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

    PubMed Central

    Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

    2014-01-01

    Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks. PMID:24949877

  12. Multilevel hierarchical kernel spectral clustering for real-life large scale complex networks.

    PubMed

    Mall, Raghvendra; Langone, Rocco; Suykens, Johan A K

    2014-01-01

    Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks. PMID:24949877

  13. Hierarchical Cluster Formation in Concentrated Monoclonal Antibody Formulations

    NASA Astrophysics Data System (ADS)

    Godfrin, P. Douglas; Zarzar, Jonathan; Zarraga, Isidro Dan; Porcar, Lionel; Falus, Peter; Wagner, Norman; Liu, Yun

    Reversible cluster formation has been identified as an underlying cause of large solution viscosities observed in some concentrated monoclonal antibody (mAb) formulations. As high solution viscosity prevents the use of subcutaneous injection as a delivery method for some mAbs, a fundamental understanding of the interactions responsible for high viscosities in concentrated mAb solutions is of significant relevance to mAb applications in human health care as well as of intellectual interest. Here, we present a detailed investigation of a well-studied IgG1 based mAb to relate the short time dynamics and microstructure to significant viscosity changes over a range of pharmaceutically relevant physiochemical conditions. Using a combination of experimental techniques, it is found that upon adding Na2SO4, these antibodies dimerize in solution. Proteins form strongly bounded reversible dimers at dilute concentrations that, when concentrated, interact with each other to form loosely bounded, large, transient clusters. The combined effect of forming strongly bounded dimers and a large transient network is a significant increase in the solution viscosity. Strongly bounded, reversible dimers may exist in many IgG1 based mAb systems such that these results contribute to a more comprehensive understanding of the physical mechanisms producing high viscosities in concentrated protein solutions.

  14. The formation of NGC 3603 young starburst cluster: `prompt' hierarchical assembly or monolithic starburst?

    NASA Astrophysics Data System (ADS)

    Banerjee, Sambaran; Kroupa, Pavel

    2015-02-01

    The formation of very young massive clusters or `starburst' clusters is currently one of the most widely debated topic in astronomy. The classical notion dictates that a star cluster is formed in situ in a dense molecular gas clump. The stellar radiative and mechanical feedback to the residual gas energizes the latter until it escapes the system. The newly born gas-free young cluster eventually readjusts with the corresponding mass-loss. Based on the observed substructured morphologies of many young stellar associations, it is alternatively suggested that even the smooth-profiled massive clusters are also assembled from migrating less massive subclusters. A very young (age ≈ 1 Myr), massive (>104 M⊙) star cluster like the Galactic NGC 3603 young cluster (HD 97950) is an appropriate testbed for distinguishing between the above `monolithic' and `hierarchical' formation scenarios. A recent study by Banerjee & Kroupa demonstrates that the monolithic scenario remarkably reproduces the HD 97950 cluster. In particular, its shape, internal motion and the mass distribution of stars are found to follow naturally and consistently from a single model calculation undergoing ≈70 per cent by mass gas dispersal. In this work, we explore the possibility of the formation of the above cluster via hierarchical assembly of subclusters. These subclusters are initially distributed over a wide range of spatial volumes and have various modes of subclustering in both absence and presence of a background gas potential. Unlike the above monolithic initial system that reproduces HD 97950 very well, the same is found to be prohibitive with hierarchical assembly alone (with/without a gas potential). Only those systems which assemble promptly into a single cluster (in ≲1 Myr) from a close separation (all within ≲2 pc) could match the observed density profile of HD 97950 after a similar gas removal. These results therefore suggest that the NGC 3603 young cluster has formed essentially

  15. HCsnip: An R Package for Semi-supervised Snipping of the Hierarchical Clustering Tree.

    PubMed

    Obulkasim, Askar; van de Wiel, Mark A

    2015-01-01

    Hierarchical clustering (HC) is one of the most frequently used methods in computational biology in the analysis of high-dimensional genomics data. Given a data set, HC outputs a binary tree leaves of which are the data points and internal nodes represent clusters of various sizes. Normally, a fixed-height cut on the HC tree is chosen, and each contiguous branch of data points below that height is considered as a separate cluster. However, the fixed-height branch cut may not be ideal in situations where one expects a complicated tree structure with nested clusters. Furthermore, due to lack of utilization of related background information in selecting the cutoff, induced clusters are often difficult to interpret. This paper describes a novel procedure that aims to automatically extract meaningful clusters from the HC tree in a semi-supervised way. The procedure is implemented in the R package HCsnip available from Bioconductor. Rather than cutting the HC tree at a fixed-height, HCsnip probes the various way of snipping, possibly at variable heights, to tease out hidden clusters ensconced deep down in the tree. The cluster extraction process utilizes, along with the data set from which the HC tree is derived, commonly available background information. Consequently, the extracted clusters are highly reproducible and robust against various sources of variations that "haunted" high-dimensional genomics data. Since the clustering process is guided by the background information, clusters are easy to interpret. Unlike existing packages, no constraint is placed on the data type on which clustering is desired. Particularly, the package accepts patient follow-up data for guiding the cluster extraction process. To our knowledge, HCsnip is the first package that is able to decomposes the HC tree into clusters with piecewise snipping under the guidance of patient time-to-event information. Our implementation of the semi-supervised HC tree snipping framework is generic, and can

  16. Hierarchical clustering of EMD based interest points for road sign detection

    NASA Astrophysics Data System (ADS)

    Khan, Jesmin; Bhuiyan, Sharif; Adhami, Reza

    2014-04-01

    This paper presents an automatic road traffic signs detection and recognition system based on hierarchical clustering of interest points and joint transform correlation. The proposed algorithm consists of the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). We propose a two-step unsupervised clustering technique, which is adaptive and based on two criterion. In this context, the detected points are initially clustered based on the stable local features related to the brightness and color, which are extracted using Gabor filter. Then points belonging to each partition are reclustered depending on the dispersion of the points in the initial cluster using position feature. This two-step hierarchical clustering yields the possible candidate road signs or the region of interests (ROIs). Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs stored in the database. The presented framework provides a novel way to detect a road sign from the natural scenes and the results demonstrate the efficacy of the proposed technique, which yields a very low false hit rate.

  17. A new artefacts resistant method for automatic lineament extraction using Multi-Hillshade Hierarchic Clustering (MHHC)

    NASA Astrophysics Data System (ADS)

    Šilhavý, Jakub; Minár, Jozef; Mentlík, Pavel; Sládek, Ján

    2016-07-01

    This paper presents a new method of automatic lineament extraction which includes the removal of the 'artefacts effect' which is associated with the process of raster based analysis. The core of the proposed Multi-Hillshade Hierarchic Clustering (MHHC) method incorporates a set of variously illuminated and rotated hillshades in combination with hierarchic clustering of derived 'protolineaments'. The algorithm also includes classification into positive and negative lineaments. MHHC was tested in two different territories in Bohemian Forest and Central Western Carpathians. The original vector-based algorithm was developed for comparison of the individual lineaments proximity. Its use confirms the compatibility of manual and automatic extraction and their similar relationships to structural data in the study areas.

  18. To Aggregate or Not and Potentially Better Questions for Clustered Data: The Need for Hierarchical Linear Modeling in CTE Research

    ERIC Educational Resources Information Center

    Nimon, Kim

    2012-01-01

    Using state achievement data that are openly accessible, this paper demonstrates the application of hierarchical linear modeling within the context of career technical education research. Three prominent approaches to analyzing clustered data (i.e., modeling aggregated data, modeling disaggregated data, modeling hierarchical data) are discussed…

  19. Recursive Hierarchical Image Segmentation by Region Growing and Constrained Spectral Clustering

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2002-01-01

    This paper describes an algorithm for hierarchical image segmentation (referred to as HSEG) and its recursive formulation (referred to as RHSEG). The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HS WO) approach to region growing, which seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing. In addition, HSEG optionally interjects between HSWO region growing iterations merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the segmentation results, especially for larger images, it also significantly increases HSEG's computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) has been devised and is described herein. Included in this description is special code that is required to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. Implementations for single processor and for multiple processor computer systems are described. Results with Landsat TM data are included comparing HSEG with classic region growing. Finally, an application to image information mining and knowledge discovery is discussed.

  20. An Intrusion Detection System Based on Multi-Level Clustering for Hierarchical Wireless Sensor Networks.

    PubMed

    Butun, Ismail; Ra, In-Ho; Sankar, Ravi

    2015-01-01

    In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) "downward-IDS (D-IDS)" to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) "upward-IDS (U-IDS)" to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented. PMID:26593915

  1. An Intrusion Detection System Based on Multi-Level Clustering for Hierarchical Wireless Sensor Networks

    PubMed Central

    Butun, Ismail; Ra, In-Ho; Sankar, Ravi

    2015-01-01

    In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) “downward-IDS (D-IDS)” to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) “upward-IDS (U-IDS)” to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented. PMID:26593915

  2. ON THE DISRUPTION OF STAR CLUSTERS IN A HIERARCHICAL INTERSTELLAR MEDIUM

    SciTech Connect

    Elmegreen, Bruce G.; Hunter, Deidre A. E-mail: dah@lowell.ed

    2010-03-20

    The distribution of the number of clusters as a function of mass M and age T suggests that clusters get eroded or dispersed in a regular way over time, such that the cluster number decreases inversely as an approximate power law with T within each fixed interval of M. This power law is inconsistent with standard dispersal mechanisms such as cluster evaporation and cloud collisions. In the conventional interpretation, it requires the unlikely situation where diverse mechanisms stitch together over time in a way that is independent of environment or M. Here, we consider another model in which the large-scale distribution of gas in each star-forming region plays an important role. We note that star clusters form with positional and temporal correlations in giant cloud complexes, and suggest that these complexes dominate the tidal force and collisional influence on a cluster during its first several hundred million years. Because the cloud complex density decreases regularly with position from the cluster birth site, the harassment and collision rates between the cluster and the cloud pieces decrease regularly with age as the cluster drifts. This decrease is typically a power law of the form required to explain the mass-age distribution. We reproduce this distribution for a variety of cases, including rapid disruption, slow erosion, combinations of these two, cluster-cloud collisions, cluster disruption by hierarchical disassembly, and partial cluster disruption. We also consider apparent cluster mass loss by fading below the surface brightness limit of a survey. In all cases, the observed log M-log T diagram can be reproduced under reasonable assumptions.

  3. On the Disruption of Star Clusters in a Hierarchical Interstellar Medium

    NASA Astrophysics Data System (ADS)

    Elmegreen, Bruce G.; Hunter, Deidre A.

    2010-03-01

    The distribution of the number of clusters as a function of mass M and age T suggests that clusters get eroded or dispersed in a regular way over time, such that the cluster number decreases inversely as an approximate power law with T within each fixed interval of M. This power law is inconsistent with standard dispersal mechanisms such as cluster evaporation and cloud collisions. In the conventional interpretation, it requires the unlikely situation where diverse mechanisms stitch together over time in a way that is independent of environment or M. Here, we consider another model in which the large-scale distribution of gas in each star-forming region plays an important role. We note that star clusters form with positional and temporal correlations in giant cloud complexes, and suggest that these complexes dominate the tidal force and collisional influence on a cluster during its first several hundred million years. Because the cloud complex density decreases regularly with position from the cluster birth site, the harassment and collision rates between the cluster and the cloud pieces decrease regularly with age as the cluster drifts. This decrease is typically a power law of the form required to explain the mass-age distribution. We reproduce this distribution for a variety of cases, including rapid disruption, slow erosion, combinations of these two, cluster-cloud collisions, cluster disruption by hierarchical disassembly, and partial cluster disruption. We also consider apparent cluster mass loss by fading below the surface brightness limit of a survey. In all cases, the observed log M-log T diagram can be reproduced under reasonable assumptions.

  4. An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks.

    PubMed

    Nasim, Mehwish; Qaisar, Saad; Lee, Sungyoung

    2012-01-01

    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. PMID:22368459

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

    PubMed Central

    Nasim, Mehwish; Qaisar, Saad; Lee, Sungyoung

    2012-01-01

    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. PMID:22368459

  6. Agglomerative percolation on the Bethe lattice and the triangular cactus

    NASA Astrophysics Data System (ADS)

    Chae, Huiseung; Yook, Soon-Hyung; Kim, Yup

    2013-08-01

    Agglomerative percolation (AP) on the Bethe lattice and the triangular cactus is studied to establish the exact mean-field theory for AP. Using the self-consistent simulation method based on the exact self-consistent equations, the order parameter P∞ and the average cluster size S are measured. From the measured P∞ and S, the critical exponents βk and γk for k = 2 and 3 are evaluated. Here, βk and γk are the critical exponents for P∞ and S when the growth of clusters spontaneously breaks the Zk symmetry of the k-partite graph. The obtained values are β2 = 1.79(3), γ2 = 0.88(1), β3 = 1.35(5) and γ3 = 0.94(2). By comparing these exponents with those for ordinary percolation (β∞ = 1 and γ∞ = 1), we also find β∞ < β3 < β2 and γ∞ > γ3 > γ2. These results quantitatively verify the conjecture that the AP model belongs to a new universality class if the Zk symmetry is broken spontaneously, and the new universality class depends on k.

  7. The Discovery of Globular Clusters in the Protospiral Galaxy NGC 2915: Implications for Hierarchical Galaxy Evolution

    NASA Astrophysics Data System (ADS)

    Meurer, Gerhardt R.; Blakeslee, J. P.; Sirianni, M.; Ford, H. C.; Illingworth, G. D.; Benítez, N.; Clampin, M.; Menanteau, F.; Tran, H. D.; Kimble, R. A.; Hartig, G. F.; Ardila, D. R.; Bartko, F.; Bouwens, R. J.; Broadhurst, T. J.; Brown, R. A.; Burrows, C. J.; Cheng, E. S.; Cross, N. J. G.; Feldman, P. D.; Golimowski, D. A.; Gronwall, C.; Infante, L.; Krist, J. E.; Lesser, M. P.; Martel, A. R.; Miley, G. K.; Postman, M.; Rosati, P.; Sparks, W. B.; Tsvetanov, Z. I.; White, R. L.; Zheng, W.

    2003-12-01

    We have discovered three globular clusters beyond the Holmberg radius in Hubble Space Telescope Advanced Camera for Surveys images of the gas-rich dark matter-dominated blue compact dwarf galaxy NGC 2915. The clusters, all of which start to resolve into stars, have MV606=-8.9 to -9.8 mag, significantly brighter than the peak of the luminosity function of Milky Way globular clusters. Their colors suggest a metallicity [Fe/H]~-1.9 dex, typical of metal-poor Galactic globular clusters. The specific frequency of clusters is at a minimum normal, compared to spiral galaxies. However, since only a small portion of the system has been surveyed, it is more likely that the luminosity and mass normalized cluster content is higher, like that seen in elliptical galaxies and galaxy clusters. This suggests that NGC 2915 resembles a key phase in the early hierarchical assembly of galaxies-the epoch when much of the old stellar population has formed but little of the stellar disk. Depending on the subsequent interaction history, such systems could go on to build up larger elliptical galaxies, evolve into normal spirals, or in rare circumstances remain suspended in their development to become systems like NGC 2915.

  8. Using Dynamic Quantum Clustering to Analyze Hierarchically Heterogeneous Samples on the Nanoscale

    SciTech Connect

    Hume, Allison; /Princeton U. /SLAC

    2012-09-07

    Dynamic Quantum Clustering (DQC) is an unsupervised, high visual data mining technique. DQC was tested as an analysis method for X-ray Absorption Near Edge Structure (XANES) data from the Transmission X-ray Microscopy (TXM) group. The TXM group images hierarchically heterogeneous materials with nanoscale resolution and large field of view. XANES data consists of energy spectra for each pixel of an image. It was determined that DQC successfully identifies structure in data of this type without prior knowledge of the components in the sample. Clusters and sub-clusters clearly reflected features of the spectra that identified chemical component, chemical environment, and density in the image. DQC can also be used in conjunction with the established data analysis technique, which does require knowledge of components present.

  9. Hierarchical Information-Based Clustering for Connectivity-Based Cortex Parcellation

    PubMed Central

    Gorbach, Nico S.; Schütte, Christoph; Melzer, Corina; Goldau, Mathias; Sujazow, Olivia; Jitsev, Jenia; Douglas, Tania; Tittgemeyer, Marc

    2011-01-01

    One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (“the functional fingerprint”) is closely related to its anatomical connections (“the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas. PMID:21977015

  10. Validation of hierarchical cluster analysis for identification of bacterial species using 42 bacterial isolates

    NASA Astrophysics Data System (ADS)

    Ghebremedhin, Meron; Yesupriya, Shubha; Luka, Janos; Crane, Nicole J.

    2015-03-01

    Recent studies have demonstrated the potential advantages of the use of Raman spectroscopy in the biomedical field due to its rapidity and noninvasive nature. In this study, Raman spectroscopy is applied as a method for differentiating between bacteria isolates for Gram status and Genus species. We created models for identifying 28 bacterial isolates using spectra collected with a 785 nm laser excitation Raman spectroscopic system. In order to investigate the groupings of these samples, partial least squares discriminant analysis (PLSDA) and hierarchical cluster analysis (HCA) was implemented. In addition, cluster analyses of the isolates were performed using various data types consisting of, biochemical tests, gene sequence alignment, high resolution melt (HRM) analysis and antimicrobial susceptibility tests of minimum inhibitory concentration (MIC) and degree of antimicrobial resistance (SIR). In order to evaluate the ability of these models to correctly classify bacterial isolates using solely Raman spectroscopic data, a set of 14 validation samples were tested using the PLSDA models and consequently the HCA models. External cluster evaluation criteria of purity and Rand index were calculated at different taxonomic levels to compare the performance of clustering using Raman spectra as well as the other datasets. Results showed that Raman spectra performed comparably, and in some cases better than, the other data types with Rand index and purity values up to 0.933 and 0.947, respectively. This study clearly demonstrates that the discrimination of bacterial species using Raman spectroscopic data and hierarchical cluster analysis is possible and has the potential to be a powerful point-of-care tool in clinical settings.

  11. CLUSTAG & WCLUSTAG: Hierarchical Clustering Algorithms for Efficient Tag-SNP Selection

    NASA Astrophysics Data System (ADS)

    Ao, Sio-Iong

    More than 6 million single nucleotide polymorphisms (SNPs) in the human genome have been genotyped by the HapMap project. Although only a pro portion of these SNPs are functional, all can be considered as candidate markers for indirect association studies to detect disease-related genetic variants. The complete screening of a gene or a chromosomal region is nevertheless an expensive undertak ing for association studies. A key strategy for improving the efficiency of association studies is to select a subset of informative SNPs, called tag SNPs, for analysis. In the chapter, hierarchical clustering algorithms have been proposed for efficient tag SNP selection.

  12. Graph kernels, hierarchical clustering, and network community structure: experiments and comparative analysis

    NASA Astrophysics Data System (ADS)

    Zhang, S.; Ning, X.-M.; Zhang, X.-S.

    2007-05-01

    There has been a quickly growing interest in properties of complex networks, such as the small world property, power-law degree distribution, network transitivity, and community structure, which seem to be common to many real world networks. In this study, we consider the community property which is also found in many real networks. Based on the diffusion kernels of networks, a hierarchical clustering approach is proposed to uncover the community structure of different extent of complex networks. We test the method on some networks with known community structures and find that it can detect significant community structure in these networks. Comparison with related methods shows the effectiveness of the method.

  13. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering

    PubMed Central

    2015-01-01

    Summary: dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing trees to one another (both statistically and visually). As such, dendextend offers a flexible framework for enhancing R's rich ecosystem of packages for performing hierarchical clustering of items. Availability and implementation: The dendextend R package (including detailed introductory vignettes) is available under the GPL-2 Open Source license and is freely available to download from CRAN at: (http://cran.r-project.org/package=dendextend) Contact: Tal.Galili@math.tau.ac.il PMID:26209431

  14. Hierarchical data clustering approach for segmenting colored three-dimensional point clouds of building interiors

    NASA Astrophysics Data System (ADS)

    Sareen, Kuldeep K.; Knopf, George K.; Canas, Roberto

    2011-07-01

    A range scan of a building's interior typically produces an immense cloud of colorized three-dimensional data that represents diverse surfaces ranging from simple planes to complex objects. To create a virtual reality model of the preexisting room, it is necessary to segment the data into meaningful clusters. Unfortunately, segmentation algorithms based solely on surface curvature have difficulty in handling such diverse interior geometries, occluded boundaries, and closely placed objects with similar curvature properties. The proposed two stage hierarchical clustering algorithm overcomes many of these challenges by exploiting the registered color and spatial information simultaneously. Large planar regions are initially identified using constraints that combine color (hue) and a measure of local planarity called planar alignment factor. This stage assigns 72 to 84% of the sampled points to clusters representing flat surfaces such as walls, ceilings, or floors. The significantly reduced data points are clustered further using local surface normal and hue deviation information. A local density driven investigation distance (fixed density distance) is used for normal computation and cluster expansion. The methodology is tested on colorized range data of a typical room interior. The combined approach enabled the successful segmentation of planar and complex geometries in both dense and sparse data regions.

  15. Analysis of genetic association in Listeria and Diabetes using Hierarchical Clustering and Silhouette Index

    NASA Astrophysics Data System (ADS)

    Pagnuco, Inti A.; Pastore, Juan I.; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L.

    2016-04-01

    It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, where significative groups of genes are defined based on some criteria. This task is usually performed by clustering algorithms, where the whole family of genes, or a subset of them, are clustered into meaningful groups based on their expression values in a set of experiment. In this work we used a methodology based on the Silhouette index as a measure of cluster quality for individual gene groups, and a combination of several variants of hierarchical clustering to generate the candidate groups, to obtain sets of co-expressed genes for two real data examples. We analyzed the quality of the best ranked groups, obtained by the algorithm, using an online bioinformatics tool that provides network information for the selected genes. Moreover, to verify the performance of the algorithm, considering the fact that it doesn’t find all possible subsets, we compared its results against a full search, to determine the amount of good co-regulated sets not detected.

  16. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

    PubMed Central

    Sherrill, Delsey M; Moy, Marilyn L; Reilly, John J; Bonato, Paolo

    2005-01-01

    Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD) undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical clustering methods are relevant

  17. Nanostring-cluster hierarchical structured Bi2O3: synthesis, evolution and application in biosensing.

    PubMed

    Yu, Ya-Nan; Lu, Shi-Yu; Bao, Shu-Juan; Sun, Qiang-Qiang; Liao, Sheng-Hui

    2016-01-21

    In the present study, a simple strategy was developed to fabricate a new Bi2O3 nanostring-cluster hierarchical structure. Precursor microrods composed of Bi(C2O4)OH were initially grown under hydrothermal conditions. After calcination in air, Bi(C2O4)OH microrods were carved into unique string-cluster structures by the gas produced during the decomposition process. To explain the formation mechanism, the effects of pyrolysis temperature and time on the morphology of the as-prepared samples were investigated and are discussed in detail. It was discovered that the nanostring-cluster-structured Bi2O3 consists of thin nanoplatelet arrays, which is advantageous for glucose enzyme immobilization and for designing biosensors. The resulting Bi2O3 structure showed an excellent capability in the modification of electrode surfaces in biosensors by enhancing the sensitivity, with good specificity and response time. Such qualities of a biosensor are ideal characteristics for glucose sensing performance and allow for further explorations of its application in other fields. PMID:26687451

  18. Evolution of hierarchical clustering in the CFHTLS-Wide since z ˜ 1

    NASA Astrophysics Data System (ADS)

    Wolk, M.; McCracken, H. J.; Colombi, S.; Fry, J. N.; Kilbinger, M.; Hudelot, P.; Mellier, Y.; Ilbert, O.

    2013-10-01

    We present measurements of higher order clustering of galaxies in the latest release of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS)-Wide. We construct a series of volume-limited sample of galaxies containing more than one million galaxies over the redshift range 0.2 < z < 1 in the four independent fields of the CFHTLS-Wide. Using a counts-in-cells technique we measure the variance {bar{ξ }}_2 and the hierarchical moments Sn= {{bar{ξ }}_n / {bar{ξ }}_2^{n-1}} (3 ≤ n ≤ 5) as a function of redshift and angular scale. We find that the measured field-to-field scatter in our estimators is in excellent agreement with analytical predictions. At small scales, corresponding to the highly non-linear regime, we find tentative evidence at the 1σ level that the hierarchical moments increase with redshift. At large scales, corresponding to the weakly non-linear regime, our measurements are marginally consistent with perturbation theory predictions for standard Λ cold dark matter cosmology using a simple linear bias. The predictions of perturbation theory tend to slightly overestimate our measurements, which may be a signature of non-linear bias.

  19. Semi-Automatically Inducing Semantic Classes of Clinical Research Eligibility Criteria Using UMLS and Hierarchical Clustering.

    PubMed

    Luo, Zhihui; Johnson, Stephen B; Weng, Chunhua

    2010-01-01

    This paper presents a novel approach to learning semantic classes of clinical research eligibility criteria. It uses the UMLS Semantic Types to represent semantic features and the Hierarchical Clustering method to group similar eligibility criteria. By establishing a gold standard using two independent raters, we evaluated the coverage and accuracy of the induced semantic classes. On 2,718 random eligibility criteria sentences, the inter-rater classification agreement was 85.73%. In a 10-fold validation test, the average Precision, Recall and F-score of the classification results of a decision-tree classifier were 87.8%, 88.0%, and 87.7% respectively. Our induced classes well aligned with 16 out of 17 eligibility criteria classes defined by the BRIDGE model. We discuss the potential of this method and our future work. PMID:21347026

  20. Clustering, hierarchical organization, and the topography of abstract and concrete nouns.

    PubMed

    Troche, Joshua; Crutch, Sebastian; Reilly, Jamie

    2014-01-01

    The empirical study of language has historically relied heavily upon concrete word stimuli. By definition, concrete words evoke salient perceptual associations that fit well within feature-based, sensorimotor models of word meaning. In contrast, many theorists argue that abstract words are "disembodied" in that their meaning is mediated through language. We investigated word meaning as distributed in multidimensional space using hierarchical cluster analysis. Participants (N = 365) rated target words (n = 400 English nouns) across 12 cognitive dimensions (e.g., polarity, ease of teaching, emotional valence). Factor reduction revealed three latent factors, corresponding roughly to perceptual salience, affective association, and magnitude. We plotted the original 400 words for the three latent factors. Abstract and concrete words showed overlap in their topography but also differentiated themselves in semantic space. This topographic approach to word meaning offers a unique perspective to word concreteness. PMID:24808876

  1. Diversity of Xiphinema americanum-group Species and Hierarchical Cluster Analysis of Morphometrics

    PubMed Central

    Lamberti, Franco; Ciancio, Aurelio

    1993-01-01

    Of the 39 species composing the Xiphinema americanum group, 14 were described originally from North America and two others have been reported from this region. Many species are very similar morphologically and can be distinguished only by a difficult comparison of various combinations of some morphometric characters. Study of morphometrics of 49 populations, including the type populations of the 39 species attributed to this group, by principal component analysis and hierarchical cluster analysis placed the populations into five subgroups, proposed here as the X. brevicolle subgroup (seven species), the X. americanum subgroup (17 species), the X. taylori subgroup (two species), the X. pachtaicum subgroup (eight species), and the X. lambertii subgroup (five species). PMID:19279776

  2. ASTEROID FAMILY IDENTIFICATION USING THE HIERARCHICAL CLUSTERING METHOD AND WISE/NEOWISE PHYSICAL PROPERTIES

    SciTech Connect

    Masiero, Joseph R.; Mainzer, A. K.; Bauer, J. M.; Nugent, C. R.; Stevenson, R.

    2013-06-10

    Using albedos from WISE/NEOWISE to separate distinct albedo groups within the Main Belt asteroids, we apply the Hierarchical Clustering Method to these subpopulations and identify dynamically associated clusters of asteroids. While this survey is limited to the {approx}35% of known Main Belt asteroids that were detected by NEOWISE, we present the families linked from these objects as higher confidence associations than can be obtained from dynamical linking alone. We find that over one-third of the observed population of the Main Belt is represented in the high-confidence cores of dynamical families. The albedo distribution of family members differs significantly from the albedo distribution of background objects in the same region of the Main Belt; however, interpretation of this effect is complicated by the incomplete identification of lower-confidence family members. In total we link 38,298 asteroids into 76 distinct families. This work represents a critical step necessary to debias the albedo and size distributions of asteroids in the Main Belt and understand the formation and history of small bodies in our solar system.

  3. An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

    PubMed

    Booma, P M; Prabhakaran, S; Dhanalakshmi, R

    2014-01-01

    Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

  4. Hierarchical Clustering of Breast Cancer Methylomes Revealed Differentially Methylated and Expressed Breast Cancer Genes

    PubMed Central

    Lin, I-Hsuan; Chen, Dow-Tien; Chang, Yi-Feng; Lee, Yu-Ling; Su, Chia-Hsin; Cheng, Ching; Tsai, Yi-Chien; Ng, Swee-Chuan; Chen, Hsiao-Tan; Lee, Mei-Chen; Chen, Hong-Wei; Suen, Shih-Hui; Chen, Yu-Cheng; Liu, Tze-Tze; Chang, Chuan-Hsiung; Hsu, Ming-Ta

    2015-01-01

    Oncogenic transformation of normal cells often involves epigenetic alterations, including histone modification and DNA methylation. We conducted whole-genome bisulfite sequencing to determine the DNA methylomes of normal breast, fibroadenoma, invasive ductal carcinomas and MCF7. The emergence, disappearance, expansion and contraction of kilobase-sized hypomethylated regions (HMRs) and the hypomethylation of the megabase-sized partially methylated domains (PMDs) are the major forms of methylation changes observed in breast tumor samples. Hierarchical clustering of HMR revealed tumor-specific hypermethylated clusters and differential methylated enhancers specific to normal or breast cancer cell lines. Joint analysis of gene expression and DNA methylation data of normal breast and breast cancer cells identified differentially methylated and expressed genes associated with breast and/or ovarian cancers in cancer-specific HMR clusters. Furthermore, aberrant patterns of X-chromosome inactivation (XCI) was found in breast cancer cell lines as well as breast tumor samples in the TCGA BRCA (breast invasive carcinoma) dataset. They were characterized with differentially hypermethylated XIST promoter, reduced expression of XIST, and over-expression of hypomethylated X-linked genes. High expressions of these genes were significantly associated with lower survival rates in breast cancer patients. Comprehensive analysis of the normal and breast tumor methylomes suggests selective targeting of DNA methylation changes during breast cancer progression. The weak causal relationship between DNA methylation and gene expression observed in this study is evident of more complex role of DNA methylation in the regulation of gene expression in human epigenetics that deserves further investigation. PMID:25706888

  5. CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks

    NASA Astrophysics Data System (ADS)

    Franke, R.

    2016-11-01

    In many networks discovered in biology, medicine, neuroscience and other disciplines special properties like a certain degree distribution and hierarchical cluster structure (also called communities) can be observed as general organizing principles. Detecting the cluster structure of an unknown network promises to identify functional subdivisions, hierarchy and interactions on a mesoscale. It is not trivial choosing an appropriate detection algorithm because there are multiple network, cluster and algorithmic properties to be considered. Edges can be weighted and/or directed, clusters overlap or build a hierarchy in several ways. Algorithms differ not only in runtime, memory requirements but also in allowed network and cluster properties. They are based on a specific definition of what a cluster is, too. On the one hand, a comprehensive network creation model is needed to build a large variety of benchmark networks with different reasonable structures to compare algorithms. On the other hand, if a cluster structure is already known, it is desirable to separate effects of this structure from other network properties. This can be done with null model networks that mimic an observed cluster structure to improve statistics on other network features. A third important application is the general study of properties in networks with different cluster structures, possibly evolving over time. Currently there are good benchmark and creation models available. But what is left is a precise sandbox model to build hierarchical, overlapping and directed clusters for undirected or directed, binary or weighted complex random networks on basis of a sophisticated blueprint. This gap shall be closed by the model CHIMERA (Cluster Hierarchy Interconnection Model for Evaluation, Research and Analysis) which will be introduced and described here for the first time.

  6. Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important.

    PubMed

    Abramyan, Tigran M; Snyder, James A; Thyparambil, Aby A; Stuart, Steven J; Latour, Robert A

    2016-08-01

    Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc. PMID:27292100

  7. Using hierarchical clustering of secreted protein families to classify and rank candidate effectors of rust fungi.

    PubMed

    Saunders, Diane G O; Win, Joe; Cano, Liliana M; Szabo, Les J; Kamoun, Sophien; Raffaele, Sylvain

    2012-01-01

    Rust fungi are obligate biotrophic pathogens that cause considerable damage on crop plants. Puccinia graminis f. sp. tritici, the causal agent of wheat stem rust, and Melampsora larici-populina, the poplar leaf rust pathogen, have strong deleterious impacts on wheat and poplar wood production, respectively. Filamentous pathogens such as rust fungi secrete molecules called disease effectors that act as modulators of host cell physiology and can suppress or trigger host immunity. Current knowledge on effectors from other filamentous plant pathogens can be exploited for the characterisation of effectors in the genome of recently sequenced rust fungi. We designed a comprehensive in silico analysis pipeline to identify the putative effector repertoire from the genome of two plant pathogenic rust fungi. The pipeline is based on the observation that known effector proteins from filamentous pathogens have at least one of the following properties: (i) contain a secretion signal, (ii) are encoded by in planta induced genes, (iii) have similarity to haustorial proteins, (iv) are small and cysteine rich, (v) contain a known effector motif or a nuclear localization signal, (vi) are encoded by genes with long intergenic regions, (vii) contain internal repeats, and (viii) do not contain PFAM domains, except those associated with pathogenicity. We used Markov clustering and hierarchical clustering to classify protein families of rust pathogens and rank them according to their likelihood of being effectors. Using this approach, we identified eight families of candidate effectors that we consider of high value for functional characterization. This study revealed a diverse set of candidate effectors, including families of haustorial expressed secreted proteins and small cysteine-rich proteins. This comprehensive classification of candidate effectors from these devastating rust pathogens is an initial step towards probing plant germplasm for novel resistance components. PMID:22238666

  8. New Alzheimer amyloid beta responsive genes identified in human neuroblastoma cells by hierarchical clustering.

    PubMed

    Uhrig, Markus; Ittrich, Carina; Wiedmann, Verena; Knyazev, Yuri; Weninger, Annette; Riemenschneider, Matthias; Hartmann, Tobias

    2009-01-01

    Alzheimer's disease (AD) is characterized by neuronal degeneration and cell loss. Abeta(42), in contrast to Abeta(40), is thought to be the pathogenic form triggering the pathological cascade in AD. In order to unravel overall gene regulation we monitored the transcriptomic responses to increased or decreased Abeta(40) and Abeta(42) levels, generated and derived from its precursor C99 (C-terminal fragment of APP comprising 99 amino acids) in human neuroblastoma cells. We identified fourteen differentially expressed transcripts by hierarchical clustering and discussed their involvement in AD. These fourteen transcripts were grouped into two main clusters each showing distinct differential expression patterns depending on Abeta(40) and Abeta(42) levels. Among these transcripts we discovered an unexpected inverse and strong differential expression of neurogenin 2 (NEUROG2) and KIAA0125 in all examined cell clones. C99-overexpression had a similar effect on NEUROG2 and KIAA0125 expression as a decreased Abeta(42)/Abeta(40) ratio. Importantly however, an increased Abeta(42)/Abeta(40) ratio, which is typical of AD, had an inverse expression pattern of NEUROG2 and KIAA0125: An increased Abeta(42)/Abeta(40) ratio up-regulated NEUROG2, but down-regulated KIAA0125, whereas the opposite regulation pattern was observed for a decreased Abeta(42)/Abeta(40) ratio. We discuss the possibilities that the so far uncharacterized KIAA0125 might be a counter player of NEUROG2 and that KIAA0125 could be involved in neurogenesis, due to the involvement of NEUROG2 in developmental neural processes. PMID:19707560

  9. New Alzheimer Amyloid β Responsive Genes Identified in Human Neuroblastoma Cells by Hierarchical Clustering

    PubMed Central

    Uhrig, Markus; Ittrich, Carina; Wiedmann, Verena; Knyazev, Yuri; Weninger, Annette; Riemenschneider, Matthias; Hartmann, Tobias

    2009-01-01

    Alzheimer's disease (AD) is characterized by neuronal degeneration and cell loss. Aβ42, in contrast to Aβ40, is thought to be the pathogenic form triggering the pathological cascade in AD. In order to unravel overall gene regulation we monitored the transcriptomic responses to increased or decreased Aβ40 and Aβ42 levels, generated and derived from its precursor C99 (C-terminal fragment of APP comprising 99 amino acids) in human neuroblastoma cells. We identified fourteen differentially expressed transcripts by hierarchical clustering and discussed their involvement in AD. These fourteen transcripts were grouped into two main clusters each showing distinct differential expression patterns depending on Aβ40 and Aβ42 levels. Among these transcripts we discovered an unexpected inverse and strong differential expression of neurogenin 2 (NEUROG2) and KIAA0125 in all examined cell clones. C99-overexpression had a similar effect on NEUROG2 and KIAA0125 expression as a decreased Aβ42/Aβ40 ratio. Importantly however, an increased Aβ42/Aβ40 ratio, which is typical of AD, had an inverse expression pattern of NEUROG2 and KIAA0125: An increased Aβ42/Aβ40 ratio up-regulated NEUROG2, but down-regulated KIAA0125, whereas the opposite regulation pattern was observed for a decreased Aβ42/Aβ40 ratio. We discuss the possibilities that the so far uncharacterized KIAA0125 might be a counter player of NEUROG2 and that KIAA0125 could be involved in neurogenesis, due to the involvement of NEUROG2 in developmental neural processes. PMID:19707560

  10. A measure of DNA sequence similarity by Fourier Transform with applications on hierarchical clustering.

    PubMed

    Yin, Changchuan; Chen, Ying; Yau, Stephen S-T

    2014-10-21

    Multiple sequence alignment (MSA) is a prominent method for classification of DNA sequences, yet it is hampered with inherent limitations in computational complexity. Alignment-free methods have been developed over past decade for more efficient comparison and classification of DNA sequences than MSA. However, most alignment-free methods may lose structural and functional information of DNA sequences because they are based on feature extractions. Therefore, they may not fully reflect the actual differences among DNA sequences. Alignment-free methods with information conservation are needed for more accurate comparison and classification of DNA sequences. We propose a new alignment-free similarity measure of DNA sequences using the Discrete Fourier Transform (DFT). In this method, we map DNA sequences into four binary indicator sequences and apply DFT to the indicator sequences to transform them into frequency domain. The Euclidean distance of full DFT power spectra of the DNA sequences is used as similarity distance metric. To compare the DFT power spectra of DNA sequences with different lengths, we propose an even scaling method to extend shorter DFT power spectra to equal the longest length of the sequences compared. After the DFT power spectra are evenly scaled, the DNA sequences are compared in the same DFT frequency space dimensionality. We assess the accuracy of the similarity metric in hierarchical clustering using simulated DNA and virus sequences. The results demonstrate that the DFT based method is an effective and accurate measure of DNA sequence similarity. PMID:24911780

  11. Hierarchical black hole triples in young star clusters: impact of Kozai-Lidov resonance on mergers

    NASA Astrophysics Data System (ADS)

    Kimpson, Thomas O.; Spera, Mario; Mapelli, Michela; Ziosi, Brunetto M.

    2016-08-01

    Mergers of compact object binaries are one of the most powerful sources of gravitational waves (GWs) in the frequency range of second-generation ground-based gravitational wave detectors (Advanced LIGO and Virgo). Dynamical simulations of young dense star clusters (SCs) indicate that ˜27 per cent of all double compact object binaries are members of hierarchical triple systems (HTs). In this paper, we consider 570 HTs composed of three compact objects (black holes or neutron stars) that formed dynamically in N-body simulations of young dense SCs. We simulate them for a Hubble time with a new code based on the Mikkola's algorithmic regularization scheme, including the 2.5 post-Newtonian term. We find that ˜88 per cent of the simulated systems develop Kozai-Lidov (KL) oscillations. KL resonance triggers the merger of the inner binary in three systems (corresponding to 0.5 per cent of the simulated HTs), by increasing the eccentricity of the inner binary. Accounting for KL oscillations leads to an increase of the total expected merger rate by ≈50 per cent. All binaries that merge because of KL oscillations were formed by dynamical exchanges (i.e. none is a primordial binary) and have chirp mass >20 M⊙. This result might be crucial to interpret the formation channel of the first recently detected GW events.

  12. Structural system identification using degree of freedom-based reduction and hierarchical clustering algorithm

    NASA Astrophysics Data System (ADS)

    Chang, Seongmin; Baek, Sungmin; Kim, Ki-Ook; Cho, Maenghyo

    2015-06-01

    A system identification method has been proposed to validate finite element models of complex structures using measured modal data. Finite element method is used for the system identification as well as the structural analysis. In perturbation methods, the perturbed system is expressed as a combination of the baseline structure and the related perturbations. The changes in dynamic responses are applied to determine the structural modifications so that the equilibrium may be satisfied in the perturbed system. In practical applications, the dynamic measurements are carried out on a limited number of accessible nodes and associated degrees of freedom. The equilibrium equation is, in principle, expressed in terms of the measured (master, primary) and unmeasured (slave, secondary) degrees of freedom. Only the specified degrees of freedom are included in the equation formulation for identification and the unspecified degrees of freedom are eliminated through the iterative improved reduction scheme. A large number of system parameters are included as the unknown variables in the system identification of large-scaled structures. The identification problem with large number of system parameters requires a large amount of computation time and resources. In the present study, a hierarchical clustering algorithm is applied to reduce the number of system parameters effectively. Numerical examples demonstrate that the proposed method greatly improves the accuracy and efficiency in the inverse problem of identification.

  13. Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering

    NASA Astrophysics Data System (ADS)

    Hirsch, Eitan; Agassi, Eyal

    2007-09-01

    The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background or any other temporal information. We introduce a novel approach for detection and discrimination of gaseous plumes in IR hyperspectral imagery using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on IR hyperspectral images of the release of two atmospheric tracers. The application of the proposed detection method on the experimental data has yielded a correct identification of all the releases without any false alarms. These encouraging results show that the presented approach can be used as a basis for a complete identification algorithm for gaseous pollutants in IR hyperspectral imagery without the need for a clear background.

  14. Hierarchical clustering of ryanodine receptors enables emergence of a calcium clock in sinoatrial node cells.

    PubMed

    Stern, Michael D; Maltseva, Larissa A; Juhaszova, Magdalena; Sollott, Steven J; Lakatta, Edward G; Maltsev, Victor A

    2014-05-01

    rate in response to β-adrenergic stimulation. The model indicates that the hierarchical clustering of surface RyRs in SANCs may be a crucial adaptive mechanism. Pathological desynchronization of the clocks may explain sinus node dysfunction in heart failure and RyR mutations. PMID:24778430

  15. Hierarchical clustering of ryanodine receptors enables emergence of a calcium clock in sinoatrial node cells

    PubMed Central

    Maltseva, Larissa A.; Juhaszova, Magdalena; Sollott, Steven J.; Lakatta, Edward G.; Maltsev, Victor A.

    2014-01-01

    beating rate in response to β-adrenergic stimulation. The model indicates that the hierarchical clustering of surface RyRs in SANCs may be a crucial adaptive mechanism. Pathological desynchronization of the clocks may explain sinus node dysfunction in heart failure and RyR mutations. PMID:24778430

  16. ClusCo: clustering and comparison of protein models

    PubMed Central

    2013-01-01

    Background The development, optimization and validation of protein modeling methods require efficient tools for structural comparison. Frequently, a large number of models need to be compared with the target native structure. The main reason for the development of Clusco software was to create a high-throughput tool for all-versus-all comparison, because calculating similarity matrix is the one of the bottlenecks in the protein modeling pipeline. Results Clusco is fast and easy-to-use software for high-throughput comparison of protein models with different similarity measures (cRMSD, dRMSD, GDT_TS, TM-Score, MaxSub, Contact Map Overlap) and clustering of the comparison results with standard methods: K-means Clustering or Hierarchical Agglomerative Clustering. Conclusions The application was highly optimized and written in C/C++, including the code for parallel execution on CPU and GPU, which resulted in a significant speedup over similar clustering and scoring computation programs. PMID:23433004

  17. The hierarchical evolution of Brightest Cluster Galaxies: red galaxies in a young universe

    NASA Astrophysics Data System (ADS)

    Tonini, Chiara

    2013-07-01

    We investigate the evolution of Brightest Cluster Galaxies (BCGs) from redshift z ~ 1.6 to z = 0. We upgrade the hierarchical semi-analytic model of Croton et al. (2006) with a new spectro-photometric model that produces realistic galaxy spectra, making use of the Maraston (2005) stellar populations and a new recipe for the dust extinction. We compare the model predictions of the K-band luminosity evolution and the J-K, V-I and I-K colour evolution with a series of datasets, including Collins et al. (Nature, 2009) who argued that semi-analytic models based on the Millennium simulation cannot reproduce the red colours and high luminosity of BCGs at z > 1. We show instead that the model is well in range of the observed luminosity and correctly reproduces the colour evolution of BCGs in the whole redshift range up to z ~ 1.6. We argue that the success of the semi-analytic model is in large part due to the implementation of a more sophisticated spectro-photometric model. An analysis of the model BCGs shows an increase in mass by a factor 2-3 since z ~ 1, and star formation activity down to low redshifts. While the consensus regarding BCGs is that they are passively evolving, we argue that this conclusion is affected by the degeneracy between star formation history and stellar population models used in SED-fitting, and by the inefficacy of toy-models of passive evolution to capture the complexity of real galaxies, especially those with rich merger histories like BCGs. Following this argument, we also show that in the semi-analytic model, the BCGs show a realistic mix of stellar populations, and that these stellar populations are mostly old. In addition, the age-redshift relation of the model BCGs follows that of the Universe, meaning that given their merger history and star formation history, the ageing of BCGs is always dominated by the ageing of their stellar populations. In a ΛCDM Universe, we define such evolution as `passive in the hierarchical sense'.

  18. Building blocks in hierarchical clustering scenarios and their connection with damped Lyα systems

    NASA Astrophysics Data System (ADS)

    Cora, Sofía A.; Tissera, Patricia B.; Lambas, Diego G.; Mosconi, Mirta B.

    2003-08-01

    We carried out a comprehensive analysis of the chemical properties of the interstellar medium (ISM) and the stellar population (SP) of current normal galaxies and their progenitors in a hierarchical clustering scenario. We compared the results with observations of damped Lyman-α systems (DLAs) under the hypothesis that, at least, part of the observed DLAs could originate in the building blocks of present-day normal galaxies. We used a hydrodynamical cosmological code which includes star formation and chemical enrichment. Galaxy-like objects are identified at z= 0 and then followed back in time. Random lines of sight (LOS) are drawn through these structures in order to mimic damped Lyman-α systems. We then analysed the chemical properties of the ISM and SP along the LOS. We found that the progenitors of current galaxies in the field with mean L < 0.5L* and virial circular velocity of 100-250 km s-1 could be the associated DLA galaxies. For these systems we detected a trend for to increase with redshift. We found moderate metallicity evolution for [Zn/H], [Fe/H] and [Si/H]. However, when we applied the observational filter suggested by Boissé et al. (1998) in order to restrict the sample to the observed limits in densities and metallicities, we found mild evolution consistent with observational results that include dust corrections. [Si/Fe] and [S/Fe] show weak α-enhancement in agreement with observations corrected by dust depletion. We found α/Fe in the ISM and SP to have more homogeneous abundances than [Fe/H] and [Zn/H]. In our models, the global metallicity evolution is driven by the high metallicity and high column density simulated DLAs, which have low impact parameters (b < 5 kpc), and SPs with more than 108 Msolar. Our results suggest that geometrical effects could be the mechanism responsible for the non-detectability of high-metallicity and high-column-density DLAs. We found sub-DLAs to map preferentially the outskirts of the simulated DLA

  19. Bimodel Galaxy Formation Through Hierarchical Clustering --- Implications from Skeleton Tree Formalism

    NASA Astrophysics Data System (ADS)

    Hanami, Hitoshi

    2004-02-01

    The formation and evolution of elliptical galaxies remain a hot topic of debate. The small scatter in the color-magnitude relation for cluster E galaxies at low and high redshifts strongly supports that their stars already formed at high redshifts. Recent observed SCUBA sources are probably these starbursting E galaxies at high redshifts. This picture seems to contradict naive hierarchical clustering scenarios in which large E galaxies were formed through starburst phase induced with merging of small spirals. The extended Press-Schechter formalism, frequently used in semi-analytic modelings, cannot distinguish the episodic merging from the continuous accretion in the mass growth processes. In order to study the correct mass growth history of the halos with the distinction between merger and accretion, we formulated the skeleton tree formalism, which can reproduce the results of N-body simulations; the violent merger forms the central part of the bound halos and the late accretion produces their envelope. This mass assembly also explains the correlation between the mass of a halo and its characteristic density remarked by Navaro, Frenk, & White (1997). In ΛCDM model, we found that the merger halo formation is stopped around z=3 and the accretion growth dominates after that at the galactic mass scale. With bimodal star formation picture of intense starburst at early merger and mild star formation at late accretion, the mass growth history can explain the observed properties of E galaxies as 1) the early merger produces the old and dense homogeneous stellar component appeared in the FP and the CM relation, and 2) the late accretion produces the extended halo observed from X-ray luminous emission which is not correlated with their starlight. Furthermore, it predicts 3) the stellar disk grows at late accretion phase, and 4) the two modes of star formation were switched around z=2-3 in Λ CDM cosmology. This bimodal galaxy formation related to bimodal mass growth should

  20. Poisson-based self-organizing feature maps and hierarchical clustering for serial analysis of gene expression data.

    PubMed

    Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

    2007-01-01

    Serial analysis of gene expression (SAGE) is a powerful technique for global gene expression profiling, allowing simultaneous analysis of thousands of transcripts without prior structural and functional knowledge. Pattern discovery and visualization have become fundamental approaches to analyzing such large-scale gene expression data. From the pattern discovery perspective, clustering techniques have received great attention. However, due to the statistical nature of SAGE data (i.e., underlying distribution), traditional clustering techniques may not be suitable for SAGE data analysis. Based on the adaptation and improvement of Self-Organizing Maps and hierarchical clustering techniques, this paper presents two new clustering algorithms, namely, PoissonS and PoissonHC, for SAGE data analysis. Tested on synthetic and experimental SAGE data, these algorithms demonstrate several advantages over traditional pattern discovery techniques. The results indicate that, by incorporating statistical properties of SAGE data, PoissonS and PoissonHC, as well as a hybrid approach (neuro-hierarchical approach) based on the combination of PoissonS and PoissonHC, offer significant improvements in pattern discovery and visualization for SAGE data. Moreover, a user-friendly platform, which may improve and accelerate SAGE data mining, was implemented. The system is freely available on request from the authors for nonprofit use. PMID:17473311

  1. Symptom Clusters in People Living with HIV Attending Five Palliative Care Facilities in Two Sub-Saharan African Countries: A Hierarchical Cluster Analysis

    PubMed Central

    Moens, Katrien; Siegert, Richard J.; Taylor, Steve; Namisango, Eve; Harding, Richard

    2015-01-01

    Background Symptom research across conditions has historically focused on single symptoms, and the burden of multiple symptoms and their interactions has been relatively neglected especially in people living with HIV. Symptom cluster studies are required to set priorities in treatment planning, and to lessen the total symptom burden. This study aimed to identify and compare symptom clusters among people living with HIV attending five palliative care facilities in two sub-Saharan African countries. Methods Data from cross-sectional self-report of seven-day symptom prevalence on the 32-item Memorial Symptom Assessment Scale-Short Form were used. A hierarchical cluster analysis was conducted using Ward’s method applying squared Euclidean Distance as the similarity measure to determine the clusters. Contingency tables, X2 tests and ANOVA were used to compare the clusters by patient specific characteristics and distress scores. Results Among the sample (N=217) the mean age was 36.5 (SD 9.0), 73.2% were female, and 49.1% were on antiretroviral therapy (ART). The cluster analysis produced five symptom clusters identified as: 1) dermatological; 2) generalised anxiety and elimination; 3) social and image; 4) persistently present; and 5) a gastrointestinal-related symptom cluster. The patients in the first three symptom clusters reported the highest physical and psychological distress scores. Patient characteristics varied significantly across the five clusters by functional status (worst functional physical status in cluster one, p<0.001); being on ART (highest proportions for clusters two and three, p=0.012); global distress (F=26.8, p<0.001), physical distress (F=36.3, p<0.001) and psychological distress subscale (F=21.8, p<0.001) (all subscales worst for cluster one, best for cluster four). Conclusions The greatest burden is associated with cluster one, and should be prioritised in clinical management. Further symptom cluster research in people living with HIV with

  2. Pareto-optimal clustering scheme using data aggregation for wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Azad, Puneet; Sharma, Vidushi

    2015-07-01

    The presence of cluster heads (CHs) in a clustered wireless sensor network (WSN) leads to improved data aggregation and enhanced network lifetime. Thus, the selection of appropriate CHs in WSNs is a challenging task, which needs to be addressed. A multicriterion decision-making approach for the selection of CHs is presented using Pareto-optimal theory and technique for order preference by similarity to ideal solution (TOPSIS) methods. CHs are selected using three criteria including energy, cluster density and distance from the sink. The overall network lifetime in this method with 50% data aggregation after simulations is 81% higher than that of distributed hierarchical agglomerative clustering in similar environment and with same set of parameters. Optimum number of clusters is estimated using TOPSIS technique and found to be 9-11 for effective energy usage in WSNs.

  3. Bulgeless Giant Galaxies Challenge Our Picture of Galaxy Formation by Hierarchical Clustering

    NASA Astrophysics Data System (ADS)

    Kormendy, John; Drory, Niv; Bender, Ralf; Cornell, Mark E.

    2010-11-01

    dark matter galaxy formation more acute: How can hierarchical clustering make so many giant, pure-disk galaxies with no evidence for merger-built bulges? Finally, we emphasize that this problem is a strong function of environment: the Virgo cluster is not a puzzle, because more than 2/3 of its stellar mass is in merger remnants. Based on observations obtained with the Hobby-Eberly Telescope, which is a joint project of the University of Texas at Austin, the Pennsylvania State University, Stanford University, Ludwig-Maximilians-Universität München, and Georg-August-Universität Göttingen. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the Data Archive at STScI, which is operated by AURA, Inc., under NASA contract NAS 5-26555. These observations are associated with program numbers 7330, 7919, 8591, 8597, 8599, 9293, 9360, 9490, 9788, and 11080.

  4. BULGELESS GIANT GALAXIES CHALLENGE OUR PICTURE OF GALAXY FORMATION BY HIERARCHICAL CLUSTERING ,

    SciTech Connect

    Kormendy, John; Cornell, Mark E.; Drory, Niv; Bender, Ralf E-mail: cornell@astro.as.utexas.ed E-mail: drory@mpe.mpg.d

    2010-11-01

    tail of a distribution of merger histories. Recognition of pseudobulges makes the biggest problem with cold dark matter galaxy formation more acute: How can hierarchical clustering make so many giant, pure-disk galaxies with no evidence for merger-built bulges? Finally, we emphasize that this problem is a strong function of environment: the Virgo cluster is not a puzzle, because more than 2/3 of its stellar mass is in merger remnants.

  5. Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

    NASA Astrophysics Data System (ADS)

    Unglert, K.; Radić, V.; Jellinek, A. M.

    2016-06-01

    Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

  6. Method for preventing plugging in the pyrolysis of agglomerative coals

    DOEpatents

    Green, Norman W.

    1979-01-23

    To prevent plugging in a pyrolysis operation where an agglomerative coal in a nondeleteriously reactive carrier gas is injected as a turbulent jet from an opening into an elongate pyrolysis reactor, the coal is comminuted to a size where the particles under operating conditions will detackify prior to contact with internal reactor surfaces while a secondary flow of fluid is introduced along the peripheral inner surface of the reactor to prevent backflow of the coal particles. The pyrolysis operation is depicted by two equations which enable preselection of conditions which insure prevention of reactor plugging.

  7. CHANDRA Observation of Galaxy Cluster A2069 - Implication for Hierarchical Mergers

    NASA Astrophysics Data System (ADS)

    Murray, Steven

    2003-09-01

    We propose a 60 ks CHANDRA observation of the irregular cluster A2069. The purpose of this proposal is twofold. EINSTEIN IPC reveals significant substructures in this cluster with at least five gas condensations. It is a good target to test the cool core merging scenario proposed by Motl et al. 2003. The complex morphology of A2069 also enables us to study rich gas dynamics phenomena inside the cluster, and shed light on cluster formation and large-scale structure.

  8. Witnessing the Hierarchical Assembly of the Brightest Cluster Galaxy in a Cluster at z=1.26

    NASA Astrophysics Data System (ADS)

    Yamada, Toru; Koyama, Yohei; Nakata, Fumiaki; Kajisawa, Masaru; Tanaka, Ichi; Kodama, Tadayuki; Okamura, Sadanori; De Propris, Roberto

    2002-10-01

    We have obtained a new high-resolution K'-band image of the central region of the rich X-ray cluster RX J0848.9+4452 at z=1.26. We found that the brightest cluster galaxy (BCG) in the cluster is clearly separated into two distinct objects. Whereas the optical to near-infrared colors of the objects are consistent with the predictions of passive evolution models for galaxies formed at high redshift, the luminosities of the two galaxies are both considerably fainter than predicted by passive evolution of BCGs in low- and intermediate-redshift clusters. We argue that this is evidence of an ongoing merger of normal cluster elliptical galaxies to form the dominant galaxy in the core of RX J0848.9+4452. The two galaxies appear to point toward the nearby cluster ClG J0848+4453 and are aligned with the outer X-ray contour of their parent cluster, supporting a model of BCG formation by collimated infall along the surrounding large-scale structure.

  9. Dynamic Key Management Schemes for Secure Group Access Control Using Hierarchical Clustering in Mobile Ad Hoc Networks

    NASA Astrophysics Data System (ADS)

    Tsaur, Woei-Jiunn; Pai, Haw-Tyng

    2008-11-01

    The applications of group computing and communication motivate the requirement to provide group access control in mobile ad hoc networks (MANETs). The operation in MANETs' groups performs a decentralized manner and accommodated membership dynamically. Moreover, due to lack of centralized control, MANETs' groups are inherently insecure and vulnerable to attacks from both within and outside the groups. Such features make access control more challenging in MANETs. Recently, several researchers have proposed group access control mechanisms in MANETs based on a variety of threshold signatures. However, these mechanisms cannot actually satisfy MANETs' dynamic environments. This is because the threshold-based mechanisms cannot be achieved when the number of members is not up to the threshold value. Hence, by combining the efficient elliptic curve cryptosystem, self-certified public key cryptosystem and secure filter technique, we construct dynamic key management schemes based on hierarchical clustering for securing group access control in MANETs. Specifically, the proposed schemes can constantly accomplish secure group access control only by renewing the secure filters of few cluster heads, when a cluster head joins or leaves a cross-cluster. In such a new way, we can find that the proposed group access control scheme can be very effective for securing practical applications in MANETs.

  10. Hierarchical cluster analysis applied to workers' exposures in fiberglass insulation manufacturing.

    PubMed

    Wu, J D; Milton, D K; Hammond, S K; Spear, R C

    1999-01-01

    The objectives of this study were to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on non-measurement-based approaches commonly used in epidemiology. Cluster analysis was performed for 37 workers simultaneously exposed to three agents (endotoxin, phenolic compounds and formaldehyde) in fiberglass insulation manufacturing. Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average and model-based clustering approaches, were used to construct the tree structures from which clusters can be formed. Differences were observed between the exposure clusters constructed by these different clustering algorithms. When contrasting the exposure classification based on tree structures with that based on non-measurement-based information, the results indicate that the exposure clusters identified from the tree structures had little in common with the classification results from either the traditional exposure zone or the work group classification approach. In terms of the defining homogeneous exposure groups or from the standpoint of health risk, some toxicological normalization in the components of the exposure vector appears to be required in order to form meaningful exposure groupings from cluster analysis. Finally, it remains important to see if the lack of correspondence between exposure groups based on epidemiological classification and measurement data is a peculiarity of the data or a more general problem in multivariate exposure analysis. PMID:10028893

  11. [The hierarchical clustering analysis of hyperspectral image based on probabilistic latent semantic analysis].

    PubMed

    Yi, Wen-Bin; Shen, Li; Qi, Yin-Feng; Tang, Hong

    2011-09-01

    The paper introduces the Probabilistic Latent Semantic Analysis (PLSA) to the image clustering and an effective image clustering algorithm using the semantic information from PLSA is proposed which is used for hyperspectral images. Firstly, the ISODATA algorithm is used to obtain the initial clustering result of hyperspectral image and the clusters of the initial clustering result are considered as the visual words of the PLSA. Secondly, the object-oriented image segmentation algorithm is used to partition the hyperspectral image and segments with relatively pure pixels are regarded as documents in PLSA. Thirdly, a variety of identification methods which can estimate the best number of cluster centers is combined to get the number of latent semantic topics. Then the conditional distributions of visual words in topics and the mixtures of topics in different documents are estimated by using PLSA. Finally, the conditional probabilistic of latent semantic topics are distinguished using statistical pattern recognition method, the topic type for each visual in each document will be given and the clustering result of hyperspectral image are then achieved. Experimental results show the clusters of the proposed algorithm are better than K-MEANS and ISODATA in terms of object-oriented property and the clustering result is closer to the distribution of real spatial distribution of surface. PMID:22097851

  12. Hierarchical Bayesian clustering for nonstationary flood frequency analysis: Application to trends of annual maximum flow in Germany

    NASA Astrophysics Data System (ADS)

    Sun, Xun; Lall, Upmanu; Merz, Bruno; Dung, Nguyen Viet

    2015-08-01

    Especially for extreme precipitation or floods, there is considerable spatial and temporal variability in long term trends or in the response of station time series to large-scale climate indices. Consequently, identifying trends or sensitivity of these extremes to climate parameters can be marked by high uncertainty. When one develops a nonstationary frequency analysis model, a key step is the identification of potential trends or effects of climate indices on the station series. An automatic clustering procedure that effectively pools stations where there are similar responses is desirable to reduce the estimation variance, thus improving the identification of trends or responses, and accounting for spatial dependence. This paper presents a new hierarchical Bayesian approach for exploring homogeneity of response in large area data sets, through a multicomponent mixture model. The approach allows the reduction of uncertainties through both full pooling and partial pooling of stations across automatically chosen subsets of the data. We apply the model to study the trends in annual maximum daily stream flow at 68 gauges over Germany. The effects of changing the number of clusters and the parameters used for clustering are demonstrated. The results show that there are large, mainly upward trends in the gauges of the River Rhine Basin in Western Germany and along the main stream of the Danube River in the south, while there are also some small upward trends at gauges in Central and Northern Germany.

  13. Density-based hierarchical clustering of pyro-sequences on a large scale—the case of fungal ITS1

    PubMed Central

    Pagni, Marco; Niculita-Hirzel, Hélène; Pellissier, Loïc; Dubuis, Anne; Xenarios, Ioannis; Guisan, Antoine; Sanders, Ian R.; Goudet, Jérôme; Guex, Nicolas

    2013-01-01

    Motivation: Analysis of millions of pyro-sequences is currently playing a crucial role in the advance of environmental microbiology. Taxonomy-independent, i.e. unsupervised, clustering of these sequences is essential for the definition of Operational Taxonomic Units. For this application, reproducibility and robustness should be the most sought after qualities, but have thus far largely been overlooked. Results: More than 1 million hyper-variable internal transcribed spacer 1 (ITS1) sequences of fungal origin have been analyzed. The ITS1 sequences were first properly extracted from 454 reads using generalized profiles. Then, otupipe, cd-hit-454, ESPRIT-Tree and DBC454, a new algorithm presented here, were used to analyze the sequences. A numerical assay was developed to measure the reproducibility and robustness of these algorithms. DBC454 was the most robust, closely followed by ESPRIT-Tree. DBC454 features density-based hierarchical clustering, which complements the other methods by providing insights into the structure of the data. Availability: An executable is freely available for non-commercial users at ftp://ftp.vital-it.ch/tools/dbc454. It is designed to run under MPI on a cluster of 64-bit Linux machines running Red Hat 4.x, or on a multi-core OSX system. Contact: dbc454@vital-it.ch or nicolas.guex@isb-sib.ch PMID:23539304

  14. Synthesis of self-assembled large area films of complex hierarchical PZT clusters

    NASA Astrophysics Data System (ADS)

    Pratap Chaudhary, Raghvendra; Saxena, Sumit; Kmar, Amit; Bharadwaj, Rajesh; Shukla, Shobha

    2016-02-01

    The ability to bridge nano-micro interface for applications in functional miniaturized devices is of fundamental interest. We have synthesized novel large area films of complex hierarchical micro-flower morphologies of piezo-ceramics using hydrothermal reactions. The overall size of the samples produced is ∼cm2. The growth morphologies are found to be dependent on concentration and pressure inside the reaction chamber. This can be used to deterministically grow these complex multi-scaled microstructures over a large area. These results outline a strategy for growth of omni-directional microstructures by utilizing self assembly processes.

  15. A comparison of hierarchical cluster analysis and league table rankings as methods for analysis and presentation of district health system performance data in Uganda.

    PubMed

    Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart

    2016-03-01

    In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards' method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. PMID:26024882

  16. A comparison of hierarchical cluster analysis and league table rankings as methods for analysis and presentation of district health system performance data in Uganda†

    PubMed Central

    Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart

    2016-01-01

    In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards’ method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. PMID:26024882

  17. Hierarchical clustering of brain activity during human nonrapid eye movement sleep

    PubMed Central

    Boly, Mélanie; Perlbarg, Vincent; Marrelec, Guillaume; Schabus, Manuel; Laureys, Steven; Doyon, Julien; Pélégrini-Issac, Mélanie; Maquet, Pierre; Benali, Habib

    2012-01-01

    Consciousness is reduced during nonrapid eye movement (NREM) sleep due to changes in brain function that are still poorly understood. Here, we tested the hypothesis that impaired consciousness during NREM sleep is associated with an increased modularity of brain activity. Cerebral connectivity was quantified in resting-state functional magnetic resonance imaging times series acquired in 13 healthy volunteers during wakefulness and NREM sleep. The analysis revealed a modification of the hierarchical organization of large-scale networks into smaller independent modules during NREM sleep, independently from EEG markers of the slow oscillation. Such modifications in brain connectivity, possibly driven by sleep ultraslow oscillations, could hinder the brain's ability to integrate information and account for decreased consciousness during NREM sleep. PMID:22451917

  18. Hierarchical clustering analysis of reading aloud data: a new technique for evaluating the performance of computational models.

    PubMed

    Robidoux, Serje; Pritchard, Stephen C

    2014-01-01

    DRC (Coltheart et al., 2001) and CDP++ (Perry et al., 2010) are two of the most successful models of reading aloud. These models differ primarily in how their sublexical systems convert letter strings into phonological codes. DRC adopts a set of grapheme-to-phoneme conversion rules (GPCs) while CDP++ uses a simple trained network that has been exposed to a combination of rules and the spellings and pronunciations of known words. Thus far the debate between fixed rules and learned associations has largely emphasized reaction time experiments, error rates in dyslexias, and item-level variance from large-scale databases. Recently, Pritchard et al. (2012) examined the models' non-word reading in a new way. They compared responses produced by the models to those produced by 45 skilled readers. Their item-by-item analysis is informative, but leaves open some questions that can be addressed with a different technique. Using hierarchical clustering techniques, we first examined the subject data to identify if there are classes of subjects that are similar to each other in their overall response profiles. We found that there are indeed two groups of subject that differ in their pronunciations for certain consonant clusters. We also tested the possibility that CDP++ is modeling one set of subjects well, while DRC is modeling a different set of subjects. We found that CDP++ does not fit any human reader's response pattern very well, while DRC fits the human readers as well as or better than any other reader. PMID:24744745

  19. Hierarchal clustering yields insight into multidrug-resistant bacteria isolated from a cattle feedlot wastewater treatment system.

    PubMed

    Jahne, Michael A; Rogers, Shane W; Ramler, Ivan P; Holder, Edith; Hayes, Gina

    2015-01-01

    Forty-two percent of Escherichia coli and 58% of Enterococcus spp. isolated from cattle feedlot runoff and associated infiltration basin and constructed wetland treatment system were resistant to at least one antibiotic of clinical importance; a high level of multidrug resistance (22% of E. coli and 37% of Enterococcus spp.) was observed. Hierarchical clustering revealed a closely associated resistance cluster among drug-resistant E. coli isolates that included cephalosporins (ceftiofur, cefoxitin, and ceftriaxone), aminoglycosides (gentamycin, kanamycin, and amikacin), and quinolone nalidixic acid; antibiotics from these classes were used at the study site, and cross-resistance may be associated with transferrable multiple-resistance elements. For Enterococcus spp., co-resistance among vancomycin, linezolid, and daptomycin was common; these antibiotics are reserved for complicated clinical infections and have not been approved for animal use. Vancomycin resistance (n = 49) only occurred when isolates were resistant to linezolid, daptomycin, and all four of the MLSB (macrolide-lincosamide-streptogramin B) antibiotics tested (tylosin, erythromycin, lincomycin, and quinipristin/dalfopristin). This suggests that developing co-resistance to MLSB antibiotics along with cyclic lipopeptides and oxazolidinones may result in resistance to vancomycin as well. Effects of the treatment system on antibiotic resistance were pronounced during periods of no rainfall and low flow (long residence time). Increased hydraulic loading (short residence time) under the influence of rain caused antibiotic-resistant bacteria to be flushed through the treatment system. This presents concern for environmental discharge of multidrug-resistant organisms relevant to public health. PMID:25504186

  20. Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

    PubMed Central

    2012-01-01

    Background An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context. Methods Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System. Results Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent

  1. A comparative study of DIGNET, average, complete, single hierarchical and k-means clustering algorithms in 2D face image recognition

    NASA Astrophysics Data System (ADS)

    Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

    2014-06-01

    The study in this paper belongs to a more general research of discovering facial sub-clusters in different ethnicity face databases. These new sub-clusters along with other metadata (such as race, sex, etc.) lead to a vector for each face in the database where each vector component represents the likelihood of participation of a given face to each cluster. This vector is then used as a feature vector in a human identification and tracking system based on face and other biometrics. The first stage in this system involves a clustering method which evaluates and compares the clustering results of five different clustering algorithms (average, complete, single hierarchical algorithm, k-means and DIGNET), and selects the best strategy for each data collection. In this paper we present the comparative performance of clustering results of DIGNET and four clustering algorithms (average, complete, single hierarchical and k-means) on fabricated 2D and 3D samples, and on actual face images from various databases, using four different standard metrics. These metrics are the silhouette figure, the mean silhouette coefficient, the Hubert test Γ coefficient, and the classification accuracy for each clustering result. The results showed that, in general, DIGNET gives more trustworthy results than the other algorithms when the metrics values are above a specific acceptance threshold. However when the evaluation results metrics have values lower than the acceptance threshold but not too low (too low corresponds to ambiguous results or false results), then it is necessary for the clustering results to be verified by the other algorithms.

  2. SEM-EDS investigation on PM10 data collected in Central Italy: Principal Component Analysis and Hierarchical Cluster Analysis

    PubMed Central

    2012-01-01

    Background Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were applied on PM10 particle data in order to: identify particle clusters that can be differentiated on the bases of their chemical composition and morphology, investigate the relationship among the chemical and morphological parameters and evaluate differences among the sampling sites. PM10 was collected in 3 different sites in central Italy characterized by different conditions: yard, urban and rural sites. The concentration of 20 chemical parameters (C, O, Na, Mg, Al, Si, P, Cd, Cl, K, Ca, Sn, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn) were determined by Scanning Electron Microscopy – Energy Dispersive X-ray Spectroscopy (SEM-EDS) and the particle images were processed by an image analysis software in order to measure: Area, Aspect Ratio, Roundness, Fractal Dimension, Box Width, Box Height and Perimeter. Result Results revealed the presence of different clusters of particles, differentiated on the bases of chemical composition and morphological parameters (aluminosilicates, calcium particles, biological particles, soot, cenosphere, sodium chloride, sulphates, metallic particles, iron spherical particles). Aluminosilicates and Calcium particles of rural and urban sites showed a similar nature due to a mainly natural origin, while those of the yard site showed a more heterogeneous composition mainly related to human activity. Biological particles and soot can be differentiated on the bases of the higher loads of Fractal Dimension, which characterizes soot, and content of Na, Mg, Ca, Cl and K which characterize the biological ones. The soot of the urban site showed higher loadings of Roundness and Fractal Dimension than the soot belonging to the yard and rural sites, this was due to the different life time of the particles. The metal particles, characterized mainly by the higher loading of iron, were present in two morphological forms: spherical and angular particles. The first were

  3. Hierarchical chlorine-doped rutile TiO{sub 2} spherical clusters of nanorods: Large-scale synthesis and high photocatalytic activity

    SciTech Connect

    Xu Hua; Zheng Zhi; Zhang Lizhi Zhang Hailu; Deng Feng

    2008-09-15

    In this study, we report the synthesis of hierarchical chlorine-doped rutile TiO{sub 2} spherical clusters of nanorods photocatalyst on a large scale via a soft interface approach. This catalyst showed much higher photocatalytic activity than the famous commercial titania (Degussa P25) under visible light ({lambda}>420 nm). The resulting sample was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution TEM (HRTEM), nitrogen adsorption, X-ray photoelectron spectroscopy (XPS), UV-vis diffuse reflectance spectroscopy, {sup 1}H solid magic-angle spinning nuclear magnetic resonance (MAS-NMR) and photoluminescence spectroscopy. On the basis of characterization results, we found that the doping of chlorine resulted in red shift of absorption and higher surface acidity as well as crystal defects in the photocatalyst, which were the reasons for high photocatalytic activity of chlorine-doped TiO{sub 2} under visible light ({lambda}>420 nm). These hierarchical chlorine-doped rutile TiO{sub 2} spherical clusters of nanorods are very attractive in the fields of environmental pollutants removal and solar cell because of their easy separation and high activity. - Graphical abstract: Hierarchical chlorine-doped rutile TiO{sub 2} spherical clusters of nanorods photocatalyst were synthesized on a large scale via a soft interface approach. This catalyst showed much higher photocatalytic activity than the famous commercial titania (Degussa P25) under visible light ({lambda}>420 nm)

  4. Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing

    PubMed Central

    Lopez-Meyer, Paulo; Schuckers, Stephanie; Makeyev, Oleksandr; Fontana, Juan M.; Sazonov, Edward

    2012-01-01

    The number of distinct foods consumed in a meal is of significant clinical concern in the study of obesity and other eating disorders. This paper proposes the use of information contained in chewing and swallowing sequences for meal segmentation by food types. Data collected from experiments of 17 volunteers were analyzed using two different clustering techniques. First, an unsupervised clustering technique, Affinity Propagation (AP), was used to automatically identify the number of segments within a meal. Second, performance of the unsupervised AP method was compared to a supervised learning approach based on Agglomerative Hierarchical Clustering (AHC). While the AP method was able to obtain 90% accuracy in predicting the number of food items, the AHC achieved an accuracy >95%. Experimental results suggest that the proposed models of automatic meal segmentation may be utilized as part of an integral application for objective Monitoring of Ingestive Behavior in free living conditions. PMID:23125872

  5. Comparing Chemistry to Outcome: The Development of a Chemical Distance Metric, Coupled with Clustering and Hierarchal Visualization Applied to Macromolecular Crystallography

    PubMed Central

    Bruno, Andrew E.; Ruby, Amanda M.; Luft, Joseph R.; Grant, Thomas D.; Seetharaman, Jayaraman; Montelione, Gaetano T.; Hunt, John F.; Snell, Edward H.

    2014-01-01

    Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications

  6. Hierarchical multiple bit clusters and patterned media enabled by novel nanofabrication techniques -- High resolution electron beam lithography and block polymer self assembly

    NASA Astrophysics Data System (ADS)

    Xiao, Qijun

    This thesis discusses the full scope of a project exploring the physics of hierarchical clusters of interacting nanomagnets. These clusters may be relevant for novel applications such as multilevel data storage devices. The work can be grouped into three main activities: micromagnetic simulation, fabrication and characterization of proof-of-concept prototype devices, and efforts to scale down the structures by creating the hierarchical structures with the aid of diblock copolymer self assembly. Theoretical micromagnetic studies and simulations based on Landau-Lifshitz-Gilbert (LLG) equation were conducted on nanoscale single domain magnetic entities. For the simulated nanomagnet clusters with perpendicular uniaxial anisotropy, the simulation showed the switching field distributions, the stability of the magnetostatic states with distinctive total cluster perpendicular moments, and the stepwise magnetic switching curves. For simulated nanomagnet clusters with in-plane shape anisotropy, the simulation showed the stepwise switching behaviors governed by thermal agitation and cluster configurations. Proof-of-concept cluster devices with three interacting Co nanomagnets were fabricated by e-beam lithography (EBL) and pulse-reverse electrochemical deposition (PRECD). EBL patterning on a suspended 100 nm SiN membrane showed improved lateral lithography resolution to 30 nm. The Co nanomagnets deposited using the PRECD method showed perpendicular anisotropy. The switching experiments with external applied fields were able to switch the Co nanomagnets through the four magnetostatic states with distinctive total perpendicular cluster magnetization, and proved the feasibility of multilevel data storage devices based on the cluster concept. Shrinking the structures size was experimented by the aid of diblock copolymer. Thick poly(styrene)-b-poly(methyl methacrylate) (PS-b-PMMA) diblock copolymer templates aligned with external electrical field were used to fabricate long Ni

  7. Cluster analysis of WIBS single particle bioaerosol data

    NASA Astrophysics Data System (ADS)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2012-09-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  8. Comparison of multianalyte proficiency test results by sum of ranking differences, principal component analysis, and hierarchical cluster analysis.

    PubMed

    Škrbić, Biljana; Héberger, Károly; Durišić-Mladenović, Nataša

    2013-10-01

    Sum of ranking differences (SRD) was applied for comparing multianalyte results obtained by several analytical methods used in one or in different laboratories, i.e., for ranking the overall performances of the methods (or laboratories) in simultaneous determination of the same set of analytes. The data sets for testing of the SRD applicability contained the results reported during one of the proficiency tests (PTs) organized by EU Reference Laboratory for Polycyclic Aromatic Hydrocarbons (EU-RL-PAH). In this way, the SRD was also tested as a discriminant method alternative to existing average performance scores used to compare mutlianalyte PT results. SRD should be used along with the z scores--the most commonly used PT performance statistics. SRD was further developed to handle the same rankings (ties) among laboratories. Two benchmark concentration series were selected as reference: (a) the assigned PAH concentrations (determined precisely beforehand by the EU-RL-PAH) and (b) the averages of all individual PAH concentrations determined by each laboratory. Ranking relative to the assigned values and also to the average (or median) values pointed to the laboratories with the most extreme results, as well as revealed groups of laboratories with similar overall performances. SRD reveals differences between methods or laboratories even if classical test(s) cannot. The ranking was validated using comparison of ranks by random numbers (a randomization test) and using seven folds cross-validation, which highlighted the similarities among the (methods used in) laboratories. Principal component analysis and hierarchical cluster analysis justified the findings based on SRD ranking/grouping. If the PAH-concentrations are row-scaled, (i.e., z scores are analyzed as input for ranking) SRD can still be used for checking the normality of errors. Moreover, cross-validation of SRD on z scores groups the laboratories similarly. The SRD technique is general in nature, i.e., it can

  9. Deterministic algorithm with agglomerative heuristic for location problems

    NASA Astrophysics Data System (ADS)

    Kazakovtsev, L.; Stupina, A.

    2015-10-01

    Authors consider the clustering problem solved with the k-means method and p-median problem with various distance metrics. The p-median problem and the k-means problem as its special case are most popular models of the location theory. They are implemented for solving problems of clustering and many practically important logistic problems such as optimal factory or warehouse location, oil or gas wells, optimal drilling for oil offshore, steam generators in heavy oil fields. Authors propose new deterministic heuristic algorithm based on ideas of the Information Bottleneck Clustering and genetic algorithms with greedy heuristic. In this paper, results of running new algorithm on various data sets are given in comparison with known deterministic and stochastic methods. New algorithm is shown to be significantly faster than the Information Bottleneck Clustering method having analogous preciseness.

  10. A new application of hierarchical cluster analysis to investigate organic peaks in bulk mass spectra obtained with an Aerodyne Aerosol Mass Spectrometer

    NASA Astrophysics Data System (ADS)

    Middlebrook, A. M.; Marcolli, C.; Canagaratna, M. R.; Worsnop, D. R.; Bahreini, R.; de Gouw, J. A.; Warneke, C.; Goldan, P. D.; Kuster, W. C.; Williams, E. J.; Lerner, B. M.; Roberts, J. M.; Meagher, J. F.; Fehsenfeld, F. C.; Marchewka, M. L.; Bertman, S. B.

    2006-12-01

    We applied hierarchical cluster analysis to an Aerodyne aerosol mass spectrometer (AMS) bulk mass spectral dataset collected aboard the NOAA research vessel Ronald H. Brown during the 2002 New England Air Quality Study off the east coast of the United States. Emphasizing the organic peaks, the cluster analysis yielded a series of categories that are distinguishable with respect to their mass spectra and their occurrence as a function of time. The differences between the categories mainly arise from relative intensity changes rather than from the presence or absence of specific peaks. The most frequent category exhibits a strong signal at m/z 44 and represents oxidized organic matter probably originating from both anthropogenic as well as biogenic sources. On the basis of spectral and trace gas correlations, the second most common category with strong signals at m/z 29, 43, and 44 contains contributions from isoprene oxidation products. The third through the fifth most common categories have peak patterns characteristic of monoterpene oxidation products and were most frequently observed when air masses from monoterpene rich regions were sampled. Taken together, the second through the fifth most common categories represent on average 17% of the total organic mass that stems likely from biogenic sources during the ship's cruise. These numbers have to be viewed as lower limits since the most common category was attributed to anthropogenic sources for this calculation. The cluster analysis was also very effective in identifying a few contaminated mass spectra that were not removed during pre-processing. This study demonstrates that hierarchical clustering is a useful tool to analyze the complex patterns of the organic peaks in bulk aerosol mass spectra from a field study.

  11. Gene-Set Local Hierarchical Clustering (GSLHC)—A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups

    PubMed Central

    Hsu, Tzu-Ting; Hsu, Chueh-Lin; Liu, Hsueh-Chuan; Lee, Hoong-Chien

    2015-01-01

    Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases. PMID:26473729

  12. Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups.

    PubMed

    Chung, Feng-Hsiang; Jin, Zhen-Hua; Hsu, Tzu-Ting; Hsu, Chueh-Lin; Liu, Hsueh-Chuan; Lee, Hoong-Chien

    2015-01-01

    Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases. PMID:26473729

  13. Prediction and characterization of P-glycoprotein substrates potentially bound to different sites by emerging chemical pattern and hierarchical cluster analysis.

    PubMed

    Pan, Xianchao; Mei, Hu; Qu, Sujun; Huang, Shuheng; Sun, Jiaying; Yang, Li; Chen, Hua

    2016-04-11

    P-glycoprotein (P-gp), an ATP-binding cassette (ABC) multidrug transporter, can actively transport a broad spectrum of chemically diverse substrates out of cells and is heavily involved in multidrug resistance (MDR) in tumors. So far, the multiple specific binding sites remain a major obstacle in developing an efficient prediction method for P-gp substrates. Herein, emerging chemical pattern (ECP) combined by hierarchical cluster analysis was utilized to predict P-gp substrates as well as their potential binding sites. An optimal ECP model using only 3 descriptors was established with prediction accuracies of 0.80, 0.81 and 0.74 for 803 training samples, 120 test samples, and 179 independent validation samples, respectively. Hierarchical cluster analysis (HCA) of the ECPs of P-gp substrates derived 2 distinct ECP groups (ECPGs). Interestingly, HCA of the P-gp substrates based on ECP similarities also showed 2 distinct classes, which happened to be dominated by the 2 ECPGs, respectively. In the light of available experimental proofs and molecular docking results, the 2 distinct ECPGs were proved to be closely related to the binding profiles of R- and H-site substrates, respectively. The present study demonstrates, for the first time, a successful ECP model, which can not only accurately predict P-gp substrates, but also identify their potential substrate-binding sites. PMID:26899974

  14. Simultaneous quantification of eleven bioactive components of male flowers of Eucommia ulmoides oliver by HPLC and their quality evaluation by chemical fingerprint analysis with hierarchical clustering analysis

    PubMed Central

    Ding, Yanxia; Dou, Deqiang; Guo, Yangjing; Li, Qin

    2014-01-01

    Background: Eucommia ulmoides Oliv (EU), a dioecious perennial angiosperm, is one of the oldest tonics in Chinese traditional medicine. The tea of male flowers of EU has been become popularities and seen as aspirational health care tea in China. There were no enough marks and effective method to control the quality of male flowers of EU. Objective: A simple and efficient HPLC method was developed for the simultaneous determination of 11 bioactive compounds (4 iridoids, 1 phenylpropanoid, 6 flavonoids). HPLC chromatographic fingerprint and hierarchical cluster analysis were used to evaluate and classify the samples of male flowers of EU which came from different locations in China. Materials and Methods: Samples were separated on a Thermal hypersil gold column (250 mm × 4.6 mm, 5 μm) and detected by an ultraviolet detector. The UV wavelength was set at 206, 236, and 206 nm. Mobile phase consisted of methanol (B) and phosphoric acid-water (0.5%) (C) using a gradient elution. Analytes were performed at 25°C with a flow rate of 1.0 mL/min. Results: In quantitative analysis, the eleven components showed good regression (r2 > 0.9996) within linear ranges, and their recoveries were in the range of 98.65-102.31%. In the chromatographic fingerprint, 16 peaks were selected as the characteristic peaks to assess the similarities of different samples. Hierarchical cluster analysis (HCA) was also applied to differentiate the samples based on the area of all the common peaks. The samples which had higher similarity in HPLC fingerprint were classified as a cluster. Conclusion: This study will provide methodological reference for the quality control and sample classification of male flowers of E. ulmoides. PMID:25422543

  15. Hierarchical quantum communication

    NASA Astrophysics Data System (ADS)

    Shukla, Chitra; Pathak, Anirban

    2013-08-01

    A general approach to study the hierarchical quantum information splitting (HQIS) is proposed and the same is used to systematically investigate the possibility of realizing HQIS using different classes of 4-qubit entangled states that are not connected by stochastic local operations and classical communication (SLOCC). Explicit examples of HQIS using 4-qubit cluster state and 4-qubit |Ω> state are provided. Further, the proposed HQIS scheme is generalized to introduce two new aspects of hierarchical quantum communication. To be precise, schemes of probabilistic hierarchical quantum information splitting and hierarchical quantum secret sharing are obtained by modifying the proposed HQIS scheme. A number of practical situations where hierarchical quantum communication would be of use, are also presented.

  16. Using LC and Hierarchical Cluster Analysis as Tools to Distinguish Timbó Collections into Two Deguelia Species: A Contribution to Chemotaxonomy.

    PubMed

    da Costa, Danielle; E Silva, Consuelo; Pinheiro, Aline; Frommenwiler, Débora; Arruda, Mara; Guilhon, Giselle; Alves, Cláudio; Arruda, Alberto; Da Silva, Milton

    2016-01-01

    The species Deguelia utilis and Deguelia rufescens var. urucu, popularly known as "timbó," have been used for many years as rotenone sources in insecticide formulations. In this work, a method was developed and validated using a high-performance liquid chromatography-photodiode array (HPLC-PDA) system, and results were analyzed using hierarchical cluster analysis (HCA). By quantifying the major rotenoids of these species, it was possible to establish a linear relation between them. The ratio between the concentrations of rotenone and deguelin for D. utilis is approximately 1:0.8, respectively, while for D. rufescens var. urucu it is 2:1. These results may help to distinguish these species contributing to their taxonomic identification. PMID:27144548

  17. Differentiation of Isodon japonica and Adulterants Based on Identification and Quantitation 14 Diterpenoids Using LC-MS-MS Library Search Approach and Hierarchical Cluster Analysis.

    PubMed

    Jin, Yiran; Tian, Tingting; Ma, Yinghua; Liu, Minyan; Xie, Weiwei; Wang, Xin; Xu, Huijun; Du, Yingfeng

    2016-03-01

    The aim of this study was to investigate the chemical differences between genunine Isodon japonica and its adulterants. A linear ion trap liquid chromatography with tandem mass spectrometry analytical method has been developed for the identification and quantification of 14 major diterpenoids in I. japonica. Data acquisition was multiple reaction monitoring transitions mode followed by an information-dependent acquisition using the enhanced product ion (EPI) scan in a single run. The target compounds were further identified and confirmed using an EPI spectral library. Overall validation of the assay was carried out including linearity, accuracy, precision, limits of detection and quantification. The results demonstrated that the method was selective, sensitive and reliable. The determination results of 21 batches of I. japonica and adulterants were then analyzed and differentiated by hierarchical clustering analysis. PMID:26489434

  18. High-throughput screening of monoclonal antibodies against plant cell wall glycans by hierarchical clustering of their carbohydrate microarray binding profiles

    PubMed Central

    Moller, Isabel; Marcus, Susan E.; Haeger, Ash; Verhertbruggen, Yves; Verhoef, Rene; Schols, Henk; Ulvskov, Peter; Mikkelsen, Jørn Dalgaard; Knox, J. Paul

    2007-01-01

    Antibody-producing hybridoma cell lines were created following immunisation with a crude extract of cell wall polymers from the plant Arabidopsis thaliana. In order to rapidly screen the specificities of individual monoclonal antibodies (mAbs), their binding to microarrays containing 50 cell wall glycans immobilized on nitrocellulose was assessed. Hierarchical clustering of microarray binding profiles from newly produced mAbs, together with the profiles for mAbs with previously defined specificities allowed the rapid assignments of mAb binding to antigen classes. mAb specificities were further investigated using subsequent immunochemical and biochemical analyses and two novel mAbs are described in detail. mAb LM13 binds to an arabinanase-sensitive pectic epitope and mAb LM14, binds to an epitope occurring on arabinogalactan-proteins. Both mAbs display novel patterns of recognition of cell walls in plant materials. PMID:17629746

  19. Fingerprint analysis of Hibiscus mutabilis L. leaves based on ultra performance liquid chromatography with photodiode array detector combined with similarity analysis and hierarchical clustering analysis methods

    PubMed Central

    Liang, Xianrui; Ma, Meiling; Su, Weike

    2013-01-01

    Background: A method for chemical fingerprint analysis of Hibiscus mutabilis L. leaves was developed based on ultra performance liquid chromatography with photodiode array detector (UPLC-PAD) combined with similarity analysis (SA) and hierarchical clustering analysis (HCA). Materials and Methods: 10 batches of Hibiscus mutabilis L. leaves samples were collected from different regions of China. UPLC-PAD was employed to collect chemical fingerprints of Hibiscus mutabilis L. leaves. Results: The relative standard deviations (RSDs) of the relative retention times (RRT) and relative peak areas (RPA) of 10 characteristic peaks (one of them was identified as rutin) in precision, repeatability and stability test were less than 3%, and the method of fingerprint analysis was validated to be suitable for the Hibiscus mutabilis L. leaves. Conclusions: The chromatographic fingerprints showed abundant diversity of chemical constituents qualitatively in the 10 batches of Hibiscus mutabilis L. leaves samples from different locations by similarity analysis on basis of calculating the correlation coefficients between each two fingerprints. Moreover, the HCA method clustered the samples into four classes, and the HCA dendrogram showed the close or distant relations among the 10 samples, which was consistent to the SA result to some extent. PMID:23930008

  20. Determination of Ruscogenin in Ophiopogonis Radix by High-performance Liquid Chromatography-evaporative Light Scattering Detector Coupled with Hierarchical Clustering Analysis

    PubMed Central

    Liu, Chun-Hua; Li, Ming; Feng, Ya-Qian; Hu, Yuan-Jia; Yu, Bo-Yang; Qi, Jin

    2016-01-01

    Background: Ophiopogonis Radix is a famous traditional Chinese medicine. It is necessary to establish a suitable quality control methods of Ophiopogonis Radix. Objective: To investigate the quality control methods of Ophiopogonis Radix by high-performance liquid chromatography (HPLC) coupled with evaporative light scattering detector (ELSD). Materials and Methods: A rapid and simple method, HPLC coupled with ELSD, was applied to determinate ruscogenin in 35 batches of Ophiopogenis Radix samples. Orthogonal tests and single factor explorations were used to optimize the extraction condition of ruscogenin. The content of ruscogenin in different origin was further analyzed by hierarchical clustering analysis (HCA). Results: The ruscogenin was successfully determined by HPLC-ELSD with a two-phase solvent system composed of methanol-water (88:12) at a flow rate 1.0 ml/min, column temperature maintained at 25°C, detector draft tube temperature at 42.2°C, nebulizer gas flow rate at 1.4 L/min, and the gain at 8. The result showed the good linearity of ruscogenin in the range of 40.20–804.00 μg/ml (R2 = 0.9996). Average of recovery was 101.3% (relative standard deviation = 1.59%). A significant difference of ruscogenin content was shown among 35 batches of Ophiopogenis Radix from different origin, varied from 0.0035% to 0.0240%. HCA based on the content of ruscogenin indicated that Ophiopogonis Radix in different origin was mainly divided into two clusters. Conclusion: This simple, rapid, low-cost, and reliable HPLC-ELSD method could be suitable for measurement of ruscogenin content rations and quality control of Ophiopogonis Radix. SUMMARY Ophiopogonis Radix is an important Traditional Chinese Medicine (TCM) to treat and prevent cardiovascular diseases and acute or chronic inflammation for thousands of years. Steroidal saponins were known as the dominant active components for their significant cardiovascular activity, and the most steroid sapogenin of them is

  1. Tight bifunctional hierarchical catalyst.

    PubMed

    Højholt, Karen T; Vennestrøm, Peter N R; Tiruvalam, Ramchandra; Beato, Pablo

    2011-12-28

    A new concept to prepare tight bifunctional catalysts has been developed, by anchoring CoMo(6) clusters on hierarchical ZSM-5 zeolites for simultaneous use in HDS and hydrocracking catalysis. The prepared material displays a significant improved activity in HDS catalysis compared to the impregnated counterpart. PMID:22048337

  2. Image Information Mining Utilizing Hierarchical Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

    2002-01-01

    The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

  3. Cluster analysis of WIBS single-particle bioaerosol data

    NASA Astrophysics Data System (ADS)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2013-02-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  4. Ultra high performance liquid chromatography with electrospray ionization tandem mass spectrometry coupled with hierarchical cluster analysis to evaluate Wikstroemia indica (L.) C. A. Mey. from different geographical regions.

    PubMed

    Wei, Lan; Wang, Xiaobo; Mu, Shanxue; Sun, Lixin; Yu, Zhiguo

    2015-06-01

    A sensitive, rapid and simple ultra high performance liquid chromatography with electrospray ionization tandem mass spectrometry method was developed to determine seven constituents (umbelliferone, apigenin, triumbelletin, daphnoretin, arctigenin, genkwanin and emodin) in Wikstroemia indica (L.) C. A. Mey. The chromatographic analysis was performed on an ACQUITY UPLC® BEH C18 column (2.1 × 50 mm, 1.7 μm) by gradient elution with the mobile phase of 0.05% formic acid aqueous solution (A) and acetonitrile (B). Multiple reaction monitoring mode with positive and negative electrospray ionization interface was carried out to detect the components. This method was validated in terms of specificity, linearity, accuracy, precision and stability. Excellent linear behavior was observed over the certain concentration ranges with the correlation coefficient values higher than 0.999. The intraday and innerday precisions were within 2.0%. The recoveries of seven analytes were 99.4-101.1% with relative standard deviation less than 1.2%. The 18 Wikstroemia indica samples from different origins were classified by hierarchical clustering analysis according to the contents of seven components. The results demonstrated that the developed method could successfully be used to quantify simultaneously of seven components in Wikstroemia indica and could be a helpful tool for the detection and confirmation of the quality of traditional Chinese medicines. PMID:25866087

  5. Quantitative and chemical fingerprint analysis for the quality evaluation of Receptaculum Nelumbinis by RP-HPLC coupled with hierarchical clustering analysis.

    PubMed

    Wu, Yan-Bin; Zheng, Li-Jun; Yi, Jun; Wu, Jian-Guo; Chen, Ti-Qiang; Wu, Jin-Zhong

    2013-01-01

    A simple and reliable method of high-performance liquid chromatography with photodiode array detection (HPLC-DAD) was developed to evaluate the quality of Receptaculum Nelumbinis (dried receptacle of Nelumbo nucifera) through establishing chromatographic fingerprint and simultaneous determination of five flavonol glycosides, including hyperoside, isoquercitrin, quercetin-3-O-β-d-glucuronide, isorhamnetin-3-O-β-d-galactoside and syringetin-3-O-β-d-glucoside. In quantitative analysis, the five components showed good regression (R > 0.9998) within linear ranges, and their recoveries were in the range of 98.31%-100.32%. In the chromatographic fingerprint, twelve peaks were selected as the characteristic peaks to assess the similarities of different samples collected from different origins in China according to the State Food and Drug Administration (SFDA) requirements. Furthermore, hierarchical cluster analysis (HCA) was also applied to evaluate the variation of chemical components among different sources of Receptaculum Nelumbinis in China. This study indicated that the combination of quantitative and chromatographic fingerprint analysis can be readily utilized as a quality control method for Receptaculum Nelumbinis and its related traditional Chinese medicinal preparations. PMID:23337200

  6. Inferring population structure and relationship using minimal independent evolutionary markers in Y-chromosome: a hybrid approach of recursive feature selection for hierarchical clustering

    PubMed Central

    Srivastava, Amit Kumar; Chopra, Rupali; Ali, Shafat; Aggarwal, Shweta; Vig, Lovekesh; Koul Bamezai, Rameshwar Nath

    2014-01-01

    Inundation of evolutionary markers expedited in Human Genome Project and 1000 Genome Consortium has necessitated pruning of redundant and dependent variables. Various computational tools based on machine-learning and data-mining methods like feature selection/extraction have been proposed to escape the curse of dimensionality in large datasets. Incidentally, evolutionary studies, primarily based on sequentially evolved variations have remained un-facilitated by such advances till date. Here, we present a novel approach of recursive feature selection for hierarchical clustering of Y-chromosomal SNPs/haplogroups to select a minimal set of independent markers, sufficient to infer population structure as precisely as deduced by a larger number of evolutionary markers. To validate the applicability of our approach, we optimally designed MALDI-TOF mass spectrometry-based multiplex to accommodate independent Y-chromosomal markers in a single multiplex and genotyped two geographically distinct Indian populations. An analysis of 105 world-wide populations reflected that 15 independent variations/markers were optimal in defining population structure parameters, such as FST, molecular variance and correlation-based relationship. A subsequent addition of randomly selected markers had a negligible effect (close to zero, i.e. 1 × 10−3) on these parameters. The study proves efficient in tracing complex population structures and deriving relationships among world-wide populations in a cost-effective and expedient manner. PMID:25030906

  7. Principal factor and hierarchical cluster analyses for the performance assessment of an urban wastewater treatment plant in the Southeast of Spain.

    PubMed

    Bayo, Javier; López-Castellanos, Joaquín

    2016-07-01

    Process performance and operation of wastewater treatment plants (WWTP) are carried out to ensure their compliance with legislative requirements imposed by European Union. Because a high amount of variables are daily measured, a coherent and structured approach of such a system is required to understand its inherent behavior and performance efficiency. In this sense, both principal factor analysis (PFA) and hierarchical cluster analysis (HCA) are multivariate techniques that have been widely applied to extract and structure information for different purposes. In this paper, both statistical tools are applied in an urban WWTP situated in the Southeast of Spain, a zone with special characteristics related to the geochemical background composition of water and an important use of fertilizers. Four main factors were extracted in association with nutrients, the ionic component, the organic load to the WWTP, and the efficiency of the whole process. HCA allowed distinguish between influent and effluent parameters, although a deeper examination resulted in a dendrogram with groupings similar to those previously reported for PFA. PMID:27108373

  8. Quantitative and Chemical Fingerprint Analysis for the Quality Evaluation of Receptaculum Nelumbinis by RP-HPLC Coupled with Hierarchical Clustering Analysis

    PubMed Central

    Wu, Yan-Bin; Zheng, Li-Jun; Yi, Jun; Wu, Jian-Guo; Chen, Ti-Qiang; Wu, Jin-Zhong

    2013-01-01

    A simple and reliable method of high-performance liquid chromatography with photodiode array detection (HPLC-DAD) was developed to evaluate the quality of Receptaculum Nelumbinis (dried receptacle of Nelumbo nucifera) through establishing chromatographic fingerprint and simultaneous determination of five flavonol glycosides, including hyperoside, isoquercitrin, quercetin-3-O-β-d-glucuronide, isorhamnetin-3-O-β-d-galactoside and syringetin-3-O-β-d-glucoside. In quantitative analysis, the five components showed good regression (R > 0.9998) within linear ranges, and their recoveries were in the range of 98.31%–100.32%. In the chromatographic fingerprint, twelve peaks were selected as the characteristic peaks to assess the similarities of different samples collected from different origins in China according to the State Food and Drug Administration (SFDA) requirements. Furthermore, hierarchical cluster analysis (HCA) was also applied to evaluate the variation of chemical components among different sources of Receptaculum Nelumbinis in China. This study indicated that the combination of quantitative and chromatographic fingerprint analysis can be readily utilized as a quality control method for Receptaculum Nelumbinis and its related traditional Chinese medicinal preparations. PMID:23337200

  9. Deterministic hierarchical networks

    NASA Astrophysics Data System (ADS)

    Barrière, L.; Comellas, F.; Dalfó, C.; Fiol, M. A.

    2016-06-01

    It has been shown that many networks associated with complex systems are small-world (they have both a large local clustering coefficient and a small diameter) and also scale-free (the degrees are distributed according to a power law). Moreover, these networks are very often hierarchical, as they describe the modularity of the systems that are modeled. Most of the studies for complex networks are based on stochastic methods. However, a deterministic method, with an exact determination of the main relevant parameters of the networks, has proven useful. Indeed, this approach complements and enhances the probabilistic and simulation techniques and, therefore, it provides a better understanding of the modeled systems. In this paper we find the radius, diameter, clustering coefficient and degree distribution of a generic family of deterministic hierarchical small-world scale-free networks that has been considered for modeling real-life complex systems.

  10. Imaging of lipids in atherosclerotic lesion in aorta from ApoE/LDLR-/- mice by FT-IR spectroscopy and Hierarchical Cluster Analysis.

    PubMed

    P Wrobel, Tomasz; Mateuszuk, Lukasz; Chlopicki, Stefan; Malek, Kamilla; Baranska, Malgorzata

    2011-12-21

    Spectroscopy-based approaches can provide an insight into the biochemical composition of a tissue sample. In the present work Fourier transform infrared (FT-IR) spectroscopy was used to develop a reliable methodology to study the content of free fatty acids, triglycerides, cholesteryl esters as well as cholesterol in aorta from mice with atherosclerosis (ApoE/LDLR(-/-) mice). In particular, distribution and concentration of palmitic, oleic and linoleic acid derivatives were analyzed. Spectral analysis of pure compounds allowed for clear discrimination between free fatty acids and other similar moieties based on the carbonyl band position (1699-1710 cm(-1) range). In order to distinguish cholesteryl esters from triglycerides a ratio of carbonyl band to signal at 1010 cm(-1) was used. Imaging of lipids in atherosclerotic aortic lesions in ApoE/LDLR(-/-) mice was followed by Hierarchical Cluster Analysis (HCA). The aorta from C57Bl/6J control mice (fed with chow diet) was used for comparison. The measurements were completed with an FT-IR spectrometer equipped with a 128 × 128 FPA detector. In cross-section of aorta from ApoE/LDLR(-/-) mice a region of atherosclerotic plaque was clearly identified by HCA, which was later divided into 2 sub-regions, one characterized by the higher content of cholesterol, while the other by higher contents of cholesteryl esters. HCA of tissues deposited on normal microscopic glass, hence limited to the 2200-3800 cm(-1) spectral range, also identified a region of atherosclerotic plaque. Importantly, this region correlates with the area stained by standard histological staining for atherosclerotic plaque (Oil Red O). In conclusion, the use of FT-IR and HCA may provide a novel tool for qualitative and quantitative analysis of contents and distribution of lipids in atherosclerotic plaque. PMID:22007352

  11. Hierarchically clustering to 1,033 genes differentially expressed in mouse superior colliculus in the courses of optic nerve development and injury.

    PubMed

    Mei, Qiang; Zhang, Yan-qi; Liu, Jian-jun; Li, Cheng-ren; Chen, Xing-shu; Li, Hong-li; Qin, Mao-lin; wu, Ya-zhou; Liu, Yun-lai; Cai, Wen-qin

    2013-11-01

    Tempo spatially specific expression of many development-related genes is the molecular basis for the formation of the central nervous system (CNS), especially those genes regulating the proliferation, differentiation, migration, axon growth, and orientation of nerve cells. The development-related genes are usually prominent during the embryonic and newborn stages, but rarely express during the adulthood. These genes are believed to be suitable target genes for promoting CNS regeneration, despite majority of which remains unknown. Hence, the aim of this study was to screen development-related genes which might contribute to CNS regeneration. In this study, 1,033 differentially-expressed genes of superior colliculus in the courses of mouse optic nerve development and injury, as previously identified by cDNA microarrays, were hierarchically clustered to display expression pattern of each gene and reveal the relationships among these genes, and infer the functions of some unknown genes based on function-identified genes with the similar expression patterns. Consequently, the expression patterns of 1,033 candidate genes were revealed at eight time points during optic nerve development or injury. According to the similarity among gene expression patterns, 1,033 genes were divided into seven groups. The potential function of genes in each group was inferred on the basis of the dynamic trend for mean gene expression values. Moreover, the expression patterns of six function-unidentified genes were extremely similar to that of the ptn gene which could promote and guide axonal extension. Therefore, these six genes are temporally regarded as candidate genes related to axon growth and guidance. The results may help to better understand the roles of function-identified genes in the stages of CNS development and injury, and offer useful clues to evaluate the functions of hundreds of unidentified genes. PMID:23526189

  12. Prediction of the fate of Hg and other contaminants in soil around a former chlor-alkali plant using Fuzzy Hierarchical Cross-Clustering approach.

    PubMed

    Frenţiu, Tiberiu; Ponta, Michaela; Sârbu, Costel

    2015-11-01

    An associative simultaneous fuzzy divisive hierarchical algorithm was used to predict the fate of Hg and other contaminants in soil around a former chlor-alkali plant. The algorithm was applied on several natural and anthropogenic characteristics of soil including water leachable, mobile, semi-mobile, non-mobile fractions and total Hg, Al, Ba, Ca, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Sr, Zn, water leachable fraction of Cl(-), NO3(-) and SO4(2)(-), pH and total organic carbon. The cross-classification algorithm provided a divisive fuzzy partition of the soil samples and associated characteristics. Soils outside the perimeter of the former chlor-alkali plant were clustered based on the natural characteristics and total Hg. In contaminated zones Hg speciation becomes relevant and the assessment of species distribution is necessary. The descending order of concentration of Hg species in the test site was semi-mobile>mobile>non-mobile>water-leachable. Physico-chemical features responsible for similarities or differences between uncontaminated soil samples or contaminated with Hg, Cu, Zn, Ba and NO3(-) were also highlighted. Other characteristics of the contaminated soil were found to be Ca, sulfate, Na and chloride, some of which with influence on Hg fate. The presence of Ca and sulfate in soil induced a higher water leachability of Hg, while Cu had an opposite effect by forming amalgam. The used algorithm provided an in-deep understanding of processes involving Hg species and allowed to make prediction of the fate of Hg and contaminants linked to chlor-alkali-industry. PMID:26057390

  13. The Metabolic Status Drives Acclimation of Iron Deficiency Responses in Chlamydomonas reinhardtii as Revealed by Proteomics Based Hierarchical Clustering and Reverse Genetics*

    PubMed Central

    Höhner, Ricarda; Barth, Johannes; Magneschi, Leonardo; Jaeger, Daniel; Niehues, Anna; Bald, Till; Grossman, Arthur; Fufezan, Christian; Hippler, Michael

    2013-01-01

    Iron is a crucial cofactor in numerous redox-active proteins operating in bioenergetic pathways including respiration and photosynthesis. Cellular iron management is essential to sustain sufficient energy production and minimize oxidative stress. To produce energy for cell growth, the green alga Chlamydomonas reinhardtii possesses the metabolic flexibility to use light and/or carbon sources such as acetate. To investigate the interplay between the iron-deficiency response and growth requirements under distinct trophic conditions, we took a quantitative proteomics approach coupled to innovative hierarchical clustering using different “distance-linkage combinations” and random noise injection. Protein co-expression analyses of the combined data sets revealed insights into cellular responses governing acclimation to iron deprivation and regulation associated with photosynthesis dependent growth. Photoautotrophic growth requirements as well as the iron deficiency induced specific metabolic enzymes and stress related proteins, and yet differences in the set of induced enzymes, proteases, and redox-related polypeptides were evident, implying the establishment of distinct response networks under the different conditions. Moreover, our data clearly support the notion that the iron deficiency response includes a hierarchy for iron allocation within organelles in C. reinhardtii. Importantly, deletion of a bifunctional alcohol and acetaldehyde dehydrogenase (ADH1), which is induced under low iron based on the proteomic data, attenuates the remodeling of the photosynthetic machinery in response to iron deficiency, and at the same time stimulates expression of stress-related proteins such as NDA2, LHCSR3, and PGRL1. This finding provides evidence that the coordinated regulation of bioenergetics pathways and iron deficiency response is sensitive to the cellular and chloroplast metabolic and/or redox status, consistent with systems approach data. PMID:23820728

  14. Patterns of comorbidity in community-dwelling older people hospitalised for fall-related injury: A cluster analysis

    PubMed Central

    2011-01-01

    Background Community-dwelling older people aged 65+ years sustain falls frequently; these can result in physical injuries necessitating medical attention including emergency department care and hospitalisation. Certain health conditions and impairments have been shown to contribute independently to the risk of falling or experiencing a fall injury, suggesting that individuals with these conditions or impairments should be the focus of falls prevention. Since older people commonly have multiple conditions/impairments, knowledge about which conditions/impairments coexist in at-risk individuals would be valuable in the implementation of a targeted prevention approach. The objective of this study was therefore to examine the prevalence and patterns of comorbidity in this population group. Methods We analysed hospitalisation data from Victoria, Australia's second most populous state, to estimate the prevalence of comorbidity in patients hospitalised at least once between 2005-6 and 2007-8 for treatment of acute fall-related injuries. In patients with two or more comorbid conditions (multicomorbidity) we used an agglomerative hierarchical clustering method to cluster comorbidity variables and identify constellations of conditions. Results More than one in four patients had at least one comorbid condition and among patients with comorbidity one in three had multicomorbidity (range 2-7). The prevalence of comorbidity varied by gender, age group, ethnicity and injury type; it was also associated with a significant increase in the average cumulative length of stay per patient. The cluster analysis identified five distinct, biologically plausible clusters of comorbidity: cardiopulmonary/metabolic, neurological, sensory, stroke and cancer. The cardiopulmonary/metabolic cluster was the largest cluster among the clusters identified. Conclusions The consequences of comorbidity clustering in terms of falls and/or injury outcomes of hospitalised patients should be investigated by

  15. Hierarchical photocatalysts.

    PubMed

    Li, Xin; Yu, Jiaguo; Jaroniec, Mietek

    2016-05-01

    As a green and sustainable technology, semiconductor-based heterogeneous photocatalysis has received much attention in the last few decades because it has potential to solve both energy and environmental problems. To achieve efficient photocatalysts, various hierarchical semiconductors have been designed and fabricated at the micro/nanometer scale in recent years. This review presents a critical appraisal of fabrication methods, growth mechanisms and applications of advanced hierarchical photocatalysts. Especially, the different synthesis strategies such as two-step templating, in situ template-sacrificial dissolution, self-templating method, in situ template-free assembly, chemically induced self-transformation and post-synthesis treatment are highlighted. Finally, some important applications including photocatalytic degradation of pollutants, photocatalytic H2 production and photocatalytic CO2 reduction are reviewed. A thorough assessment of the progress made in photocatalysis may open new opportunities in designing highly effective hierarchical photocatalysts for advanced applications ranging from thermal catalysis, separation and purification processes to solar cells. PMID:26963902

  16. Delineation of river bed-surface patches by clustering high-resolution spatial grain size data

    NASA Astrophysics Data System (ADS)

    Nelson, Peter A.; Bellugi, Dino; Dietrich, William E.

    2014-01-01

    The beds of gravel-bed rivers commonly display distinct sorting patterns, which at length scales of ~ 0.1 - 1 channel widths appear to form an organization of patches or facies. This paper explores alternatives to traditional visual facies mapping by investigating methods of patch delineation in which clustering analysis is applied to a high-resolution grid of spatial grain-size distributions (GSDs) collected during a flume experiment. Specifically, we examine four clustering techniques: 1) partitional clustering of grain-size distributions with the k-means algorithm (assigning each GSD to a type of patch based solely on its distribution characteristics), 2) spatially-constrained agglomerative clustering ("growing" patches by merging adjacent GSDs, thus generating a hierarchical structure of patchiness), 3) spectral clustering using Normalized Cuts (using the spatial distance between GSDs and the distribution characteristics to generate a matrix describing the similarity between all GSDs, and using the eigenvalues of this matrix to divide the bed into patches), and 4) fuzzy clustering with the fuzzy c-means algorithm (assigning each GSD a membership probability to every patch type). For each clustering method, we calculate metrics describing how well-separated cluster-average GSDs are and how patches are arranged in space. We use these metrics to compute optimal clustering parameters, to compare the clustering methods against each other, and to compare clustering results with patches mapped visually during the flume experiment.All clustering methods produced better-separated patch GSDs than the visually-delineated patches. Although they do not produce crisp cluster assignment, fuzzy algorithms provide useful information that can characterize the uncertainty of a location on the bed belonging to any particular type of patch, and they can be used to characterize zones of transition from one patch to another. The extent to which spatial information influences

  17. The Case for a Hierarchical Cosmology

    ERIC Educational Resources Information Center

    Vaucouleurs, G. de

    1970-01-01

    The development of modern theoretical cosmology is presented and some questionable assumptions of orthodox cosmology are pointed out. Suggests that recent observations indicate that hierarchical clustering is a basic factor in cosmology. The implications of hierarchical models of the universe are considered. Bibliography. (LC)

  18. Hyperspectral remote sensing of paddy crop using insitu measurement and clustering technique

    NASA Astrophysics Data System (ADS)

    Moharana, S.; Dutta, S.

    2014-11-01

    Rice Agriculture, mainly cultivated in South Asia regions, is being monitored for extracting crop parameter, crop area, crop growth profile, crop yield using both optical and microwave remote sensing. Hyperspectral data provide more detailed information of rice agriculture. The present study was carried out at the experimental station of the Regional Rainfed Low land Rice Research Station, Assam, India (26.1400° N, 91.7700° E) and the overall climate of the study area comes under Lower Brahmaputra Valley (LBV) Agro Climatic Zones. The hyperspectral measurements were made in the year 2009 from 72 plots that include eight rice varieties along with three different level of nitrogen treatments (50, 100, 150 kg/ha) covering rice transplanting to the crop harvesting period. With an emphasis to varieties, hyperspectral measurements were taken in the year 2014 from 24 plots having 24 rice genotypes with different crop developmental ages. All the measurements were performed using a spectroradiometer with a spectral range of 350-1050 nm under direct sunlight of a cloud free sky and stable condition of the atmosphere covering more than 95 % canopy. In this study, reflectance collected from canopy of rice were expressed in terms of waveforms. Furthermore, generated waveforms were analysed for all combinations of nitrogen applications and varieties. A hierarchical clustering technique was employed to classify these waveforms into different groups. By help of agglomerative clustering algorithm a few number of clusters were finalized for different rice varieties along with nitrogen treatments. By this clustering approach, observational error in spectroradiometer reflectance was also nullified. From this hierarchical clustering, appropriate spectral signature for rice canopy were identified and will help to create rice crop classification accurately and therefore have a prospect to make improved information on rice agriculture at both local and regional scales. From this

  19. An X-Ray Spectral Classification Algorithm with Application to Young Stellar Clusters

    NASA Astrophysics Data System (ADS)

    Hojnacki, S. M.; Kastner, J. H.; Micela, G.; Feigelson, E. D.; LaLonde, S. M.

    2007-04-01

    A large volume of low signal-to-noise, multidimensional data is available from the CCD imaging spectrometers aboard the Chandra X-Ray Observatory and the X-Ray Multimirror Mission (XMM-Newton). To make progress analyzing this data, it is essential to develop methods to sort, classify, and characterize the vast library of X-ray spectra in a nonparametric fashion (complementary to current parametric model fits). We have developed a spectral classification algorithm that handles large volumes of data and operates independently of the requirement of spectral model fits. We use proven multivariate statistical techniques including principal component analysis and an ensemble classifier consisting of agglomerative hierarchical clustering and K-means clustering applied for the first time for spectral classification. The algorithm positions the sources in a multidimensional spectral sequence and then groups the ordered sources into clusters based on their spectra. These clusters appear more distinct for sources with harder observed spectra. The apparent diversity of source spectra is reduced to a three-dimensional locus in principal component space, with spectral outliers falling outside this locus. The algorithm was applied to a sample of 444 strong sources selected from the 1616 X-ray emitting sources detected in deep Chandra imaging spectroscopy of the Orion Nebula Cluster. Classes form sequences in NH, AV, and accretion activity indicators, demonstrating that the algorithm efficiently sorts the X-ray sources into a physically meaningful sequence. The algorithm also isolates important classes of very deeply embedded, active young stellar objects, and yields trends between X-ray spectral parameters and stellar parameters for the lowest mass, pre-main-sequence stars.

  20. The relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

    PubMed Central

    2015-01-01

    Background Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data. Results We propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data. Conclusions Our proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID Bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner. PMID:25734691

  1. Delineation of Stenotrophomonas maltophilia isolates from cystic fibrosis patients by fatty acid methyl ester profiles and matrix-assisted laser desorption/ionization time-of-flight mass spectra using hierarchical cluster analysis and principal component analysis.

    PubMed

    Vidigal, Pedrina Gonçalves; Mosel, Frank; Koehling, Hedda Luise; Mueller, Karl Dieter; Buer, Jan; Rath, Peter Michael; Steinmann, Joerg

    2014-12-01

    Stenotrophomonas maltophilia is an opportunist multidrug-resistant pathogen that causes a wide range of nosocomial infections. Various cystic fibrosis (CF) centres have reported an increasing prevalence of S. maltophilia colonization/infection among patients with this disease. The purpose of this study was to assess specific fingerprints of S. maltophilia isolates from CF patients (n = 71) by investigating fatty acid methyl esters (FAMEs) through gas chromatography (GC) and highly abundant proteins by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and to compare them with isolates obtained from intensive care unit (ICU) patients (n = 20) and the environment (n = 11). Principal component analysis (PCA) of GC-FAME patterns did not reveal a clustering corresponding to distinct CF, ICU or environmental types. Based on the peak area index, it was observed that S. maltophilia isolates from CF patients produced significantly higher amounts of fatty acids in comparison with ICU patients and the environmental isolates. Hierarchical cluster analysis (HCA) based on the MALDI-TOF MS peak profiles of S. maltophilia revealed the presence of five large clusters, suggesting a high phenotypic diversity. Although HCA of MALDI-TOF mass spectra did not result in distinct clusters predominantly composed of CF isolates, PCA revealed the presence of a distinct cluster composed of S. maltophilia isolates from CF patients. Our data suggest that S. maltophilia colonizing CF patients tend to modify not only their fatty acid patterns but also their protein patterns as a response to adaptation in the unfavourable environment of the CF lung. PMID:25266870

  2. Hierarchical Cluster Analysis of Three-Dimensional Reconstructions of Unbiased Sampled Microglia Shows not Continuous Morphological Changes from Stage 1 to 2 after Multiple Dengue Infections in Callithrix penicillata

    PubMed Central

    Diniz, Daniel G.; Silva, Geane O.; Naves, Thaís B.; Fernandes, Taiany N.; Araújo, Sanderson C.; Diniz, José A. P.; de Farias, Luis H. S.; Sosthenes, Marcia C. K.; Diniz, Cristovam G.; Anthony, Daniel C.; da Costa Vasconcelos, Pedro F.; Picanço Diniz, Cristovam W.

    2016-01-01

    It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous. PMID:27047345

  3. Hierarchical Cluster Analysis of Three-Dimensional Reconstructions of Unbiased Sampled Microglia Shows not Continuous Morphological Changes from Stage 1 to 2 after Multiple Dengue Infections in Callithrix penicillata.

    PubMed

    Diniz, Daniel G; Silva, Geane O; Naves, Thaís B; Fernandes, Taiany N; Araújo, Sanderson C; Diniz, José A P; de Farias, Luis H S; Sosthenes, Marcia C K; Diniz, Cristovam G; Anthony, Daniel C; da Costa Vasconcelos, Pedro F; Picanço Diniz, Cristovam W

    2016-01-01

    It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous. PMID:27047345

  4. Molecular Clustering Interrelationships and Carbohydrate Conformation in Hull and Seeds Among Barley Cultivars

    SciTech Connect

    N Liu; P Yu

    2011-12-31

    The objective of this study was to use molecular spectral analyses with the diffuse reflectance Fourier transform infrared spectroscopy (DRIFT) bioanlytical technique to study carbohydrate conformation features, molecular clustering and interrelationships in hull and seed among six barley cultivars (AC Metcalfe, CDC Dolly, McLeod, CDC Helgason, CDC Trey, CDC Cowboy), which had different degradation kinetics in rumen. The molecular structure spectral analyses in both hull and seed involved the fingerprint regions of ca. 1536-1484 cm{sup -1} (attributed mainly to aromatic lignin semicircle ring stretch), ca. 1293-1212 cm{sup -1} (attributed mainly to cellulosic compounds in the hull), ca. 1269-1217 cm{sup -1} (attributed mainly to cellulosic compound in the seeds), and ca. 1180-800 cm{sup -1} (attributed mainly to total CHO C-O stretching vibrations) together with an agglomerative hierarchical cluster (AHCA) and principal component spectral analyses (PCA). The results showed that the DRIFT technique plus AHCA and PCA molecular analyses were able to reveal carbohydrate conformation features and identify carbohydrate molecular structure differences in both hull and seeds among the barley varieties. The carbohydrate molecular spectral analyses at the region of ca. 1185-800 cm{sup -1} together with the AHCA and PCA were able to show that the barley seed inherent structures exhibited distinguishable differences among the barley varieties. CDC Helgason had differences from AC Metcalfe, MeLeod, CDC Cowboy and CDC Dolly in carbohydrate conformation in the seed. Clear molecular cluster classes could be distinguished and identified in AHCA analysis and the separate ellipses could be grouped in PCA analysis. But CDC Helgason had no distinguished differences from CDC Trey in carbohydrate conformation. These carbohydrate conformation/structure difference could partially explain why the varieties were different in digestive behaviors in animals. The molecular spectroscopy

  5. Quantitative Analysis and Comparison of Four Major Flavonol Glycosides in the Leaves of Toona sinensis (A. Juss.) Roemer (Chinese Toon) from Various Origins by High-Performance Liquid Chromatography-Diode Array Detector and Hierarchical Clustering Analysis

    PubMed Central

    Sun, Xiaoxiang; Zhang, Liting; Cao, Yaqi; Gu, Qinying; Yang, Huan; Tam, James P.

    2016-01-01

    Background: Toona sinensis (A. Juss.) Roemer is an endemic species of Toona genus native to Asian area. Its dried leaves are applied in the treatment of many diseases; however, few investigations have been reported for the quantitative analysis and comparison of major bioactive flavonol glycosides in the leaves harvested from various origins. Objective: To quantitatively analyze four major flavonol glycosides including rutinoside, quercetin-3-O-β-D-glucoside, quercetin-3-O-α-L-rhamnoside, and kaempferol-3-O-α-L-rhamnoside in the leaves from different production sites and classify them according to the content of these glycosides. Materials and Methods: A high-performance liquid chromatography-diode array detector (HPLC-DAD) method for their simultaneous determination was developed and validated for linearity, precision, accuracy, stability, and repeatability. Moreover, the method established was then employed to explore the difference in the content of these four glycosides in raw materials. Finally, a hierarchical clustering analysis was performed to classify 11 voucher specimens. Results: The separation was performed on a Waters XBridge Shield RP18 column (150 mm × 4.6 mm, 3.5 μm) kept at 35°C, and acetonitrile and H2O containing 0.30% trifluoroacetic acid as mobile phase was driven at 1.0 mL/min during the analysis. Ten microliters of solution were injected and 254 nm was selected to monitor the separation. A strong linear relationship between the peak area and concentration of four analytes was observed. And, the method was also validated to be repeatable, stable, precise, and accurate. Conclusion: An efficient and reliable HPLC-DAD method was established and applied in the assays for the samples from 11 origins successfully. Moreover, the content of those flavonol glycosides varied much among different batches, and the flavonoids could be considered as biomarkers to control the quality of Chinese Toon. SUMMARY Four major flavonol glycosides in the leaves

  6. Random sequential renormalization and agglomerative percolation in networks: Application to Erdös-Rényi and scale-free graphs

    NASA Astrophysics Data System (ADS)

    Bizhani, Golnoosh; Grassberger, Peter; Paczuski, Maya

    2011-12-01

    We study the statistical behavior under random sequential renormalization (RSR) of several network models including Erdös-Rényi (ER) graphs, scale-free networks, and an annealed model related to ER graphs. In RSR the network is locally coarse grained by choosing at each renormalization step a node at random and joining it to all its neighbors. Compared to previous (quasi-)parallel renormalization methods [Song , Nature (London)NATUAS0028-083610.1038/nature03248 433, 392 (2005)], RSR allows a more fine-grained analysis of the renormalization group (RG) flow and unravels new features that were not discussed in the previous analyses. In particular, we find that all networks exhibit a second-order transition in their RG flow. This phase transition is associated with the emergence of a giant hub and can be viewed as a new variant of percolation, called agglomerative percolation. We claim that this transition exists also in previous graph renormalization schemes and explains some of the scaling behavior seen there. For critical trees it happens as N/N0→0 in the limit of large systems (where N0 is the initial size of the graph and N its size at a given RSR step). In contrast, it happens at finite N/N0 in sparse ER graphs and in the annealed model, while it happens for N/N0→1 on scale-free networks. Critical exponents seem to depend on the type of the graph but not on the average degree and obey usual scaling relations for percolation phenomena. For the annealed model they agree with the exponents obtained from a mean-field theory. At late times, the networks exhibit a starlike structure in agreement with the results of Radicchi [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.101.148701 101, 148701 (2008)]. While degree distributions are of main interest when regarding the scheme as network renormalization, mass distributions (which are more relevant when considering “supernodes” as clusters) are much easier to study using the fast Newman-Ziff algorithm for

  7. The Sensitivity of Atmospheric Trajectory Cluster Analysis Results to Clustering Methods Using Trajectories to the PICO-NARE Station

    NASA Astrophysics Data System (ADS)

    Owen, R. C.; Honrath, R. E.; Merrill, J.

    2003-12-01

    The use of cluster analysis to group atmospheric trajectories according to similar flow paths has become a common tool in atmospheric studies. Many methods are available to conduct a cluster analysis. However, the dependence of the resulting clusters upon the specific clustering method chosen has not been fully characterized. Specifically, the use of hierarchical versus non-hierarchical clustering algorithms has received little focus. This study presents the results of two cluster analyses: one using the hierarchical clustering algorithm average linkage, and one using the non-hierarchical clustering algorithm k-means. These results demonstrate the sensitivity of this cluster analysis to the use of a hierarchical method versus a non-hierarchical method. In addition, this study analyzes methods for dealing with the vertical component of trajectories during the clustering process. The analyses were performed using a 40-year set of trajectories to the PICO-NARE station, located atop Pico Mountain in the Azores Islands in the central North Atlantic.

  8. An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features.

    PubMed

    De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D A; Norberto de Souza, Osmar

    2015-01-01

    Protein receptor conformations, obtained from molecular dynamics (MD) simulations, have become a promising treatment of its explicit flexibility in molecular docking experiments applied to drug discovery and development. However, incorporating the entire ensemble of MD conformations in docking experiments to screen large candidate compound libraries is currently an unfeasible task. Clustering algorithms have been widely used as a means to reduce such ensembles to a manageable size. Most studies investigate different algorithms using pairwise Root-Mean Square Deviation (RMSD) values for all, or part of the MD conformations. Nevertheless, the RMSD only may not be the most appropriate gauge to cluster conformations when the target receptor has a plastic active site, since they are influenced by changes that occur on other parts of the structure. Hence, we have applied two partitioning methods (k-means and k-medoids) and four agglomerative hierarchical methods (Complete linkage, Ward's, Unweighted Pair Group Method and Weighted Pair Group Method) to analyze and compare the quality of partitions between a data set composed of properties from an enzyme receptor substrate-binding cavity and two data sets created using different RMSD approaches. Ensembles of representative MD conformations were generated by selecting a medoid of each group from all partitions analyzed. We investigated the performance of our new method for evaluating binding conformation of drug candidates to the InhA enzyme, which were performed by cross-docking experiments between a 20 ns MD trajectory and 20 different ligands. Statistical analyses showed that the novel ensemble, which is represented by only 0.48% of the MD conformations, was able to reproduce 75% of all dynamic behaviors within the binding cavity for the docking experiments performed. Moreover, this new approach not only outperforms the other two RMSD-clustering solutions, but it also shows to be a promising strategy to distill

  9. An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features

    PubMed Central

    Ruiz, Duncan D. A.; Norberto de Souza, Osmar

    2015-01-01

    Protein receptor conformations, obtained from molecular dynamics (MD) simulations, have become a promising treatment of its explicit flexibility in molecular docking experiments applied to drug discovery and development. However, incorporating the entire ensemble of MD conformations in docking experiments to screen large candidate compound libraries is currently an unfeasible task. Clustering algorithms have been widely used as a means to reduce such ensembles to a manageable size. Most studies investigate different algorithms using pairwise Root-Mean Square Deviation (RMSD) values for all, or part of the MD conformations. Nevertheless, the RMSD only may not be the most appropriate gauge to cluster conformations when the target receptor has a plastic active site, since they are influenced by changes that occur on other parts of the structure. Hence, we have applied two partitioning methods (k-means and k-medoids) and four agglomerative hierarchical methods (Complete linkage, Ward’s, Unweighted Pair Group Method and Weighted Pair Group Method) to analyze and compare the quality of partitions between a data set composed of properties from an enzyme receptor substrate-binding cavity and two data sets created using different RMSD approaches. Ensembles of representative MD conformations were generated by selecting a medoid of each group from all partitions analyzed. We investigated the performance of our new method for evaluating binding conformation of drug candidates to the InhA enzyme, which were performed by cross-docking experiments between a 20 ns MD trajectory and 20 different ligands. Statistical analyses showed that the novel ensemble, which is represented by only 0.48% of the MD conformations, was able to reproduce 75% of all dynamic behaviors within the binding cavity for the docking experiments performed. Moreover, this new approach not only outperforms the other two RMSD-clustering solutions, but it also shows to be a promising strategy to distill

  10. Topological properties of hierarchical networks

    NASA Astrophysics Data System (ADS)

    Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Guerra, Francesco; Tantari, Daniele; Tavani, Flavia

    2015-06-01

    Hierarchical networks are attracting a renewal interest for modeling the organization of a number of biological systems and for tackling the complexity of statistical mechanical models beyond mean-field limitations. Here we consider the Dyson hierarchical construction for ferromagnets, neural networks, and spin glasses, recently analyzed from a statistical-mechanics perspective, and we focus on the topological properties of the underlying structures. In particular, we find that such structures are weighted graphs that exhibit a high degree of clustering and of modularity, with a small spectral gap; the robustness of such features with respect to the presence of thermal noise is also studied. These outcomes are then discussed and related to the statistical-mechanics scenario in full consistency. Last, we look at these weighted graphs as Markov chains and we show that in the limit of infinite size, the emergence of ergodicity breakdown for the stochastic process mirrors the emergence of metastabilities in the corresponding statistical mechanical analysis.

  11. Relation chain based clustering analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Cheng-ning; Zhao, Ming-yang; Luo, Hai-bo

    2011-08-01

    Clustering analysis is currently one of well-developed branches in data mining technology which is supposed to find the hidden structures in the multidimensional space called feature or pattern space. A datum in the space usually possesses a vector form and the elements in the vector represent several specifically selected features. These features are often of efficiency to the problem oriented. Generally, clustering analysis goes into two divisions: one is based on the agglomerative clustering method, and the other one is based on divisive clustering method. The former refers to a bottom-up process which regards each datum as a singleton cluster while the latter refers to a top-down process which regards entire data as a cluster. As the collected literatures, it is noted that the divisive clustering is currently overwhelming both in application and research. Although some famous divisive clustering methods are designed and well developed, clustering problems are still far from being solved. The k - means algorithm is the original divisive clustering method which initially assigns some important index values, such as the clustering number and the initial clustering prototype positions, and that could not be reasonable in some certain occasions. More than the initial problem, the k - means algorithm may also falls into local optimum, clusters in a rigid way and is not available for non-Gaussian distribution. One can see that seeking for a good or natural clustering result, in fact, originates from the one's understanding of the concept of clustering. Thus, the confusion or misunderstanding of the definition of clustering always derives some unsatisfied clustering results. One should consider the definition deeply and seriously. This paper demonstrates the nature of clustering, gives the way of understanding clustering, discusses the methodology of designing a clustering algorithm, and proposes a new clustering method based on relation chains among 2D patterns. In

  12. Bayesian Hierarchical Classes Analysis

    ERIC Educational Resources Information Center

    Leenen, Iwin; Van Mechelen, Iven; Gelman, Andrew; De Knop, Stijn

    2008-01-01

    Hierarchical classes models are models for "N"-way "N"-mode data that represent the association among the "N" modes and simultaneously yield, for each mode, a hierarchical classification of its elements. In this paper we present a stochastic extension of the hierarchical classes model for two-way two-mode binary data. In line with the original…

  13. Optimization of hierarchical management of technological processes

    NASA Astrophysics Data System (ADS)

    Bayas, Marcia M.; Dubovoy, B. M.; Shegebaeva, Jibek; Gromaszek, Konrad

    2015-12-01

    The advisability of clustering of control tasks providing reduction of information flows needed to coordinate operational processes is demonstrated. Criterion and algorithm for optimal aggregation are proposed. The influence of the hierarchical structure of control system on the filling factor of information flow matrix is discussed.

  14. Modular and hierarchical structure of social contact networks

    NASA Astrophysics Data System (ADS)

    Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

    2013-10-01

    Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

  15. The Supervised Hierarchical Dirichlet Process.

    PubMed

    Dai, Andrew M; Storkey, Amos J

    2015-02-01

    We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method on two real-world classification problems and two real-world regression problems. Bayesian nonparametric regression models based on the Dirichlet process, such as the Dirichlet process-generalised linear models (DP-GLM) have previously been explored; these models allow flexibility in modelling nonlinear relationships. However, until now, hierarchical Dirichlet process (HDP) mixtures have not seen significant use in supervised problems with grouped data since a straightforward application of the HDP on the grouped data results in learnt clusters that are not predictive of the responses. The sHDP solves this problem by allowing for clusters to be learnt jointly from the group structure and from the label assigned to each group. PMID:26353239

  16. Optical bias and hierarchical clustering. [Of galaxies

    SciTech Connect

    Bonometto, S.A.; Lucchin, F.; Matarrese, S.

    1987-12-01

    The present transfer of statistical results for biased theories of galaxy origin to a direct analysis of the celestial sphere's luminosity field notes that magnitude-limited galaxy catalogs are interpretable as sets of luminosity peaks bypassing suitable luminosity limits. The relationship between this view and that based on the Limber equation is discussed, and a tentative explanation is proposed for peculiarities arising in observed spatial correlations. Zwicky catalog data appear to confirm the validity of the concepts presented. 30 references.

  17. Mokken Scale Analysis Using Hierarchical Clustering Procedures

    ERIC Educational Resources Information Center

    van Abswoude, Alexandra A. H.; Vermunt, Jeroen K.; Hemker, Bas T.; van der Ark, L. Andries

    2004-01-01

    Mokken scale analysis (MSA) can be used to assess and build unidimensional scales from an item pool that is sensitive to multiple dimensions. These scales satisfy a set of scaling conditions, one of which follows from the model of monotone homogeneity. An important drawback of the MSA program is that the sequential item selection and scale…

  18. Hierarchical Models of Attitude.

    ERIC Educational Resources Information Center

    Reddy, Srinivas K.; LaBarbera, Priscilla A.

    1985-01-01

    The application and use of hierarchical models is illustrated, using the example of the structure of attitudes toward a new product and a print advertisement. Subjects were college students who responded to seven-point bipolar scales. Hierarchical models were better than nonhierarchical models in conceptualizing attitude but not intention. (GDC)

  19. Statistical properties of convex clustering

    PubMed Central

    Tan, Kean Ming; Witten, Daniela

    2016-01-01

    In this manuscript, we study the statistical properties of convex clustering. We establish that convex clustering is closely related to single linkage hierarchical clustering and k-means clustering. In addition, we derive the range of the tuning parameter for convex clustering that yields a non-trivial solution. We also provide an unbiased estimator of the degrees of freedom, and provide a finite sample bound for the prediction error for convex clustering. We compare convex clustering to some traditional clustering methods in simulation studies.

  20. Non-Trivial Feature Derivation for Intensifying Feature Detection Using LIDAR Datasets Through Allometric Aggregation Data Analysis Applying Diffused Hierarchical Clustering for Discriminating Agricultural Land Cover in Portions of Northern Mindanao, Philippines

    NASA Astrophysics Data System (ADS)

    Villar, Ricardo G.; Pelayo, Jigg L.; Mozo, Ray Mari N.; Salig, James B., Jr.; Bantugan, Jojemar

    2016-06-01

    Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m2 of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this

  1. Hierarchical coefficient of a multifractal based network

    NASA Astrophysics Data System (ADS)

    Moreira, Darlan A.; Lucena, Liacir dos Santos; Corso, Gilberto

    2014-02-01

    The hierarchical property for a general class of networks stands for a power-law relation between clustering coefficient, CC and connectivity k: CC∝kβ. This relation is empirically verified in several biologic and social networks, as well as in random and deterministic network models, in special for hierarchical networks. In this work we show that the hierarchical property is also present in a Lucena network. To create a Lucena network we use the dual of a multifractal lattice ML, the vertices are the sites of the ML and links are established between neighbouring lattices, therefore this network is space filling and planar. Besides a Lucena network shows a scale-free distribution of connectivity. We deduce a relation for the maximal local clustering coefficient CCimax of a vertex i in a planar graph. This condition expresses that the number of links among neighbour, N△, of a vertex i is equal to its connectivity ki, that means: N△=ki. The Lucena network fulfils the condition N△≃ki independent of ki and the anisotropy of ML. In addition, CCmax implies the threshold β=1 for the hierarchical property for any scale-free planar network.

  2. Cluster analysis of water-quality data for Lake Sakakawea, Audubon Lake, and McClusky Canal, central North Dakota, 1990-2003

    USGS Publications Warehouse

    Ryberg, Karen R.

    2006-01-01

    As a result of the Dakota Water Resources Act of 2000, the Bureau of Reclamation, U.S. Department of the Interior, identified eight water-supply alternatives (including a no-action alternative) to meet future water needs in portions of the Red River of the North (Red River) Basin. Of those alternatives, four include the interbasin transfer of water from the Missouri River Basin to the Red River Basin. Three of the interbasin transfer alternatives would use the McClusky Canal, located in central North Dakota, to transport the water. Therefore, the water quality of the McClusky Canal and the sources of its water, Lake Sakakawea and Audubon Lake, is of interest to water-quality stakeholders. The Bureau of Reclamation collected water-quality samples at 23 sites on Lake Sakakawea, Audubon Lake, and the McClusky Canal system from 1990 through 2003. Physical properties and water-quality constituents from these samples were summarized and analyzed by the U.S. Geological Survey using hierarchical agglomerative cluster analysis (HACA). HACA separated the samples into related clusters, or groups. These groups were examined for statistical significance and relation to structure of the McClusky Canal system. Statistically, the sample groupings found using HACA were significantly different from each other and appear to result from spatial and temporal water-quality differences corresponding with different sections of the canal and different operational conditions. Future operational changes of the canal system may justify additional water-quality sampling to characterize possible water-quality changes.

  3. Dynamic Organization of Hierarchical Memories.

    PubMed

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2016-01-01

    In the brain, external objects are categorized in a hierarchical way. Although it is widely accepted that objects are represented as static attractors in neural state space, this view does not take account interaction between intrinsic neural dynamics and external input, which is essential to understand how neural system responds to inputs. Indeed, structured spontaneous neural activity without external inputs is known to exist, and its relationship with evoked activities is discussed. Then, how categorical representation is embedded into the spontaneous and evoked activities has to be uncovered. To address this question, we studied bifurcation process with increasing input after hierarchically clustered associative memories are learned. We found a "dynamic categorization"; neural activity without input wanders globally over the state space including all memories. Then with the increase of input strength, diffuse representation of higher category exhibits transitions to focused ones specific to each object. The hierarchy of memories is embedded in the transition probability from one memory to another during the spontaneous dynamics. With increased input strength, neural activity wanders over a narrower state space including a smaller set of memories, showing more specific category or memory corresponding to the applied input. Moreover, such coarse-to-fine transitions are also observed temporally during transient process under constant input, which agrees with experimental findings in the temporal cortex. These results suggest the hierarchy emerging through interaction with an external input underlies hierarchy during transient process, as well as in the spontaneous activity. PMID:27618549

  4. Hierarchical Auxetic Mechanical Metamaterials

    PubMed Central

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I.; Azzopardi, Keith M.; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N.

    2015-01-01

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts. PMID:25670400

  5. Perception and Hierarchical Dynamics

    PubMed Central

    Kiebel, Stefan J.; Daunizeau, Jean; Friston, Karl J.

    2009-01-01

    In this paper, we suggest that perception could be modeled by assuming that sensory input is generated by a hierarchy of attractors in a dynamic system. We describe a mathematical model which exploits the temporal structure of rapid sensory dynamics to track the slower trajectories of their underlying causes. This model establishes a proof of concept that slowly changing neuronal states can encode the trajectories of faster sensory signals. We link this hierarchical account to recent developments in the perception of human action; in particular artificial speech recognition. We argue that these hierarchical models of dynamical systems are a plausible starting point to develop robust recognition schemes, because they capture critical temporal dependencies induced by deep hierarchical structure. We conclude by suggesting that a fruitful computational neuroscience approach may emerge from modeling perception as non-autonomous recognition dynamics enslaved by autonomous hierarchical dynamics in the sensorium. PMID:19649171

  6. Hierarchical Auxetic Mechanical Metamaterials

    NASA Astrophysics Data System (ADS)

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I.; Azzopardi, Keith M.; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N.

    2015-02-01

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts.

  7. Hierarchical auxetic mechanical metamaterials.

    PubMed

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I; Azzopardi, Keith M; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N

    2015-01-01

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts. PMID:25670400

  8. Hierarchical structure of Turkey's foreign trade

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Deviren, Bayram; Keskin, Mustafa

    2011-10-01

    We examine the hierarchical structures of Turkey's foreign trade by using real prices of their commodity export and import move together over time. We obtain the topological properties among the countries based on Turkey's foreign trade during the 1996-2010 period by using the concept of hierarchical structure methods (minimal spanning tree, (MST) and hierarchical tree, (HT)). These periods are divided into two subperiods, such as 1996-2002 and 2003-2010, in order to test various time-window and observe the temporal evolution. We perform the bootstrap techniques to investigate a value of the statistical reliability to the links of the MSTs and HTs. We also use a clustering linkage procedure in order to observe the cluster structure much better. From the structural topologies of these trees, we identify different clusters of countries according to their geographical location and economic ties. Our results show that the DE (Germany), UK (United Kingdom), FR (France), IT (Italy) and RU (Russia) are more important within the network, due to a tighter connection with other countries. We have also found that these countries play a significant role for Turkey's foreign trade and have important implications for the design of portfolio and investment strategies.

  9. Hierarchical Kohonenen net for anomaly detection in network security.

    PubMed

    Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie

    2005-04-01

    A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate. PMID:15828658

  10. EINSTEIN Cluster Alignments Revisited

    NASA Astrophysics Data System (ADS)

    Chambers, S. W.; Melott, A. L.; Miller, C. J.

    2000-12-01

    We have examined whether the major axes of rich galaxy clusters tend to point (in projection) toward their nearest neighboring cluster. We used the data of Ulmer, McMillan and Kowalski, who used x-ray morphology to define position angles. Our cluster samples, with well measured redshifts and updated positions, were taken from the MX Northern Abell Cluster Survey. The usual Kolmogorov-Smirnov test shows no significant alignment signal for nonrandom angles for all separations less than 100 Mpc/h. Refining the null hypothesis, however, with the Wilcoxon rank-sum test, reveals a high confidence signal for alignment. This confidence is highest when we restrict our sample to small nearest neighbor separations. We conclude that we have identified a more powerful tool for testing cluster-cluster alignments. Moreover, there is a strong signal in the data for alignment, consistent with a picture of hierarchical cluster formation in which matter falls into clusters along large scale filamentary structures.

  11. [Cluster analysis in biomedical researches].

    PubMed

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

    2013-01-01

    Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research. PMID:24640781

  12. Hierarchical Structure in Dark Matter Distributions

    NASA Astrophysics Data System (ADS)

    Makishima, K.

    Galaxy observers argue for the dark halo surrounding each galaxy, while cluster investigators discuss the domiance of dark matter (DM) in the intra-cluster space. Then, what is the relation between the galaxy DM and the cluster DM? Some believe that the galaxy DM is nothing but a local portion of the cluster DM, while others claim that the cluster DM is merely a sum of the galaxy DM. ASCA studies of the Fornax, Centaurus, Hydra-A, and A1795 clusters have shown that neither is correct: in reality, while a cluster has a massive dark halo, the cD galaxy also has its own smaller-scale dark halo, thus making a halo-in-halo hierarchical structure. The narrower of the two DM concentrations produces a potential dimple at the cluster center, as suggested by gravitational lensing measurements. This dimple in turn produces the central X-ray brightness excess, which was previously thought to arise from cooling flows. Such a nested potential structure has also been discovered around several X-ray bright non-cD elliptical galaxies, including NGC 4636 in particular. These studies will provide us with an understanding of spatial Fourier power-density spectrum of the DM distribution.

  13. Hierarchical features of large-scale cortical connectivity

    NASA Astrophysics Data System (ADS)

    da F. Costa, L.; Sporns, O.

    2005-12-01

    The analysis of complex networks has revealed patterns of organization in a variety of natural and artificial systems, including neuronal networks of the brain at multiple scales. In this paper, we describe a novel analysis of the large-scale connectivity between regions of the mammalian cerebral cortex, utilizing a set of hierarchical measurements proposed recently. We examine previously identified functional clusters of brain regions in macaque visual cortex and cat cortex and find significant differences between such clusters in terms of several hierarchical measures, revealing differences in how these clusters are embedded in the overall cortical architecture. For example, the ventral cluster of visual cortex maintains structurally more segregated, less divergent connections than the dorsal cluster, which may point to functionally different roles of their constituent brain regions.

  14. Parallel hierarchical radiosity rendering

    SciTech Connect

    Carter, M.

    1993-07-01

    In this dissertation, the step-by-step development of a scalable parallel hierarchical radiosity renderer is documented. First, a new look is taken at the traditional radiosity equation, and a new form is presented in which the matrix of linear system coefficients is transformed into a symmetric matrix, thereby simplifying the problem and enabling a new solution technique to be applied. Next, the state-of-the-art hierarchical radiosity methods are examined for their suitability to parallel implementation, and scalability. Significant enhancements are also discovered which both improve their theoretical foundations and improve the images they generate. The resultant hierarchical radiosity algorithm is then examined for sources of parallelism, and for an architectural mapping. Several architectural mappings are discussed. A few key algorithmic changes are suggested during the process of making the algorithm parallel. Next, the performance, efficiency, and scalability of the algorithm are analyzed. The dissertation closes with a discussion of several ideas which have the potential to further enhance the hierarchical radiosity method, or provide an entirely new forum for the application of hierarchical methods.

  15. Discovering hierarchical motion structure.

    PubMed

    Gershman, Samuel J; Tenenbaum, Joshua B; Jäkel, Frank

    2016-09-01

    Scenes filled with moving objects are often hierarchically organized: the motion of a migrating goose is nested within the flight pattern of its flock, the motion of a car is nested within the traffic pattern of other cars on the road, the motion of body parts are nested in the motion of the body. Humans perceive hierarchical structure even in stimuli with two or three moving dots. An influential theory of hierarchical motion perception holds that the visual system performs a "vector analysis" of moving objects, decomposing them into common and relative motions. However, this theory does not specify how to resolve ambiguity when a scene admits more than one vector analysis. We describe a Bayesian theory of vector analysis and show that it can account for classic results from dot motion experiments, as well as new experimental data. Our theory takes a step towards understanding how moving scenes are parsed into objects. PMID:25818905

  16. Seismotectonic Implications Of Clustered Regional GPS Velocities In The San Francisco Bay Region, California

    NASA Astrophysics Data System (ADS)

    Graymer, R. W.; Simpson, R.

    2012-12-01

    We have used a hierarchical agglomerative clustering algorithm with Euclidean distance and centroid linkage, applied to continuous GPS observations for the Bay region available from the U.S. Geological Survey website. This analysis reveals 4 robust, spatially coherent clusters that coincide with 4 first-order structural blocks separated by 3 major fault systems: San Andreas (SA), Southern/Central Calaveras-Hayward-Rodgers Creek-Maacama (HAY), and Northern Calaveras-Concord-Green Valley-Berryessa-Bartlett Springs (NCAL). Because observations seaward of the San Gregorio (SG) fault are few in number, the cluster to the west of SA may actually contain 2 major structural blocks not adequately resolved: the Pacific plate to the west of the northern SA and a Peninsula block between the Peninsula SA and the SG fault. The average inter-block velocities are 11, 10, and 9 mm/yr across SA, HAY, and NCAL respectively. There appears to be a significant component of fault-normal compression across NCAL, whereas SA and HAY faults appear to be, on regional average, purely strike-slip. The velocities for the Sierra Nevada - Great Valley (SNGV) block to the west of NCAL are impressive in their similarity. The cluster of these velocities in a velocity plot forms a tighter grouping compared with the groupings for the other cluster blocks, suggesting a more rigid behavior for this block than the others. We note that for 4 clusters, none of the 3 cluster boundaries illuminate geologic structures other than north-northwest trending dominantly strike-slip faults, so plate motion is not accommodated by large-scale fault-parallel compression or extension in the region or by significant plastic deformation , at least over the time span of the GPS observations. Complexities of interseismic deformation of the upper crust do not allow simple application of inter-block velocities as long-term slip rates on bounding faults. However, 2D dislocation models using inter-block velocities and typical

  17. Systolic architecture for heirarchical clustering

    SciTech Connect

    Ku, L.C.

    1984-01-01

    Several hierarchical clustering methods (including single-linkage complete-linkage, centroid, and absolute overlap methods) are reviewed. The absolute overlap clustering method is selected for the design of systolic architecture mainly due to its simplicity. Two versions of systolic architectures for the absolute overlap hierarchical clustering algorithm are proposed: one-dimensional version that leads to the development of a two dimensional version which fully takes advantage of the underlying data structure of the problems. The two dimensional systolic architecture can achieve a time complexity of O(m + n) in comparison with the conventional computer implementation of a time complexity of O(m/sup 2*/n).

  18. Clustering of Resting State Networks

    PubMed Central

    Lee, Megan H.; Hacker, Carl D.; Snyder, Abraham Z.; Corbetta, Maurizio; Zhang, Dongyang; Leuthardt, Eric C.; Shimony, Joshua S.

    2012-01-01

    Background 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. Methodology/Principal Findings 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. Conclusions/Significance 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. PMID:22792291

  19. Microparticles with hierarchical porosity

    DOEpatents

    Petsev, Dimiter N; Atanassov, Plamen; Pylypenko, Svitlana; Carroll, Nick; Olson, Tim

    2012-12-18

    The present disclosure provides oxide microparticles with engineered hierarchical porosity and methods of manufacturing the same. Also described are structures that are formed by templating, impregnating, and/or precipitating the oxide microparticles and method for forming the same. Suitable applications include catalysts, electrocatalysts, electrocatalysts support materials, capacitors, drug delivery systems, sensors and chromatography.

  20. Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network.

    PubMed

    Balaguer, Jan; Spiers, Hugo; Hassabis, Demis; Summerfield, Christopher

    2016-05-18

    Planning allows actions to be structured in pursuit of a future goal. However, in natural environments, planning over multiple possible future states incurs prohibitive computational costs. To represent plans efficiently, states can be clustered hierarchically into "contexts". For example, representing a journey through a subway network as a succession of individual states (stations) is more costly than encoding a sequence of contexts (lines) and context switches (line changes). Here, using functional brain imaging, we asked humans to perform a planning task in a virtual subway network. Behavioral analyses revealed that humans executed a hierarchically organized plan. Brain activity in the dorsomedial prefrontal cortex and premotor cortex scaled with the cost of hierarchical plan representation and unique neural signals in these regions signaled contexts and context switches. These results suggest that humans represent hierarchical plans using a network of caudal prefrontal structures. VIDEO ABSTRACT. PMID:27196978

  1. Mining a Web Citation Database for Author Co-Citation Analysis.

    ERIC Educational Resources Information Center

    He, Yulan; Hui, Siu Cheung

    2002-01-01

    Proposes a mining process to automate author co-citation analysis based on the Web Citation Database, a data warehouse for storing citation indices of Web publications. Describes the use of agglomerative hierarchical clustering for author clustering and multidimensional scaling for displaying author cluster maps, and explains PubSearch, a…

  2. Cost functions for pairwise data clustering

    NASA Astrophysics Data System (ADS)

    Angelini, L.; Nitti, L.; Pellicoro, M.; Stramaglia, S.

    2001-07-01

    Cost functions for non-hierarchical pairwise clustering are introduced, in the probabilistic autoencoder framework, by the request of maximal average similarity between input and the output of the autoencoder. Clustering is thus formulated as the problem of finding the ground state of Potts spins Hamiltonians. The partition, provided by this procedure, identifies clusters with dense connected regions in the data space.

  3. Evaluating Faculty Salary Equity Using Hierarchical Linear Modeling.

    ERIC Educational Resources Information Center

    Stapleton, Laura M.; Lissitz, Robert W.

    This paper presents results from a comparison of the multiple regression (MR) approach to examining faculty salary equity (with clusters for the various disciplines) and hierarchical linear modeling (HLM) for the same problem. The comparison was done in two steps. First, a practical example of applying both techniques, using empirical data, is…

  4. Nested Hierarchical Dirichlet Processes.

    PubMed

    Paisley, John; Wang, Chong; Blei, David M; Jordan, Michael I

    2015-02-01

    We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP generalizes the nested Chinese restaurant process (nCRP) to allow each word to follow its own path to a topic node according to a per-document distribution over the paths on a shared tree. This alleviates the rigid, single-path formulation assumed by the nCRP, allowing documents to easily express complex thematic borrowings. We derive a stochastic variational inference algorithm for the model, which enables efficient inference for massive collections of text documents. We demonstrate our algorithm on 1.8 million documents from The New York Times and 2.7 million documents from Wikipedia. PMID:26353240

  5. A Computer Program for Clustering Large Matrices

    ERIC Educational Resources Information Center

    Koch, Valerie L.

    1976-01-01

    A Fortran V program is described derived for the Univac 1100 Series Computer for clustering into hierarchical structures large matrices, up to 1000 x 1000 and larger, of interassociations between objects. (RC)

  6. Hierarchical Star Formation in the Milky Way Disk

    NASA Astrophysics Data System (ADS)

    de la Fuente Marcos, R.; de la Fuente Marcos, C.

    2009-07-01

    Hierarchical star formation leads to a progressive decrease in the clustering of star clusters both in terms of spatial scale and age. Consistently, statistical analysis of the positions and ages of clusters in the Milky Way disk strongly suggests that a correlation between the duration of star formation in a region and its size does exist. The average age difference between pairs of open clusters increases with their separation as the ~0.16 power. In contrast, for the Large Magellanic Cloud, Efremov & Elmegreen found that the age difference scales with the ~0.35 power of the region size. This discrepancy may be tentatively interpreted as an argument in support of intrinsically shorter (faster) star formation timescales in smaller galaxies. However, if both the effects of cluster dissolution and incompleteness are taken into consideration, the average age difference between cluster pairs in the Galaxy increases with their separation as the ~0.4 power. This result implies that the characteristic timescale for coherent, clustered-mode star formation is nearly 1 Myr. Therefore, the overall consequence of ignoring the effect of cluster dissolution is to overestimate the star formation timescale. On the other hand, in the Galactic disk and for young clusters separated by less than three times the characteristic cluster tidal radius (10 pc), the average age difference is 16 Myr, which suggests common origin. A close pair classification scheme is introduced and a list of 11 binary cluster candidates with physical separation less than 30 pc is compiled. Two of these pairs are likely primordial: ASCC 18/ASCC 21 and NGC 3293/NGC 3324. A triple cluster candidate in a highly hierarchical configuration is also identified: NGC 1981/NGC 1976/Collinder 70 in Orion. We find that binary cluster candidates seem to show a tendency to have components of different size—evidence for dynamical interaction.

  7. HIERARCHICAL STAR FORMATION IN THE MILKY WAY DISK

    SciTech Connect

    De la Fuente Marcos, R.; De la Fuente Marcos, C.

    2009-07-20

    Hierarchical star formation leads to a progressive decrease in the clustering of star clusters both in terms of spatial scale and age. Consistently, statistical analysis of the positions and ages of clusters in the Milky Way disk strongly suggests that a correlation between the duration of star formation in a region and its size does exist. The average age difference between pairs of open clusters increases with their separation as the {approx}0.16 power. In contrast, for the Large Magellanic Cloud, Efremov and Elmegreen found that the age difference scales with the {approx}0.35 power of the region size. This discrepancy may be tentatively interpreted as an argument in support of intrinsically shorter (faster) star formation timescales in smaller galaxies. However, if both the effects of cluster dissolution and incompleteness are taken into consideration, the average age difference between cluster pairs in the Galaxy increases with their separation as the {approx}0.4 power. This result implies that the characteristic timescale for coherent, clustered-mode star formation is nearly 1 Myr. Therefore, the overall consequence of ignoring the effect of cluster dissolution is to overestimate the star formation timescale. On the other hand, in the Galactic disk and for young clusters separated by less than three times the characteristic cluster tidal radius (10 pc), the average age difference is 16 Myr, which suggests common origin. A close pair classification scheme is introduced and a list of 11 binary cluster candidates with physical separation less than 30 pc is compiled. Two of these pairs are likely primordial: ASCC 18/ASCC 21 and NGC 3293/NGC 3324. A triple cluster candidate in a highly hierarchical configuration is also identified: NGC 1981/NGC 1976/Collinder 70 in Orion. We find that binary cluster candidates seem to show a tendency to have components of different size-evidence for dynamical interaction.

  8. The nature and nurture of star clusters

    NASA Astrophysics Data System (ADS)

    Elmegreen, Bruce G.

    2010-01-01

    Star clusters have hierarchical patterns in space and time, suggesting formation processes in the densest regions of a turbulent interstellar medium. Clusters also have hierarchical substructure when they are young, which makes them all look like the inner mixed parts of a pervasive stellar hierarchy. Young field stars share this distribution, presumably because some of them came from dissolved clusters and others formed in a dispersed fashion in the same gas. The fraction of star formation that ends up in clusters is apparently not constant, but may increase with interstellar pressure. Hierarchical structure explains why stars form in clusters and why many of these clusters are self-bound. It also explains the cluster mass function. Halo globular clusters share many properties of disk clusters, including what appears to be an upper cluster cutoff mass. However, halo globulars are self-enriched and often connected with dwarf galaxy streams. The mass function of halo globulars could have initially been like the power-law mass function of disk clusters, but the halo globulars have lost their low-mass members. The reasons for this loss are not understood. It could have happened slowly over time as a result of cluster evaporation, or it could have happened early after cluster formation as a result of gas loss. The latter model explains best the observation that the globular cluster mass function has no radial gradient in galaxies.

  9. Data Clustering

    NASA Astrophysics Data System (ADS)

    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

  10. A Complete Hierarchical Key Management Scheme for Heterogeneous Wireless Sensor Networks

    PubMed Central

    Zheng, Xinying

    2014-01-01

    Heterogeneous cluster-based wireless sensor networks (WSN) attracted increasing attention recently. Obviously, the clustering makes the entire networks hierarchical; thus, several kinds of keys are required for hierarchical network topology. However, most existing key management schemes for it place more emphasis on pairwise key management schemes or key predistribution schemes and neglect the property of hierarchy. In this paper, we propose a complete hierarchical key management scheme which only utilizes symmetric cryptographic algorithms and low cost operations for heterogeneous cluster-based WSN. Our scheme considers four kinds of keys, which are an individual key, a cluster key, a master key, and pairwise keys, for each sensor node. Finally, the analysis and experiments demonstrate that the proposed scheme is secure and efficient; thus, it is suitable for heterogeneous cluster-based WSN. PMID:24983001

  11. A Hierarchical Approach to Forest Landscape Pattern Characterization

    NASA Astrophysics Data System (ADS)

    Wang, Jialing; Yang, Xiaojun

    2012-01-01

    Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

  12. An exactly solvable model of hierarchical self-assembly

    NASA Astrophysics Data System (ADS)

    Dudowicz, Jacek; Douglas, Jack F.; Freed, Karl F.

    2009-06-01

    Many living and nonliving structures in the natural world form by hierarchical organization, but physical theories that describe this type of organization are scarce. To address this problem, a model of equilibrium self-assembly is formulated in which dynamically associating species organize into hierarchical structures that preserve their shape at each stage of assembly. In particular, we consider symmetric m-gons that associate at their vertices into Sierpinski gasket structures involving the hierarchical association of triangles, squares, hexagons, etc., at their corner vertices, thereby leading to fractal structures after many generations of assembly. This rather idealized model of hierarchical assembly yields an infinite sequence of self-assembly transitions as the morphology progressively organizes to higher levels of the hierarchy, and these structures coexists at dynamic equilibrium, as found in real hierarchically self-assembling systems such as amyloid fiber forming proteins. Moreover, the transition sharpness progressively grows with increasing m, corresponding to larger and larger loops in the assembled structures. Calculations are provided for several basic thermodynamic properties (including the order parameters for assembly for each stage of the hierarchy, average mass of clusters, specific heat, transition sharpness, etc.) that are required for characterizing the interaction parameters governing this type of self-assembly and for elucidating other basic qualitative aspects of these systems. Our idealized model of hierarchical assembly gives many insights into this ubiquitous type of self-organization process.

  13. Hierarchical image enhancement

    NASA Astrophysics Data System (ADS)

    Qi, Wei; Han, Jing; Zhang, Yi; Bai, Lian-fa

    2016-05-01

    Image enhancement is an important technique in computer vision. In this paper, we propose a hierarchical image enhancement approach based on the structure layer and texture layer. In the structure layer, we propose a structure-based method based on GMM, which better exploits structure details with fewer noise. In the texture layer, we present a structure-filtering method to filter unwanted texture with keeping completeness of detected salient structure. Next, we introduce a structure constraint prior to integrate them, leading to an improved enhancement result. Extensive experiments demonstrate that the proposed approach achieves higher quality results than previous approaches.

  14. Hierarchical Safety Cases

    NASA Technical Reports Server (NTRS)

    Denney, Ewen W.; Whiteside, Iain J.

    2012-01-01

    We introduce hierarchical safety cases (or hicases) as a technique to overcome some of the difficulties that arise creating and maintaining industrial-size safety cases. Our approach extends the existing Goal Structuring Notation with abstraction structures, which allow the safety case to be viewed at different levels of detail. We motivate hicases and give a mathematical account of them as well as an intuition, relating them to other related concepts. We give a second definition which corresponds closely to our implementation of hicases in the AdvoCATE Assurance Case Editor and prove the correspondence between the two. Finally, we suggest areas of future enhancement, both theoretically and practically.

  15. Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo

    PubMed Central

    JACOB, BENJAMIN G.; NOVAK, ROBERT J.; TOE, LAURENT; SANFO, MOUSSA S.; AFRIYIE, ABENA N.; IBRAHIM, MOHAMMED A.; GRIFFITH, DANIEL A.; UNNASCH, THOMAS R.

    2013-01-01

    The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter

  16. HIERARCHICAL STAR FORMATION IN NEARBY LEGUS GALAXIES

    SciTech Connect

    Elmegreen, Debra Meloy; Elmegreen, Bruce G.; Adamo, Angela; Gouliermis, Dimitrios A.; Aloisi, Alessandra; Bright, Stacey N.; Cignoni, Michele; Lee, Janice; Sabbi, Elena; Andrews, Jennifer; Calzetti, Daniela; Annibali, Francesca; Evans, Aaron S.; Johnson, Kelsey; Gallagher III, John S.; Grebel, Eva K.; Hunter, Deidre A.; Kim, Hwihyun; Smith, Linda J.; Thilker, David; and others

    2014-05-20

    Hierarchical structure in ultraviolet images of 12 late-type LEGUS galaxies is studied by determining the numbers and fluxes of nested regions as a function of size from ∼1 to ∼200 pc, and the number as a function of flux. Two starburst dwarfs, NGC 1705 and NGC 5253, have steeper number-size and flux-size distributions than the others, indicating high fractions of the projected areas filled with star formation. Nine subregions in seven galaxies have similarly steep number-size slopes, even when the whole galaxies have shallower slopes. The results suggest that hierarchically structured star-forming regions several hundred parsecs or larger represent common unit structures. Small galaxies dominated by only a few of these units tend to be starbursts. The self-similarity of young stellar structures down to parsec scales suggests that star clusters form in the densest parts of a turbulent medium that also forms loose stellar groupings on larger scales. The presence of super star clusters in two of our starburst dwarfs would follow from the observed structure if cloud and stellar subregions more readily coalesce when self-gravity in the unit cell contributes more to the total gravitational potential.

  17. Hierarchical Star Formation in Nearby LEGUS Galaxies

    NASA Astrophysics Data System (ADS)

    Elmegreen, Debra Meloy; Elmegreen, Bruce G.; Adamo, Angela; Aloisi, Alessandra; Andrews, Jennifer; Annibali, Francesca; Bright, Stacey N.; Calzetti, Daniela; Cignoni, Michele; Evans, Aaron S.; Gallagher, John S., III; Gouliermis, Dimitrios A.; Grebel, Eva K.; Hunter, Deidre A.; Johnson, Kelsey; Kim, Hwihyun; Lee, Janice; Sabbi, Elena; Smith, Linda J.; Thilker, David; Tosi, Monica; Ubeda, Leonardo

    2014-05-01

    Hierarchical structure in ultraviolet images of 12 late-type LEGUS galaxies is studied by determining the numbers and fluxes of nested regions as a function of size from ~1 to ~200 pc, and the number as a function of flux. Two starburst dwarfs, NGC 1705 and NGC 5253, have steeper number-size and flux-size distributions than the others, indicating high fractions of the projected areas filled with star formation. Nine subregions in seven galaxies have similarly steep number-size slopes, even when the whole galaxies have shallower slopes. The results suggest that hierarchically structured star-forming regions several hundred parsecs or larger represent common unit structures. Small galaxies dominated by only a few of these units tend to be starbursts. The self-similarity of young stellar structures down to parsec scales suggests that star clusters form in the densest parts of a turbulent medium that also forms loose stellar groupings on larger scales. The presence of super star clusters in two of our starburst dwarfs would follow from the observed structure if cloud and stellar subregions more readily coalesce when self-gravity in the unit cell contributes more to the total gravitational potential.

  18. A Rasch Hierarchical Measurement Model.

    ERIC Educational Resources Information Center

    Maier, Kimberly S.

    This paper describes a model that integrates an item response theory (IRT) Rasch model and a hierarchical linear model and presents a method of estimating model parameter values that does not rely on large-sample theory and normal approximations. The model resulting from the integration of a hierarchical linear model and the Rasch model allows one…

  19. Segmenting Student Markets with a Student Satisfaction and Priorities Survey.

    ERIC Educational Resources Information Center

    Borden, Victor M. H.

    1995-01-01

    A market segmentation analysis of 872 university students compared 2 hierarchical clustering procedures for deriving market segments: 1 using matching-type measures and an agglomerative clustering algorithm, and 1 using the chi-square based automatic interaction detection. Results and implications for planning, evaluating, and improving academic…

  20. How hierarchical is language use?

    PubMed Central

    Frank, Stefan L.; Bod, Rens; Christiansen, Morten H.

    2012-01-01

    It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science. PMID:22977157

  1. Incremental hierarchical discriminant regression.

    PubMed

    Weng, Juyang; Hwang, Wey-Shiuan

    2007-03-01

    This paper presents incremental hierarchical discriminant regression (IHDR) which incrementally builds a decision tree or regression tree for very high-dimensional regression or decision spaces by an online, real-time learning system. Biologically motivated, it is an approximate computational model for automatic development of associative cortex, with both bottom-up sensory inputs and top-down motor projections. At each internal node of the IHDR tree, information in the output space is used to automatically derive the local subspace spanned by the most discriminating features. Embedded in the tree is a hierarchical probability distribution model used to prune very unlikely cases during the search. The number of parameters in the coarse-to-fine approximation is dynamic and data-driven, enabling the IHDR tree to automatically fit data with unknown distribution shapes (thus, it is difficult to select the number of parameters up front). The IHDR tree dynamically assigns long-term memory to avoid the loss-of-memory problem typical with a global-fitting learning algorithm for neural networks. A major challenge for an incrementally built tree is that the number of samples varies arbitrarily during the construction process. An incrementally updated probability model, called sample-size-dependent negative-log-likelihood (SDNLL) metric is used to deal with large sample-size cases, small sample-size cases, and unbalanced sample-size cases, measured among different internal nodes of the IHDR tree. We report experimental results for four types of data: synthetic data to visualize the behavior of the algorithms, large face image data, continuous video stream from robot navigation, and publicly available data sets that use human defined features. PMID:17385628

  2. Re-shaping colloidal clusters

    NASA Astrophysics Data System (ADS)

    Kraft, Daniela

    2015-03-01

    Controlling the geometry and yield of anisotropic colloidal particles remains a challenge for hierarchical self-assembly. I will discuss a synthetic strategy for fabricating colloidal clusters by creating order in randomly aggregated polymer spheres using surface tension and geometrical constraints. The technique can be extended to a variety of charge-stabilized polymer spheres and offers control over the cluster size distribution. VENI grant from The Netherlands Organization for Scientific Research (NWO).

  3. Hierarchical multifunctional nanocomposites

    NASA Astrophysics Data System (ADS)

    Ghasemi-Nejhad, Mehrdad N.

    2014-03-01

    properties of the fibers can also be improved by the growth of nanotubes on the fibers. The combination of the two will produce super-performing materials, not currently available. Since the improvement of fiber starts with carbon nanotube grown on micron-size fibers (and matrix with a nanomaterial) to give the macro-composite, this process is a bottom-up "hierarchical" advanced manufacturing process, and since the resulting nanocomposites will have "multifunctionality" with improve properties in various functional areas such as chemical and fire resistance, damping, stiffness, strength, fracture toughness, EMI shielding, and electrical and thermal conductivity, the resulting nanocomposites are in fact "multifunctional hierarchical nanocomposites." In this paper, the current state of knowledge in processing, performance, and characterization of these materials are addressed.

  4. Hierarchical Interaction Structure of Neural Activities in Cortical Slice Cultures

    PubMed Central

    Santos, Gustavo S.; Gireesh, Elakkat D.; Plenz, Dietmar; Nakahara, Hiroyuki

    2010-01-01

    Recent advances in the analysis of neuronal activities suggest that the instantaneous activity patterns can be mostly explained by considering only first-order and pairwise interactions between recorded elements, i.e., action potentials or local field potentials (LFP), and do not require higher-than-pairwise-order interactions. If generally applicable, this pairwise approach greatly simplifies the description of network interactions. However, an important question remains: are the recorded elements the units of interaction that best describe neuronal activity patterns? To explore this, we recorded spontaneous LFP peak activities in cortical organotypic cultures using planar, integrated 60-microelectrode arrays. We compared predictions obtained using a pairwise approach with those using a hierarchical approach that uses two different spatial units for describing the activity interactions: single electrodes and electrode clusters. In this hierarchical model, short-range interactions within each cluster were modeled by pairwise interactions of electrode activities and long-range interactions were modeled by pairwise interactions of cluster activities. Despite the relatively low number of parameters used, the hierarchical model provided a more accurate description of the activity patterns than the pairwise model when applied to ensembles of 10 electrodes. Furthermore, the hierarchical model was successfully applied to a larger-scale data of ~60 electrodes. Electrode activities within clusters were highly correlated and spatially contiguous. In contrast, long-range interactions were diffuse, suggesting the presence of higher-than-pairwise-order interactions involved in the LFP peak activities. Thus, the identification of appropriate units of interaction may allow for the successful characterization of neuronal activities in large-scale networks. PMID:20592194

  5. Hierarchical Fast Multipole Simulation of Magnetic Colloids

    NASA Astrophysics Data System (ADS)

    Günal, Yüksel; Visscher, Pieter

    1997-03-01

    We have extended the well-known "fast multipole"footnote L. F. Greengard and V. Rokhlin, J. Comp. Phys. 73 p. 325, 1987. methods for molecular-dynamics simulation of large systems of point charges to continuum systems, such as magnetic films or particulate suspensions. (These methods reduce the computational labor from O(N^2) to O(N log N) or O(N), the number of particles). We apply the method to the particular case of a colloidal dispersion of magnetized cylindrical particles. Our method is fully hierarchical, both upward and downward from the particle size scale. The force on each particle is calculated by grouping distant particles into large clusters, nearer particles into smaller clusters, and dividing the nearest particles into segments. The fineness with which the particles are divided is controlled by an error tolerance parameter. The field of each cluster or segment is computed from a multipole expansion. Distant periodic images are also treated as multipoles - this is much faster than standard Fourier-transform or Ewald summation techniques.

  6. Onboard hierarchical network

    NASA Astrophysics Data System (ADS)

    Tunesi, Luca; Armbruster, Philippe

    2004-02-01

    The objective of this paper is to demonstrate a suitable hierarchical networking solution to improve capabilities and performances of space systems, with significant recurrent costs saving and more efficient design & manufacturing flows. Classically, a satellite can be split in two functional sub-systems: the platform and the payload complement. The platform is in charge of providing power, attitude & orbit control and up/down-link services, whereas the payload represents the scientific and/or operational instruments/transponders and embodies the objectives of the mission. One major possibility to improve the performance of payloads, by limiting the data return to pertinent information, is to process data on board thanks to a proper implementation of the payload data system. In this way, it is possible to share non-recurring development costs by exploiting a system that can be adopted by the majority of space missions. It is believed that the Modular and Scalable Payload Data System, under development by ESA, provides a suitable solution to fulfil a large range of future mission requirements. The backbone of the system is the standardised high data rate SpaceWire network http://www.ecss.nl/. As complement, a lower speed command and control bus connecting peripherals is required. For instance, at instrument level, there is a need for a "local" low complexity bus, which gives the possibility to command and control sensors and actuators. Moreover, most of the connections at sub-system level are related to discrete signals management or simple telemetry acquisitions, which can easily and efficiently be handled by a local bus. An on-board hierarchical network can therefore be defined by interconnecting high-speed links and local buses. Additionally, it is worth stressing another important aspect of the design process: Agencies and ESA in particular are frequently confronted with a big consortium of geographically spread companies located in different countries, each one

  7. Hierarchical Linked Views

    SciTech Connect

    Erbacher, Robert; Frincke, Deb

    2007-07-02

    Coordinated views have proven critical to the development of effective visualization environments. This results from the fact that a single view or representation of the data cannot show all of the intricacies of a given data set. Additionally, users will often need to correlate more data parameters than can effectively be integrated into a single visual display. Typically, development of multiple-linked views results in an adhoc configuration of views and associated interactions. The hierarchical model we are proposing is geared towards more effective organization of such environments and the views they encompass. At the same time, this model can effectively integrate much of the prior work on interactive and visual frameworks. Additionally, we expand the concept of views to incorporate perceptual views. This is related to the fact that visual displays can have information encoded at various levels of focus. Thus, a global view of the display provides overall trends of the data while focusing in on individual elements provides detailed specifics. By integrating interaction and perception into a single model, we show how one impacts the other. Typically, interaction and perception are considered separately, however, when interaction is being considered at a fundamental level and allowed to direct/modify the visualization directly we must consider them simultaneously and how they impact one another.

  8. Jamming in hierarchical networks

    NASA Astrophysics Data System (ADS)

    Cheng, Xiang; Boettcher, Stefan

    2015-11-01

    We study the Biroli-Mezard model for lattice glasses on a number of hierarchical networks. These networks combine certain lattice-like features with a recursive structure that makes them suitable for exact renormalization group studies and provide an alternative to the mean-field approach. In our numerical simulations here, we first explore their equilibrium properties with the Wang-Landau algorithm. Then, we investigate their dynamical behavior using a grand-canonical annealing algorithm. We find that the dynamics readily falls out of equilibrium and jams in many of our networks with certain constraints on the neighborhood occupation imposed by the Biroli-Mezard model, even in cases where exact results indicate that no ideal glass transition exists. But while we find that time-scales for the jams diverge, our simulations cannot ascertain such a divergence for a packing fraction distinctly above random close packing. In cases where we allow hopping in our dynamical simulations, the jams on these networks generally disappear.

  9. Improvement in Recursive Hierarchical Segmentation of Data

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2006-01-01

    A further modification has been made in the algorithm and implementing software reported in Modified Recursive Hierarchical Segmentation of Data (GSC- 14681-1), NASA Tech Briefs, Vol. 30, No. 6 (June 2006), page 51. That software performs recursive hierarchical segmentation of data having spatial characteristics (e.g., spectral-image data). The output of a prior version of the software contained artifacts, including spurious segmentation-image regions bounded by processing-window edges. The modification for suppressing the artifacts, mentioned in the cited article, was addition of a subroutine that analyzes data in the vicinities of seams to find pairs of regions that tend to lie adjacent to each other on opposite sides of the seams. Within each such pair, pixels in one region that are more similar to pixels in the other region are reassigned to the other region. The present modification provides for a parameter ranging from 0 to 1 for controlling the relative priority of merges between spatially adjacent and spatially non-adjacent regions. At 1, spatially-adjacent-/spatially- non-adjacent-region merges have equal priority. At 0, only spatially-adjacent-region merges (no spectral clustering) are allowed. Between 0 and 1, spatially-adjacent- region merges have priority over spatially- non-adjacent ones.

  10. Bimodal Color Distribution in Hierarchical Galaxy Formation

    NASA Astrophysics Data System (ADS)

    Menci, N.; Fontana, A.; Giallongo, E.; Salimbeni, S.

    2005-10-01

    We show how the observed bimodality in the color distribution of galaxies can be explained in the framework of the hierarchical clustering picture in terms of the interplay between the properties of the merging histories and the feedback/star formation processes in the progenitors of local galaxies. Using a semianalytic model of hierarchical galaxy formation, we compute the color distributions of galaxies with different luminosities and compare them with the observations. Our fiducial model matches the fundamental properties of the observed distributions, namely: (1) the distribution of objects brighter than Mr<~-18 is clearly bimodal, with a fraction of red objects increasing with luminosity; (2) for objects brighter than Mr<~-21, the color distribution is dominated by red objects with color u-r~2.2-2.4 (3) the spread on the distribution of the red population is smaller than that of the blue population; (4) the fraction of red galaxies is larger in denser environments, even for low-luminosity objects; and (5) the bimodality in the distribution persists up to z~1.5. We discuss the role of the different physical processes included in the model in producing the above results.

  11. Direct hierarchical assembly of nanoparticles

    DOEpatents

    Xu, Ting; Zhao, Yue; Thorkelsson, Kari

    2014-07-22

    The present invention provides hierarchical assemblies of a block copolymer, a bifunctional linking compound and a nanoparticle. The block copolymers form one micro-domain and the nanoparticles another micro-domain.

  12. Advanced hierarchical distance sampling

    USGS Publications Warehouse

    Royle, Andy

    2016-01-01

    In this chapter, we cover a number of important extensions of the basic hierarchical distance-sampling (HDS) framework from Chapter 8. First, we discuss the inclusion of “individual covariates,” such as group size, in the HDS model. This is important in many surveys where animals form natural groups that are the primary observation unit, with the size of the group expected to have some influence on detectability. We also discuss HDS integrated with time-removal and double-observer or capture-recapture sampling. These “combined protocols” can be formulated as HDS models with individual covariates, and thus they have a commonality with HDS models involving group structure (group size being just another individual covariate). We cover several varieties of open-population HDS models that accommodate population dynamics. On one end of the spectrum, we cover models that allow replicate distance sampling surveys within a year, which estimate abundance relative to availability and temporary emigration through time. We consider a robust design version of that model. We then consider models with explicit dynamics based on the Dail and Madsen (2011) model and the work of Sollmann et al. (2015). The final major theme of this chapter is relatively newly developed spatial distance sampling models that accommodate explicit models describing the spatial distribution of individuals known as Point Process models. We provide novel formulations of spatial DS and HDS models in this chapter, including implementations of those models in the unmarked package using a hack of the pcount function for N-mixture models.

  13. Conceptual hierarchical modeling to describe wetland plant community organization

    USGS Publications Warehouse

    Little, A.M.; Guntenspergen, G.R.; Allen, T.F.H.

    2010-01-01

    Using multivariate analysis, we created a hierarchical modeling process that describes how differently-scaled environmental factors interact to affect wetland-scale plant community organization in a system of small, isolated wetlands on Mount Desert Island, Maine. We followed the procedure: 1) delineate wetland groups using cluster analysis, 2) identify differently scaled environmental gradients using non-metric multidimensional scaling, 3) order gradient hierarchical levels according to spatiotem-poral scale of fluctuation, and 4) assemble hierarchical model using group relationships with ordination axes and post-hoc tests of environmental differences. Using this process, we determined 1) large wetland size and poor surface water chemistry led to the development of shrub fen wetland vegetation, 2) Sphagnum and water chemistry differences affected fen vs. marsh / sedge meadows status within small wetlands, and 3) small-scale hydrologic differences explained transitions between forested vs. non-forested and marsh vs. sedge meadow vegetation. This hierarchical modeling process can help explain how upper level contextual processes constrain biotic community response to lower-level environmental changes. It creates models with more nuanced spatiotemporal complexity than classification and regression tree procedures. Using this process, wetland scientists will be able to generate more generalizable theories of plant community organization, and useful management models. ?? Society of Wetland Scientists 2009.

  14. Hierarchical Velocity Structure in the Core of Abell 2597

    NASA Technical Reports Server (NTRS)

    Still, Martin; Mushotzky, Richard

    2004-01-01

    We present XMM-Newton RGS and EPIC data of the putative cooling flow cluster Abell 2597. Velocities of the low-ionization emission lines in the spectrum are blue shifted with respect to the high-ionization lines by 1320 (sup +660) (sub -210) kilometers per second, which is consistent with the difference in the two peaks of the galaxy velocity distribution and may be the signature of bulk turbulence, infall, rotation or damped oscillation in the cluster. A hierarchical velocity structure such as this could be the direct result of galaxy mergers in the cluster core, or the injection of power into the cluster gas from a central engine. The uniform X-ray morphology of the cluster, the absence of fine scale temperature structure and the random distribution of the the galaxy positions, independent of velocity, suggests that our line of sight is close to the direction of motion. These results have strong implications for cooling flow models of the cluster Abell 2597. They give impetus to those models which account for the observed temperature structure of some clusters using mergers instead of cooling flows.

  15. Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.

    PubMed

    Froyen, Vicky; Feldman, Jacob; Singh, Manish

    2015-10-01

    We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian hierarchical grouping (BHG). In BHG, we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are "owned" by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of Heller and Ghahramani (2005). We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz. PMID:26322548

  16. Parallel hierarchical global illumination

    SciTech Connect

    Snell, Q.O.

    1997-10-08

    Solving the global illumination problem is equivalent to determining the intensity of every wavelength of light in all directions at every point in a given scene. The complexity of the problem has led researchers to use approximation methods for solving the problem on serial computers. Rather than using an approximation method, such as backward ray tracing or radiosity, the authors have chosen to solve the Rendering Equation by direct simulation of light transport from the light sources. This paper presents an algorithm that solves the Rendering Equation to any desired accuracy, and can be run in parallel on distributed memory or shared memory computer systems with excellent scaling properties. It appears superior in both speed and physical correctness to recent published methods involving bidirectional ray tracing or hybrid treatments of diffuse and specular surfaces. Like progressive radiosity methods, it dynamically refines the geometry decomposition where required, but does so without the excessive storage requirements for ray histories. The algorithm, called Photon, produces a scene which converges to the global illumination solution. This amounts to a huge task for a 1997-vintage serial computer, but using the power of a parallel supercomputer significantly reduces the time required to generate a solution. Currently, Photon can be run on most parallel environments from a shared memory multiprocessor to a parallel supercomputer, as well as on clusters of heterogeneous workstations.

  17. A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

    ERIC Educational Resources Information Center

    Chahine, Firas Safwan

    2012-01-01

    Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

  18. Energy Aware Clustering Algorithms for Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian

    2011-09-01

    The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.

  19. GPU-based Multilevel Clustering.

    PubMed

    Chiosa, Iurie; Kolb, Andreas

    2010-04-01

    The processing power of parallel co-processors like the Graphics Processing Unit (GPU) are dramatically increasing. However, up until now only a few approaches have been presented to utilize this kind of hardware for mesh clustering purposes. In this paper we introduce a Multilevel clustering technique designed as a parallel algorithm and solely implemented on the GPU. Our formulation uses the spatial coherence present in the cluster optimization and hierarchical cluster merging to significantly reduce the number of comparisons in both parts . Our approach provides a fast, high quality and complete clustering analysis. Furthermore, based on the original concept we present a generalization of the method to data clustering. All advantages of the meshbased techniques smoothly carry over to the generalized clustering approach. Additionally, this approach solves the problem of the missing topological information inherent to general data clustering and leads to a Local Neighbors k-means algorithm. We evaluate both techniques by applying them to Centroidal Voronoi Diagram (CVD) based clustering. Compared to classical approaches, our techniques generate results with at least the same clustering quality. Our technique proves to scale very well, currently being limited only by the available amount of graphics memory. PMID:20421676

  20. Hierarchical Microaggressions in Higher Education

    ERIC Educational Resources Information Center

    Young, Kathryn; Anderson, Myron; Stewart, Saran

    2015-01-01

    Although there has been substantial research examining the effects of microaggressions in the public sphere, there has been little research that examines microaggressions in the workplace. This study explores the types of microaggressions that affect employees at universities. We coin the term "hierarchical microaggression" to represent…

  1. Hierarchical storage management system evaluation

    NASA Technical Reports Server (NTRS)

    Woodrow, Thomas S.

    1993-01-01

    The Numerical Aerodynamic Simulation (NAS) Program at NASA Ames Research Center has been developing a hierarchical storage management system, NAStore, for some 6 years. This evaluation compares functionality, performance, reliability, and other factors of NAStore and three commercial alternatives. FileServ is found to be slightly better overall than NAStore and DMF. UniTree is found to be severely lacking in comparison.

  2. Memory Stacking in Hierarchical Networks.

    PubMed

    Westö, Johan; May, Patrick J C; Tiitinen, Hannu

    2016-02-01

    Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns. PMID:26654206

  3. Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation Algorithm. Chapter 5

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Plaza, Antonio J. (Editor); Chang, Chein-I. (Editor)

    2008-01-01

    The hierarchical image segmentation algorithm (referred to as HSEG) is a hybrid of hierarchical step-wise optimization (HSWO) and constrained spectral clustering that produces a hierarchical set of image segmentations. HSWO is an iterative approach to region grooving segmentation in which the optimal image segmentation is found at N(sub R) regions, given a segmentation at N(sub R+1) regions. HSEG's addition of constrained spectral clustering makes it a computationally intensive algorithm, for all but, the smallest of images. To counteract this, a computationally efficient recursive approximation of HSEG (called RHSEG) has been devised. Further improvements in processing speed are obtained through a parallel implementation of RHSEG. This chapter describes this parallel implementation and demonstrates its computational efficiency on a Landsat Thematic Mapper test scene.

  4. Cities and regions in Britain through hierarchical percolation

    PubMed Central

    Arcaute, Elsa; Molinero, Carlos; Hatna, Erez; Murcio, Roberto; Vargas-Ruiz, Camilo; Masucci, A. Paolo; Batty, Michael

    2016-01-01

    Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations which are the outcome of complex geographical, political and historical processes which leave almost indelible footprints on infrastructure such as the street network. In this work, we uncover a set of hierarchies in Britain at different scales using percolation theory on the street network and on its intersections which are the primary points of interaction and urban agglomeration. At the larger scales, the observed hierarchical structures can be interpreted as regional fractures of Britain, observed in various forms, from natural boundaries, such as National Parks, to regional divisions based on social class and wealth such as the well-known North–South divide. At smaller scales, cities are generated through recursive percolations on each of the emerging regional clusters. We examine the evolution of the morphology of the system as a whole, by measuring the fractal dimension of the clusters at each distance threshold in the percolation. We observe that this reaches a maximum plateau at a specific distance. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images, and from previous methods using population density. PMID:27152211

  5. Cities and regions in Britain through hierarchical percolation.

    PubMed

    Arcaute, Elsa; Molinero, Carlos; Hatna, Erez; Murcio, Roberto; Vargas-Ruiz, Camilo; Masucci, A Paolo; Batty, Michael

    2016-04-01

    Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations which are the outcome of complex geographical, political and historical processes which leave almost indelible footprints on infrastructure such as the street network. In this work, we uncover a set of hierarchies in Britain at different scales using percolation theory on the street network and on its intersections which are the primary points of interaction and urban agglomeration. At the larger scales, the observed hierarchical structures can be interpreted as regional fractures of Britain, observed in various forms, from natural boundaries, such as National Parks, to regional divisions based on social class and wealth such as the well-known North-South divide. At smaller scales, cities are generated through recursive percolations on each of the emerging regional clusters. We examine the evolution of the morphology of the system as a whole, by measuring the fractal dimension of the clusters at each distance threshold in the percolation. We observe that this reaches a maximum plateau at a specific distance. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images, and from previous methods using population density. PMID:27152211

  6. The Case for A Hierarchal System Model for Linux Clusters

    SciTech Connect

    Seager, M; Gorda, B

    2009-06-05

    The computer industry today is no longer driven, as it was in the 40s, 50s and 60s, by High-performance computing requirements. Rather, HPC systems, especially Leadership class systems, sit on top of a pyramid investment mode. Figure 1 shows a representative pyramid investment model for systems hardware. At the base of the pyramid is the huge investment (order 10s of Billions of US Dollars per year) in semiconductor fabrication and process technologies. These costs, which are approximately doubling with every generation, are funded from investments multiple markets: enterprise, desktops, games, embedded and specialized devices. Over and above these base technology investments are investments for critical technology elements such as microprocessor, chipsets and memory ASIC components. Investments for these components are spread across the same markets as the base semiconductor processes investments. These second tier investments are approximately half the size of the lower level of the pyramid. The next technology investment layer up, tier 3, is more focused on scalable computing systems such as those needed for HPC and other markets. These tier 3 technology elements include networking (SAN, WAN and LAN), interconnects and large scalable SMP designs. Above these is tier 4 are relatively small investments necessary to build very large, scalable systems high-end or Leadership class systems. Primary among these are the specialized network designs of vertically integrated systems, etc.

  7. Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering.

    PubMed

    Knowles, David A; Ghahramani, Zoubin

    2015-02-01

    In this paper we introduce the Pitman Yor Diffusion Tree (PYDT), a Bayesian non-parametric prior over tree structures which generalises the Dirichlet Diffusion Tree [30] and removes the restriction to binary branching structure. The generative process is described and shown to result in an exchangeable distribution over data points. We prove some theoretical properties of the model including showing its construction as the continuum limit of a nested Chinese restaurant process model. We then present two alternative MCMC samplers which allow us to model uncertainty over tree structures, and a computationally efficient greedy Bayesian EM search algorithm. Both algorithms use message passing on the tree structure. The utility of the model and algorithms is demonstrated on synthetic and real world data, both continuous and binary. PMID:26353241

  8. Galaxy satellites; building blocks and the hierarchical clustering paradigm

    NASA Astrophysics Data System (ADS)

    Padilla, N. D.; Lagos, C.; Cora, S. A.

    We study the properties of building blocks (BBs, i.e. accreted satellites) and surviving satellites of present-day galaxies using a semi-analytic model of galaxy formation in the context of a concordance Cold Dark Mat- ter (CDM) cosmology. We consider large numbers of dark-matter (DM) halo merger trees spanning a wide range of masses ( 1 × 1010 to 2.14 × 1015 M ). Our simulated galaxies show higher metallicities for BBs with respect to surviving satellites, an effect produced by the same processes re- sponsible for the build-up of the mass-metallicity relation. We prove that these metallicity differences arise from the higher peak in the density fluctu- ation field occupied by BBs and central galaxies which have collapsed into a single object earlier than surviving satellites. BBs start to form stars ear- lier, during the peak of the merger activity in the universe, and build up half of their final stellar mass (measured at the moment of disruption) up to four times faster than surviving satellites. Surviving satellites keep increasing their stellar masses rather quiescently from their formation until z 1. The difference between the metallicities of satellites, BBs and central galaxies depends on the host DM halo mass, in a way that can be used as a further test for the concordance cosmology.

  9. Polyoxometalate Cluster-Incorporated Metal-Organic Framework Hierarchical Nanotubes.

    PubMed

    Xu, Xiaobin; Chen, Shuangming; Chen, Yifeng; Sun, Hongyu; Song, Li; He, Wei; Wang, Xun

    2016-06-01

    A simple method to prepare metal-organic framework (MOF) nanotubes is developed by employing polyoxometalates (POMs) as modulators. The local structure of the MOF nanotubes is investigated combining XANES and EXAFS studies. These nanotubes show both an excellent catalytic performance in the detoxification of sulfur compounds in O2 atmosphere and a remarkable cycling stability as the anode material for lithium-ion batteries. PMID:27101564

  10. Hierarchical structures in the Large and Small Magellanic Clouds

    NASA Astrophysics Data System (ADS)

    Bonatto, C.; Bica, E.

    2010-04-01

    We investigate the degree of spatial correlation among extended structures in the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC). To this purpose, we work with subsamples characterized by different properties such as age and size, taken from the updated catalogue of Bica et al. or gathered in the present work. The structures are classified as star clusters or non-clusters (basically, nebular complexes and their stellar associations). The radius distribution functions follow power laws (dN/dR ~ R-α) with slopes and maximum radius (Rmax) that depend on object class (and age). Non-clusters are characterized by α ~ 1.9 and Rmax <~ 472pc, while young clusters (age <~10Myr) have α ~ 3.6 and Rmax <~ 15pc and old ones (age >~600Myr) have α ~ 2.5 and Rmax <~ 40pc. Young clusters present a high degree of spatial self-correlation and, especially, correlate with star-forming structures, which does not occur with the old ones. This is consistent with the old clusters having been heavily mixed up, since their ages correspond to several LMC and SMC crossing times. On the other hand, with ages corresponding to fractions of the respective crossing times, the young clusters still trace most of their birthplace structural pattern. Also, small clusters (R < 10pc), as well as small non-clusters (R < 100pc), are spatially self-correlated, while their large counterparts of both classes are not. The above results are consistent with a hierarchical star formation scenario for the LMC and SMC.

  11. Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

    ERIC Educational Resources Information Center

    Raykov, Tenko

    2011-01-01

    Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

  12. Estimation of Reliability for Multiple-Component Measuring Instruments in Hierarchical Designs

    ERIC Educational Resources Information Center

    Raykov, Tenko; du Toit, Stephen H. C.

    2005-01-01

    A method for estimation of reliability for multiple-component measuring instruments with clustered data is outlined. The approach is applicable with hierarchical designs where individuals are nested within higher order units and exhibit possibly related performance on components of a scale of interest. The procedure is developed within the…

  13. Dynamics of hierarchical Brownian oscillators

    NASA Astrophysics Data System (ADS)

    Knapp, E. W.

    1988-11-01

    Two hierarchical Brownian oscillator models are introduced by using a discretized Brownian oscillator model. The dynamical behavior of these models is solved exactly. Closed-form expressions for the intermediate scattering function and the energy relaxation function governing the frequency-dependent specific heat are given. A general relationship between the two relaxation functions is established. The case of a rectangular distribution of activation energies for the different hierarchy levels of the oscillator is considered in more detail. There the energy relaxation function decays with a logarithmic slope and the intermediate scattering function exhibits an algebraic long-time decay. The two hierarchical Brownian oscillator models have the same dynamical behavior though they possess a quite different coupling scheme.

  14. Parallel hierarchical method in networks

    NASA Astrophysics Data System (ADS)

    Malinochka, Olha; Tymchenko, Leonid

    2007-09-01

    This method of parallel-hierarchical Q-transformation offers new approach to the creation of computing medium - of parallel -hierarchical (PH) networks, being investigated in the form of model of neurolike scheme of data processing [1-5]. The approach has a number of advantages as compared with other methods of formation of neurolike media (for example, already known methods of formation of artificial neural networks). The main advantage of the approach is the usage of multilevel parallel interaction dynamics of information signals at different hierarchy levels of computer networks, that enables to use such known natural features of computations organization as: topographic nature of mapping, simultaneity (parallelism) of signals operation, inlaid cortex, structure, rough hierarchy of the cortex, spatially correlated in time mechanism of perception and training [5].

  15. Hierarchical Bayesian inverse reinforcement learning.

    PubMed

    Choi, Jaedeug; Kim, Kee-Eung

    2015-04-01

    Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the expert's behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal. Another difficulty comes from the noisy behavior data due to sub-optimal experts. We propose a hierarchical Bayesian framework, which subsumes most of the previous IRL algorithms as well as models the sub-optimality of the expert's behavior. Using a number of experiments on a synthetic problem, we demonstrate the effectiveness of our approach including the robustness of our hierarchical Bayesian framework to the sub-optimal expert behavior data. Using a real dataset from taxi GPS traces, we additionally show that our approach predicts the driving behavior with a high accuracy. PMID:25291805

  16. Multicast Routing of Hierarchical Data

    NASA Technical Reports Server (NTRS)

    Shacham, Nachum

    1992-01-01

    The issue of multicast of broadband, real-time data in a heterogeneous environment, in which the data recipients differ in their reception abilities, is considered. Traditional multicast schemes, which are designed to deliver all the source data to all recipients, offer limited performance in such an environment, since they must either force the source to overcompress its signal or restrict the destination population to those who can receive the full signal. We present an approach for resolving this issue by combining hierarchical source coding techniques, which allow recipients to trade off reception bandwidth for signal quality, and sophisticated routing algorithms that deliver to each destination the maximum possible signal quality. The field of hierarchical coding is briefly surveyed and new multicast routing algorithms are presented. The algorithms are compared in terms of network utilization efficiency, lengths of paths, and the required mechanisms for forwarding packets on the resulting paths.

  17. Hierarchical structure of biological systems

    PubMed Central

    Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M

    2014-01-01

    A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems. PMID:24145961

  18. A Clustering Classification of Spare Parts for Improving Inventory Policies

    NASA Astrophysics Data System (ADS)

    Meri Lumban Raja, Anton; Ai, The Jin; Diar Astanti, Ririn

    2016-02-01

    Inventory policies in a company may consist of storage, control, and replenishment policy. Since the result of common ABC inventory classification can only affect the replenishment policy, we are proposing a clustering based classification technique as a basis for developing inventory policy especially for storage and control policy. Hierarchical clustering procedure is used after clustering variables are defined. Since hierarchical clustering procedure requires metric variables only, therefore a step to convert non-metric variables to metric variables is performed. The clusters resulted from the clustering techniques are analyzed in order to define each cluster characteristics. Then, the inventory policies are determined for each group according to its characteristics. A real data, which consists of 612 items from a local manufacturer's spare part warehouse, are used in the research of this paper to show the applicability of the proposed methodology.

  19. Hierarchical Molecular Modelling with Ellipsoids

    SciTech Connect

    Max, N

    2004-03-29

    Protein and DNA structures are represented at varying levels of details using ellipsoidal RGBA textured splats. The splat texture at each level is generated by rendering its children in a hierarchical model, from a distribution of viewing directions, and averaging the result. For rendering, the ellipsoids to be used are chosen adaptively, depending on the distance to the viewpoint. This technique is applied to visualize DNA coiling around nucleosomes in chromosomes.

  20. Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters

    PubMed Central

    Tellaroli, Paola; Bazzi, Marco; Donato, Michele; Brazzale, Alessandra R.; Drăghici, Sorin

    2016-01-01

    Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository. PMID:27015427

  1. Hierarchical analysis of rainfall variability across Nigeria

    NASA Astrophysics Data System (ADS)

    Nnaji, Chidozie Charles; Mama, Cordelia Nnennaya; Ukpabi, Okechukwu

    2016-01-01

    Rainfall in Nigeria is subjected to wide variability both in time and space. This variability has assumed a more pronounced dimension as a result of climate change. In this paper, cluster analyses were used to study rainfall variability in Nigeria. Rainfall data in 20 locations spread across the geopolitical and ecological zones of Nigeria were subjected to hierarchical cluster analysis and analysis of time series and coefficient of variation for over periods spanning 30 years. Maps of spatial variations of mean annual rainfall and mean rainfall coefficient of variation were produced using ARCGIS 10.1. Furthermore, a better understanding of temporal variation of rainfall was sought by an investigation into the time series of rainfall coefficients of variation. It was found that the southern parts of the country were given to more severe rainfall variability/fluctuations than the northern parts. The north central parts exhibited more similarity to the southern parts than the other northern locations. The relationship between average annual rainfall and the coefficient of rainfall variation was found to follow a power law with R 2 value approximately 0.7. With respect to variability of annual rainfall, three zones emerged as follows: a linear relationship ( R 2 = 0.90) exists between coefficient of variation and average annual rainfall for the rainforest zone of the southsouth; a power law ( R 2 = 0.86) exists between coefficient of variation and average annual rainfall for all rainforest and derived guinea savannah zones of the southeastern and southwestern states; and a logarithmic relationship ( R 2 = 0.54) exists between coefficient of variation and average annual rainfall for all northern states regardless of ecological zone. Generally, in-year rainfall variability increases from the northwest to the southwest; while between-year (yearly) rainfall variability increases from the north central to the southeast. This study further confirms that rainfall variability

  2. SPECTRAL CLUSTERING STRATEGIES FOR HETEROGENEOUS DISEASE EXPRESSION DATA†

    PubMed Central

    HUANG, GRACE T.; CUNNINGHAM, KATHRYN I.; BENOS, PANAYIOTIS V.; CHENNUBHOTLA, CHAKRA S.

    2015-01-01

    Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (number of clusters for K-means, and a threshold to cut the tree for hierarchical clustering). Human disease gene expression data are in general more difficult to cluster efficiently due to background (genotype) heterogeneity, disease stage and progression differences and disease subtyping; all of which cause gene expression datasets to be more heterogeneous. Spectral clustering has been recently introduced in many fields as a promising alternative to standard clustering methods. The idea is that pairwise comparisons can help reveal global features through the eigen techniques. In this paper, we developed a new recursive K-means spectral clustering method (ReKS) for disease gene expression data. We benchmarked ReKS on three large-scale cancer datasets and we compared it to different clustering methods with respect to execution time, background models and external biological knowledge. We found ReKS to be superior to the hierarchical methods and equally good to K-means, but much faster than them and without the requirement for a priori knowledge of K. Overall, ReKS offers an attractive alternative for efficient clustering of human disease data. PMID:23424126

  3. CLUSTER CHEMISTRY

    SciTech Connect

    Muetterties, Earl L.

    1980-05-01

    Metal cluster chemistry is one of the most rapidly developing areas of inorganic and organometallic chemistry. Prior to 1960 only a few metal clusters were well characterized. However, shortly after the early development of boron cluster chemistry, the field of metal cluster chemistry began to grow at a very rapid rate and a structural and a qualitative theoretical understanding of clusters came quickly. Analyzed here is the chemistry and the general significance of clusters with particular emphasis on the cluster research within my group. The importance of coordinately unsaturated, very reactive metal clusters is the major subject of discussion.

  4. Dynamic hierarchical algorithm for accelerated microfossil identification

    NASA Astrophysics Data System (ADS)

    Wong, Cindy M.; Joseph, Dileepan

    2015-02-01

    Marine microfossils provide a useful record of the Earth's resources and prehistory via biostratigraphy. To study Hydrocarbon reservoirs and prehistoric climate, geoscientists visually identify the species of microfossils found in core samples. Because microfossil identification is labour intensive, automation has been investigated since the 1980s. With the initial rule-based systems, users still had to examine each specimen under a microscope. While artificial neural network systems showed more promise for reducing expert labour, they also did not displace manual identification for a variety of reasons, which we aim to overcome. In our human-based computation approach, the most difficult step, namely taxon identification is outsourced via a frontend website to human volunteers. A backend algorithm, called dynamic hierarchical identification, uses unsupervised, supervised, and dynamic learning to accelerate microfossil identification. Unsupervised learning clusters specimens so that volunteers need not identify every specimen during supervised learning. Dynamic learning means interim computation outputs prioritize subsequent human inputs. Using a dataset of microfossils identified by an expert, we evaluated correct and incorrect genus and species rates versus simulated time, where each specimen identification defines a moment. The proposed algorithm accelerated microfossil identification effectively, especially compared to benchmark results obtained using a k-nearest neighbour method.

  5. Hierarchical Star Formation in LEGUS Galaxies

    NASA Astrophysics Data System (ADS)

    Elmegreen, Debra M.; Elmegreen, Bruce

    2014-06-01

    Star formation generally follows a hierarchical distribution in galaxies from kpc scales in giant star complexes down to sub-pc scales in embedded clusters. This hierarchy corresponds to a power law distribution function for the number of star forming regions as a function of size or luminosity. Using the Legacy ExtraGalactic Ultraviolet Survey (LEGUS), we examine six galaxies, NGC 1566, NGC 1705, NGC 2500, NGC 5253, NGC 5477, and IC 4247, which span types from grand design and flocculent spirals to irregulars and starburst irregulars. Power law size and luminosity distributions were measured from Gaussian-blurred images in the NUV and UV using SExtractor. Slopes ranged from -1 to -1.8, with the steepest slopes corresponding to the starburst galaxies. The slopes did not vary from the NUV to the UV. The fraction of light contained within the largest scales ranged from 85 to 95 percent, independent of galaxy type. We acknowledge support from grant HST-GO-13364.

  6. Hierarchical Generative Biclustering for MicroRNA Expression Analysis

    NASA Astrophysics Data System (ADS)

    Caldas, José; Kaski, Samuel

    Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie each cluster, changing the setup to biclustering. Furthermore, we make the indicators hierarchical, resulting in a hierarchy of progressively more specific biclusters. A non-parametric Bayesian formulation makes the model rigorous and yet flexible, and computations feasible. The formulation additionally offers a natural information retrieval relevance measure that allows relating samples in a principled manner. We show that the model outperforms other four biclustering procedures in a large miRNA data set. We also demonstrate the model's added interpretability and information retrieval capability in a case study that highlights the potential and novel role of miR-224 in the association between melanoma and non-Hodgkin lymphoma. Software is publicly available.

  7. Hierarchical generative biclustering for microRNA expression analysis.

    PubMed

    Caldas, José; Kaski, Samuel

    2011-03-01

    Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie each cluster, changing the setup to biclustering. Furthermore, we make the indicators hierarchical, resulting in a hierarchy of progressively more specific biclusters. A non-parametric Bayesian formulation makes the model rigorous yet flexible and computations feasible. The model can additionally be used in information retrieval for relating relevant samples. We show that the model outperforms four other biclustering procedures on a large miRNA data set. We also demonstrate the model's added interpretability and information retrieval capability in a case study. Software is publicly available at http://research.ics.tkk.fi/mi/software/treebic/. PMID:21385032

  8. Entropy bounds for hierarchical molecular networks.

    PubMed

    Dehmer, Matthias; Borgert, Stephan; Emmert-Streib, Frank

    2008-01-01

    In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of non-hierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the entropy of a single hierarchical graph, we see that the derived bounds can also be used for characterizing graph classes. Our contribution is an important extension to previous results about the entropy of non-hierarchical networks because for practical applications hierarchical networks are playing an important role in chemistry and biology. In addition to the derivation of the entropy bounds, we provide a numerical analysis for two special graph classes, rooted trees and generalized trees, and demonstrate hereby not only the computational feasibility of our method but also learn about its characteristics and interpretability with respect to data analysis. PMID:18769487

  9. Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach

    NASA Astrophysics Data System (ADS)

    Ulusoy, Tolga; Keskin, Mustafa; Shirvani, Ayoub; Deviren, Bayram; Kantar, Ersin; Çaǧrı Dönmez, Cem

    2012-11-01

    This study reports on topology of the top 40 UK companies that have been analysed for predictive verification of markets for the period 2006-2010, applying the concept of minimal spanning tree and hierarchical tree (HT) analysis. Construction of the minimal spanning tree (MST) and the hierarchical tree (HT) is confined to a brief description of the methodology and a definition of the correlation function between a pair of companies based on the London Stock Exchange (LSE) index in order to quantify synchronization between the companies. A derivation of hierarchical organization and the construction of minimal-spanning and hierarchical trees for the 2006-2008 and 2008-2010 periods have been used and the results validate the predictive verification of applied semantics. The trees are known as useful tools to perceive and detect the global structure, taxonomy and hierarchy in financial data. From these trees, two different clusters of companies in 2006 were detected. They also show three clusters in 2008 and two between 2008 and 2010, according to their proximity. The clusters match each other as regards their common production activities or their strong interrelationship. The key companies are generally given by major economic activities as expected. This work gives a comparative approach between MST and HT methods from statistical physics and information theory with analysis of financial markets that may give new valuable and useful information of the financial market dynamics.

  10. A comparison of clustering methods for biogeography with fossil datasets

    PubMed Central

    2016-01-01

    Cluster analysis is one of the most commonly used methods in palaeoecological studies, particularly in studies investigating biogeographic patterns. Although a number of different clustering methods are widely used, the approach and underlying assumptions of many of these methods are quite different. For example, methods may be hierarchical or non-hierarchical in their approaches, and may use Euclidean distance or non-Euclidean indices to cluster the data. In order to assess the effectiveness of the different clustering methods as compared to one another, a simulation was designed that could assess each method over a range of both cluster distinctiveness and sampling intensity. Additionally, a non-hierarchical, non-Euclidean, iterative clustering method implemented in the R Statistical Language is described. This method, Non-Euclidean Relational Clustering (NERC), creates distinct clusters by dividing the data set in order to maximize the average similarity within each cluster, identifying clusters in which each data point is on average more similar to those within its own group than to those in any other group. While all the methods performed well with clearly differentiated and well-sampled datasets, when data are less than ideal the linkage methods perform poorly compared to non-Euclidean based k-means and the NERC method. Based on this analysis, Unweighted Pair Group Method with Arithmetic Mean and neighbor joining methods are less reliable with incomplete datasets like those found in palaeobiological analyses, and the k-means and NERC methods should be used in their place. PMID:26966658

  11. Some physical applications of random hierarchical matrices

    SciTech Connect

    Avetisov, V. A.; Bikulov, A. Kh.; Vasilyev, O. A.; Nechaev, S. K.; Chertovich, A. V.

    2009-09-15

    The investigation of spectral properties of random block-hierarchical matrices as applied to dynamic and structural characteristics of complex hierarchical systems with disorder is proposed for the first time. Peculiarities of dynamics on random ultrametric energy landscapes are discussed and the statistical properties of scale-free and polyscale (depending on the topological characteristics under investigation) random hierarchical networks (graphs) obtained by multiple mapping are considered.

  12. Interactive explorations of hierarchical segmentations

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    1992-01-01

    The authors report on the implementation of an interactive tool, called HSEGEXP, to interactively explore the hierarchical segmentation produced by the iterative parallel region growing (IPRG) algorithm to select the best segmentation result. This combination of the HSEGEXP tool with the IPRG algorithm amounts to a computer-assisted image segmentation system guided by human interaction. The initial application of the HSEGEXP tool is in the refinement of ground reference data based on the IPRG/HSEGEXP segmentation of the corresponding remotely sensed image data. The HSEGEXP tool is being used to help evaluate the effectiveness of an automatic 'best' segmentation process under development.

  13. Einstein imaging observations of clusters with a bimodal mass distribution

    NASA Technical Reports Server (NTRS)

    Forman, W.; Bechtold, J.; Blair, W.; Giacconi, R.; Van Speybroeck, L.; Jones, C.

    1981-01-01

    Einstein imaging observations of four X-ray clusters of galaxies characterized by a double X-ray surface brightness and thus mass distribution are presented. The clusters A98, A115, A1750 and SC 0627-54 were found to exhibit two enhancements in their X-ray surface brightness distributions in observations made with the Einstein Imaging Proportional Counter. Calculations of the probability that the clusters represent chance superpositions indicate that the double clusters are physically associated. The radial distributions of the components are inconsistent with those of single point sources, and have been used to derive cluster luminosities which are typical of rich clusters. Masses of the subclusters are also found to be typical of bound and virialized clusters with gas contributing 10%. Within the framework of the hierarchical theory of galactic clustering, the double clusters are suggested to represent an intermediate evolutionary stage before the merger of subclusters into a relaxed Coma-type cluster.

  14. Evidence for evolution of the luminosity function of clusters of galaxies

    NASA Technical Reports Server (NTRS)

    Edge, A. C.; Stewart, G. C.; Fabian, A. C.; Arnaud, K. A.

    1991-01-01

    From an all sky, X-ray flux limited sample of clusters of galaxies evidence for a significant deficit in the number of high luminosity clusters is found in the redshift range z approximately 0.1 to 0.2 compared with numbers of nearby clusters. This indicates that the X-ray luminous clusters are undergoing strong evolution. The strength of the effect is consistent with hierarchical merging models. The implications of such strong evolution for clusters are discussed.

  15. Evidence for evolution of the luminosity function of clusters of galaxies

    NASA Technical Reports Server (NTRS)

    Edge, Alastair C.; Stewart, G. C.; Fabian, A. C.; Arnaud, K. A.

    1991-01-01

    From an all sky, x-ray flux limited sample of clusters of galaxies evidence for a significant deficit in the number of high luminosity clusters is found in the redshift range z approximately 0.1 to 0.2 compared with numbers of nearby clusters. This indicates that the x-ray luminous clusters are undergoing strong evolution. The strength of the effect is consistent with hierarchical merging models. The implications of such strong evolution for clusters are discussed.

  16. An Algorithm for Testing Unidimensionality and Clustering Items in Rasch Measurement

    ERIC Educational Resources Information Center

    Debelak, Rudolf; Arendasy, Martin

    2012-01-01

    A new approach to identify item clusters fitting the Rasch model is described and evaluated using simulated and real data. The proposed method is based on hierarchical cluster analysis and constructs clusters of items that show a good fit to the Rasch model. It thus gives an estimate of the number of independent scales satisfying the postulates of…

  17. Clusters of Occupations Based on Systematically Derived Work Dimensions: An Exploratory Study.

    ERIC Educational Resources Information Center

    Cunningham, J. W.; And Others

    The study explored the feasibility of deriving an educationally relevant occupational cluster structure based on Occupational Analysis Inventory (OAI) work dimensions. A hierarchical cluster analysis was applied to the factor score profiles of 814 occupations on 22 higher-order OAI work dimensions. From that analysis, 73 occupational clusters were…

  18. A Multiple-Methods Approach to the Investigation of WAIS-R Constructs Employing Cluster Analysis.

    ERIC Educational Resources Information Center

    Fraboni, Maryann; And Others

    1989-01-01

    Seven hierarchical clustering methods were applied to the Wechsler Adult Intelligence Scale-Revised (WAIS-R) scores of 121 medical rehabilitation clients to investigate the possibility of method-dependent results and determine the stability of the clusters. This multiple-methods cluster analysis suggests that the underlying constructs of the…

  19. Hierarchical classification of glycoside hydrolases.

    PubMed

    Naumoff, D G

    2011-06-01

    This review deals with structural and functional features of glycoside hydrolases, a widespread group of enzymes present in almost all living organisms. Their catalytic domains are grouped into 120 amino acid sequence-based families in the international classification of the carbohydrate-active enzymes (CAZy database). At a higher hierarchical level some of these families are combined in 14 clans. Enzymes of the same clan have common evolutionary origin of their genes and share the most important functional characteristics such as composition of the active center, anomeric configuration of cleaved glycosidic bonds, and molecular mechanism of the catalyzed reaction (either inverting, or retaining). There are now extensive data in the literature concerning the relationship between glycoside hydrolase families belonging to different clans and/or included in none of them, as well as information on phylogenetic protein relationship within particular families. Summarizing these data allows us to propose a multilevel hierarchical classification of glycoside hydrolases and their homologs. It is shown that almost the whole variety of the enzyme catalytic domains can be brought into six main folds, large groups of proteins having the same three-dimensional structure and the supposed common evolutionary origin. PMID:21639842

  20. Clustering of Variables for Mixed Data

    NASA Astrophysics Data System (ADS)

    Saracco, J.; Chavent, M.

    2016-05-01

    This chapter presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described: a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the R packages ClustOfVar and PCAmixdata are illustrated on real mixed data. The PCAmix and ClustOfVar approaches are first used for dimension reduction (step 1) before applying in step 2 a standard clustering method to obtain groups of individuals.

  1. Star Clusters in the Magellanic Clouds-1: Parameterisation and Classification of 1072 Clusters in the LMC

    NASA Astrophysics Data System (ADS)

    Nayak, P. K.; Subramaniam, A.; Choudhury, S.; Indu, G.; Sagar, Ram

    2016-08-01

    We have introduced a semi-automated quantitative method to estimate the age and reddening of 1072 star clusters in the Large Magellanic Cloud (LMC) using the Optical Gravitational Lensing Experiment (OGLE) III survey data. This study brings out 308 newly parameterised clusters. In a first of its kind, the LMC clusters are classified into groups based on richness/mass as very poor, poor, moderate and rich clusters, similar to the classification scheme of open clusters in the Galaxy. A major cluster formation episode is found to happen at 125±25 Myr in the inner LMC. The bar region of the LMC appears prominently in the age range 60 - 250 Myr and is found to have a relatively higher concentration of poor and moderate clusters. The eastern and the western ends of the bar are found to form clusters initially, which later propagates to the central part. We demonstrate that there is a significant difference in the distribution of clusters as a function of mass, using a movie based on the propagation (in space and time) of cluster formation in various groups. The importance of including the low mass clusters in the cluster formation history is demonstrated. The catalog with parameters, classification, and cleaned and isochrone fitted CMDs of 1072 clusters, which are available as online material, can be further used to understand the hierarchical formation of clusters in selected regions of the LMC.

  2. Six clustering algorithms applied to the WAIS-R: the problem of dissimilar cluster results.

    PubMed

    Fraboni, M; Cooper, D

    1989-11-01

    Clusterings of the Wechsler Adult Intelligence Scale-Revised subtests were obtained from the application of six hierarchical clustering methods (N = 113). These sets of clusters were compared for similarities using the Rand index. The calculated indices suggested similarities of cluster group membership between the Complete Linkage and Centroid methods; Complete Linkage and Ward's methods; Centroid and Ward's methods; and Single Linkage and Average Linkage Between Groups methods. Cautious use of single clustering methods is implied, though the authors suggest some advantages of knowing specific similarities and differences. If between-method comparisons consistently reveal similar cluster membership, a choice could be made from those algorithms that tend to produce similar partitions, thereby enhancing cluster interpretation. PMID:2613904

  3. Discursive Hierarchical Patterning in Economics Cases

    ERIC Educational Resources Information Center

    Lung, Jane

    2011-01-01

    This paper attempts to apply Lung's (2008) model of the discursive hierarchical patterning of cases to a closer and more specific study of Economics cases and proposes a model of the distinct discursive hierarchical patterning of the same. It examines a corpus of 150 Economics cases with a view to uncovering the patterns of discourse construction.…

  4. Hierarchical Latent Trait Approach in Test Analysis.

    ERIC Educational Resources Information Center

    Dimitrov, Dimiter M.

    An approach is described that reveals the hierarchical test structure (HTS) based on the cognitive demands of the test items, and conducts a linear trait modeling by using the HST elements as item difficulty components. This approach, referred to as the Hierarchical Latent Trait Approach (HLTA), employs an algorithm that allows all test items to…

  5. Hierarchical Classes Modeling of Rating Data

    ERIC Educational Resources Information Center

    Van Mechelen, Iven; Lombardi, Luigi; Ceulemans, Eva

    2007-01-01

    Hierarchical classes (HICLAS) models constitute a distinct family of structural models for N-way N-mode data. All members of the family include N simultaneous and linked classifications of the elements of the N modes implied by the data; those classifications are organized in terms of hierarchical, if-then-type relations. Moreover, the models are…

  6. Genetic Network Inference Using Hierarchical Structure.

    PubMed

    Kimura, Shuhei; Tokuhisa, Masato; Okada-Hatakeyama, Mariko

    2016-01-01

    Many methods for inferring genetic networks have been proposed, but the regulations they infer often include false-positives. Several researchers have attempted to reduce these erroneous regulations by proposing the use of a priori knowledge about the properties of genetic networks such as their sparseness, scale-free structure, and so on. This study focuses on another piece of a priori knowledge, namely, that biochemical networks exhibit hierarchical structures. Based on this idea, we propose an inference approach that uses the hierarchical structure in a target genetic network. To obtain a reasonable hierarchical structure, the first step of the proposed approach is to infer multiple genetic networks from the observed gene expression data. We take this step using an existing method that combines a genetic network inference method with a bootstrap method. The next step is to extract a hierarchical structure from the inferred networks that is consistent with most of the networks. Third, we use the hierarchical structure obtained to assign confidence values to all candidate regulations. Numerical experiments are also performed to demonstrate the effectiveness of using the hierarchical structure in the genetic network inference. The improvement accomplished by the use of the hierarchical structure is small. However, the hierarchical structure could be used to improve the performances of many existing inference methods. PMID:26941653

  7. Hierarchical structure of the countries based on electricity consumption and economic growth

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Aslan, Alper; Deviren, Bayram; Keskin, Mustafa

    2016-07-01

    We investigate the hierarchical structures of countries based on electricity consumption and economic growth by using the real amounts of their consumption over a certain time period. We use electricity consumption data to detect the topological properties of 64 countries from 1971 to 2008. These countries are divided into three clusters: low income group, middle income group and high income group countries. Firstly, a relationship between electricity consumption and economic growth is investigated by using the concept of hierarchical structure methods (minimal spanning tree (MST) and hierarchical tree (HT)). Secondly, we perform bootstrap techniques to investigate a value of the statistical reliability to the links of the MST. Finally, we use a clustering linkage procedure in order to observe the cluster structure more clearly. The results of the structural topologies of these trees are as follows: (i) we identified different clusters of countries according to their geographical location and economic growth, (ii) we found a strong relation between energy consumption and economic growth for all the income groups considered in this study and (iii) the results are in good agreement with the causal relationship between electricity consumption and economic growth.

  8. DEDICATED FILTER FOR DEFECTS CLUSTERING IN RADIOGRAPHIC IMAGE

    SciTech Connect

    Sikora, R.; Swiadek, K.; Chady, T.

    2009-03-03

    Defect clusters such as linear or clustered porosity are in some cases even more important than single flaws. This paper presents two methods of defect clustering and algorithm for calculation of distances between flaws in digital radiographic image. Dedicated lookup table based filter is used for calculation of distances between objects in the specified range. For defect clustering two functions were developed. First one is based on MMD (Minimum Mean Distance) algorithm. Second one uses hierarchical procedures for clustering defects of various types, shapes and size.

  9. Hierarchically nanostructured materials for sustainable environmental applications

    PubMed Central

    Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian

    2013-01-01

    This review presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions, and multiple functionalities toward water remediation, biosensing, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing, and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology. PMID:24790946

  10. Processing of hierarchical syntactic structure in music

    PubMed Central

    Koelsch, Stefan; Rohrmeier, Martin; Torrecuso, Renzo; Jentschke, Sebastian

    2013-01-01

    Hierarchical structure with nested nonlocal dependencies is a key feature of human language and can be identified theoretically in most pieces of tonal music. However, previous studies have argued against the perception of such structures in music. Here, we show processing of nonlocal dependencies in music. We presented chorales by J. S. Bach and modified versions in which the hierarchical structure was rendered irregular whereas the local structure was kept intact. Brain electric responses differed between regular and irregular hierarchical structures, in both musicians and nonmusicians. This finding indicates that, when listening to music, humans apply cognitive processes that are capable of dealing with long-distance dependencies resulting from hierarchically organized syntactic structures. Our results reveal that a brain mechanism fundamental for syntactic processing is engaged during the perception of music, indicating that processing of hierarchical structure with nested nonlocal dependencies is not just a key component of human language, but a multidomain capacity of human cognition. PMID:24003165

  11. Hierarchically Nanostructured Materials for Sustainable Environmental Applications

    NASA Astrophysics Data System (ADS)

    Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian

    2013-11-01

    This article presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions and multiple functionalities towards water remediation, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology.

  12. Hierarchically nanostructured materials for sustainable environmental applications.

    PubMed

    Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian

    2013-01-01

    This review presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions, and multiple functionalities toward water remediation, biosensing, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing, and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology. PMID:24790946

  13. Luminous red galaxies in hierarchical cosmologies

    NASA Astrophysics Data System (ADS)

    Almeida, C.; Baugh, C. M.; Wake, D. A.; Lacey, C. G.; Benson, A. J.; Bower, R. G.; Pimbblet, K.

    2008-06-01

    Luminous red galaxies (LRGs) are much rarer and more massive than L* galaxies. Coupled with their extreme colours, LRGs therefore provide a demanding testing ground for the physics of massive galaxy formation. We present the first self-consistent predictions for the abundance and properties of LRGs in hierarchical structure formation models. We test two published models which use quite different mechanisms to suppress the formation of massive galaxies: the Bower et al. model which invokes `active galactic nuclei (AGN) feedback' to prevent gas from cooling in massive haloes and the Baugh et al. model which relies upon a `superwind' to eject gas before it is turned into stars. Without adjusting any parameters, the Bower et al. model gives an excellent match to the observed luminosity function of LRGs in the Sloan Digital Sky Survey (with a median redshift of z = 0.24) and to their clustering; the Baugh et al. model is less successful in these respects. Both models fail to match the observed abundance of LRGs at z = 0.5 to better than a factor of ~2. In the models, LRGs are typically bulge-dominated systems with stellar masses of ~2 × 1011h-1Msolar and velocity dispersions of σ ~ 250kms-1. Around half of the stellar mass in the model LRGs is already formed by z ~ 2.2 and is assembled into one main progenitor by z ~ 1.5; on average, only 25 per cent of the mass of the main progenitor is added after z ~ 1. LRGs are predicted to be found in a wide range of halo masses, a conclusion which relies on properly taking into account the scatter in the formation histories of haloes. Remarkably, we find that the correlation function of LRGs is predicted to be a power law down to small pair separations, in excellent agreement with observational estimates. Neither the Bower et al. nor the Baugh et al. model is able to reproduce the observed radii of LRGs.

  14. Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

    2011-01-01

    The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

  15. Adaptive Sampling in Hierarchical Simulation

    SciTech Connect

    Knap, J; Barton, N R; Hornung, R D; Arsenlis, A; Becker, R; Jefferson, D R

    2007-07-09

    We propose an adaptive sampling methodology for hierarchical multi-scale simulation. The method utilizes a moving kriging interpolation to significantly reduce the number of evaluations of finer-scale response functions to provide essential constitutive information to a coarser-scale simulation model. The underlying interpolation scheme is unstructured and adaptive to handle the transient nature of a simulation. To handle the dynamic construction and searching of a potentially large set of finer-scale response data, we employ a dynamic metric tree database. We study the performance of our adaptive sampling methodology for a two-level multi-scale model involving a coarse-scale finite element simulation and a finer-scale crystal plasticity based constitutive law.

  16. Hierarchical condensation near phase equilibrium

    NASA Astrophysics Data System (ADS)

    Olemskoi, A. I.; Yushchenko, O. V.; Borisyuk, V. N.; Zhilenko, T. I.; Kosminska, Yu. O.; Perekrestov, V. I.

    2012-06-01

    A novel mechanism of new phase formation is studied both experimentally and theoretically in the example of quasi-equilibrium stationary condensation in an ion-plasma sputterer. Copper condensates are obtained to demonstrate that a specific network structure is formed as a result of self-assembly in the course of deposition. The fractal pattern related is inherent in the phenomena of diffusion limited aggregation. Condensate nuclei are shown to form statistical ensemble of hierarchically subordinated objects distributed in ultrametric space. The Langevin equation and the Fokker-Planck equation related are found to describe stationary distribution of thermodynamic potential variations at condensation. Time dependence of the formation probability of branching structures is found to clarify the experimental situation.

  17. Core Recursive Hierarchical Image Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James

    2011-01-01

    The Recursive Hierarchical Image Segmentation (RHSEG) software has been repackaged to provide a version of the RHSEG software that is not subject to patent restrictions and that can be released to the general public through NASA GSFC's Open Source release process. Like the Core HSEG Software Package, this Core RHSEG Software Package also includes a visualization program called HSEGViewer along with a utility program HSEGReader. It also includes an additional utility program called HSEGExtract. The unique feature of the Core RHSEG package is that it is a repackaging of the RHSEG technology designed to specifically avoid the inclusion of the certain software technology. Unlike the Core HSEG package, it includes the recursive portions of the technology, but does not include processing window artifact elimination technology.

  18. Hierarchical analysis of molecular spectra

    SciTech Connect

    Davis, M.J.

    1996-03-01

    A novel representation of molecular spectra in terms of hierarchical trees has proven to be an important aid for the study of many significant problems in gas-phase chemical dynamics. Trees are generated from molecular spectra by monitoring the changes that occur in a spectrum as resolution is changed in a continuous manner. A tree defines a genealogy among all lines of a spectrum. This allows for a detailed understanding of the assignment of features of a spectrum that may be difficult to obtain any other way as well as an understanding of intramolecular energy transfer time scales, mechanisms, and pathways. The methodology has been applied to several problems: transition state spectroscopy, intramolecular energy transfer in highly excited molecules, high-resolution overtone spectroscopy, and the nature of the classical-quantum correspondence when there is classical chaos (``quantum chaos``).

  19. Meaningful Clusters

    SciTech Connect

    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.

  20. Hierarchical drivers of reef-fish metacommunity structure.

    PubMed

    MacNeil, M Aaron; Graham, Nicholas A J; Polunin, Nicholas V C; Kulbicki, Michel; Galzin, René; Harmelin-Vivien, Mireille; Rushton, Steven P

    2009-01-01

    multiple spatial scales; and (3) inter-atoll connectedness was poorly correlated with the nonrandom clustering of reef-fish species. These results demonstrate the importance of modeling hierarchical data and processes in understanding reef-fish metacommunity structure. PMID:19294930

  1. Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2003-01-01

    A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic

  2. Spectral clustering of protein sequences

    PubMed Central

    Paccanaro, Alberto; Casbon, James A.; Saqi, Mansoor A. S.

    2006-01-01

    An important problem in genomics is automatically clustering homologous proteins when only sequence information is available. Most methods for clustering proteins are local, and are based on simply thresholding a measure related to sequence distance. We first show how locality limits the performance of such methods by analysing the distribution of distances between protein sequences. We then present a global method based on spectral clustering and provide theoretical justification of why it will have a remarkable improvement over local methods. We extensively tested our method and compared its performance with other local methods on several subsets of the SCOP (Structural Classification of Proteins) database, a gold standard for protein structure classification. We consistently observed that, the number of clusters that we obtain for a given set of proteins is close to the number of superfamilies in that set; there are fewer singletons; and the method correctly groups most remote homologs. In our experiments, the quality of the clusters as quantified by a measure that combines sensitivity and specificity was consistently better [on average, improvements were 84% over hierarchical clustering, 34% over Connected Component Analysis (CCA) (similar to GeneRAGE) and 72% over another global method, TribeMCL]. PMID:16547200

  3. Exploring the hierarchical structure in road network

    NASA Astrophysics Data System (ADS)

    He, Jing; Zhang, Hong; Lan, Tian; Cao, Weiwei; Wu, Xun

    2015-12-01

    Hierarchical structure of road network has received intensive attention either in urban planning or multi-scale representation. On the one hand, high-efficiency traffic flow counts on a reasonable hierarchical structure. On the other hand, it is a guide-line for cartographic generalization of road network. The paper attempts to investigate the hierarchical structure of a road network from two perspectives, a) the ht-index in terms of the degree connectivity, which was proposed to quantify the scaling and hierarchical structure of the network, b) the renormalization process, originated from complex network analysis, which is able to uncover the self-similarity of a network and reveal its hierarchical structure. We argue that the first point exhibits a big picture of the whole network, revealing the depth of the hierarchy, while the second point further illustrates how the nodes are organized to form a hierarchical structure at different scales. The hierarchical structures of 6 road networks in reality are examined accordingly. Results show that both indices are able to reveal the complexity of the hierarchy of a network. These conclusions can be beneficial to the road network generalization.

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

    PubMed Central

    Abdullah, Ahmed; Sabbir Alam, S.M.; Sultana, Munawar; Hossain, M. Anwar

    2015-01-01

    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/. PMID:26216453

  5. Asymmetric Clustering Index in a Case Study of 5-HT1A Receptor Ligands

    PubMed Central

    Śmieja, Marek; Warszycki, Dawid; Tabor, Jacek; Bojarski, Andrzej J.

    2014-01-01

    The automatic clustering of chemical compounds is an important branch of chemoinformatics. In this paper the Asymmetric Clustering Index (Aci) is proposed to assess how well an automatically created partition reflects the reference. The asymmetry allows for a distinction between the fixed reference and the numerically constructed partition. The introduced index is applied to evaluate the quality of hierarchical clustering procedures for 5-HT1A receptor ligands. We find that the most appropriate combination of parameters for the hierarchical clustering of compounds with a determined activity for this biological target is the Klekota Roth fingerprint combined with the complete linkage function and the Buser similarity metric. PMID:25019251

  6. Clustering PPI data by combining FA and SHC method

    PubMed Central

    2015-01-01

    Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value. PMID:25707632

  7. Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

    PubMed Central

    Havemann, Frank; Gläser, Jochen; Heinz, Michael; Struck, Alexander

    2012-01-01

    The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields. PMID:22479376

  8. Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

    PubMed

    Havemann, Frank; Gläser, Jochen; Heinz, Michael; Struck, Alexander

    2012-01-01

    The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields. PMID:22479376

  9. Worldwide clustering of the corruption perception

    NASA Astrophysics Data System (ADS)

    Paulus, Michal; Kristoufek, Ladislav

    2015-06-01

    We inspect a possible clustering structure of the corruption perception among 134 countries. Using the average linkage clustering, we uncover a well-defined hierarchy in the relationships among countries. Four main clusters are identified and they suggest that countries worldwide can be quite well separated according to their perception of corruption. Moreover, we find a strong connection between corruption levels and a stage of development inside the clusters. The ranking of countries according to their corruption perfectly copies the ranking according to the economic performance measured by the gross domestic product per capita of the member states. To the best of our knowledge, this study is the first one to present an application of hierarchical and clustering methods to the specific case of corruption.

  10. NASA thesaurus. Volume 1: Hierarchical Listing

    NASA Technical Reports Server (NTRS)

    1988-01-01

    There are over 17,000 postable terms and nearly 4,000 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary and Volume 3 - Definitions.

  11. NASA Thesaurus. Volume 1: Hierarchical listing

    NASA Technical Reports Server (NTRS)

    1982-01-01

    There are 16,713 postable terms and 3,716 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary.

  12. NASA thesaurus. Volume 1: Hierarchical listing

    NASA Technical Reports Server (NTRS)

    1985-01-01

    There are 16,835 postable terms and 3,765 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary.

  13. Hierarchical structure of the European countries based on debts as a percentage of GDP during the 2000-2011 period

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Deviren, Bayram; Keskin, Mustafa

    2014-11-01

    We investigate hierarchical structures of the European countries by using debt as a percentage of Gross Domestic Product (GDP) of the countries as they change over a certain period of time. We obtain the topological properties among the countries based on debt as a percentage of GDP of European countries over the period 2000-2011 by using the concept of hierarchical structure methods (minimal spanning tree, (MST) and hierarchical tree, (HT)). This period is also divided into two sub-periods related to 2004 enlargement of the European Union, namely 2000-2004 and 2005-2011, in order to test various time-window and observe the temporal evolution. The bootstrap techniques is applied to see a value of statistical reliability of the links of the MSTs and HTs. The clustering linkage procedure is also used to observe the cluster structure more clearly. From the structural topologies of these trees, we identify different clusters of countries according to their level of debts. Our results show that by the debt crisis, the less and most affected Eurozone’s economies are formed as a cluster with each other in the MSTs and hierarchical trees.

  14. An evolutionary and visual framework for clustering of DNA microarray data.

    PubMed

    Castellanos-Garzón, José A; Díaz, Fernando

    2013-01-01

    This paper presents a case study to show the competence of our evolutionary and visual framework for cluster analysis of DNA microarray data. The proposed framework joins a genetic algorithm for hierarchical clustering with a set of visual components of cluster tasks given by a tool. The cluster visualization tool allows us to display different views of clustering results as a means of cluster visual validation. The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set. Thus, this shows the reliability of the proposed framework. PMID:24231146

  15. Hierarchical model for distributed seismicity

    SciTech Connect

    Tejedor, Alejandro; Gomez, Javier B.; Pacheco, Amalio F.

    2010-07-15

    A cellular automata model for the interaction between seismic faults in an extended region is presented. Faults are represented by boxes formed by a different number of sites and located in the nodes of a fractal tree. Both the distribution of box sizes and the interaction between them is assumed to be hierarchical. Load particles are randomly added to the system, simulating the action of external tectonic forces. These particles fill the sites of the boxes progressively. When a box is full it topples, some of the particles are redistributed to other boxes and some of them are lost. A box relaxation simulates the occurrence of an earthquake in the region. The particle redistributions mostly occur upwards (to larger faults) and downwards (to smaller faults) in the hierarchy producing new relaxations. A simple and efficient bookkeeping of the information allows the running of systems with more than fifty million faults. This model is consistent with the definition of magnitude, i.e., earthquakes of magnitude m take place in boxes with a number of sites ten times bigger than those boxes responsible for earthquakes with a magnitude m-1 which are placed in the immediate lower level of the hierarchy. The three parameters of the model have a geometrical nature: the height or number of levels of the fractal tree, the coordination of the tree and the ratio of areas between boxes in two consecutive levels. Besides reproducing several seismicity properties and regularities, this model is used to test the performance of some precursory patterns.

  16. Dynamic visualization of hierarchical data

    NASA Astrophysics Data System (ADS)

    Senay, Hikmet; Saltz, Jeffrey S.

    1997-06-01

    Many business data applications involve several hierarchies reflecting inherent structure of the underlying domains. Often, these hierarchies are presented in a spreadsheet like format, showing a single hierarchy in a tabular form with drill down and roll up capabilities. Although this approach works for simple hierarchies and small data sets, they tend to break as the hierarchies become more complex or the size of data set increases. An alternative approach, which has the potential to work equally well with complex hierarchies and large data sets, is to devise a visualization front-end for dynamically altering and displaying views of data under user control. Toward this end, we have developed a prototype of a visualization tool to view large financial data sets which typically involve multiple hierarchies. The tool not only acts as a data presentation medium, but also serves as a graphical means to form complex queries and interact with data. Its presentation structure uses hierarchical labels, which may be drilled down or rolled up, and displayable cell objects, which may have arbitrary complexity. Its data interface allows users to select data of interest, map data parameters to visual attributes, set threshold values to focus on interesting data, and read data values associated with cell objects.

  17. Geophysical Inversion Through Hierarchical Scheme

    NASA Astrophysics Data System (ADS)

    Furman, A.; Huisman, J. A.

    2010-12-01

    Geophysical investigation is a powerful tool that allows non-invasive and non-destructive mapping of subsurface states and properties. However, non-uniqueness associated with the inversion process prevents the quantitative use of these methods. One major direction researchers are going is constraining the inverse problem by hydrological observations and models. An alternative to the commonly used direct inversion methods are global optimization schemes (such as genetic algorithms and Monte Carlo Markov Chain methods). However, the major limitation here is the desired high resolution of the tomographic image, which leads to a large number of parameters and an unreasonably high computational effort when using global optimization schemes. Two innovative schemes are presented here. First, a hierarchical approach is used to reduce the computational effort for the global optimization. Solution is achieved for coarse spatial resolution, and this solution is used as the starting point for finer scheme. We show that the computational effort is reduced in this way dramatically. Second, we use a direct ERT inversion as the starting point for global optimization. In this case preliminary results show that the outcome is not necessarily beneficial, probably because of spatial mismatch between the results of the direct inversion and the true resistivity field.

  18. Hierarchical streamline bundles for visualizing 2D flow fields.

    SciTech Connect

    Shene, Ching-Kuang; Wang, Chaoli; Yu, Hongfeng; Chen, Jacqueline H.

    2010-08-01

    We present hierarchical streamline bundles, a new approach to simplifying and visualizing 2D flow fields. Our method first densely seeds a flow field and produces a large number of streamlines that capture important flow features such as critical points. Then, we group spatially neighboring and geometrically similar streamlines to construct a hierarchy from which we extract streamline bundles at different levels of detail. Streamline bundles highlight multiscale flow features and patterns through a clustered yet non-cluttered display. This selective visualization strategy effectively accentuates visual foci and therefore is able to convey the desired insight into the flow fields. The hierarchical streamline bundles we have introduced offer a new way to characterize and visualize the flow structure and patterns in multiscale fashion. Streamline bundles highlight critical points clearly and concisely. Exploring the hierarchy allows a complete visualization of important flow features. Thanks to selective streamline display and flexible LOD refinement, our multiresolution technique is scalable and is promising for viewing large and complex flow fields. In the future, we would like to seek a cost-effective way to generate streamlines without enforcing the dense seeding condition. We will also extend this approach to handle real-world 3D complex flow fields.

  19. A Hierarchical Analysis of Environmental Attitudes.

    ERIC Educational Resources Information Center

    Richmond, James M.; Baumgart, Neil

    1981-01-01

    Reported is a reanalysis of responses of a survey of environmental attitudes of English high school students. The purpose of the analysis was to try to determine if there was a hierarchical relationship among the items. Relationships were identified. (RH)

  20. Hierarchically Structured Nanomaterials for Electrochemical Energy Conversion.

    PubMed

    Trogadas, Panagiotis; Ramani, Vijay; Strasser, Peter; Fuller, Thomas F; Coppens, Marc-Olivier

    2016-01-01

    Hierarchical nanomaterials are highly suitable as electrocatalysts and electrocatalyst supports in electrochemical energy conversion devices. The intrinsic kinetics of an electrocatalyst are associated with the nanostructure of the active phase and the support, while the overall properties are also affected by the mesostructure. Therefore, both structures need to be controlled. A comparative state-of-the-art review of catalysts and supports is provided along with detailed synthesis methods. To further improve the design of these hierarchical nanomaterials, in-depth research on the effect of materials architecture on reaction and transport kinetics is necessary. Inspiration can be derived from nature, which is full of very effective hierarchical structures. Developing fundamental understanding of how desired properties of biological systems are related to their hierarchical architecture can guide the development of novel catalytic nanomaterials and nature-inspired electrochemical devices. PMID:26549054

  1. Hierarchically UVO patterned elastomeric and thermoplastic structures

    NASA Astrophysics Data System (ADS)

    Chen, Ying; Kulkarni, Manish; Marshall, Allan; Karim, Alamgir

    2014-03-01

    We demonstrate a simple yet versatile method to fabricate tunable hierarchical micro-nanostructures on flexible Poly(dimethylsiloxane) (PDMS) elastomer and thermoplastic polymer surface by a two-step process. Nanoscale patterned PDMS was obtained by imprinting compact disc (CD)/digital video disc (DVD) patterns. The second micro pattern was superposed by selective densification of PDMS by exposing to ultraviolet-ozone radiation (UVO) through micro-patterned TEM grid as a mask. The nanoscale patterns are preserved through UVO exposure step leading to formation of deep hierarchical patterns, so that for a 19 um square mesh, the micro pattern has a depth of 600nm with 6h PDMS UVO exposure time. This simple method can be promoted to fabricate hierarchical structures of thermoplastic materials (such as polystyrene), from which the mechanism of capillary imprinting and thermal stability of hierarchical patterns are investigated. This study is potentially important to various applications ranging from biomimetic scaffolds to solar cell.

  2. Hierarchically sculptured plant surfaces and superhydrophobicity.

    PubMed

    Koch, Kerstin; Bohn, Holger Florian; Barthlott, Wilhelm

    2009-12-15

    More than 400 million years of evolution of land plants led to a high diversity of adapted surface structures. Superhydrophobic biological surfaces are of special interest for the development of biomimetic materials for self-cleaning, drag reduction, and energy conservation. The key innovation in superhydrophobic biological surfaces is hierarchical sculpturing. In plants, a hydrophobic wax coating creates water-repelling surfaces that in combination with two or more levels of sculpturing leads to superhydrophobicity. Hierarchical structuring is of special interest for technical "biomimetic" materials with low adhesion and self-cleaning properties. Here we introduce hierarchical surface sculptures of plants with up to six levels. The article gives an overview of the composition of hierarchical surfaces for superhydrophobicity and their use as models for the development of artificial self-cleaning or drag-reducing surfaces. PMID:19634871

  3. Hierarchical mutual information for the comparison of hierarchical community structures in complex networks.

    PubMed

    Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

    2015-12-01

    The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks. PMID:26764762

  4. Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

    NASA Astrophysics Data System (ADS)

    Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

    2015-12-01

    The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

  5. A generic algorithm for constructing hierarchical representations of geometric objects

    SciTech Connect

    Xavier, P.G.

    1995-10-01

    For a number of years, robotics researchers have exploited hierarchical representations of geometrical objects and scenes in motion-planning, collision-avoidance, and simulation. However, few general techniques exist for automatically constructing them. We present a generic, bottom-up algorithm that uses a heuristic clustering technique to produced balanced, coherent hierarchies. Its worst-case running time is O(N{sup 2}logN), but for non-pathological cases it is O(NlogN), where N is the number of input primitives. We have completed a preliminary C++ implementation for input collections of 3D convex polygons and 3D convex polyhedra and conducted simple experiments with scenes of up to 12,000 polygons, which take only a few minutes to process. We present examples using spheres and convex hulls as hierarchy primitives.

  6. Complex networks as an emerging property of hierarchical preferential attachment

    NASA Astrophysics Data System (ADS)

    Hébert-Dufresne, Laurent; Laurence, Edward; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

    2015-12-01

    Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

  7. Analysis of the effects of the global financial crisis on the Turkish economy, using hierarchical methods

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Keskin, Mustafa; Deviren, Bayram

    2012-04-01

    We have analyzed the topology of 50 important Turkish companies for the period 2006-2010 using the concept of hierarchical methods (the minimal spanning tree (MST) and hierarchical tree (HT)). We investigated the statistical reliability of links between companies in the MST by using the bootstrap technique. We also used the average linkage cluster analysis (ALCA) technique to observe the cluster structures much better. The MST and HT are known as useful tools to perceive and detect global structure, taxonomy, and hierarchy in financial data. We obtained four clusters of companies according to their proximity. We also observed that the Banks and Holdings cluster always forms in the centre of the MSTs for the periods 2006-2007, 2008, and 2009-2010. The clusters match nicely with their common production activities or their strong interrelationship. The effects of the Automobile sector increased after the global financial crisis due to the temporary incentives provided by the Turkish government. We find that Turkish companies were not very affected by the global financial crisis.

  8. Hierarchical Nanoceramics for Industrial Process Sensors

    SciTech Connect

    Ruud, James, A.; Brosnan, Kristen, H.; Striker, Todd; Ramaswamy, Vidya; Aceto, Steven, C.; Gao, Yan; Willson, Patrick, D.; Manoharan, Mohan; Armstrong, Eric, N., Wachsman, Eric, D.; Kao, Chi-Chang

    2011-07-15

    This project developed a robust, tunable, hierarchical nanoceramics materials platform for industrial process sensors in harsh-environments. Control of material structure at multiple length scales from nano to macro increased the sensing response of the materials to combustion gases. These materials operated at relatively high temperatures, enabling detection close to the source of combustion. It is anticipated that these materials can form the basis for a new class of sensors enabling widespread use of efficient combustion processes with closed loop feedback control in the energy-intensive industries. The first phase of the project focused on materials selection and process development, leading to hierarchical nanoceramics that were evaluated for sensing performance. The second phase focused on optimizing the materials processes and microstructures, followed by validation of performance of a prototype sensor in a laboratory combustion environment. The objectives of this project were achieved by: (1) synthesizing and optimizing hierarchical nanostructures; (2) synthesizing and optimizing sensing nanomaterials; (3) integrating sensing functionality into hierarchical nanostructures; (4) demonstrating material performance in a sensing element; and (5) validating material performance in a simulated service environment. The project developed hierarchical nanoceramic electrodes for mixed potential zirconia gas sensors with increased surface area and demonstrated tailored electrocatalytic activity operable at high temperatures enabling detection of products of combustion such as NOx close to the source of combustion. Methods were developed for synthesis of hierarchical nanostructures with high, stable surface area, integrated catalytic functionality within the structures for gas sensing, and demonstrated materials performance in harsh lab and combustion gas environments.

  9. Cosmic clustering

    NASA Astrophysics Data System (ADS)

    Anninos, Dionysios; Denef, Frederik

    2016-06-01

    We show that the late time Hartle-Hawking wave function for a free massless scalar in a fixed de Sitter background encodes a sharp ultrametric structure for the standard Euclidean distance on the space of field configurations. This implies a hierarchical, tree-like organization of the state space, reflecting its genesis as a branched diffusion process. An equivalent mathematical structure organizes the state space of the Sherrington-Kirkpatrick model of a spin glass.

  10. OT2_baltieri_5: Star formation in proto-clusters

    NASA Astrophysics Data System (ADS)

    Altieri, B.

    2011-09-01

    Massive clusters of galaxies have been found to date from as early as 3-4 billion years after the Big Bang. Cosmological simulations using the current cold dark matter model predict that these systems should descend from 'proto-clusters' - early overdensities of massive galaxies that merge hierarchically to form a cluster. These protocluster regions themselves are built up hierarchically and so are expected to contain extremely massive galaxies, progenitors of the quiescent behemoths observed in cores of the present day massive galaxy clusters. Observational evidence for this picture, however, is sparse because high-redshift proto-clusters are rare and difficult to observe. Here we propose to probe with Herschel SPIRE the very beginning of the cluster and massive galaxies formation process by observing 5 proto-clusters at 3cluster galaxies with those of field galaxies at similar redshift. Determining whether cluster galaxies differ from field galaxies when the proto-cluster was still forming, tells us whether any of the difference observed today is driven by nature as apposed to nurture.

  11. Hierarchical model for distributed seismicity.

    PubMed

    Tejedor, Alejandro; Gómez, Javier B; Pacheco, Amalio F

    2010-07-01

    A cellular automata model for the interaction between seismic faults in an extended region is presented. Faults are represented by boxes formed by a different number of sites and located in the nodes of a fractal tree. Both the distribution of box sizes and the interaction between them is assumed to be hierarchical. Load particles are randomly added to the system, simulating the action of external tectonic forces. These particles fill the sites of the boxes progressively. When a box is full it topples, some of the particles are redistributed to other boxes and some of them are lost. A box relaxation simulates the occurrence of an earthquake in the region. The particle redistributions mostly occur upwards (to larger faults) and downwards (to smaller faults) in the hierarchy producing new relaxations. A simple and efficient bookkeeping of the information allows the running of systems with more than fifty million faults. This model is consistent with the definition of magnitude, i.e., earthquakes of magnitude m take place in boxes with a number of sites ten times bigger than those boxes responsible for earthquakes with a magnitude m-1 which are placed in the immediate lower level of the hierarchy. The three parameters of the model have a geometrical nature: the height or number of levels of the fractal tree, the coordination of the tree and the ratio of areas between boxes in two consecutive levels. Besides reproducing several seismicity properties and regularities, this model is used to test the performance of some precursory patterns. PMID:20866700

  12. Performance impact of dynamic parallelism on different clustering algorithms

    NASA Astrophysics Data System (ADS)

    DiMarco, Jeffrey; Taufer, Michela

    2013-05-01

    In this paper, we aim to quantify the performance gains of dynamic parallelism. The newest version of CUDA, CUDA 5, introduces dynamic parallelism, which allows GPU threads to create new threads, without CPU intervention, and adapt to its data. This effectively eliminates the superfluous back and forth communication between the GPU and CPU through nested kernel computations. The change in performance will be measured using two well-known clustering algorithms that exhibit data dependencies: the K-means clustering and the hierarchical clustering. K-means has a sequential data dependence wherein iterations occur in a linear fashion, while the hierarchical clustering has a tree-like dependence that produces split tasks. Analyzing the performance of these data-dependent algorithms gives us a better understanding of the benefits or potential drawbacks of CUDA 5's new dynamic parallelism feature.

  13. Automatic style clustering of printed characters in form images

    NASA Astrophysics Data System (ADS)

    Liu, Changsong; Ding, Xiaoqing

    2005-01-01

    Style is an important feature of printed or handwritten characters. But it is not studied thoroughly compared with character recognition. In this paper, we try to learn how many typical styles exist in a kind of real world form images. A hierarchical clustering method has been developed and tested. A cross recognition error rate constraint is proposed to reduce the false combinations in the hierarchical clustering process, and a cluster selecting method is used to delete redundant or unsuitable clusters. Only a similarity measure between any patterns is needed by the algorithm. It is tested on a template matching based similarity measure which can be extended to any other feature and distance measure easily. The detailed comparing on every step"s effects is shown in the paper. Total 16 kinds of typical styles are found out, and by giving each character in each style a prototype for recognition, a 0.78% error rate is achieved by recognizing the testing set.

  14. Automatic style clustering of printed characters in form images

    NASA Astrophysics Data System (ADS)

    Liu, Changsong; Ding, Xiaoqing

    2004-12-01

    Style is an important feature of printed or handwritten characters. But it is not studied thoroughly compared with character recognition. In this paper, we try to learn how many typical styles exist in a kind of real world form images. A hierarchical clustering method has been developed and tested. A cross recognition error rate constraint is proposed to reduce the false combinations in the hierarchical clustering process, and a cluster selecting method is used to delete redundant or unsuitable clusters. Only a similarity measure between any patterns is needed by the algorithm. It is tested on a template matching based similarity measure which can be extended to any other feature and distance measure easily. The detailed comparing on every step"s effects is shown in the paper. Total 16 kinds of typical styles are found out, and by giving each character in each style a prototype for recognition, a 0.78% error rate is achieved by recognizing the testing set.

  15. Quintuplet Cluster

    NASA Technical Reports Server (NTRS)

    1999-01-01

    Penetrating 25,000 light-years of obscuring dust and myriad stars, NASA's Hubble Space Telescope has provided the clearest view yet of one of the largest young clusters of stars inside our Milky Way galaxy, located less than 100 light-years from the very center of the Galaxy. Having the equivalent mass greater than 10,000 stars like our sun, the monster cluster is ten times larger than typical young star clusters scattered throughout our Milky Way. It is destined to be ripped apart in just a few million years by gravitational tidal forces in the galaxy's core. But in its brief lifetime it shines more brightly than any other star cluster in the Galaxy. Quintuplet Cluster is 4 million years old. It has stars on the verge of blowing up as supernovae. It is the home of the brightest star seen in the galaxy, called the Pistol star. This image was taken in infrared light by Hubble's NICMOS camera in September 1997. The false colors correspond to infrared wavelengths. The galactic center stars are white, the red stars are enshrouded in dust or behind dust, and the blue stars are foreground stars between us and the Milky Way's center. The cluster is hidden from direct view behind black dust clouds in the constellation Sagittarius. If the cluster could be seen from earth it would appear to the naked eye as a 3rd magnitude star, 1/6th of a full moon's diameter apart.

  16. Hierarchical Structures in Granular Matter

    NASA Astrophysics Data System (ADS)

    González-Gutiérrez, J.; Carrillo-Estrada, J. L.; Ruiz-Suárez, J. C.

    2013-12-01

    Granular matter, under the proper conditions of vibration, exhibits a behavior that closely resembles that of gases, liquids or solids. In a vibrated mix of glass particles and magnetic steel particles, it is also possible to observe aggregation phenomena, as well as, processes of reconstruction of the generated clusters. In this work we discuss the effects of the so called granular temperature on the evolution of the agglomerates generated by the magnetic interactions. On the basis of a fractal analysis and the measured mass distribution, we analyze experimental results on the static structural aspects of the aggregates originated by two methods we call: granular diffusion limited aggregation (GDLA) and growth limited by concentration (GLC).

  17. Multiscale ensemble clustering for finding modules in complex networks

    NASA Astrophysics Data System (ADS)

    Kim, Eun-Youn; Hwang, Dong-Uk; Ko, Tae-Wook

    2012-02-01

    The identification of modules in complex networks is important for the understanding of systems. Here, we propose an ensemble clustering method incorporating node groupings in various sizes and the sequential removal of weak ties between nodes which are rarely grouped together. This method successfully detects modules in various networks, such as hierarchical random networks and the American college football network, with known modular structures. Some of the results are compared with those obtained by modularity optimization and K-means clustering.

  18. A Survey on Clustering Routing Protocols in Wireless Sensor Networks

    PubMed Central

    Liu, Xuxun

    2012-01-01

    The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions. PMID:23112649

  19. Statistical label fusion with hierarchical performance models

    PubMed Central

    Asman, Andrew J.; Dagley, Alexander S.; Landman, Bennett A.

    2014-01-01

    Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally – fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy. PMID:24817809

  20. Hierarchical regression for analyses of multiple outcomes.

    PubMed

    Richardson, David B; Hamra, Ghassan B; MacLehose, Richard F; Cole, Stephen R; Chu, Haitao

    2015-09-01

    In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical "shrinkage" approach to stabilize estimates of an exposure's associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes. PMID:26232395

  1. Self-assembled biomimetic superhydrophobic hierarchical arrays.

    PubMed

    Yang, Hongta; Dou, Xuan; Fang, Yin; Jiang, Peng

    2013-09-01

    Here, we report a simple and inexpensive bottom-up technology for fabricating superhydrophobic coatings with hierarchical micro-/nano-structures, which are inspired by the binary periodic structure found on the superhydrophobic compound eyes of some insects (e.g., mosquitoes and moths). Binary colloidal arrays consisting of exemplary large (4 and 30 μm) and small (300 nm) silica spheres are first assembled by a scalable Langmuir-Blodgett (LB) technology in a layer-by-layer manner. After surface modification with fluorosilanes, the self-assembled hierarchical particle arrays become superhydrophobic with an apparent water contact angle (CA) larger than 150°. The throughput of the resulting superhydrophobic coatings with hierarchical structures can be significantly improved by templating the binary periodic structures of the LB-assembled colloidal arrays into UV-curable fluoropolymers by a soft lithography approach. Superhydrophobic perfluoroether acrylate hierarchical arrays with large CAs and small CA hysteresis can be faithfully replicated onto various substrates. Both experiments and theoretical calculations based on the Cassie's dewetting model demonstrate the importance of the hierarchical structure in achieving the final superhydrophobic surface states. PMID:23786830

  2. Analysis hierarchical model for discrete event systems

    NASA Astrophysics Data System (ADS)

    Ciortea, E. M.

    2015-11-01

    The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

  3. Hierarchical Ensemble Methods for Protein Function Prediction

    PubMed Central

    2014-01-01

    Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

  4. Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

    PubMed Central

    Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T.

    2015-01-01

    We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging. PMID:25786703

  5. Relation between financial market structure and the real economy: comparison between clustering methods.

    PubMed

    Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

    2015-01-01

    We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected]. PMID:25786703

  6. Galaxy Clusters

    NASA Astrophysics Data System (ADS)

    Miller, Christopher J. Miller

    2012-03-01

    There are many examples of clustering in astronomy. Stars in our own galaxy are often seen as being gravitationally bound into tight globular or open clusters. The Solar System's Trojan asteroids cluster at the gravitational Langrangian in front of Jupiter’s orbit. On the largest of scales, we find gravitationally bound clusters of galaxies, the Virgo cluster (in the constellation of Virgo at a distance of ˜50 million light years) being a prime nearby example. The Virgo cluster subtends an angle of nearly 8◦ on the sky and is known to contain over a thousand member galaxies. Galaxy clusters play an important role in our understanding of theUniverse. Clusters exist at peaks in the three-dimensional large-scale matter density field. Their sky (2D) locations are easy to detect in astronomical imaging data and their mean galaxy redshifts (redshift is related to the third spatial dimension: distance) are often better (spectroscopically) and cheaper (photometrically) when compared with the entire galaxy population in large sky surveys. Photometric redshift (z) [Photometric techniques use the broad band filter magnitudes of a galaxy to estimate the redshift. Spectroscopic techniques use the galaxy spectra and emission/absorption line features to measure the redshift] determinations of galaxies within clusters are accurate to better than delta_z = 0.05 [7] and when studied as a cluster population, the central galaxies form a line in color-magnitude space (called the the E/S0 ridgeline and visible in Figure 16.3) that contains galaxies with similar stellar populations [15]. The shape of this E/S0 ridgeline enables astronomers to measure the cluster redshift to within delta_z = 0.01 [23]. The most accurate cluster redshift determinations come from spectroscopy of the member galaxies, where only a fraction of the members need to be spectroscopically observed [25,42] to get an accurate redshift to the whole system. If light traces mass in the Universe, then the locations

  7. A Cluster Analysis of Personality Style in Adults with ADHD

    ERIC Educational Resources Information Center

    Robin, Arthur L.; Tzelepis, Angela; Bedway, Marquita

    2008-01-01

    Objective: The purpose of this study was to use hierarchical linear cluster analysis to examine the normative personality styles of adults with ADHD. Method: A total of 311 adults with ADHD completed the Millon Index of Personality Styles, which consists of 24 scales assessing motivating aims, cognitive modes, and interpersonal behaviors. Results:…

  8. Determining the Number of Clusters by Sampling With Replacement

    ERIC Educational Resources Information Center

    Tonidandel, Scott; Overall, John E.

    2004-01-01

    A split-sample replication criterion originally proposed by J. E. Overall and K. N. Magee (1992) as a stopping rule for hierarchical cluster analysis is applied to multiple data sets generated by sampling with replacement from an original simulated primary data set. An investigation of the validity of this bootstrap procedure was undertaken using…

  9. NOA: A Scalable Multi-Parent Clustering Hierarchy for WSNs

    SciTech Connect

    Cree, Johnathan V.; Delgado-Frias, Jose; Hughes, Michael A.; Burghard, Brion J.; Silvers, Kurt L.

    2012-08-10

    NOA is a multi-parent, N-tiered, hierarchical clustering algorithm that provides a scalable, robust and reliable solution to autonomous configuration of large-scale wireless sensor networks. The novel clustering hierarchy's inherent benefits can be utilized by in-network data processing techniques to provide equally robust, reliable and scalable in-network data processing solutions capable of reducing the amount of data sent to sinks. Utilizing a multi-parent framework, NOA reduces the cost of network setup when compared to hierarchical beaconing solutions by removing the expense of r-hop broadcasting (r is the radius of the cluster) needed to build the network and instead passes network topology information among shared children. NOA2, a two-parent clustering hierarchy solution, and NOA3, the three-parent variant, saw up to an 83% and 72% reduction in overhead, respectively, when compared to performing one round of a one-parent hierarchical beaconing, as well as 92% and 88% less overhead when compared to one round of two- and three-parent hierarchical beaconing hierarchy.

  10. Occupational Clusters.

    ERIC Educational Resources Information Center

    Pottawattamie County School System, Council Bluffs, IA.

    The 15 occupational clusters (transportation, fine arts and humanities, communications and media, personal service occupations, construction, hospitality and recreation, health occupations, marine science occupations, consumer and homemaking-related occupations, agribusiness and natural resources, environment, public service, business and office…

  11. Hierarchical self-organization of cytoskeletal active networks

    NASA Astrophysics Data System (ADS)

    Gordon, Daniel; Bernheim-Groswasser, Anne; Keasar, Chen; Farago, Oded

    2012-04-01

    The structural reorganization of the actin cytoskeleton is facilitated through the action of motor proteins that crosslink the actin filaments and transport them relative to each other. Here, we present a combined experimental-computational study that probes the dynamic evolution of mixtures of actin filaments and clusters of myosin motors. While on small spatial and temporal scales the system behaves in a very noisy manner, on larger scales it evolves into several well distinct patterns such as bundles, asters and networks. These patterns are characterized by junctions with high connectivity, whose formation is possible due to the organization of the motors in ‘oligoclusters’ (intermediate-size aggregates). The simulations reveal that the self-organization process proceeds through a series of hierarchical steps, starting from local microscopic moves and ranging up to the macroscopic large scales where the steady-state structures are formed. Our results shed light on the mechanisms involved in processes such as cytokinesis and cellular contractility, where myosin motors organized in clusters operate cooperatively to induce the structural organization of cytoskeletal networks.

  12. Superoleophobic Surfaces Obtained via Hierarchical Metallic Meshes.

    PubMed

    Grynyov, Roman; Bormashenko, Edward; Whyman, Gene; Bormashenko, Yelena; Musin, Albina; Pogreb, Roman; Starostin, Anton; Valtsifer, Viktor; Strelnikov, Vladimir; Schechter, Alex; Kolagatla, Srikanth

    2016-05-01

    Hierarchical metallic surfaces demonstrating pronounced water and oil repellence are reported. The surfaces were manufactured with stainless-steel microporous meshes, which were etched with perfluorononanoic acid. As a result, a hierarchical relief was created, characterized by roughness at micro- and sub-microscales. Pronounced superoleophobicity was registered with regard to canola, castor, sesame, flax, crude (petroleum), and engine oils. Relatively high sliding angles were recorded for 5 μL turpentine, olive, and silicone oil droplets. The stability of the Cassie-like air trapping wetting state, established with water/ethanol solutions, is reported. The omniphobicity of the surfaces is due to the interplay of their hierarchical relief and surface fluorination. PMID:27077637

  13. Hierarchical Micro-Nano Coatings by Painting

    NASA Astrophysics Data System (ADS)

    Kirveslahti, Anna; Korhonen, Tuulia; Suvanto, Mika; Pakkanen, Tapani A.

    2016-03-01

    In this paper, the wettability properties of coatings with hierarchical surface structures and low surface energy were studied. Hierarchically structured coatings were produced by using hydrophobic fumed silica nanoparticles and polytetrafluoroethylene (PTFE) microparticles as additives in polyester (PES) and polyvinyldifluoride (PVDF). These particles created hierarchical micro-nano structures on the paint surfaces and lowered or supported the already low surface energy of the paint. Two standard application techniques for paint application were employed and the presented coatings are suitable for mass production and use in large surface areas. By regulating the particle concentrations, it was possible to modify wettability properties gradually. Highly hydrophobic surfaces were achieved with the highest contact angle of 165∘. Dynamic contact angle measurements were carried out for a set of selected samples and low hysteresis was obtained. Produced coatings possessed long lasting durability in the air and in underwater conditions.

  14. Optimal retesting configurations for hierarchical group testing

    PubMed Central

    Black, Michael S.; Bilder, Christopher R.; Tebbs, Joshua M.

    2014-01-01

    SUMMARY Hierarchical group testing is widely used to test individuals for diseases. This testing procedure works by first amalgamating individual specimens into groups for testing. Groups testing negatively have their members declared negative. Groups testing positively are subsequently divided into smaller subgroups and are then retested to search for positive individuals. In our paper, we propose a new class of informative retesting procedures for hierarchical group testing that acknowledges heterogeneity among individuals. These procedures identify the optimal number of groups and their sizes at each testing stage in order to minimize the expected number of tests. We apply our proposals in two settings: 1) HIV testing programs that currently use three-stage hierarchical testing and 2) chlamydia and gonorrhea screening practices that currently use individual testing. For both applications, we show that substantial savings can be realized by our new procedures. PMID:26166904

  15. A hierarchical cellular logic for pyramid computers

    SciTech Connect

    Tanimoto, S.L.

    1984-11-01

    Hierarchical structure occurs in biological vision systems and there is good reason to incorporate it into a model of computation for processing binary images. A mathematical formalism is presented which can describe a wide variety of operations useful in image processing and graphics. The formalism allows for two kinds of simple transformations on the values (called pyramids) of a set of cells called a hierarchical domain: the first are binary operations on boolean values, and the second are neighborhood-matching operations. The implied model of computation is more structured than previously discussed pyramidal models, and is more readily realized in parallel hardware, while it remains sufficiently rich to provide efficient solutions to a wide variety of problems. The model has a simplicity which is due to the restricted nature of the operations and the implied synchronization across the hierarchical domain. A corresponding algebraic simplicity in the logic makes possible the concise representation of many cellular-data operations.

  16. Cluster generator

    DOEpatents

    Donchev, Todor I.; Petrov, Ivan G.

    2011-05-31

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

  17. Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems

    PubMed Central

    Rosvall, Martin; Bergstrom, Carl T.

    2011-01-01

    To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network — the optimal number of levels and modular partition at each level — with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks. PMID:21494658

  18. Nanoclusters first: a hierarchical phase transformation in a novel Mg alloy

    PubMed Central

    Okuda, Hiroshi; Yamasaki, Michiaki; Kawamura, Yoshihito; Tabuchi, Masao; Kimizuka, Hajime

    2015-01-01

    The Mg-Y-Zn ternary alloy system contains a series of novel structures known as long-period stacking ordered (LPSO) structures. The formation process and its key concept from a viewpoint of phase transition are not yet clear. The current study reveals that the phase transformation process is not a traditional spinodal decomposition or structural transformation but, rather a novel hierarchical phase transformation. In this transformation, clustering occurs first, and the spatial rearrangement of the clusters induce a secondary phase transformation that eventually lead to two-dimensional ordering of the clusters. The formation process was examined using in situ synchrotron radiation small-angle X-ray scattering (SAXS). Rapid quenching from liquid alloy into thin ribbons yielded strongly supersaturated amorphous samples. The samples were heated at a constant rate of 10 K/min. and the scattering patterns were acquired. The SAXS analysis indicated that small clusters grew to sizes of 0.2 nm after they crystallized. The clusters distributed randomly in space grew and eventually transformed into a microstructure with two well-defined cluster-cluster distances, one for the segregation periodicity of LPSO and the other for the in-plane ordering in segregated layer. This transformation into the LPSO structure concomitantly introduces the periodical stacking fault required for the 18R structures. PMID:26387813

  19. Hierarchical bioinspired adhesive surfaces-a review.

    PubMed

    Brodoceanu, D; Bauer, C T; Kroner, E; Arzt, E; Kraus, T

    2016-01-01

    The extraordinary adherence and climbing agility of geckos on rough surfaces has been attributed to the multiscale hierarchical structures on their feet. Hundreds of thousands of elastic hairs called setae, each of which split into several spatulae, create a large number of contact points that generate substantial adhesion through van der Waals interactions. The hierarchical architecture provides increased structural compliance on surfaces with roughness features ranging from micrometers to millimeters. We review synthetic adhesion surfaces that mimic the naturally occurring hierarchy with an emphasis on microfabrication strategies, material choice and the adhesive performance achieved. PMID:27529743

  20. Hierarchical Analysis of the Omega Ontology

    SciTech Connect

    Joslyn, Cliff A.; Paulson, Patrick R.

    2009-12-01

    Initial delivery for mathematical analysis of the Omega Ontology. We provide an analysis of the hierarchical structure of a version of the Omega Ontology currently in use within the US Government. After providing an initial statistical analysis of the distribution of all link types in the ontology, we then provide a detailed order theoretical analysis of each of the four main hierarchical links present. This order theoretical analysis includes the distribution of components and their properties, their parent/child and multiple inheritance structure, and the distribution of their vertical ranks.

  1. Hierarchical social networks and information flow

    NASA Astrophysics Data System (ADS)

    López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.

    2002-12-01

    Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.

  2. Interface fluctuations on a hierarchical lattice

    NASA Astrophysics Data System (ADS)

    Iglói, Ferenc; Szalma, Ferenc

    1996-08-01

    We consider interface fluctuations on a two-dimensional layered lattice where the couplings follow a hierarchical sequence. This problem is equivalent to the diffusion process of a quantum particle in the presence of a one-dimensional hierarchical potential. According to a modified Harris criterion, this type of perturbation is relevant and one expects anomalous fluctuating behavior. By transfer-matrix techniques and by an exact renormalization-group transformation we have obtained analytical results for the interface fluctuation exponents, which are discontinuous at the homogeneous lattice limit.

  3. Hierarchical assembly of diphenylalanine into dendritic nanoarchitectures.

    PubMed

    Han, Tae Hee; Oh, Jun Kyun; Lee, Gyoung-Ja; Pyun, Su-Il; Kim, Sang Ouk

    2010-09-01

    Highly ordered, multi-dimensional dendritic nanoarchitectures were created via self-assembly of diphenylalanine from an acidic buffer solution. The self-similarity of dendritic structures was characterized by examining their fractal dimensions with the box-counting method. The fractal dimension was determined to be 1.7, which demonstrates the fractal dimension of structures generated by diffusion limited aggregation on a two-dimensional substrate surface. By confining the dendritic assembly of diphenylalanine within PDMS microchannels, the self-similar dendritic growth could be hierarchically directed to create linearly assembled nanoarchitectures. Our approach offers a novel pathway for creating and directing hierarchical nanoarchitecture from biomolecular assembly. PMID:20605423

  4. Frontoparietal Connectivity and Hierarchical Structure of the Brain’s Functional Network during Sleep

    PubMed Central

    Spoormaker, Victor I.; Gleiser, Pablo M.; Czisch, Michael

    2011-01-01

    Frontal and parietal regions are associated with some of the most complex cognitive functions, and several frontoparietal resting-state networks can be observed in wakefulness. We used functional magnetic resonance imaging data acquired in polysomnographically validated wakefulness, light sleep, and slow-wave sleep to examine the hierarchical structure of a low-frequency functional brain network, and to examine whether frontoparietal connectivity would disintegrate in sleep. Whole-brain analyses with hierarchical cluster analysis on predefined atlases were performed, as well as regression of inferior parietal lobules (IPL) seeds against all voxels in the brain, and an evaluation of the integrity of voxel time-courses in subcortical regions-of-interest. We observed that frontoparietal functional connectivity disintegrated in sleep stage 1 and was absent in deeper sleep stages. Slow-wave sleep was characterized by strong hierarchical clustering of local submodules. Frontoparietal connectivity between IPL and superior medial and right frontal gyrus was lower in sleep stages than in wakefulness. Moreover, thalamus voxels showed maintained integrity in sleep stage 1, making intrathalamic desynchronization an unlikely source of reduced thalamocortical connectivity in this sleep stage. Our data suggest a transition from a globally integrated functional brain network in wakefulness to a disintegrated network consisting of local submodules in slow-wave sleep, in which frontoparietal inter-modular nodes may play a role, possibly in combination with the thalamus. PMID:22629253

  5. Frontoparietal Connectivity and Hierarchical Structure of the Brain's Functional Network during Sleep.

    PubMed

    Spoormaker, Victor I; Gleiser, Pablo M; Czisch, Michael

    2012-01-01

    Frontal and parietal regions are associated with some of the most complex cognitive functions, and several frontoparietal resting-state networks can be observed in wakefulness. We used functional magnetic resonance imaging data acquired in polysomnographically validated wakefulness, light sleep, and slow-wave sleep to examine the hierarchical structure of a low-frequency functional brain network, and to examine whether frontoparietal connectivity would disintegrate in sleep. Whole-brain analyses with hierarchical cluster analysis on predefined atlases were performed, as well as regression of inferior parietal lobules (IPL) seeds against all voxels in the brain, and an evaluation of the integrity of voxel time-courses in subcortical regions-of-interest. We observed that frontoparietal functional connectivity disintegrated in sleep stage 1 and was absent in deeper sleep stages. Slow-wave sleep was characterized by strong hierarchical clustering of local submodules. Frontoparietal connectivity between IPL and superior medial and right frontal gyrus was lower in sleep stages than in wakefulness. Moreover, thalamus voxels showed maintained integrity in sleep stage 1, making intrathalamic desynchronization an unlikely source of reduced thalamocortical connectivity in this sleep stage. Our data suggest a transition from a globally integrated functional brain network in wakefulness to a disintegrated network consisting of local submodules in slow-wave sleep, in which frontoparietal inter-modular nodes may play a role, possibly in combination with the thalamus. PMID:22629253

  6. Investigation of major international and Turkish companies via hierarchical methods and bootstrap approach

    NASA Astrophysics Data System (ADS)

    Kantar, E.; Deviren, B.; Keskin, M.

    2011-11-01

    We present a study, within the scope of econophysics, of the hierarchical structure of 98 among the largest international companies including 18 among the largest Turkish companies, namely Banks, Automobile, Software-hardware, Telecommunication Services, Energy and the Oil-Gas sectors, viewed as a network of interacting companies. We analyze the daily time series data of the Boerse-Frankfurt and Istanbul Stock Exchange. We examine the topological properties among the companies over the period 2006-2010 by using the concept of hierarchical structure methods (the minimal spanning tree (MST) and the hierarchical tree (HT)). The period is divided into three subperiods, namely 2006-2007, 2008 which was the year of global economic crisis, and 2009-2010, in order to test various time-windows and observe temporal evolution. We carry out bootstrap analyses to associate the value of statistical reliability to the links of the MSTs and HTs. We also use average linkage clustering analysis (ALCA) in order to better observe the cluster structure. From these studies, we find that the interactions among the Banks/Energy sectors and the other sectors were reduced after the global economic crisis; hence the effects of the Banks and Energy sectors on the correlations of all companies were decreased. Telecommunication Services were also greatly affected by the crisis. We also observed that the Automobile and Banks sectors, including Turkish companies as well as some companies from the USA, Japan and Germany were strongly correlated with each other in all periods.

  7. X-ray selected AGN in a Merging Cluster Environment

    NASA Astrophysics Data System (ADS)

    Norman, Dara; Coldwell, Georgina; Soechting, Ilona

    2010-08-01

    Although a general understanding of the overall AGN population is being addressed by optical large area surveys, a complete picture of cluster AGN is hampered by survey biases and a lack of information about AGN environments where we can trace cluster interactions. Optically selected AGN are under-represented in cluster environments due to color selection criteria insensitive to red and dusty sources. X-ray selection of AGN can overcome this bias. Hierarchical simulations suggest the gravitational processes which govern the evolution of clusters also have a role in AGN triggering, and AGN feedback is critically important to galaxy evolution. Confirmation of models requires comparisons not just of the cluster galaxy population, but also of the distributions of AGN in clusters especially forming clusters, which are not fully identified in surveys. Weak lensing cluster selection can overcome this bias. We propose to obtain GMOS-S spectra for a sample of 23 X-ray selected , optically faint (20 with R>20.3) AGN cluster candidates in an X-ray cluster discovered by its weak lensing shear signal. This cluster is unique in the large number of 1) X-ray AGN candidates identified in its environment and 2) X-ray sub-clusters identified. We require redshift data to identify AGN cluster members.

  8. Decentralized Cooperative TOA/AOA Target Tracking for Hierarchical Wireless Sensor Networks

    PubMed Central

    Chen, Ying-Chih; Wen, Chih-Yu

    2012-01-01

    This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation. PMID:23202212

  9. 3D Pharmacophore, hierarchical methods, and 5-HT4 receptor binding data.

    PubMed

    Varin, Thibault; Saettel, Nicolas; Villain, Jonathan; Lesnard, Aurelien; Dauphin, François; Bureau, Ronan; Rault, Sylvain

    2008-10-01

    5-Hydroxytryptamine subtype-4 (5-HT(4)) receptors have stimulated considerable interest amongst scientists and clinicians owing to their importance in neurophysiology and potential as therapeutic targets. A comparative analysis of hierarchical methods applied to data from one thousand 5-HT(4) receptor-ligand binding interactions was carried out. The chemical structures were described as chemical and pharmacophore fingerprints. The definitions of indices, related to the quality of the hierarchies in being able to distinguish between active and inactive compounds, revealed two interesting hierarchies with the Unity (1 active cluster) and pharmacophore fingerprints (4 active clusters). The results of this study also showed the importance of correct choice of metrics as well as the effectiveness of a new alternative of the Ward clustering algorithm named Energy (Minimum E-Distance method). In parallel, the relationship between these classifications and a previously defined 3D 5-HT(4) antagonist pharmacophore was established. PMID:18821249

  10. Utilizing Hierarchical Segmentation to Generate Water and Snow Masks to Facilitate Monitoring Change with Remotely Sensed Image Data

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Lawrence, William T.; Plaza, Antonio J.

    2006-01-01

    The hierarchical segmentation (HSEG) algorithm is a hybrid of hierarchical step-wise optimization and constrained spectral clustering that produces a hierarchical set of image segmentations. This segmentation hierarchy organizes image data in a manner that makes the image's information content more accessible for analysis by enabling region-based analysis. This paper discusses data analysis with HSEG and describes several measures of region characteristics that may be useful analyzing segmentation hierarchies for various applications. Segmentation hierarchy analysis for generating landwater and snow/ice masks from MODIS (Moderate Resolution Imaging Spectroradiometer) data was demonstrated and compared with the corresponding MODIS standard products. The masks based on HSEG segmentation hierarchies compare very favorably to the MODIS standard products. Further, the HSEG based landwater mask was specifically tailored to the MODIS data and the HSEG snow/ice mask did not require the setting of a critical threshold as required in the production of the corresponding MODIS standard product.

  11. Progressive Image Coding by Hierarchical Linear Approximation.

    ERIC Educational Resources Information Center

    Wu, Xiaolin; Fang, Yonggang

    1994-01-01

    Proposes a scheme of hierarchical piecewise linear approximation as an adaptive image pyramid. A progressive image coder comes naturally from the proposed image pyramid. The new pyramid is semantically more powerful than regular tessellation but syntactically simpler than free segmentation. This compromise between adaptability and complexity…

  12. Performance Measurement Framework for Hierarchical Text Classification.

    ERIC Educational Resources Information Center

    Sun, Aixin; Lim, Ee-Peng; Ng, Wee-Keong

    2003-01-01

    Discusses hierarchical text classification for electronic information retrieval and the measures used to evaluate performance. Proposes new performance measures that consist of category similarity measures and distance-based measures that consider the contributions of misclassified documents, and explains a blocking measure that identifies…

  13. A Hierarchical Grouping of Great Educators

    ERIC Educational Resources Information Center

    Barker, Donald G.

    1977-01-01

    Great educators of history were categorized on the basis of their: aims of education, fundamental ideas, and educational theories. They were classed by Ward's method of hierarchical analysis into six groupings: Socrates, Ausonius, Jerome, Abelard; Quintilian, Origen, Melanchthon, Ascham, Loyola; Alciun, Comenius; Vittorino, Basedow, Pestalozzi,…

  14. Hierarchical interconnection networks for multicomputer systems

    SciTech Connect

    Dandamudi, S.P. ); Eager, D.L. )

    1990-06-01

    Multicomputer systems are distributed-memory MIMD systems. Communication in these systems occurs through explicit message passing. Therefore, the underlying processor interconnection network plays an important and direct role in determining their performance. Several types f interconnection networks have been proposed. Unfortunately, no network is universally better. Ideally, therefore, systems should use more than one such network. Furthermore, systems that have large numbers of processors should be able to exploit locality in communication in order to obtain improved performance. This paper proposes the use of hierarchical interconnection networks to meet both these requirements. A performance analysis of a class of hierarchical interconnection networks is presented. This analysis includes both static analysis (queuing delays are neglected) and queuing analysis. In both cases, the hierarchical networks are shown to have better cost-benefit ratios. The queuing analysis is also validated (within our model) by several simulation experiments. The impact of two performance enhancement schemes---replication of links and improved routing algorithms---on hierarchical interconnection network performance is also presented.

  15. Parallel temporal dynamics in hierarchical cognitive control.

    PubMed

    Ranti, Carolyn; Chatham, Christopher H; Badre, David

    2015-09-01

    Cognitive control allows us to follow abstract rules in order to choose appropriate responses given our desired outcomes. Cognitive control is often conceptualized as a hierarchical decision process, wherein decisions made at higher, more abstract levels of control asymmetrically influence lower-level decisions. These influences could evolve sequentially across multiple levels of a hierarchical decision, consistent with much prior evidence for central bottlenecks and seriality in decision-making processes. However, here, we show that multiple levels of hierarchical cognitive control are processed primarily in parallel. Human participants selected responses to stimuli using a complex, multiply contingent (third order) rule structure. A response deadline procedure allowed assessment of the accuracy and timing of decisions made at each level of the hierarchy. In contrast to a serial decision process, error rates across levels of the decision mostly declined simultaneously and at identical rates, with only a slight tendency to complete the highest level decision first. Simulations with a biologically plausible neural network model demonstrate how such parallel processing could emerge from a previously developed hierarchically nested frontostriatal architecture. Our results support a parallel processing model of cognitive control, in which uncertainty on multiple levels of a decision is reduced simultaneously. PMID:26051820

  16. A Hierarchical Process-Dissociation Model

    ERIC Educational Resources Information Center

    Rouder, Jeffrey N.; Lu, Jun; Morey, Richard D.; Sun, Dongchu; Speckman, Paul L.

    2008-01-01

    In fitting the process-dissociation model (L. L. Jacoby, 1991) to observed data, researchers aggregate outcomes across participant, items, or both. T. Curran and D. L. Hintzman (1995) demonstrated how biases from aggregation may lead to artifactual support for the model. The authors develop a hierarchical process-dissociation model that does not…

  17. Cognitive Diagnostic Analysis Using Hierarchically Structured Skills

    ERIC Educational Resources Information Center

    Su, Yu-Lan

    2013-01-01

    This dissertation proposes two modified cognitive diagnostic models (CDMs), the deterministic, inputs, noisy, "and" gate with hierarchy (DINA-H) model and the deterministic, inputs, noisy, "or" gate with hierarchy (DINO-H) model. Both models incorporate the hierarchical structures of the cognitive skills in the model estimation…

  18. Transforming Hierarchical Relationships in Student Conduct Administration

    ERIC Educational Resources Information Center

    Jacobson, Kelly A.

    2013-01-01

    Conflict transformation theory provided a philosophical lens for this critical cultural, constructivist study, wherein four student conduct administrators who engage in leveling hierarchical relationships with students in conduct processes shared ways they make meaning of their professional practice. Through informal, unstructured interviews, a…

  19. Metal oxide nanostructures with hierarchical morphology

    DOEpatents

    Ren, Zhifeng; Lao, Jing Yu; Banerjee, Debasish

    2007-11-13

    The present invention relates generally to metal oxide materials with varied symmetrical nanostructure morphologies. In particular, the present invention provides metal oxide materials comprising one or more metallic oxides with three-dimensionally ordered nanostructural morphologies, including hierarchical morphologies. The present invention also provides methods for producing such metal oxide materials.

  20. Types of Online Hierarchical Repository Structures

    ERIC Educational Resources Information Center

    Hershkovitz, Arnon; Azran, Ronit; Hardof-Jaffe, Sharon; Nachmias, Rafi

    2011-01-01

    This study presents an empirical investigation of online hierarchical repositories of items presented to university students in Web-supported course websites, using Web mining methods. To this end, data from 1747 courses were collected, and the use of online repositories of content items in these courses was examined. At a later stage, courses…

  1. Hierarchical Forms Processing in Adults and Children

    ERIC Educational Resources Information Center

    Harrison, Tamara B.; Stiles, Joan

    2009-01-01

    Two experiments examined child and adult processing of hierarchical stimuli composed of geometric forms. Adults (ages 18-23 years) and children (ages 7-10 years) performed a forced-choice task gauging similarity between visual stimuli consisting of large geometric objects (global level) composed of small geometric objects (local level). The…

  2. Rehabilitation Goals: Their Hierarchical and Multifaceted Nature.

    ERIC Educational Resources Information Center

    Livneh, Hanoch

    1988-01-01

    Rehabilitation goals are analyzed from a hierarchical, multifaceted perspective, illustrating reduction of the ultimate goal of life adjustment to smaller goals. Addressed are: the contexts within which rehabilitation takes place, the activity levels defining human performance, and the functional levels achieved. A matrix of 12 sets of goals is…

  3. Multi-Hierarchical Modeling of Driving Behavior Using Dynamics-Based Mode Segmentation

    NASA Astrophysics Data System (ADS)

    Okuda, Hiroyuki; Suzuki, Tatsuya; Nakano, Ato; Inagaki, Shinkichi; Hayakawa, Soichiro

    This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.

  4. Hydrodynamical simulations of realistic massive cluster populations

    NASA Astrophysics Data System (ADS)

    Barnes, David J.; Henson, Monique A.; Kay, Scott T.; McCarthy, Ian G.; Bahe, Yannick M.; Eagle Collaboration

    2015-09-01

    Galaxy clusters are seeded by density fluctuations in the early Universe and grow via hierarchical collapse to become the most massive virialised objects we observed today. They are powerful probes that study both cosmology and astrophysical processes. Their internal structure at the current epoch is the result of a non-trivial interplay between gravitational collapse and the energy fed into the intra-cluster medium (ICM) by star formation and active galactic nuclei (AGN). These processes shape the ICM during its formation at high redshift, but current observations of galaxy clusters are limited to z<0.5. The resolution and sensitivity of textit{Athena+} will allow it to study galaxy clusters in unprecedented detail. It will constrain cluster properties, such as its entropy, temperature and gas fraction, out to z˜2, enabling it to investigate the progenitors of today's massive clusters and observing the evolution of the properties of the ICM for the first time. Athena+ will produce a significant change in our understanding of the formation of galaxy clusters. Recently the theoretical modelling of clusters has advanced significantly and issues, such as the 'cooling catastophea', have been overcome by including feedback from star formation and AGN. We present the MAssive ClusterS and Intercluster Structures (MACSIS) project. The MACSIS project is a representative sample of 390 of galaxy clusters, with M_{FOF} > 10(15} M_{⊙) , re-simulated using the cosmo-OWLS model (Le Brun et al. 2014, McCarthy et al. in prep.) to extend it to the most massive and rarest objects. We demonstrate that this sample reproduces the scaling relations, with intrinsic scatter, observed with current instruments at low redshift. Under the hierarchical paradigm, the progenitors of these systems will be the first objects to collapse at high redshift and we examine to z=2 how the scaling relations of these massive objects evolve with redshift. Finally, we investigate methods of defining a

  5. Clustering under the line graph transformation: application to reaction network

    PubMed Central

    Nacher, Jose C; Ueda, Nobuhisa; Yamada, Takuji; Kanehisa, Minoru; Akutsu, Tatsuya

    2004-01-01

    Background Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. In general, the second network may be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. While the chemical compound network has been classified as hierarchical network, a detailed study of the chemical reaction network had not been carried out. Results We have applied the line graph transformation to a hierarchical network and the degree-dependent clustering coefficient C(k) is calculated for the transformed network. C(k) indicates the probability that two nearest neighbours of a vertex of degree k are connected to each other. While C(k) follows the scaling law C(k) ~ k-1.1 for the initial hierarchical network, C(k) scales weakly as k0.08 for the transformed network. This theoretical prediction was compared with the experimental data of chemical reactions from the KEGG database finding a good agreement. Conclusions The weak scaling found for the transformed network indicates that the reaction network can be identified as a degree-independent clustering network. By using this result, the hierarchical classification of the reaction network is discussed. PMID:15617578

  6. Dynamical Evolution of Open Star Clusters

    NASA Astrophysics Data System (ADS)

    de La Fuente Marcos, Raúl

    1998-09-01

    hierarchical systems is also considered in detail. Some questions concerning multicomponent clusters, such as the preferential evaporation of light stars, are reviewed. The global results are compared with the observational data of actual clusters from The Database for Stars in Open Clusters: BDA1 (J.-C. Mermilliod, in ASP Conf. Ser. 90, The Origins, Evolution, and Destinies of Binary Stars in Clusters, ed. E. F. Milone & J.-C. Mermilliod [San Francisco: ASP, 1996]). The likely observational properties of the final stages of the evolution of open clusters are also investigated, and the results are compared with the available observational data (L. O. Loden, Ap&SS, 199, 165 [1993]; R. P. Stefanik, J. R. Caruso, G. Torres, S. Jha, & D. W. Latham, Baltic Astron., 6, 137 [1997]). It is found that they depend on the membership of the cluster, the abundance of primordial binaries, and the initial mass function. The final cluster remnant is very rich in binaries and hierarchical systems. Remnants of poorly populated clusters are relatively easy to identify because they contain early-type stars. Remnants of rich open clusters are difficult to detect and might exist in large numbers. The detection of rich open clusters' remnants (OCRs) is a great challenge for the largest available telescopes because they contain only faint stars. It is possible that some of the oldest known open clusters are in fact remnants of densely populated (N 40,000) clusters. The existence of a large number of OCRs may be relevant for dark matter in the Galactic disk. This hypothesis could be tested by using the capabilities of the proposed global astrometry mission Global Astrometric Interferometer for Astrophysics (GAIA).

  7. Nanotribological and wetting performance of hierarchical patterns.

    PubMed

    Grewal, H S; Piao, Shuxue; Cho, Il-Joo; Jhang, Kyung-Young; Yoon, Eui-Sung

    2016-01-21

    Surface modification is a promising method to solve the tribological problems in microsystems. To modify the surface, we fabricated hierarchical patterns with different pitches of nano-scale features and different surface chemistries. Micro- and nano-patterns with similar geometrical configurations were also fabricated for comparison. The nano-tribological behavior of the patterns was investigated using an atomic force microscope at different relative humidity levels (5% to 80%) and applied normal loads (40 nN to 120 nN) under a constant sliding velocity. The results showed significant enhancement in the de-wetting and tribological performance of the hierarchical patterns compared with those of flat and micro- and nano-patterned surfaces. The PTFE-coated hierarchical patterns showed similar dynamic contact angles (advancing and receding) to those of the real lotus leaf. The influence of relative humidity on adhesion and friction behavior was found to be significant for all the tested surfaces. The tribological performance was improved as the pitch of the nano-scale geometry of the hierarchical pattern increased, even though the wetting property was not influenced significantly. A model was proposed based on the role of intermolecular force to explain the effect of the pitch of the hierarchical patterns on the adhesion and friction behavior. According to the model based on the molecular force, the contact between a ball and the patterned surface was a multi-asperity contact, contrary to the single-asperity contact predicted by the Johnson-Kendall-Roberts (JKR) and Maugis-Dugdale (MD) models. The strong intermolecular forces, which are activated in the confined spaces between the adjacent nano-pillars and the ball, contributed to the contact area and hence the adhesion and friction forces. PMID:26549103

  8. Inference and Hierarchical Modeling in the Social Sciences.

    ERIC Educational Resources Information Center

    Draper, David

    1995-01-01

    The use of hierarchical models in social science research is discussed, with emphasis on causal inference and consideration of the limitations of hierarchical models. The increased use of Gibbs sampling and other Markov-chain Monte Carlo methods in the application of hierarchical models is recommended. (SLD)

  9. Application of a hierarchical structure stochastic learning automation

    NASA Technical Reports Server (NTRS)

    Neville, R. G.; Chrystall, M. S.; Mars, P.

    1979-01-01

    A hierarchical structure automaton was developed using a two state stochastic learning automato (SLA) in a time shared model. Application of the hierarchical SLA to systems with multidimensional, multimodal performance criteria is described. Results of experiments performed with the hierarchical SLA using a performance index with a superimposed noise component of ? or - delta distributed uniformly over the surface are discussed.

  10. Incorporating Usability Criteria into the Development of Animated Hierarchical Maps

    ERIC Educational Resources Information Center

    Shih, Yu-Cheng; Huang, Pei-Ren; Chen, Sherry Y.

    2013-01-01

    Nowadays, Web-based learning systems have become popular because they can provide multiple tools, among which hierarchical maps are widely used to support teaching and learning. However, traditional hierarchical maps may let learners easily get lost within large information space. This study proposes an animated hierarchical map to address this…

  11. Some Basic Elements in Clustering and Classification

    NASA Astrophysics Data System (ADS)

    Grégoire, G.

    2016-05-01

    This chapter deals with basic tools useful in clustering and classification and present some commonly used approaches for these two problems. Since several chapters in these proceedings are devoted to approaches to deal with classification, we give more attention in this chapter to clustering issues. We are first concerned with notions of distances or dissimilarities between objects we are to group in clusters. Then based on these inter-objects distances we define distances between sets of objects, such as single linkage, complete linkage or Ward distance. Three clustering algorithms are presented with some details and compared: Kmeans, Ascendant Hierarchical and DBSCAN algorithms. The comparison between partitions and the issue of choosing the correct number of clusters are investigated and the proposed procedures are tested on two data sets. We emphasize the fact that the results provided by the numerous indices available in the literature for selecting the number of clusters is largely depending upon the shape and the dispersion we are assuming for these clusters. Finally the last section is devoted to classification. Some basic notions such as training sets, test sets and cross-validation are discussed. Two particular approaches are detailed, the K-nearest neighbors method and the logistic regression, and comparisons with LDA (Linear Discriminant Analysis) and QDA (Quadratic Discriminant Analysis) are analyzed.

  12. The Origin of the Brightest Cluster Galaxies

    NASA Astrophysics Data System (ADS)

    Dubinski, John

    1998-07-01

    Most clusters and groups of galaxies contain a giant elliptical galaxy in their centers that far outshines and outweighs normal ellipticals. The origin of these brightest cluster galaxies is intimately related to the collapse and formation of the cluster. Using an N-body simulation of a cluster of galaxies in a hierarchical cosmological model, we show that galaxy merging naturally produces a massive central galaxy with surface brightness and velocity dispersion profiles similar to those of observed BCGs. To enhance the resolution of the simulation, 100 dark halos at z = 2 are replaced with self-consistent disk + bulge + halo galaxy models following a Tully-Fisher relation using 100,000 particles for the 20 largest galaxies and 10,000 particles for the remaining ones. This technique allows us to analyze the stellar and dark-matter components independently. The central galaxy forms through the merger of several massive galaxies along a filament early in the cluster's history. Galactic cannibalism of smaller galaxies through dynamical friction over a Hubble time only accounts for a small fraction of the accreted mass. The galaxy is a flattened, triaxial object whose long axis aligns with the primordial filament and the long axis of the cluster galaxy distribution, agreeing with observed trends for galaxy cluster alignment.

  13. Methods for analyzing cost effectiveness data from cluster randomized trials

    PubMed Central

    Bachmann, Max O; Fairall, Lara; Clark, Allan; Mugford, Miranda

    2007-01-01

    Background Measurement of individuals' costs and outcomes in randomized trials allows uncertainty about cost effectiveness to be quantified. Uncertainty is expressed as probabilities that an intervention is cost effective, and confidence intervals of incremental cost effectiveness ratios. Randomizing clusters instead of individuals tends to increase uncertainty but such data are often analysed incorrectly in published studies. Methods We used data from a cluster randomized trial to demonstrate five appropriate analytic methods: 1) joint modeling of costs and effects with two-stage non-parametric bootstrap sampling of clusters then individuals, 2) joint modeling of costs and effects with Bayesian hierarchical models and 3) linear regression of net benefits at different willingness to pay levels using a) least squares regression with Huber-White robust adjustment of errors, b) a least squares hierarchical model and c) a Bayesian hierarchical model. Results All five methods produced similar results, with greater uncertainty than if cluster randomization was not accounted for. Conclusion Cost effectiveness analyses alongside cluster randomized trials need to account for study design. Several theoretically coherent methods can be implemented with common statistical software. PMID:17822546

  14. Fuzzy clustering analysis of the first 10 MEIC chemicals.

    PubMed

    Sârbu, C; Pop, H F

    2000-03-01

    In this paper, we discuss the classification results of the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering and a new clustering technique, namely fuzzy hierarchical cross-classification. The characteristics clustering technique produces fuzzy partitions of the characteristics (chemicals) involved and thus it is a useful tool for studying the (dis)similarities between different chemicals and for essential chemicals selection. The cross-classification algorithm produces not only a fuzzy partition of the test systems analyzed, but also a fuzzy partition of the considered 10 MEIC (multicentre evaluation of in vitro cytotoxicity) chemicals. In this way it is possible to identify which chemicals are responsible for the similarities or differences observed between different groups of test systems. In another way, there is a specific sensitivity of a chemical for one or more toxicological tests. PMID:10665388

  15. SVM clustering

    PubMed Central

    Winters-Hilt, Stephen; Merat, Sam

    2007-01-01

    Background Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being considered in a number of different ways. Results An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labelled, this is repeated until an initial convergence occurs. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabelled data) then has its' labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labelled data) and is guaranteed to converge in this situation since it converged previously, and now it has fewer data points to carry with mislabelling penalties. This approach appears to limit exposure to the local minima traps that can occur with other approaches. Thus, the algorithm then improves on its weakly convergent result by SVM re-training after each re-labeling on the worst of the misclassified vectors – i.e., those feature vectors with confidence factor values beyond some threshold. The repetition of the above process improves the accuracy, here a measure of separability, until there are no misclassifications. Variations on this type of clustering approach are shown. Conclusion Non-parametric SVM-based clustering methods may allow for much improved performance over parametric approaches, particularly if they can be designed to inherit the strengths of their supervised SVM counterparts. PMID:18047717

  16. Hierarchical structures of correlations networks among Turkey’s exports and imports by currencies

    NASA Astrophysics Data System (ADS)

    Kocakaplan, Yusuf; Deviren, Bayram; Keskin, Mustafa

    2012-12-01

    We have examined the hierarchical structures of correlations networks among Turkey’s exports and imports by currencies for the 1996-2010 periods, using the concept of a minimal spanning tree (MST) and hierarchical tree (HT) which depend on the concept of ultrametricity. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial markets. We derived a hierarchical organization and build the MSTs and HTs during the 1996-2001 and 2002-2010 periods. The reason for studying two different sub-periods, namely 1996-2001 and 2002-2010, is that the Euro (EUR) came into use in 2001, and some countries have made their exports and imports with Turkey via the EUR since 2002, and in order to test various time-windows and observe temporal evolution. We have carried out bootstrap analysis to associate a value of the statistical reliability to the links of the MSTs and HTs. We have also used the average linkage cluster analysis (ALCA) to observe the cluster structure more clearly. Moreover, we have obtained the bidimensional minimal spanning tree (BMST) due to economic trade being a bidimensional problem. From the structural topologies of these trees, we have identified different clusters of currencies according to their proximity and economic ties. Our results show that some currencies are more important within the network, due to a tighter connection with other currencies. We have also found that the obtained currencies play a key role for Turkey’s exports and imports and have important implications for the design of portfolio and investment strategies.

  17. New resampling method for evaluating stability of clusters

    PubMed Central

    Gana Dresen, Irina M; Boes, Tanja; Huesing, Johannes; Neuhaeuser, Markus; Joeckel, Karl-Heinz

    2008-01-01

    Background Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in clustering procedures. Statistical methods are required to distinguish between real and random clusters. Several methods for assessing cluster stability have been published, including resampling methods such as the bootstrap. We propose a new resampling method based on continuous weights to assess the stability of clusters in hierarchical clustering. While in bootstrapping approximately one third of the original items is lost, continuous weights avoid zero elements and instead allow non integer diagonal elements, which leads to retention of the full dimensionality of space, i.e. each variable of the original data set is represented in the resampling sample. Results Comparison of continuous weights and bootstrapping using real datasets and simulation studies reveals the advantage of continuous weights especially when the dataset has only few observations, few differentially expressed genes and the fold change of differentially expressed genes is low. Conclusion We recommend the use of continuous weights in small as well as in large datasets, because according to our results they produce at least the same results as conventional bootstrapping and in some cases they surpass it. PMID:18218074

  18. Hierarchical curiosity loops and active sensing.

    PubMed

    Gordon, Goren; Ahissar, Ehud

    2012-08-01

    A curious agent acts so as to optimize its learning about itself and its environment, without external supervision. We present a model of hierarchical curiosity loops for such an autonomous active learning agent, whereby each loop selects the optimal action that maximizes the agent's learning of sensory-motor correlations. The model is based on rewarding the learner's prediction errors in an actor-critic reinforcement learning (RL) paradigm. Hierarchy is achieved by utilizing previously learned motor-sensory mapping, which enables the learning of other mappings, thus increasing the extent and diversity of knowledge and skills. We demonstrate the relevance of this architecture to active sensing using the well-studied vibrissae (whiskers) system, where rodents acquire sensory information by virtue of repeated whisker movements. We show that hierarchical curiosity loops starting from optimally learning the internal models of whisker motion and then extending to object localization result in free-air whisking and object palpation, respectively. PMID:22386787

  19. Biomimetic silicification of demineralized hierarchical collagenous tissues

    PubMed Central

    Ryou, Heonjune; Diogenes, Anibal; Yiu, Cynthia K.Y.; Mazzoni, Annalisa; Chen, Ji-hua; Arola, Dwayne D.; Hargreaves, Kenneth M.; Pashley, David H.; Tay, Franklin R.

    2013-01-01

    Unlike man-made composite materials, natural biominerals containing composites usually demonstrate different levels of sophisticated hierarchical structures which are responsible for their mechanical properties and other metabolic functions. However, the complex spatial organizations of the organic-inorganic phases are far beyond what they be achieved by contemporary engineering techniques. Here, we demonstrate that carbonated apatite present in collagen matrices derived from fish scale and bovine bone may be replaced by amorphous silica, using an approach that simulates what is utilized by phylogenetically ancient glass sponges. The structural hierarchy of these collagen-based biomaterials is replicated by the infiltration and condensation of fluidic polymer-stabilized silicic acid precursors within the intrafibrillar milieu of type I collagen fibrils. This facile biomimetic silicification strategy may be used for fabricating silica-based, three-dimensional functional materials with specific morphological and hierarchical requirements. PMID:23586938

  20. An Hierarchical approach to Big Data

    NASA Astrophysics Data System (ADS)

    Allen, Mark G.; Fernique, Pierre

    2015-08-01

    The increasing volumes of astronomical data require practical methods for data access, visualisation and analysis. Hierarchical methods based on sky tessellation techniques enable a multi-resolution approach to astronomy data from the individual pixels up to the whole sky. The Hierarchical Progressive Survey (HiPS) scheme based on the HEALPix is able to describe images, catalogues and 3-dimensional data cubes and is a practical solution for managing large volumes of heterogeneous data. We present the development of HiPS, and its implementation for ~200 diverse data sets at the CDS and other data centres. We highlight the ease of implementation and the use of HiPS with Aladin Lite and other applications.

  1. First-passage phenomena in hierarchical networks

    NASA Astrophysics Data System (ADS)

    Tavani, Flavia; Agliari, Elena

    2016-02-01

    In this paper we study Markov processes and related first-passage problems on a class of weighted, modular graphs which generalize the Dyson hierarchical model. In these networks, the coupling strength between two nodes depends on their distance and is modulated by a parameter σ . We find that, in the thermodynamic limit, ergodicity is lost and the "distant" nodes cannot be reached. Moreover, for finite-sized systems, there exists a threshold value for σ such that, when σ is relatively large, the inhomogeneity of the coupling pattern prevails and "distant" nodes are hardly reached. The same analysis is carried on also for generic hierarchical graphs, where interactions are meant to involve p -plets (p >2 ) of nodes, finding that ergodicity is still broken in the thermodynamic limit, but no threshold value for σ is evidenced, ultimately due to a slow growth of the network diameter with the size.

  2. Non-Homogeneous Fractal Hierarchical Weighted Networks

    PubMed Central

    Dong, Yujuan; Dai, Meifeng; Ye, Dandan

    2015-01-01

    A model of fractal hierarchical structures that share the property of non-homogeneous weighted networks is introduced. These networks can be completely and analytically characterized in terms of the involved parameters, i.e., the size of the original graph Nk and the non-homogeneous weight scaling factors r1, r2, · · · rM. We also study the average weighted shortest path (AWSP), the average degree and the average node strength, taking place on the non-homogeneous hierarchical weighted networks. Moreover the AWSP is scrupulously calculated. We show that the AWSP depends on the number of copies and the sum of all non-homogeneous weight scaling factors in the infinite network order limit. PMID:25849619

  3. Hierarchical networks, power laws, and neuronal avalanches

    NASA Astrophysics Data System (ADS)

    Friedman, Eric J.; Landsberg, Adam S.

    2013-03-01

    We show that in networks with a hierarchical architecture, critical dynamical behaviors can emerge even when the underlying dynamical processes are not critical. This finding provides explicit insight into current studies of the brain's neuronal network showing power-law avalanches in neural recordings, and provides a theoretical justification of recent numerical findings. Our analysis shows how the hierarchical organization of a network can itself lead to power-law distributions of avalanche sizes and durations, scaling laws between anomalous exponents, and universal functions—even in the absence of self-organized criticality or critical points. This hierarchy-induced phenomenon is independent of, though can potentially operate in conjunction with, standard dynamical mechanisms for generating power laws.

  4. Hierarchical porous polymer scaffolds from block copolymers.

    PubMed

    Sai, Hiroaki; Tan, Kwan Wee; Hur, Kahyun; Asenath-Smith, Emily; Hovden, Robert; Jiang, Yi; Riccio, Mark; Muller, David A; Elser, Veit; Estroff, Lara A; Gruner, Sol M; Wiesner, Ulrich

    2013-08-01

    Hierarchical porous polymer materials are of increasing importance because of their potential application in catalysis, separation technology, or bioengineering. Examples for their synthesis exist, but there is a need for a facile yet versatile conceptual approach to such hierarchical scaffolds and quantitative characterization of their nonperiodic pore systems. Here, we introduce a synthesis method combining well-established concepts of macroscale spinodal decomposition and nanoscale block copolymer self-assembly with porosity formation on both length scales via rinsing with protic solvents. We used scanning electron microscopy, small-angle x-ray scattering, transmission electron tomography, and nanoscale x-ray computed tomography for quantitative pore-structure characterization. The method was demonstrated for AB- and ABC-type block copolymers, and resulting materials were used as scaffolds for calcite crystal growth. PMID:23908232

  5. Hierarchical structure of the logical Internet graph

    NASA Astrophysics Data System (ADS)

    Ge, Zihui; Figueiredo, Daniel R.; Jaiswal, Sharad; Gao, Lixin

    2001-07-01

    The study of the Internet topology has recently received much attention from the research community. In particular, the observation that the network graph has interesting properties, such as power laws, that might be explored in a myriad of ways. Most of the work in characterizing the Internet graph is based on the physical network graph, i.e., the connectivity graph. In this paper we investigate how logical relationships between nodes of the AS graph can be used to gain insight to its structure. We characterize the logical graph using various metrics and identify the presence of power laws in the number of customers that a provider has. Using these logical relationships we define a structural model of the AS graph. The model highlights the hierarchical nature of logical relationships and the preferential connection to larger providers. We also investigate the consistency of this model over time and observe interesting properties of the hierarchical structure.

  6. Hierarchical Robot Control In A Multisensor Environment

    NASA Astrophysics Data System (ADS)

    Bhanu, Bir; Thune, Nils; Lee, Jih Kun; Thune, Mari

    1987-03-01

    Automatic recognition, inspection, manipulation and assembly of objects will be a common denominator in most of tomorrow's highly automated factories. These tasks will be handled by intelligent computer controlled robots with multisensor capabilities which contribute to desired flexibility and adaptability. The control of a robot in such a multisensor environment becomes of crucial importance as the complexity of the problem grows exponentially with the number of sensors, tasks, commands and objects. In this paper we present an approach which uses CAD (Computer-Aided Design) based geometric and functional models of objects together with action oriented neuroschemas to recognize and manipulate objects by a robot in a multisensor environment. The hierarchical robot control system is being implemented on a BBN Butterfly multi processor. Index terms: CAD, Hierarchical Control, Hypothesis Generation and Verification, Parallel Processing, Schemas

  7. Noise enhances information transfer in hierarchical networks.

    PubMed

    Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A

    2013-01-01

    We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. PMID:23390574

  8. Noise enhances information transfer in hierarchical networks

    PubMed Central

    Czaplicka, Agnieszka; Holyst, Janusz A.; Sloot, Peter M. A.

    2013-01-01

    We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. PMID:23390574

  9. Hierarchical self-assembly of colloidal magnetic particles into reconfigurable spherical structures

    NASA Astrophysics Data System (ADS)

    Morphew, Daniel; Chakrabarti, Dwaipayan

    2015-04-01

    Colloidal self-assembly has enormous potential as a bottom-up means of structure fabrication. Here we demonstrate hierarchical self-assembly of rationally designed charge-stabilised colloidal magnetic particles into ground state structures that are topologically equivalent to a snub cube and a snub dodecahedron, the only two chiral Archimedean solids, for size-selected clusters. These spherical structures open up in response to an external magnetic field and demonstrate controllable porosity. Such features are critical to their applications as functional materials.

  10. Why Do Only Some Galaxy Clusters Have Cool Cores?

    NASA Astrophysics Data System (ADS)

    Burns, Jack O.; Hallman, Eric J.; Gantner, Brennan; Motl, Patrick M.; Norman, Michael L.

    2008-03-01

    Flux-limited X-ray samples indicate that about half of rich galaxy clusters have cool cores. Why do only some clusters have cool cores while others do not? In this paper, cosmological N-body + Eulerian hydrodynamic simulations, including radiative cooling and heating, are used to address this question as we examine the formation and evolution of cool core (CC) and noncool core (NCC) clusters. These adaptive mesh refinement simulations produce both CC and NCC clusters in the same volume. They have a peak resolution of 15.6 h-1 kpc within a (256 h-1 Mpc)3 box. Our simulations suggest that there are important evolutionary differences between CC clusters and their NCC counterparts. Many of the numerical CC clusters accreted mass more slowly over time and grew enhanced CCs via hierarchical mergers; when late major mergers occurred, the CCs survived the collisions. By contrast, NCC clusters experienced major mergers early in their evolution that destroyed embryonic CCs and produced conditions that prevented CC reformation. As a result, our simulations predict observationally testable distinctions in the properties of CC and NCC beyond the core regions in clusters. In particular, we find differences between CC versus NCC clusters in the shapes of X-ray surface brightness profiles, between the temperatures and hardness ratios beyond the cores, between the distribution of masses, and between their supercluster environs. It also appears that CC clusters are no closer to hydrostatic equilibrium than NCC clusters, an issue important for precision cosmology measurements.

  11. The Effect of Mergers on Galaxy Cluster Mass Estimates

    NASA Astrophysics Data System (ADS)

    Johnson, Ryan E.; Zuhone, John A.; Thorsen, Tessa; Hinds, Andre

    2015-08-01

    At vertices within the filamentary structure that describes the universal matter distribution, clusters of galaxies grow hierarchically through merging with other clusters. As such, the most massive galaxy clusters should have experienced many such mergers in their histories. Though we cannot see them evolve over time, these mergers leave lasting, measurable effects in the cluster galaxies' phase space. By simulating several different galaxy cluster mergers here, we examine how the cluster galaxies kinematics are altered as a result of these mergers. Further, we also examine the effect of our line of sight viewing angle with respect to the merger axis. In projecting the 6-dimensional galaxy phase space onto a 3-dimensional plane, we are able to simulate how these clusters might actually appear to optical redshift surveys. We find that for those optical cluster statistics which are most often used as a proxy for the cluster mass (variants of σv), the uncertainty due to an inprecise or unknown line of sight may alter the derived cluster masses moreso than the kinematic disturbance of the merger itself. Finally, by examining these, and several other clustering statistics, we find that significant events (such as pericentric crossings) are identifiable over a range of merger initial conditions and from many different lines of sight.

  12. Hierarchical Fiber Structures Made by Electrospinning Polymers

    NASA Astrophysics Data System (ADS)

    Reneker, Darrell H.

    2009-03-01

    A filter for water purification that is very thin, with small interstices and high surface area per unit mass, can be made with nanofibers. The mechanical strength of a very thin sheet of nanofibers is not great enough to withstand the pressure drop of the fluid flowing through. If the sheet of nanofibers is made thicker, the strength will increase, but the flow will be reduced to an impractical level. An optimized filter can be made with nanometer scale structures supported on micron scale structures, which are in turn supported on millimeter scale structures. This leads to a durable hierarchical structure to optimize the filtration efficiency with a minimum amount of material. Buckling coils,ootnotetextTao Han, Darrell H Reneker, Alexander L. Yarin, Polymer, Volume 48, issue 20 (September 21, 2007), p. 6064-6076. electrical bending coilsootnotetextDarrell H. Reneker and Alexander L. Yarin, Polymer, Volume 49, Issue 10 (2008) Pages 2387-2425, DOI:10.1016/j.polymer.2008.02.002. Feature Article. and pendulum coilsootnotetextT. Han, D.H. Reneker, A.L. Yarin, Polymer, Volume 49, (2008) Pages 2160-2169, doi:10.1016/jpolymer.2008.01.0487878. spanning dimensions from a few microns to a few centimeters can be collected from a single jet by controlling the position and motion of a collector. Attractive routes to the design and construction of hierarchical structures for filtration are based on nanofibers supported on small coils that are in turn supported on larger coils, which are supported on even larger overlapping coils. ``Such top-down'' hierarchical structures are easy to make by electrospinning. In one example, a thin hierarchical structure was made, with a high surface area and small interstices, having an open area of over 50%, with the thinnest fibers supported at least every 15 microns.

  13. Modular, Hierarchical Learning By Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  14. Learning by imitation: a hierarchical approach.

    PubMed

    Byrne, R W; Russon, A E

    1998-10-01

    To explain social learning without invoking the cognitively complex concept of imitation, many learning mechanisms have been proposed. Borrowing an idea used routinely in cognitive psychology, we argue that most of these alternatives can be subsumed under a single process, priming, in which input increases the activation of stored internal representations. Imitation itself has generally been seen as a "special faculty." This has diverted much research towards the all-or-none question of whether an animal can imitate, with disappointingly inconclusive results. In the great apes, however, voluntary, learned behaviour is organized hierarchically. This means that imitation can occur at various levels, of which we single out two clearly distinct ones: the "action level," a rather detailed and linear specification of sequential acts, and the "program level," a broader description of subroutine structure and the hierarchical layout of a behavioural "program." Program level imitation is a high-level, constructive mechanism, adapted for the efficient learning of complex skills and thus not evident in the simple manipulations used to test for imitation in the laboratory. As examples, we describe the food-preparation techniques of wild mountain gorillas and the imitative behaviour of orangutans undergoing "rehabilitation" to the wild. Representing and manipulating relations between objects seems to be one basic building block in their hierarchical programs. There is evidence that great apes suffer from a stricter capacity limit than humans in the hierarchical depth of planning. We re-interpret some chimpanzee behaviour previously described as "emulation" and suggest that all great apes may be able to imitate at the program level. Action level imitation is seldom observed in great ape skill learning, and may have a largely social role, even in humans. PMID:10097023

  15. Asymptotic analysis of hierarchical martensitic microstructure

    NASA Astrophysics Data System (ADS)

    Cesana, Pierluigi; Porta, Marcel; Lookman, Turab

    2014-12-01

    We consider a hierarchical nested microstructure, which also contains a point of singularity (disclination) at the origin, observed in lead orthovanadate. We show how to exactly compute the energy cost and associated displacement field within linearized elasticity by enforcing geometric compatibility of strains across interfaces of the three-phase mixture of distortions (variants) in the microstructure. We prove that the mechanical deformation is purely elastic and discuss the behavior of the system close to the origin.

  16. Ecobionanocomposites: Hierarchical supramolecular materials incorporating stereocomplexation

    NASA Astrophysics Data System (ADS)

    Dorgan, John

    2015-03-01

    Polylactides (PLAs) are a leading class of renewable plastics with several favorable sustainability metrics. However, for many applications basic PLA has insufficient properties. The combination of nanoscopic filler particles can be combined with the phenomena of stereocomplexation to create a new class of hierarchically structured materials. Recent progress on the development of these novel ecobionanocomposites is discussed. The following grants are deeply appreciated: USDA 2012-33610-19945 and NSF CMMI 1335338.

  17. TRANSIMS and the hierarchical data format

    SciTech Connect

    Bush, B.W.

    1997-06-12

    The Hierarchical Data Format (HDF) is a general-purposed scientific data format developed at the National Center for Supercomputing Applications. It supports metadata, compression, and a variety of data structures (multidimensional arrays, raster images, tables). FORTRAN 77 and ANSI C programming interfaces are available for it and a wide variety of visualization tools read HDF files. The author discusses the features of this file format and its possible uses in TRANSIMS.

  18. Hierarchical optimization for neutron scattering problems

    NASA Astrophysics Data System (ADS)

    Bao, Feng; Archibald, Rick; Bansal, Dipanshu; Delaire, Olivier

    2016-06-01

    We present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

  19. Metal hierarchical patterning by direct nanoimprint lithography

    PubMed Central

    Radha, Boya; Lim, Su Hui; Saifullah, Mohammad S. M.; Kulkarni, Giridhar U.

    2013-01-01

    Three-dimensional hierarchical patterning of metals is of paramount importance in diverse fields involving photonics, controlling surface wettability and wearable electronics. Conventionally, this type of structuring is tedious and usually involves layer-by-layer lithographic patterning. Here, we describe a simple process of direct nanoimprint lithography using palladium benzylthiolate, a versatile metal-organic ink, which not only leads to the formation of hierarchical patterns but also is amenable to layer-by-layer stacking of the metal over large areas. The key to achieving such multi-faceted patterning is hysteretic melting of ink, enabling its shaping. It undergoes transformation to metallic palladium under gentle thermal conditions without affecting the integrity of the hierarchical patterns on micro- as well as nanoscale. A metallic rice leaf structure showing anisotropic wetting behavior and woodpile-like structures were thus fabricated. Furthermore, this method is extendable for transferring imprinted structures to a flexible substrate to make them robust enough to sustain numerous bending cycles. PMID:23446801

  20. Resilient 3D hierarchical architected metamaterials.

    PubMed

    Meza, Lucas R; Zelhofer, Alex J; Clarke, Nigel; Mateos, Arturo J; Kochmann, Dennis M; Greer, Julia R

    2015-09-15

    Hierarchically designed structures with architectural features that span across multiple length scales are found in numerous hard biomaterials, like bone, wood, and glass sponge skeletons, as well as manmade structures, like the Eiffel Tower. It has been hypothesized that their mechanical robustness and damage tolerance stem from sophisticated ordering within the constituents, but the specific role of hierarchy remains to be fully described and understood. We apply the principles of hierarchical design to create structural metamaterials from three material systems: (i) polymer, (ii) hollow ceramic, and (iii) ceramic-polymer composites that are patterned into self-similar unit cells in a fractal-like geometry. In situ nanomechanical experiments revealed (i) a nearly theoretical scaling of structural strength and stiffness with relative density, which outperforms existing nonhierarchical nanolattices; (ii) recoverability, with hollow alumina samples recovering up to 98% of their original height after compression to ≥ 50% strain; (iii) suppression of brittle failure and structural instabilities in hollow ceramic hierarchical nanolattices; and (iv) a range of deformation mechanisms that can be tuned by changing the slenderness ratios of the beams. Additional levels of hierarchy beyond a second order did not increase the strength or stiffness, which suggests the existence of an optimal degree of hierarchy to amplify resilience. We developed a computational model that captures local stress distributions within the nanolattices under compression and explains some of the underlying deformation mechanisms as well as validates the measured effective stiffness to be interpreted as a metamaterial property. PMID:26330605

  1. The traveling salesman problem: a hierarchical model.

    PubMed

    Graham, S M; Joshi, A; Pizlo, Z

    2000-10-01

    Our review of prior literature on spatial information processing in perception, attention, and memory indicates that these cognitive functions involve similar mechanisms based on a hierarchical architecture. The present study extends the application of hierarchical models to the area of problem solving. First, we report results of an experiment in which human subjects were tested on a Euclidean traveling salesman problem (TSP) with 6 to 30 cities. The subject's solutions were either optimal or near-optimal in length and were produced in a time that was, on average, a linear function of the number of cities. Next, the performance of the subjects is compared with that of five representative artificial intelligence and operations research algorithms, that produce approximate solutions for Euclidean problems. None of these algorithms was found to be an adequate psychological model. Finally, we present a new algorithm for solving the TSP, which is based on a hierarchical pyramid architecture. The performance of this new algorithm is quite similar to the performance of the subjects. PMID:11126941

  2. Hierarchical image classification in the bioscience literature.

    PubMed

    Kim, Daehyun; Yu, Hong

    2009-01-01

    Our previous work has shown that images appearing in bioscience articles can be classified into five types: Gel-Image, Image-of-Thing, Graph, Model, and Mix. For this paper, we explored and analyzed features strongly associated with each image type and developed a hierarchical image classification approach for classifying an image into one of the five types. First, we applied texture features to separate images into two groups: 1) a texture group comprising Gel Image, Image-of-Thing, and Mix, and 2) a non-texture group comprising Graph and Model. We then applied entropy, skewness, and uniformity for the first group, and edge difference, uniformity, and smoothness for the second group to classify images into specific types. Our results show that hierarchical image classification accurately divided images into the two groups during the initial classification and that the overall accuracy of the image classification was higher than that of our previous approach. In particular, the recall of hierarchical image classification was greatly improved due to the high accuracy of the initial classification. PMID:20351874

  3. Hierarchically Structured Materials for Lithium Batteries

    SciTech Connect

    Xiao, Jie; Zheng, Jianming; Li, Xiaolin; Shao, Yuyan; Zhang, Jiguang

    2013-09-25

    Lithium-ion battery (LIB) is one of the most promising power sources to be deployed in electric vehicles (EV), including solely battery powered vehicles, plug-in hybrid electric vehicles, and hybrid electrical vehicles. With the increasing demand on devices of high energy densities (>500 Wh/kg) , new energy storage systems, such as lithium-oxygen (Li-O2) batteries and other emerging systems beyond the conventional LIB also attracted worldwide interest for both transportation and grid energy storage applications in recent years. It is well known that the electrochemical performances of these energy storage systems depend not only on the composition of the materials, but also on the structure of electrode materials used in the batteries. Although the desired performances characteristics of batteries often have conflict requirements on the micro/nano-structure of electrodes, hierarchically designed electrodes can be tailored to satisfy these conflict requirements. This work will review hierarchically structured materials that have been successfully used in LIB and Li-O2 batteries. Our goal is to elucidate 1) how to realize the full potential of energy materials through the manipulation of morphologies, and 2) how the hierarchical structure benefits the charge transport, promotes the interfacial properties, prolongs the electrode stability and battery lifetime.

  4. Hierarchically structured materials for lithium batteries

    NASA Astrophysics Data System (ADS)

    Xiao, Jie; Zheng, Jianming; Li, Xiaolin; Shao, Yuyan; Zhang, Ji-Guang

    2013-10-01

    The lithium-ion battery (LIB) is one of the most promising power sources to be deployed in electric vehicles, including solely battery powered vehicles, plug-in hybrid electric vehicles, and hybrid electric vehicles. With the increasing demand for devices of high-energy densities (>500 Wh kg-1), new energy storage systems, such as lithium-oxygen (Li-O2) batteries and other emerging systems beyond the conventional LIB, have attracted worldwide interest for both transportation and grid energy storage applications in recent years. It is well known that the electrochemical performance of these energy storage systems depends not only on the composition of the materials, but also on the structure of the electrode materials used in the batteries. Although the desired performance characteristics of batteries often have conflicting requirements with the micro/nano-structure of electrodes, hierarchically designed electrodes can be tailored to satisfy these conflicting requirements. This work will review hierarchically structured materials that have been successfully used in LIB and Li-O2 batteries. Our goal is to elucidate (1) how to realize the full potential of energy materials through the manipulation of morphologies, and (2) how the hierarchical structure benefits the charge transport, promotes the interfacial properties and prolongs the electrode stability and battery lifetime.

  5. Hierarchically structured materials for lithium batteries.

    PubMed

    Xiao, Jie; Zheng, Jianming; Li, Xiaolin; Shao, Yuyan; Zhang, Ji-Guang

    2013-10-25

    The lithium-ion battery (LIB) is one of the most promising power sources to be deployed in electric vehicles, including solely battery powered vehicles, plug-in hybrid electric vehicles, and hybrid electric vehicles. With the increasing demand for devices of high-energy densities (>500 Wh kg(-1)), new energy storage systems, such as lithium-oxygen (Li-O2) batteries and other emerging systems beyond the conventional LIB, have attracted worldwide interest for both transportation and grid energy storage applications in recent years. It is well known that the electrochemical performance of these energy storage systems depends not only on the composition of the materials, but also on the structure of the electrode materials used in the batteries. Although the desired performance characteristics of batteries often have conflicting requirements with the micro/nano-structure of electrodes, hierarchically designed electrodes can be tailored to satisfy these conflicting requirements. This work will review hierarchically structured materials that have been successfully used in LIB and Li-O2 batteries. Our goal is to elucidate (1) how to realize the full potential of energy materials through the manipulation of morphologies, and (2) how the hierarchical structure benefits the charge transport, promotes the interfacial properties and prolongs the electrode stability and battery lifetime. PMID:24067410

  6. The generality of negative hierarchically restrictive behaviours.

    PubMed

    Brown, Jennie; Trafimow, David; Gregory, W Larry

    2005-03-01

    Previous research has shown that when an actor engages in a negative hierarchically restrictive behaviour, a strong correspondent trait attribution is made and this behaviour is expected to generalize across situations (Trafimow, 2001). This paper discusses three experiments that examined the effects of extreme situations and perceived morality of the actor on the way in which participants make trait attributions, and the extent to which those behaviours are expected to generalize to other situations. Findings from Experiments 1 and 2 indicate that even negative hierarchically restrictive behaviours do not always lead to strong correspondent inferences if the situation in which the initial behaviour was performed was sufficiently extreme. Experiment 3 served to support these findings and cleared up questions from the first two experiments. Findings supported the hypothesis that some situations inhibit negative trait attributions and behaviour generalizations. Furthermore, findings indicate that the perception of the morality of the actor determines whether or not a negative hierarchically restrictive behaviour will lead to a negative trait attribution and generalization. PMID:15901388

  7. [Comparative hierarchic structure of the genetic language].

    PubMed

    Ratner, V A

    1993-05-01

    The genetical texts and genetic language are built according to hierarchic principle and contain no less than 6 levels of coding sequences, separated by marks of punctuation, separation and indication: codons, cistrons, scriptons, replicons, linkage groups, genomes. Each level has all the attributes of the language. This hierarchic system expresses some general properties and regularities. The rules of genetic language being determined, the variability of genetical texts is generated by block-modular combinatorics on each level. Between levels there are some intermediate sublevels and module types capable of being combined. The genetic language is compared with two different independent linguistic systems: human natural languages and artificial programming languages. Genetic language is a natural one by its origin, but it is a typical technical language of the functioning genetic regulatory system--by its predestination. All three linguistic systems under comparison have evident similarity of the organization principles and hierarchical structures. This argues for similarity of their principles of appearance and evolution. PMID:8335232

  8. Hierarchical Bayesian model updating for structural identification

    NASA Astrophysics Data System (ADS)

    Behmanesh, Iman; Moaveni, Babak; Lombaert, Geert; Papadimitriou, Costas

    2015-12-01

    A new probabilistic finite element (FE) model updating technique based on Hierarchical Bayesian modeling is proposed for identification of civil structural systems under changing ambient/environmental conditions. The performance of the proposed technique is investigated for (1) uncertainty quantification of model updating parameters, and (2) probabilistic damage identification of the structural systems. Accurate estimation of the uncertainty in modeling parameters such as mass or stiffness is a challenging task. Several Bayesian model updating frameworks have been proposed in the literature that can successfully provide the "parameter estimation uncertainty" of model parameters with the assumption that there is no underlying inherent variability in the updating parameters. However, this assumption may not be valid for civil structures where structural mass and stiffness have inherent variability due to different sources of uncertainty such as changing ambient temperature, temperature gradient, wind speed, and traffic loads. Hierarchical Bayesian model updating is capable of predicting the overall uncertainty/variability of updating parameters by assuming time-variability of the underlying linear system. A general solution based on Gibbs Sampler is proposed to estimate the joint probability distributions of the updating parameters. The performance of the proposed Hierarchical approach is evaluated numerically for uncertainty quantification and damage identification of a 3-story shear building model. Effects of modeling errors and incomplete modal data are considered in the numerical study.

  9. Hierarchical models of animal abundance and occurrence

    USGS Publications Warehouse

    Royle, J. Andrew; Dorazio, R.M.

    2006-01-01

    Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

  10. Resilient 3D hierarchical architected metamaterials

    PubMed Central

    Meza, Lucas R.; Zelhofer, Alex J.; Clarke, Nigel; Mateos, Arturo J.; Kochmann, Dennis M.; Greer, Julia R.

    2015-01-01

    Hierarchically designed structures with architectural features that span across multiple length scales are found in numerous hard biomaterials, like bone, wood, and glass sponge skeletons, as well as manmade structures, like the Eiffel Tower. It has been hypothesized that their mechanical robustness and damage tolerance stem from sophisticated ordering within the constituents, but the specific role of hierarchy remains to be fully described and understood. We apply the principles of hierarchical design to create structural metamaterials from three material systems: (i) polymer, (ii) hollow ceramic, and (iii) ceramic–polymer composites that are patterned into self-similar unit cells in a fractal-like geometry. In situ nanomechanical experiments revealed (i) a nearly theoretical scaling of structural strength and stiffness with relative density, which outperforms existing nonhierarchical nanolattices; (ii) recoverability, with hollow alumina samples recovering up to 98% of their original height after compression to ≥50% strain; (iii) suppression of brittle failure and structural instabilities in hollow ceramic hierarchical nanolattices; and (iv) a range of deformation mechanisms that can be tuned by changing the slenderness ratios of the beams. Additional levels of hierarchy beyond a second order did not increase the strength or stiffness, which suggests the existence of an optimal degree of hierarchy to amplify resilience. We developed a computational model that captures local stress distributions within the nanolattices under compression and explains some of the underlying deformation mechanisms as well as validates the measured effective stiffness to be interpreted as a metamaterial property. PMID:26330605

  11. Hierarchical microimaging of bone structure and function.

    PubMed

    Müller, Ralph

    2009-07-01

    With recent advances in molecular medicine and disease treatment in osteoporosis, quantitative image processing of three-dimensional bone structures is critical in the context of bone quality assessment. Biomedical imaging technology such as MRI or CT is readily available, but few attempts have been made to expand the capabilities of these systems by integrating quantitative analysis tools and by exploring structure-function relationships in a hierarchical fashion. Nevertheless, such quantitative end points are an important factor for success in basic research and in the development of novel therapeutic strategies. CT is key to these developments, as it images and quantifies bone in three dimensions and provides multiscale biological imaging capabilities with isotropic resolutions of a few millimeters (clinical CT), a few tens of micrometers (microCT) and even as high as 100 nanometers (nanoCT). The technology enables the assessment of the relationship between microstructural and ultrastructural measures of bone quality and certain diseases or therapies. This Review focuses on presenting strategies for three-dimensional approaches to hierarchical biomechanical imaging in the study of microstructural and ultrastructural bone failure. From this Review, it can be concluded that biomechanical imaging is extremely valuable for the study of bone failure mechanisms at different hierarchical levels. PMID:19568252

  12. A Technique of Two-Stage Clustering Applied to Environmental and Civil Engineering and Related Methods of Citation Analysis.

    ERIC Educational Resources Information Center

    Miyamoto, S.; Nakayama, K.

    1983-01-01

    A method of two-stage clustering of literature based on citation frequency is applied to 5,065 articles from 57 journals in environmental and civil engineering. Results of related methods of citation analysis (hierarchical graph, clustering of journals, multidimensional scaling) applied to same set of articles are compared. Ten references are…

  13. Spontaneous Motion in Hierarchically Assembled Active Cellular Materials

    NASA Astrophysics Data System (ADS)

    Chen, Daniel

    2013-03-01

    With exquisite precision and reproducibility, cells orchestrate the cooperative action of thousands of nanometer-sized molecular motors to carry out mechanical tasks at much larger length scales, such as cell motility, division and replication. Besides their biological importance, such inherently far-from-equilibrium processes are an inspiration for the development of soft materials with highly sought after biomimetic properties such as autonomous motility and self-healing. I will describe our exploration of such a class of biologically inspired soft active materials. Starting from extensile bundles comprised of microtubules and kinesin, we hierarchically assemble active analogs of polymeric gels, liquid crystals and emulsions. At high enough concentration, microtubule bundles form an active gel network capable of generating internally driven chaotic flows that enhance transport and fluid mixing. When confined to emulsion droplets, these 3D networks buckle onto the water-oil interface forming a dense thin film of bundles exhibiting cascades of collective buckling, fracture, and self-healing driven by internally generated stresses from the kinesin clusters. When compressed against surfaces, this active nematic cortex exerts traction stresses that propel the locomotion of the droplet. Taken together, these observations exemplify how assemblies of animate microscopic objects exhibit collective biomimetic properties that are fundamentally distinct from those found in materials assembled from inanimate building blocks. These assemblies, in turn, enable the generation of a new class of materials that exhibit macroscale flow phenomena emerging from nanoscale components.

  14. A Bayesian, exemplar-based approach to hierarchical shape matching.

    PubMed

    Gavrila, Dariu M

    2007-08-01

    This paper presents a novel probabilistic approach to hierarchical, exemplar-based shape matching. No feature correspondence is needed among exemplars, just a suitable pairwise similarity measure. The approach uses a template tree to efficiently represent and match the variety of shape exemplars. The tree is generated offline by a bottom-up clustering approach using stochastic optimization. Online matching involves a simultaneous coarse-to-fine approach over the template tree and over the transformation parameters. The main contribution of this paper is a Bayesian model to estimate the a posteriori probability of the object class, after a certain match at a node of the tree. This model takes into account object scale and saliency and allows for a principled setting of the matching thresholds such that unpromising paths in the tree traversal process are eliminated early on. The proposed approach was tested in a variety of application domains. Here, results are presented on one of the more challenging domains: real-time pedestrian detection from a moving vehicle. A significant speed-up is obtained when comparing the proposed probabilistic matching approach with a manually tuned nonprobabilistic variant, both utilizing the same template tree structure. PMID:17568144

  15. Complex networks with scale-free nature and hierarchical modularity

    NASA Astrophysics Data System (ADS)

    Shekatkar, Snehal M.; Ambika, G.

    2015-09-01

    Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

  16. Failure tolerance of load-bearing hierarchical networks

    NASA Astrophysics Data System (ADS)

    Kachhvah, Ajay Deep; Gupte, Neelima

    2011-03-01

    We investigate the statistics and dynamics of failure in a two-dimensional load-bearing network with branching hierarchical structure, and its variants. The variants strengthen the original lattice by using connectivity strategies which add new sites to the maximal cluster in top-to-bottom or bottom-to-top versions. We study the load-bearing capacity and the failure tolerance of all versions, as well as that of the strongest realization of the original lattice, the V lattice. The average number of failures as a function of the test load shows power-law behavior with power 5/2 for the V lattice, but sigmoidal behavior for all other versions. Thus the V lattice turns out to be the critical case of the load-bearing lattices. The distribution of failures is Gaussian for the original lattice, the V lattice, and the bottom-to-top strategy, but is non-Gaussian for the top-to-bottom one. The bottom-to-top strategy leads to stable and strong lattices, and can resist failure even when tested with weights which greatly exceed the capacity of its backbone. We also examine the behavior of asymmetric lattices and discover that the mean failure rates are minimized if the probability of connection p is symmetric with respect to both neighbors. Our results can be of relevance in the context of realistic networks.

  17. Hierarchical structure and physicochemical properties of plasticized chitosan.

    PubMed

    Meng, Qingkai; Heuzey, Marie-Claude; Carreau, Pierre J

    2014-04-14

    Plasticized chitosan with hierarchical structure, including multiple length scale structural units, was prepared by a "melt"-based method, that is, thermomechanical mixing, as opposed to the usual casting-evaporation procedure. Chitosan was successfully plasticized by thermomechanical mixing in the presence of concentrated lactic acid and glycerol using a batch mixer. Different plasticization formulations were compared in this study, in which concentrated lactic acid was used as protonation agent as well as plasticizer. The microstructure of thermomechanically plasticized chitosan was investigated by X-ray diffraction, scanning electron microscopy, and optical microscopy. With increasing amount of additional plasticizers (glycerol or water), the crystallinity of the plasticized chitosan decreased from 63.7% for the original chitosan powder to almost zero for the sample plasticized with additional water. Salt linkage between lactic acid molecules and amino side chains of chitosan was confirmed by FTIR spectroscopy: the lactic acid molecules expanded the space between the chitosan molecules of the crystalline phase. In the presence of other plasticizers (glycerol and water), various levels of structural units including an amorphous phase, nanofibrils, nanofibril clusters, and microfibers were produced under mechanical shear and thermal energy and identified for the first time. The thermal and thermomechanical properties of the plasticized chitosan were measured by thermogravimetric analysis, differential scanning calorimetric, and DMA. These properties were correlated with the different levels of microstructure, including multiple structural units. PMID:24564751

  18. Cheetah: A Framework for Scalable Hierarchical Collective Operations

    SciTech Connect

    Graham, Richard L; Gorentla Venkata, Manjunath; Ladd, Joshua S; Shamis, Pavel; Rabinovitz, Ishai; Filipov, Vasily; Shainer, Gilad

    2011-01-01

    Collective communication operations, used by many scientific applications, tend to limit overall parallel application performance and scalability. Computer systems are becoming more heterogeneous with increasing node and core-per-node counts. Also, a growing number of data-access mechanisms, of varying characteristics, are supported within a single computer system. We describe a new hierarchical collective communication framework that takes advantage of hardware-specific data-access mechanisms. It is flexible, with run-time hierarchy specification, and sharing of collective communication primitives between collective algorithms. Data buffers are shared between levels in the hierarchy reducing collective communication management overhead. We have implemented several versions of the Message Passing Interface (MPI) collective operations, MPI Barrier() and MPI Bcast(), and run experiments using up to 49, 152 processes on a Cray XT5, and a small InfiniBand based cluster. At 49, 152 processes our barrier implementation outperforms the optimized native implementation by 75%. 32 Byte and one Mega-Byte broadcasts outperform it by 62% and 11%, respectively, with better scalability characteristics. Improvements relative to the default Open MPI implementation are much larger.

  19. The globular cluster system of NGC 5128

    NASA Astrophysics Data System (ADS)

    Woodley, Kristin Anne

    2010-11-01

    17 +/- 14 km s-1 around no clearly defined axis and its dynamics are dominated by dispersion. The metal-rich globular cluster system has a mild rotation of 41 +/- 15 km s-1 about the galaxy's isophotal major axis, following the rotation of a representative field star population, the planetary nebulae. The motion of the metal-rich globular cluster system is also dominated by random motion. We estimate the mass of the galaxy to be (5.5 +/- 1.9) x 1011 M⊙ with a mass-to-light ratio of 15.35 M⊙/LB⊙ using the globular cluster population out to 20'. This estimate places NGC 5128 on a mass scale similar to other giant elliptical galaxies. The evidence collected suggests that NGC 5128 formed in a hierarchical scenario, gradually building up larger structure from smaller protogalaxies at early times in the history of the Universe. The group environment of NGC 5128 may have prolonged star formation within the galaxy as well, enabling a small spread in the old ages of globular clusters and also slowing the formation timescales compared to globular clusters in other giant elliptical galaxies. Results from this thesis also support more recent accretions in the history of NGC 5128, building up the more metal-rich and young globular clusters, which have a different rotation axis than the rest of the population.

  20. Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

    NASA Astrophysics Data System (ADS)

    He, Li'ao; Wang, Qianqian; Zhao, Yu; Liu, Li; Peng, Zhong

    2016-06-01

    Supervised learning methods (eg. PLS-DA, SVM, etc.) have been widely used with laser-induced breakdown spectroscopy (LIBS) to classify materials; however, it may induce a low correct classification rate if a test sample type is not included in the training dataset. Unsupervised cluster analysis methods (hierarchical clustering analysis, K-means clustering analysis, and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper. The results of hierarchical clustering analysis using four different similarity measuring methods (single linkage, complete linkage, unweighted pair-group average, and weighted pair-group average) are compared. In K-means clustering analysis, four kinds of choosing initial centers methods are applied in our case and their results are compared. The classification results of hierarchical clustering analysis, K-means clustering analysis, and ISODATA are analyzed. The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS. supported by Beijing Natural Science Foundation of China (No. 4132063)

  1. Clusters of Galaxies as a Probe of the Cosmic Density

    NASA Astrophysics Data System (ADS)

    Richstone, Douglas

    1994-05-01

    We focus on the influence of cosmological model on the process of formation of clusters of galaxies. Richstone, Loeb and Turner (1992 ApJ 393, 477) have shown that under the assumptions of hierarchical formation and a Gaussian random field of perturbations, the rate at which matter is currently being added to the most massive virialized structures is a strong function of Omega_0 , and suggested that the observed frequency of substructure in clusters might be a probe of Omega . Evrard, Mohr, Fabricant and Geller (1993 ApJ Letters 419, L9) have shown that it is possible to compare SPH simulations of clusters to X-ray images of clusters using a test measuring the skewness of the image, to explore this effect. We report on calculations done in collaboration with Crone and Evrard, which explore the cosmological dependence of the cluster density profile and various tests of substructure in N-body simulations.

  2. Application of cluster analysis to aerometric data (journal version)

    SciTech Connect

    Crutcher, H.L.; Rhodes, R.C.; Graves, M.E.; Fairbairn, B.; Nelson, A.C.

    1986-01-01

    The NORMIX data-analysis program, which incorporates cluster-analysis and multivariate statistical-analysis routines, was modified and revised for use in a UNIVAC 1110 computer. The revised program was tested on three sample data sets and produced results in agreement with those from the original program. The NORMIX program was then used to evaluate and analyze eight sets of aerometric data from various sources. Comparison of the performance of NORMIX with two other cluster analysis algorithms, MIKCA and SAS CLUSTER, revealed that all three programs produce similar results in terms of hierarchical clustering, but NORMIX produces considerably more statistical evaluation and information to the user. Thus NORMIX is recommended as the most useful cluster analysis program of these three.

  3. A systematic comparison of genome-scale clustering algorithms

    PubMed Central

    2012-01-01

    Background A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each cluster's agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further

  4. Cluster fusion-fission dynamics in the Singapore stock exchange

    NASA Astrophysics Data System (ADS)

    Teh, Boon Kin; Cheong, Siew Ann

    2015-10-01

    In this paper, we investigate how the cross-correlations between stocks in the Singapore stock exchange (SGX) evolve over 2008 and 2009 within overlapping one-month time windows. In particular, we examine how these cross-correlations change before, during, and after the Sep-Oct 2008 Lehman Brothers Crisis. To do this, we extend the complete-linkage hierarchical clustering algorithm, to obtain robust clusters of stocks with stronger intracluster correlations, and weaker intercluster correlations. After we identify the robust clusters in all time windows, we visualize how these change in the form of a fusion-fission diagram. Such a diagram depicts graphically how the cluster sizes evolve, the exchange of stocks between clusters, as well as how strongly the clusters mix. From the fusion-fission diagram, we see a giant cluster growing and disintegrating in the SGX, up till the Lehman Brothers Crisis in September 2008 and the market crashes of October 2008. After the Lehman Brothers Crisis, clusters in the SGX remain small for few months before giant clusters emerge once again. In the aftermath of the crisis, we also find strong mixing of component stocks between clusters. As a result, the correlation between initially strongly-correlated pairs of stocks decay exponentially with average life time of about a month. These observations impact strongly how portfolios and trading strategies should be formulated.

  5. Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

    NASA Technical Reports Server (NTRS)

    Alexander, June; Corwin, Edward; Lloyd, David; Logar, Antonette; Welch, Ronald

    1996-01-01

    This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from

  6. Star Formation in Galaxy Clusters Over the Past 10 Billion Years

    NASA Astrophysics Data System (ADS)

    Tran, Kim-Vy

    2012-01-01

    Galaxy clusters are the largest gravitationally bound systems in the universe and include the most massive galaxies in the universe; this makes galaxy clusters ideal laboratories for disentangling the nature versus nurture aspect of how galaxies evolve. Understanding how galaxies form and evolve in clusters continues to be a fundamental question in astronomy. The ages and assembly histories of galaxies in rich clusters test both stellar population models and hierarchical formation scenarios. Is star formation in cluster galaxies simply accelerated relative to their counterparts in the lower density field, or do cluster galaxies assemble their stars in a fundamentally different manner? To answer this question, I review multi-wavelength results on star formation in galaxy clusters from Coma to the most distant clusters yet discovered at look-back times of 10 billion years (z 2).

  7. Identification of asthma clusters in two independent Korean adult asthma cohorts.

    PubMed

    Kim, Tae-Bum; Jang, An-Soo; Kwon, Hyouk-Soo; Park, Jong-Sook; Chang, Yoon-Seok; Cho, Sang-Heon; Choi, Byoung Whui; Park, Jung-Won; Nam, Dong-Ho; Yoon, Ho-Joo; Cho, Young-Joo; Moon, Hee-Bom; Cho, You Sook; Park, Choon-Sik

    2013-06-01

    Asthma is a heterogeneous airway disease with various clinical phenotypes. It is crucial to clearly identify clinical phenotypes to achieve better asthma management. We used cluster analysis to classify the clinical groups of 724 asthmatic patients from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA), and in 1843 subjects from another independent Korean asthma cohort of Soonchunhyang University Asthma Genome Research Centre (SCH) (Bucheon, Republic of Korea). Hierarchical cluster analysis was performed by Ward's method, followed by κ-means cluster analysis. Cluster analysis of the COREA cohort indicated four asthma subtypes: 1) smoking asthma; 2) severe obstructive asthma; 3) early-onset atopic asthma; and 4) late-onset mild asthma. An independent cluster analysis of the SCH cohort also indicated four clusters that were similar to the COREA clusters. Our results indicate that adult Korean asthma patients can be classified into four distinct clusters. PMID:23060627

  8. The relationship between carbon dioxide emission and economic growth: Hierarchical structure methods

    NASA Astrophysics Data System (ADS)

    Deviren, Seyma Akkaya; Deviren, Bayram

    2016-06-01

    Carbon dioxide (CO2) emission has an essential role in the current debate on sustainable development and environmental protection. CO2 emission is also directly linked with use of energy which plays a focal role both for production and consumption in the world economy. Therefore the relationship between the CO2 emission and economic growth has a significant implication for the environmental and economical policies. In this study, within the scope of sociophysics, the topology, taxonomy and relationships among the 33 countries, which have almost the high CO2 emission and economic growth values, are investigated by using the hierarchical structure methods, such as the minimal spanning tree (MST) and hierarchical tree (HT), over the period of 1970-2010. The average linkage cluster analysis (ALCA) is also used to examine the cluster structure more clearly in HTs. According to their proximity, economic ties and economic growth, different clusters of countries are identified from the structural topologies of these trees. We have found that the high income & OECD countries are closely connected to each other and are isolated from the upper middle and lower middle income countries from the MSTs, which are obtained both for the CO2 emission and economic growth. Moreover, the high income & OECD clusters are homogeneous with respect to the economic activities and economic ties of the countries. It is also mentioned that the Group of Seven (G7) countries (CAN, ENG, FRA, GER, ITA, JPN, USA) are connected to each other and these countries are located at the center of the MST for the results of CO2 emission. The same analysis may also successfully apply to the other environmental sources and different countries.

  9. Evaluating δ(15)N-body size relationships across taxonomic levels using hierarchical models.

    PubMed

    Reum, Jonathan C P; Marshall, Kristin N

    2013-12-01

    Ecologists routinely set out to estimate the trophic position of individuals, populations, and species composing food webs, and nitrogen stable isotopes (δ(15)N) are a widely used proxy for trophic position. Although δ(15)N values are often sampled at the level of individuals, estimates and confidence intervals are frequently sought for aggregations of individuals. If individual δ(15)N values are correlated as an artifact of sampling design (e.g., clustering of samples in space or time) or due to intrinsic groupings (e.g., life history stages, social groups, taxonomy), such estimates may be biased and exhibit overly optimistic confidence intervals. However, these issues can be accommodated using hierarchical modeling methods. Here, we demonstrate how hierarchical models offer an additional quantitative tool for investigating δ(15)N variability and we explicitly evaluate how δ(15)N varies with body size at successively higher levels of taxonomic aggregation in a diverse fish assemblage. The models take advantage of all available data, better account for uncertainty in parameters estimates, may improve inferences on coefficients corresponding to groups with small to moderate sample sizes, and partition variation across model levels, which provides convenient summaries of the 'importance' of each level in terms of unexplained heterogeneity in the data. These methods can easily be applied to diet-based studies of trophic position. Although hierarchical models are well-understood and established tools, their benefits have yet to be fully reaped by stable isotope and food web ecologists. We suggest that hierarchical models can provide a robust framework for conceptualizing and statistically modeling trophic position at multiple levels of aggregation. PMID:23812110

  10. Exploiting context in mammograms: a hierarchical neural network for detecting microcalcifications

    NASA Astrophysics Data System (ADS)

    Sajda, Paul; Spence, Clay D.; Pearson, John C.; Nishikawa, Robert M.

    1996-04-01

    Microcalcifications are important cues used by radiologists for early detection in breast cancer. Individually, microcalcifications are difficult to detect, and often contextual information (e.g. clustering, location relative to ducts) can be exploited to aid in their detection. We have developed an algorithm for constructing a hierarchical pyramid/neural network (HPNN) architecture to automatically learn context information for detection. To test the HPNN we first examined if the hierarchical architecture improves detection of individual microcalcifications and if context is in fact extracted by the network hierarchy. We compared the performance of our hierarchical architecture versus a single neural network receiving input from all resolutions of a feature pyramid. Receiver operator characteristic (ROC) analysis shows that the hierarchical architecture reduces false positives by a factor of two. We examined hidden units at various levels of the processing hierarchy and found what appears to be representations of ductal location. We next investigated the utility of the HPNN if integrated as part of a complete computer-aided diagnosis (CAD) system for microcalcification detection, such as that being developed at the University of Chicago. Using ROC analysis, we tested the HPNN's ability to eliminate false positive regions of interest generated by the computer, comparing its performance to the neural network currently used in the Chicago system. The HPNN achieves an area under the ROC curve of Az equal to .94 and a false positive fraction of FPF equal to .21 at TPF equals 1.0. This is in comparison to the results reported for the Chicago network; Az equal to .91, FPF equal to .43 at TPF equal to 1.0. These differences are statistically significant. We conclude that the HPNN algorithm is able to utilize contextual information for improving microcalcifications detection and potentially reduce the false positive rates in CAD systems.

  11. Structural analysis of hierarchically organized zeolites

    NASA Astrophysics Data System (ADS)

    Mitchell, Sharon; Pinar, Ana B.; Kenvin, Jeffrey; Crivelli, Paolo; Kärger, Jörg; Pérez-Ramírez, Javier

    2015-10-01

    Advances in materials synthesis bring about many opportunities for technological applications, but are often accompanied by unprecedented complexity. This is clearly illustrated by the case of hierarchically organized zeolite catalysts, a class of crystalline microporous solids that has been revolutionized by the engineering of multilevel pore architectures, which combine unique chemical functionality with efficient molecular transport. Three key attributes, the crystal, the pore and the active site structure, can be expected to dominate the design process. This review examines the adequacy of the palette of techniques applied to characterize these distinguishing features and their catalytic impact.

  12. Structural analysis of hierarchically organized zeolites.

    PubMed

    Mitchell, Sharon; Pinar, Ana B; Kenvin, Jeffrey; Crivelli, Paolo; Kärger, Jörg; Pérez-Ramírez, Javier

    2015-01-01

    Advances in materials synthesis bring about many opportunities for technological applications, but are often accompanied by unprecedented complexity. This is clearly illustrated by the case of hierarchically organized zeolite catalysts, a class of crystalline microporous solids that has been revolutionized by the engineering of multilevel pore architectures, which combine unique chemical functionality with efficient molecular transport. Three key attributes, the crystal, the pore and the active site structure, can be expected to dominate the design process. This review examines the adequacy of the palette of techniques applied to characterize these distinguishing features and their catalytic impact. PMID:26482337

  13. Hierarchical modelling of mobile, seeing robots

    NASA Technical Reports Server (NTRS)

    Luh, Cheng-Jye; Zeigler, Bernard P.

    1990-01-01

    This paper describes the implementation of a hierarchical robot simulation which supports the design of robots with vision and mobility. A seeing robot applies a classification expert system for visual identification of laboratory objects. The visual data acquisition algorithm used by the robot vision system has been developed to exploit multiple viewing distances and perspectives. Several different simulations have been run testing the visual logic in a laboratory environment. Much work remains to integrate the vision system with the rest of the robot system.

  14. Hierarchical nucleus segmentation in digital pathology images

    PubMed Central

    Gao, Yi; Ratner, Vadim; Zhu, Liangjia; Diprima, Tammy; Kurc, Tahsin; Tannenbaum, Allen; Saltz, Joel

    2016-01-01

    Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set. PMID:27375315

  15. Modified Recursive Hierarchical Segmentation of Data

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2006-01-01

    An algorithm and a computer program that implements the algorithm that performs recursive hierarchical segmentation (RHSEG) of data have been developed. While the current implementation is for two-dimensional data having spatial characteristics (e.g., image, spectral, or spectral-image data), the generalized algorithm also applies to three-dimensional or higher dimensional data and also to data with no spatial characteristics. The algorithm and software are modified versions of a prior RHSEG algorithm and software, the outputs of which often contain processing-window artifacts including, for example, spurious segmentation-image regions along the boundaries of processing-window edges.

  16. Flow and transport in hierarchically fractured systems

    SciTech Connect

    Karasaki, K.

    1993-01-01

    Preliminary results indicate that flow in the saturated zone at Yucca Mountain is controlled by fractures. A current conceptual model assumes that the flow in the fracture system can be approximately by a three-dimensionally interconnected network of linear conduits. The overall flow system of rocks at Yucca Mountain is considered to consist of hierarchically structured heterogeneous fracture systems of multiple scales. A case study suggests that it is more appropriate to use the flow parameters of the large fracture system for predicting the first arrival time, rather than using the bulk average parameters of the total system.

  17. Flow and transport in hierarchically fractured systems

    SciTech Connect

    Karasaki, K.

    1993-12-31

    Preliminary results indicate that flow in the saturated zone at Yucca Mountain is controlled by fractures. A current conceptual model assumes that the flow in the fracture system can be approximated by a three-dimensionally interconnected network of linear conduits. The overall flow system of rocks at Yucca Mountain is considered to consist of hierarchically structured heterogeneous fracture systems of multiple scales. A case study suggests that it is more appropriate to use the flow parameters of the large fracture system for predicting the first arrival time, rather than using the bulk average parameters of the total system.

  18. Structural analysis of hierarchically organized zeolites

    PubMed Central

    Mitchell, Sharon; Pinar, Ana B.; Kenvin, Jeffrey; Crivelli, Paolo; Kärger, Jörg; Pérez-Ramírez, Javier

    2015-01-01

    Advances in materials synthesis bring about many opportunities for technological applications, but are often accompanied by unprecedented complexity. This is clearly illustrated by the case of hierarchically organized zeolite catalysts, a class of crystalline microporous solids that has been revolutionized by the engineering of multilevel pore architectures, which combine unique chemical functionality with efficient molecular transport. Three key attributes, the crystal, the pore and the active site structure, can be expected to dominate the design process. This review examines the adequacy of the palette of techniques applied to characterize these distinguishing features and their catalytic impact. PMID:26482337

  19. CARTILAGE CELL CLUSTERS

    PubMed Central

    Lotz, Martin K.; Otsuki, Shuhei; Grogan, Shawn P.; Sah, Robert; Terkeltaub, Robert; D’Lima, Darryl

    2010-01-01

    The formation of new cell clusters is a histological hallmark of arthritic cartilage but the biology of clusters and their role in disease are poorly understood. This is the first comprehensive review of clinical and experimental conditions associated with cluster formation. Genes and proteins that are expressed in cluster cells, the cellular origin of the clusters, mechanisms that lead to cluster formation and the role of cluster cells in pathogenesis are discussed. PMID:20506158

  20. Spatio-Temporal Clustering of Monitoring Network

    NASA Astrophysics Data System (ADS)

    Hussain, I.; Pilz, J.

    2009-04-01

    Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters

  1. Prevalence of and Epidemic Spreading on Hierarchical Networks

    NASA Astrophysics Data System (ADS)

    He, Jiankui; Deem, Michael

    2009-03-01

    Recent studies show that real networks are organized in a modular or even hierarchical fashion. However, there is no clear mathematical definition of hierarchy and current studies do not tell us the degree to which a network is hierarchical. In this talk, we will discuss a quantitative measurement of hierarchy. We find that networks of protein interactions, metabolic pathways, electronic circuits, power grids, and emails display strong hierarchy compared with networks generated at random or scale free networks of the Barab'asi-Albert model. Further, we investigated the spread of virus in hierarchical networks. Viral spread on hierarchical networks displays quite different pattern from scale free and random networks.

  2. Fractal image perception provides novel insights into hierarchical cognition.

    PubMed

    Martins, M J; Fischmeister, F P; Puig-Waldmüller, E; Oh, J; Geissler, A; Robinson, S; Fitch, W T; Beisteiner, R

    2014-08-01

    Hierarchical structures play a central role in many aspects of human cognition, prominently including both language and music. In this study we addressed hierarchy in the visual domain, using a novel paradigm based on fractal images. Fractals are self-similar patterns generated by repeating the same simple rule at multiple hierarchical levels. Our hypothesis was that the brain uses different resources for processing hierarchies depending on whether it applies a "fractal" or a "non-fractal" cognitive strategy. We analyzed the neural circuits activated by these complex hierarchical patterns in an event-related fMRI study of 40 healthy subjects. Brain activation was compared across three different tasks: a similarity task, and two hierarchical tasks in which subjects were asked to recognize the repetition of a rule operating transformations either within an existing hierarchical level, or generating new hierarchical levels. Similar hierarchical images were generated by both rules and target images were identical. We found that when processing visual hierarchies, engagement in both hierarchical tasks activated the visual dorsal stream (occipito-parietal cortex, intraparietal sulcus and dorsolateral prefrontal cortex). In addition, the level-generating task specifically activated circuits related to the integration of spatial and categorical information, and with the integration of items in contexts (posterior cingulate cortex, retrosplenial cortex, and medial, ventral and anterior regions of temporal cortex). These findings provide interesting new clues about the cognitive mechanisms involved in the generation of new hierarchical levels as required for fractals. PMID:24699014

  3. A study of hierarchical structure on South China industrial electricity-consumption correlation

    NASA Astrophysics Data System (ADS)

    Yao, Can-Zhong; Lin, Ji-Nan; Liu, Xiao-Feng

    2016-02-01

    Based on industrial electricity-consumption data of five southern provinces of China from 2005 to 2013, we study the industrial correlation mechanism with MST (minimal spanning tree) and HT (hierarchical tree) models. First, we comparatively analyze the industrial electricity-consumption correlation structure in pre-crisis and after-crisis period using MST model and Bootstrap technique of statistical reliability test of links. Results exhibit that all industrial electricity-consumption trees of five southern provinces of China in pre-crisis and after-crisis time are in formation of chain, and the "center-periphery structure" of those chain-like trees is consistent with industrial specialization in classical industrial chain theory. Additionally, the industrial structure of some provinces is reorganized and transferred in pre-crisis and after-crisis time. Further, the comparative analysis with hierarchical tree and Bootstrap technique demonstrates that as for both observations of GD and overall NF, the industrial electricity-consumption correlation is non-significant clustered in pre-crisis period, whereas it turns significant clustered in after-crisis time. Therefore we propose that in perspective of electricity-consumption, their industrial structures are directed to optimized organization and global correlation. Finally, the analysis of distance of HTs verifies that industrial reorganization and development may strengthen market integration, coordination and correlation of industrial production. Except GZ, other four provinces have a shorter distance of industrial electricity-consumption correlation in after-crisis period, revealing a better performance of regional specialization and integration.

  4. Implementation of system intelligence in a 3-tier telemedicine/PACS hierarchical storage management system

    NASA Astrophysics Data System (ADS)

    Chao, Woodrew; Ho, Bruce K. T.; Chao, John T.; Sadri, Reza M.; Huang, Lu J.; Taira, Ricky K.

    1995-05-01

    Our tele-medicine/PACS archive system is based on a three-tier distributed hierarchical architecture, including magnetic disk farms, optical jukebox, and tape jukebox sub-systems. The hierarchical storage management (HSM) architecture, built around a low cost high performance platform [personal computers (PC) and Microsoft Windows NT], presents a very scaleable and distributed solution ideal for meeting the needs of client/server environments such as tele-medicine, tele-radiology, and PACS. These image based systems typically require storage capacities mirroring those of film based technology (multi-terabyte with 10+ years storage) and patient data retrieval times at near on-line performance as demanded by radiologists. With the scaleable architecture, storage requirements can be easily configured to meet the needs of the small clinic (multi-gigabyte) to those of a major hospital (multi-terabyte). The patient data retrieval performance requirement was achieved by employing system intelligence to manage migration and caching of archived data. Relevant information from HIS/RIS triggers prefetching of data whenever possible based on simple rules. System intelligence embedded in the migration manger allows the clustering of patient data onto a single tape during data migration from optical to tape medium. Clustering of patient data on the same tape eliminates multiple tape loading and associated seek time during patient data retrieval. Optimal tape performance can then be achieved by utilizing the tape drives high performance data streaming capabilities thereby reducing typical data retrieval delays associated with streaming tape devices.

  5. Self-similarity, small-world, scale-free scaling, disassortativity, and robustness in hierarchical lattices

    NASA Astrophysics Data System (ADS)

    Zhang, Z.-Z.; Zhou, S.-G.; Zou, T.

    2007-04-01

    In this paper, firstly, we study analytically the topological features of a family of hierarchical lattices (HLs) from the view point of complex networks. We derive some basic properties of HLs controlled by a parameter q: scale-free degree distribution with exponent γ=2+ln 2/(ln q), null clustering coefficient, power-law behavior of grid coefficient, exponential growth of average path length (non-small-world), fractal scaling with dimension dB=ln (2q)/(ln 2), and disassortativity. Our results show that scale-free networks are not always small-world, and support the conjecture that self-similar scale-free networks are not assortative. Secondly, we define a deterministic family of graphs called small-world hierarchical lattices (SWHLs). Our construction preserves the structure of hierarchical lattices, including its degree distribution, fractal architecture, clustering coefficient, while the small-world phenomenon arises. Finally, the dynamical processes of intentional attacks and collective synchronization are studied and the comparisons between HLs and Barabási-Albert (BA) networks as well as SWHLs are shown. We find that the self-similar property of HLs and SWHLs significantly increases the robustness of such networks against targeted damage on hubs, as compared to the very vulnerable non fractal BA networks, and that HLs have poorer synchronizability than their counterparts SWHLs and BA networks. We show that degree distribution of scale-free networks does not suffice to characterize their synchronizability, and that networks with smaller average path length are not always easier to synchronize.

  6. Cluster analysis of spontaneous preterm birth phenotypes identifies potential associations among preterm birth mechanisms

    PubMed Central

    Esplin, M Sean; Manuck, Tracy A.; Varner, Michael W.; Christensen, Bryce; Biggio, Joseph; Bukowski, Radek; Parry, Samuel; Zhang, Heping; Huang, Hao; Andrews, William; Saade, George; Sadovsky, Yoel; Reddy, Uma M.; Ilekis, John

    2015-01-01

    Objective We sought to employ an innovative tool based on common biological pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB), in order to enhance investigators' ability to identify to highlight common mechanisms and underlying genetic factors responsible for SPTB. Study Design A secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks gestation. Each woman was assessed for the presence of underlying SPTB etiologies. A hierarchical cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis using VEGAS software. Results 1028 women with SPTB were assigned phenotypes. Hierarchical clustering of the phenotypes revealed five major clusters. Cluster 1 (N=445) was characterized by maternal stress, cluster 2 (N=294) by premature membrane rupture, cluster 3 (N=120) by familial factors, and cluster 4 (N=63) by maternal comorbidities. Cluster 5 (N=106) was multifactorial, characterized by infection (INF), decidual hemorrhage (DH) and placental dysfunction (PD). These three phenotypes were highly correlated by Chi-square analysis [PD and DH (p<2.2e-6); PD and INF (p=6.2e-10); INF and DH (p=0.0036)]. Gene-based testing identified the INS (insulin) gene as significantly associated with cluster 3 of SPTB. Conclusion We identified 5 major clusters of SPTB based on a phenotype tool and hierarchal clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors underlying SPTB. PMID:26070700

  7. Multiple Manifold Clustering Using Curvature Constrained Path

    PubMed Central

    Babaeian, Amir; Bayestehtashk, Alireza; Bandarabadi, Mojtaba

    2015-01-01

    The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering. PMID:26375819

  8. Cluster Analysis for CTBT Seismic Event Monitoring

    SciTech Connect

    Carr, Dorthe B.; Young, Chris J.; Aster, Richard C.; Zhang, Xioabing

    1999-08-03

    Mines at regional distances are expected to be continuing sources of small, ambiguous events which must be correctly identified as part of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) monitoring process. Many of these events are small enough that they are only seen by one or two stations, so locating them by traditional methods maybe impossible or at best leads to poorly resolved parameters. To further complicate matters, these events have parametric characteristics (explosive sources, shallow depths) which make them difficult to identify as definite non-nuclear events using traditional discrimination methods. Fortunately, explosions from the same mines tend to have similar waveforms, making it possible to identify an unknown event by comparison with characteristic archived events that have been associated with specific mines. In this study we examine the use of hierarchical cluster methods to identify groups of similar events. These methods produce dendrograms, which are tree-like structures showing the relationships between entities. Hierarchical methods are well-suited to use for event clustering because they are well documented, easy to implement, computationally cheap enough to run multiple times for a given data set, and because these methods produce results which can be readily interpreted. To aid in determining the proper threshold value for defining event families for a given dendrogram, we use cophenetic correlation (which compares a model of the similarity behavior to actual behavior), variance, and a new metric developed for this study. Clustering methods are compared using archived regional and local distance mining blasts recorded at two sites in the western U.S. with different tectonic and instrumentation characteristics: the three-component broadband DSVS station in Pinedale, Wyoming and the short period New Mexico Tech (NMT) network in central New Mexico. Ground truth for the events comes from the mining industry and local network locations

  9. Efficient scalable algorithms for hierarchically semiseparable matrices

    SciTech Connect

    Wang, Shen; Xia, Jianlin; Situ, Yingchong; Hoop, Maarten V. de

    2011-09-14

    Hierarchically semiseparable (HSS) matrix algorithms are emerging techniques in constructing the superfast direct solvers for both dense and sparse linear systems. Here, we develope a set of novel parallel algorithms for the key HSS operations that are used for solving large linear systems. These include the parallel rank-revealing QR factorization, the HSS constructions with hierarchical compression, the ULV HSS factorization, and the HSS solutions. The HSS tree based parallelism is fully exploited at the coarse level. The BLACS and ScaLAPACK libraries are used to facilitate the parallel dense kernel operations at the ne-grained level. We have appplied our new parallel HSS-embedded multifrontal solver to the anisotropic Helmholtz equations for seismic imaging, and were able to solve a linear system with 6.4 billion unknowns using 4096 processors, in about 20 minutes. The classical multifrontal solver simply failed due to high demand of memory. To our knowledge, this is the first successful demonstration of employing the HSS algorithms in solving the truly large-scale real-world problems. Our parallel strategies can be easily adapted to the parallelization of the other rank structured methods.

  10. A hierarchical exact accelerated stochastic simulation algorithm

    PubMed Central

    Orendorff, David; Mjolsness, Eric

    2012-01-01

    A new algorithm, “HiER-leap” (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled “blocks” and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms. PMID:23231214

  11. Hierarchical majorana neutrinos from democratic mass matrices

    NASA Astrophysics Data System (ADS)

    Yang, Masaki J. S.

    2016-09-01

    In this paper, we obtain the light neutrino masses and mixings consistent with the experiments, in the democratic texture approach. The essential ansatz is that νRi are assumed to transform as "right-handed fields" 2R +1R under the S3L ×S3R symmetry. The symmetry breaking terms are assumed to be diagonal and hierarchical. This setup only allows the normal hierarchy of the neutrino mass, and excludes both of inverted hierarchical and degenerated neutrinos. Although the neutrino sector has nine free parameters, several predictions are obtained at the leading order. When we neglect the smallest parameters ζν and ζR, all components of the mixing matrix UPMNS are expressed by the masses of light neutrinos and charged leptons. From the consistency between predicted and observed UPMNS, we obtain the lightest neutrino masses m1 = (1.1 → 1.4) meV, and the effective mass for the double beta decay ≃ 4.5 meV.

  12. A Hierarchical Bayesian Model for Crowd Emotions

    PubMed Central

    Urizar, Oscar J.; Baig, Mirza S.; Barakova, Emilia I.; Regazzoni, Carlo S.; Marcenaro, Lucio; Rauterberg, Matthias

    2016-01-01

    Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds. PMID:27458366

  13. Hierarchical approaches to analysis of natural textures

    NASA Astrophysics Data System (ADS)

    Lutsiv, Vadim R.; Malyshev, Igor A.; Novikova, Tatiana A.

    2004-09-01

    The surface textures of natural objects often have the visible fractal-like properties. A similar pattern of texture could be found looking at the forests in the aerial photographs or at the trees in the outdoor scenes when the image spatial resolution was changed. Or the texture patterns are different at different spatial resolution levels in the aerial photographs of villages. It creates the difficulties in image segmentation and object recognition because the levels of spatial resolution necessary to get the homogeneously and correctly labeled texture regions differ for different types of landscape. E.g. if the spatial resolution was sufficient for distinguishing between the textures of agricultural fields, water, and asphalt, the texture labeled areas of forest or suburbs are hardly fragmented, because the texture peculiarities corresponding to two stable levels of texture spatial resolution will be visible in this case. A hierarchical texture analysis could solve this problem, and we did it in two different ways: we performed the texture segmentation simultaneously for several levels of image spatial resolution, or we subjected the texture labeled image of highest spatial resolution to a recurring texture segmentation using the texture cells of larger sizes. The both approaches turned out to be rather fruitful for the aerial photographs as well as for the outdoor images. They generalize and support the hierarchical image analysis technique presented in another our paper. Some of the methods applied were borrowed from the living vision systems.

  14. A neural network with modular hierarchical learning

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F. (Inventor); Toomarian, Nikzad (Inventor)

    1994-01-01

    This invention provides a new hierarchical approach for supervised neural learning of time dependent trajectories. The modular hierarchical methodology leads to architectures which are more structured than fully interconnected networks. The networks utilize a general feedforward flow of information and sparse recurrent connections to achieve dynamic effects. The advantages include the sparsity of units and connections, the modular organization. A further advantage is that the learning is much more circumscribed learning than in fully interconnected systems. The present invention is embodied by a neural network including a plurality of neural modules each having a pre-established performance capability wherein each neural module has an output outputting present results of the performance capability and an input for changing the present results of the performance capabilitiy. For pattern recognition applications, the performance capability may be an oscillation capability producing a repeating wave pattern as the present results. In the preferred embodiment, each of the plurality of neural modules includes a pre-established capability portion and a performance adjustment portion connected to control the pre-established capability portion.

  15. Hierarchically structured activated carbon for ultracapacitors

    NASA Astrophysics Data System (ADS)

    Kim, Mok-Hwa; Kim, Kwang-Bum; Park, Sun-Min; Roh, Kwang Chul

    2016-02-01

    To resolve the pore-associated bottleneck problem observed in the electrode materials used for ultracapacitors, which inhibits the transport of the electrolyte ions, we designed hierarchically structured activated carbon (HAC) by synthesizing a mesoporous silica template/carbon composite and chemically activating it to simultaneously remove the silica template and increase the pore volume. The resulting HAC had a well-designed, unique porous structure, which allowed for large interfaces for efficient electric double-layer formation. Given the unique characteristics of the HAC, we believe that the developed synthesis strategy provides important insights into the design and fabrication of hierarchical carbon nanostructures. The HAC, which had a specific surface area of 1,957 m2 g-1, exhibited an extremely high specific capacitance of 157 F g-1 (95 F cc-1), as well as a high rate capability. This indicated that it had superior energy storage capability and was thus suitable for use in advanced ultracapacitors.

  16. Hierarchical structures in a turbulent pipe flow

    NASA Astrophysics Data System (ADS)

    She, Zhen-Su; Zou, Zhengping; Zhu, Yuanjie; Zhou, Mingde

    2003-11-01

    Statistical structures of a series of longitudinal velocity fluctuation signals at different distances (10hierarchical structural (HS) model. The so-called beta-test and gamma-test are designed to study the correlation between intermittent structures of different intensities and the similarity property of the most intense structures. It is shown that at all locations the velocity fluctuations satisfy the She-Leveque hierarchical symmetry (She and Leveque, 1994). The measured HS parameters, beta and gamma, are interpreted in a fluid structure dynamics context. For instance, intense anisotropic fluid structures generated near the wall show smaller gamma and beta. As turbulence migrates into the logarithmic region, small-scale motions are generated by an energy cascade and large-scale organized structures emerge with a less singular character than the most intermittent structures of isotropic turbulence. At the center, turbulence is nearly isotropic, and beta and gamma are close to the 1994 She-Leveque predictions. A transition is observed from the logarithmic region to the center in which gamma drops and the large-scale organized structures break down. We speculate that it is due to the growing eddy viscosity effects of widely spread turbulent fluctuations. Similar effects are observed in the breakdown of the Taylor vortices in a turbulent Couette-Taylor flow at moderately high Reynolds numbers.

  17. Layer like porous materials with hierarchical structure.

    PubMed

    Roth, Wieslaw J; Gil, Barbara; Makowski, Wacław; Marszalek, Bartosz; Eliášová, Pavla

    2016-06-13

    Many chemical compositions produce layered solids consisting of extended sheets with thickness not greater than a few nanometers. The layers are weakly bonded together in a crystal and can be modified into various nanoarchitectures including porous hierarchical structures. Several classes of 2-dimensional (2D) materials have been extensively studied and developed because of their potential usefulness as catalysts and sorbents. They are discussed in this review with focus on clays, layered transition metal oxides, silicates, layered double hydroxides, metal(iv) phosphates and phosphonates, especially zirconium, and zeolites. Pillaring and delamination are the primary methods for structural modification and pore tailoring. The reported approaches are described and compared for the different classes of materials. The methods of characterization include identification by X-ray diffraction and microscopy, pore size analysis and activity assessment by IR spectroscopy and catalytic testing. The discovery of layered zeolites was a fundamental breakthrough that created unprecedented opportunities because of (i) inherent strong acid sites that make them very active catalytically, (ii) porosity through the layers and (iii) bridging of 2D and 3D structures. Approximately 16 different types of layered zeolite structures and modifications have been identified as distinct forms. It is also expected that many among the over 200 recognized zeolite frameworks can produce layered precursors. Additional advances enabled by 2D zeolites include synthesis of layered materials by design, hierarchical structures obtained by direct synthesis and top-down preparation of layered materials from 3D frameworks. PMID:26489452

  18. A self-defining hierarchical data system

    NASA Technical Reports Server (NTRS)

    Bailey, J.

    1992-01-01

    The Self-Defining Data System (SDS) is a system which allows the creation of self-defining hierarchical data structures in a form which allows the data to be moved between different machine architectures. Because the structures are self-defining they can be used for communication between independent modules in a distributed system. Unlike disk-based hierarchical data systems such as Starlink's HDS, SDS works entirely in memory and is very fast. Data structures are created and manipulated as internal dynamic structures in memory managed by SDS itself. A structure may then be exported into a caller supplied memory buffer in a defined external format. This structure can be written as a file or sent as a message to another machine. It remains static in structure until it is reimported into SDS. SDS is written in portable C and has been run on a number of different machine architectures. Structures are portable between machines with SDS looking after conversion of byte order, floating point format, and alignment. A Fortran callable version is also available for some machines.

  19. Oracle Database DBFS Hierarchical Storage Overview

    SciTech Connect

    Rivenes, A

    2011-07-25

    The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory creates large numbers of images during each shot cycle for the analysis of optics, target inspection and target diagnostics. These images must be readily accessible once they are created and available for the 30 year lifetime of the facility. The Livermore Computing Center (LC) runs a High Performance Storage System (HPSS) that is capable of storing NIF's estimated 1 petabyte of diagnostic images at a fraction of what it would cost NIF to operate its own automated tape library. With Oracle 11g Release 2 database, it is now possible to create an application transparent, hierarchical storage system using the LC's HPSS. Using the Oracle DBMS-LOB and DBMS-DBFS-HS packages a SecureFile LOB can now be archived to storage outside of the database and accessed seamlessly through a DBFS 'link'. NIF has chosen to use this technology to implement a hierarchical store for its image based SecureFile LOBs. Using a modified external store and DBFS links, files are written to and read from a disk 'staging area' using Oracle's backup utility. Database external procedure calls invoke OS based scripts to manage a staging area and the transfer of the backup files between the staging area and the Lab's HPSS.

  20. Trabecular bone as a hierarchical material

    NASA Astrophysics Data System (ADS)

    Jasiuk, Iwona

    2004-03-01

    Trabecular bone is studied as a hierarchical material. The analysis includes the experimental characterization of bone's structure, the measurement of its mechanical properties, and the mechanics modeling at several different length scales: nanoscale (under 1 micron, crystal/fiber level), sub-microscale (1-10 microns, single lamella level), microscale (10-500 microns, single trabecula level), and mesoscale (1 mm - 10 cm, trabecular structure, random network of struts or plates). Experiments include the characterization of bone's ultrastructure using SEM (scanning electron microscopy) and TEM (transmission electron microscopy) at nano and sub-microscale levels. In addition, we use the x-ray microtomography, a nondestructive technique, which can provide the three-dimensional details of bone at mesostructural level. Measurements of mechanical properties are done using the MTS machine and nanoindentation apparatus. We use the MTS testing machine to determine constitutive relations of bone at mesoscale and the nanoindentation technique to determine the properties at lower scales. The experimental observations of bone's hierarchical structure are used in the theoretical analysis of bone's mechanical properties. The calculated results are compared with the experimentally measured ones. The material properties are determined at each scale both analytically (using micromechanics theories) and numerically (using a finite element method, a spring network, and beam network approaches). The computational challenges include a complex irregular, random structure at each level, spatial heterogeneity of bone's structure, applicability of separation of scales law, size of the representative volume element, and in general the dependence of properties on specimen size and boundary conditions.