Science.gov

Sample records for agglomerative hierarchical cluster

  1. Implementing Agglomerative Hierarchic Clustering Algorithms for Use in Document Retrieval.

    ERIC Educational Resources Information Center

    Voorhees, Ellen M.

    1986-01-01

    Describes a computerized information retrieval system that uses three agglomerative hierarchic clustering algorithms--single link, complete link, and group average link--and explains their implementations. It is noted that these implementations have been used to cluster a collection of 12,000 documents. (LRW)

  2. Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

    NASA Astrophysics Data System (ADS)

    Munandar, T. A.; Azhari; Mushdholifah, A.; Arsyad, L.

    2017-03-01

    Disparities in regional development methods are commonly identified using the Klassen Typology and Location Quotient. Both methods typically use the data on the gross regional domestic product (GRDP) sectors of a particular region. The Klassen approach can identify regional disparities by classifying the GRDP sector data into four classes, namely Quadrants I, II, III, and IV. Each quadrant indicates a certain level of regional disparities based on the GRDP sector value of the said region. Meanwhile, the Location Quotient (LQ) is usually used to identify potential sectors in a particular region so as to determine which sectors are potential and which ones are not potential. LQ classifies each sector into three classes namely, the basic sector, the non-basic sector with a competitive advantage, and the non-basic sector which can only meet its own necessities. Both Klassen Typology and LQ are unable to visualize the relationship of achievements in the development clearly of each region and sector. This research aimed to develop a new approach to the identification of disparities in regional development in the form of hierarchical clustering. The method of Hierarchical Agglomerative Clustering (HAC) was employed as the basis of the hierarchical clustering model for identifying disparities in regional development. Modifications were made to HAC using the Klassen Typology and LQ. Then, HAC which had been modified using the Klassen Typology was called MHACK while HAC which had been modified using LQ was called MACLoQ. Both algorithms can be used to identify regional disparities (MHACK) and potential sectors (MACLoQ), respectively, in the form of hierarchical clusters. Based on the MHACK in 31 regencies in Central Java Province, it is identified that 3 regencies (Demak, Jepara, and Magelang City) fall into the category of developed and rapidly-growing regions, while the other 28 regencies fall into the category of developed but depressed regions. Results of the MACLo

  3. Combining analytical hierarchy process and agglomerative hierarchical clustering in search of expert consensus in green corridors development management.

    PubMed

    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.

  4. Classifying airborne radiometry data with Agglomerative Hierarchical Clustering: A tool for geological mapping in context of rainforest (French Guiana)

    NASA Astrophysics Data System (ADS)

    Martelet, G.; Truffert, C.; Tourlière, B.; Ledru, P.; Perrin, J.

    2006-09-01

    In highly weathered environments, it is crucial that geological maps provide information concerning both the regolith and the bedrock, for societal needs, such as land-use, mineral or water resources management. Often, geologists are facing the challenge of upgrading existing maps, as relevant information concerning weathering processes and pedogenesis is currently missing. In rugged areas in particular, where access to the field is difficult, ground observations are sparsely available, and need therefore to be complemented using methods based on remotely sensed data. For this purpose, we discuss the use of Agglomerative Hierarchical Clustering (AHC) on eU, K and eTh airborne gamma-ray spectrometry grids. The AHC process allows primarily to segment the geophysical maps into zones having coherent U, K and Th contents. The analysis of these contents are discussed in terms of geochemical signature for lithological attribution of classes, as well as the use of a dendrogram, which gives indications on the hierarchical relations between classes. Unsupervised classification maps resulting from AHC can be considered as spatial models of the distribution of the radioelement content in surface and sub-surface formations. The source of gamma rays emanating from the ground is primarily related to the geochemistry of the bedrock and secondarily to modifications of the radioelement distribution by weathering and other secondary mechanisms, such as mobilisation by wind or water. The interpretation of the obtained predictive classified maps, their U, K, Th contents, and the dendrogram, in light of available geological knowledge, allows to separate signatures related to regolith and solid geology. Consequently, classification maps can be integrated within a GIS environment and used by the geologist as a support for mapping bedrock lithologies and their alteration. We illustrate the AHC classification method in the region of Cayenne using high-resolution airborne radiometric data

  5. Hierarchical clustering in minimum spanning trees.

    PubMed

    Yu, Meichen; Hillebrand, Arjan; Tewarie, Prejaas; Meier, Jil; van Dijk, Bob; Van Mieghem, Piet; Stam, Cornelis Jan

    2015-02-01

    The identification of clusters or communities in complex networks is a reappearing problem. The minimum spanning tree (MST), the tree connecting all nodes with minimum total weight, is regarded as an important transport backbone of the original weighted graph. We hypothesize that the clustering of the MST reveals insight in the hierarchical structure of weighted graphs. However, existing theories and algorithms have difficulties to define and identify clusters in trees. Here, we first define clustering in trees and then propose a tree agglomerative hierarchical clustering (TAHC) method for the detection of clusters in MSTs. We then demonstrate that the TAHC method can detect clusters in artificial trees, and also in MSTs of weighted social networks, for which the clusters are in agreement with the previously reported clusters of the original weighted networks. Our results therefore not only indicate that clusters can be found in MSTs, but also that the MSTs contain information about the underlying clusters of the original weighted network.

  6. Hierarchical clustering in minimum spanning trees

    NASA Astrophysics Data System (ADS)

    Yu, Meichen; Hillebrand, Arjan; Tewarie, Prejaas; Meier, Jil; van Dijk, Bob; Van Mieghem, Piet; Stam, Cornelis Jan

    2015-02-01

    The identification of clusters or communities in complex networks is a reappearing problem. The minimum spanning tree (MST), the tree connecting all nodes with minimum total weight, is regarded as an important transport backbone of the original weighted graph. We hypothesize that the clustering of the MST reveals insight in the hierarchical structure of weighted graphs. However, existing theories and algorithms have difficulties to define and identify clusters in trees. Here, we first define clustering in trees and then propose a tree agglomerative hierarchical clustering (TAHC) method for the detection of clusters in MSTs. We then demonstrate that the TAHC method can detect clusters in artificial trees, and also in MSTs of weighted social networks, for which the clusters are in agreement with the previously reported clusters of the original weighted networks. Our results therefore not only indicate that clusters can be found in MSTs, but also that the MSTs contain information about the underlying clusters of the original weighted network.

  7. 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.

  8. Ultrametric Hierarchical Clustering Algorithms.

    ERIC Educational Resources Information Center

    Milligan, Glenn W.

    1979-01-01

    Johnson has shown that the single linkage and complete linkage hierarchical clustering algorithms induce a metric on the data known as the ultrametric. Johnson's proof is extended to four other common clustering algorithms. Two additional methods also produce hierarchical structures which can violate the ultrametric inequality. (Author/CTM)

  9. CLAG: an unsupervised non hierarchical clustering algorithm handling biological data

    PubMed Central

    2012-01-01

    Background Searching for similarities in a set of biological data is intrinsically difficult due to possible data points that should not be clustered, or that should group within several clusters. Under these hypotheses, hierarchical agglomerative clustering is not appropriate. Moreover, if the dataset is not known enough, like often is the case, supervised classification is not appropriate either. Results CLAG (for CLusters AGgregation) is an unsupervised non hierarchical clustering algorithm designed to cluster a large variety of biological data and to provide a clustered matrix and numerical values indicating cluster strength. CLAG clusterizes correlation matrices for residues in protein families, gene-expression and miRNA data related to various cancer types, sets of species described by multidimensional vectors of characters, binary matrices. It does not ask to all data points to cluster and it converges yielding the same result at each run. Its simplicity and speed allows it to run on reasonably large datasets. Conclusions CLAG can be used to investigate the cluster structure present in biological datasets and to identify its underlying graph. It showed to be more informative and accurate than several known clustering methods, as hierarchical agglomerative clustering, k-means, fuzzy c-means, model-based clustering, affinity propagation clustering, and not to suffer of the convergence problem proper to this latter. PMID:23216858

  10. Hierarchical clustering techniques for image database organization and summarization

    NASA Astrophysics Data System (ADS)

    Vellaikal, Asha; Kuo, C.-C. Jay

    1998-10-01

    This paper investigates clustering techniques as a method of organizing image databases to support popular visual management functions such as searching, browsing and navigation. Different types of hierarchical agglomerative clustering techniques are studied as a method of organizing features space as well as summarizing image groups by the selection of a few appropriate representatives. Retrieval performance using both single and multiple level hierarchies are experimented with and the algorithms show an interesting relationship between the top k correct retrievals and the number of comparisons required. Some arguments are given to support the use of such cluster-based techniques for managing distributed image databases.

  11. 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.

  12. Galaxy formation through hierarchical clustering

    NASA Technical Reports Server (NTRS)

    White, Simon D. M.; Frenk, Carlos S.

    1991-01-01

    Analytic methods for studying the formation of galaxies by gas condensation within massive dark halos are presented. The present scheme applies to cosmogonies where structure grows through hierarchical clustering of a mixture of gas and dissipationless dark matter. The simplest models consistent with the current understanding of N-body work on dissipationless clustering, and that of numerical and analytic work on gas evolution and cooling are adopted. Standard models for the evolution of the stellar population are also employed, and new models for the way star formation heats and enriches the surrounding gas are constructed. Detailed results are presented for a cold dark matter universe with Omega = 1 and H(0) = 50 km/s/Mpc, but the present methods are applicable to other models. The present luminosity functions contain significantly more faint galaxies than are observed.

  13. Evaluation of Hierarchical Clustering Algorithms for Document Datasets

    DTIC Science & Technology

    2002-06-03

    new class of agglomerative algorithms, in which we introduced intermediate clusters obtained by partitional clustering algorithms to constrain the space ...of the corresponding clusters. The various clustering algorithms that are described in this paper use the vector- space model [26] to represent each...document. In this model, each document d is considered to be a vector in the term- space . In particular, we employed the t f id f term weighting model

  14. Improved initialisation of model-based clustering using Gaussian hierarchical partitions

    PubMed Central

    Scrucca, Luca; Raftery, Adrian E.

    2015-01-01

    Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting points in the parameter space often lead to different local maxima of the likelihood function and, so to different clustering partitions. Among the several approaches available in the literature, model-based agglomerative hierarchical clustering is used to provide initial partitions in the popular mclust R package. This choice is computationally convenient and often yields good clustering partitions. However, in certain circumstances, poor initial partitions may cause the EM algorithm to converge to a local maximum of the likelihood function. We propose several simple and fast refinements based on data transformations and illustrate them through data examples. PMID:26949421

  15. A hierarchical clustering algorithm for MIMD architecture.

    PubMed

    Du, Zhihua; Lin, Feng

    2004-12-01

    Hierarchical clustering is the most often used method for grouping similar patterns of gene expression data. A fundamental problem with existing implementations of this clustering method is the inability to handle large data sets within a reasonable time and memory resources. We propose a parallelized algorithm of hierarchical clustering to solve this problem. Our implementation on a multiple instruction multiple data (MIMD) architecture shows considerable reduction in computational time and inter-node communication overhead, especially for large data sets. We use the standard message passing library, message passing interface (MPI) for any MIMD systems.

  16. 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

  17. 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…

  18. 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

  19. Multiple sequence alignment with hierarchical clustering.

    PubMed Central

    Corpet, F

    1988-01-01

    An algorithm is presented for the multiple alignment of sequences, either proteins or nucleic acids, that is both accurate and easy to use on microcomputers. The approach is based on the conventional dynamic-programming method of pairwise alignment. Initially, a hierarchical clustering of the sequences is performed using the matrix of the pairwise alignment scores. The closest sequences are aligned creating groups of aligned sequences. Then close groups are aligned until all sequences are aligned in one group. The pairwise alignments included in the multiple alignment form a new matrix that is used to produce a hierarchical clustering. If it is different from the first one, iteration of the process can be performed. The method is illustrated by an example: a global alignment of 39 sequences of cytochrome c. PMID:2849754

  20. Hierarchical video summarization based on context clustering

    NASA Astrophysics Data System (ADS)

    Tseng, Belle L.; Smith, John R.

    2003-11-01

    A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.

  1. Constructing storyboards based on hierarchical clustering analysis

    NASA Astrophysics Data System (ADS)

    Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

    2005-07-01

    There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

  2. 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.

  3. Cluster assembly in hierarchically collapsing molecular clouds

    NASA Astrophysics Data System (ADS)

    Vazquez-Semadeni, Enrique

    2015-08-01

    I will discuss the mechanism of cluster formation in hierarchically collapsing molecular clouds. Recent evidence, both observational and numerical, suggests that molecular clouds (MCs) may be undergoing global, hierarchical gravitational collapse. The "hierarchical" regime consists of small-scale collapses within larger-scale ones. The former occur in a more scattered fashion and at slightly earlier times, and are themselves falling into the larger potential well of the still-ongoing large-scale collapse. Instead, the large-scale collapse culminates a few Myr later, in a highly focused region, of higher density, mass, and velocity dispersion. The stars formed in the early, small-scale collapses share the infall velocity of their parent clumps towards the larger potential trough, while those formed later, in the aforementioned trough, form from gas that has already dissipated some of its kinetic energy, and thus have a lower velocity dispersion. This leads to a radial age gradient in the stellar population, in agreement with recent observations.

  4. 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...

  5. 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.

  6. 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...

  7. 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...

  8. 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)

  9. A hierarchical clustering methodology for the estimation of toxicity.

    PubMed

    Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M

    2008-01-01

    ABSTRACT 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 similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

  10. The Hierarchical Distribution of Young Stellar Clusters in Nearby Galaxies

    NASA Astrophysics Data System (ADS)

    Grasha, Kathryn; Calzetti, Daniela

    2017-01-01

    We investigate the spatial distributions of young stellar clusters in six nearby galaxies to trace the large scale hierarchical star-forming structures. The six galaxies are drawn from the Legacy ExtraGalactic UV Survey (LEGUS). We quantify the strength of the clustering among stellar clusters as a function of spatial scale and age to establish the survival timescale of the substructures. We separate the clusters into different classes, compact (bound) clusters and associations (unbound), and compare the clustering among them. We find that younger star clusters are more strongly clustered over small spatial scales and that the clustering disappears rapidly for ages as young as a few tens of Myr, consistent with clusters slowly losing the fractal dimension inherited at birth from their natal molecular clouds.

  11. 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).

  12. Breaking the hierarchy - a new cluster selection mechanism for hierarchical clustering methods

    PubMed Central

    Zahoránszky, László A; Katona, Gyula Y; Hári, Péter; Málnási-Csizmadia, András; Zweig, Katharina A; Zahoránszky-Köhalmi, Gergely

    2009-01-01

    Background Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the hierarchy by a so-called level selection algorithm. In 2005, a new kind of hierarchical clustering method was introduced by Palla et al. that differs in two ways from Ward's method: it can be used on data on which no full similarity matrix is defined and it can produce overlapping clusters, i.e., allow for multiple membership of items in clusters. These features are optimal for biological and chemical data sets but until now no level selection algorithm has been published for this method. Results In this article we provide a general selection scheme, the level independent clustering selection method, called LInCS. With it, clusters can be selected from any level in quadratic time with respect to the number of clusters. Since hierarchically clustered data is not necessarily associated with a similarity measure, the selection is based on a graph theoretic notion of cohesive clusters. We present results of our method on two data sets, a set of drug like molecules and set of protein-protein interaction (PPI) data. In both cases the method provides a clustering with very good sensitivity and specificity values according to a given reference clustering. Moreover, we can show for the PPI data set that our graph theoretic cohesiveness measure indeed chooses biologically homogeneous clusters and disregards inhomogeneous ones in most cases. We finally discuss how the method can be generalized to other hierarchical clustering methods to allow for a level independent cluster selection. Conclusion Using our new cluster selection method together with the method by Palla et al. provides a new interesting clustering mechanism that allows to compute overlapping clusters, which is especially valuable for biological and chemical data sets. PMID:19840391

  13. 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.

  14. Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

    PubMed Central

    Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

    2012-01-01

    Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043

  15. Formation of Globular Clusters in Hierarchical Cosmology: ART and Science

    NASA Astrophysics Data System (ADS)

    Gnedin, Oleg Y.; Prieto, José L.

    We test the hypothesis that globular clusters form in supergiant molecular clouds within high-redshift galaxies. Numerical simulations demonstrate that such large, dense, and cold gas clouds assemble naturally in current hierarchical models of galaxy formation. These clouds are enriched with heavy elements from earlier stars and could produce star clusters in a similar way to nearby molecular clouds. The masses and sizes of the model clusters are in excellent agreement with the observations of young massive clusters. Do these model clusters evolve into globular clusters that we see in our and external galaxies? In order to study their dynamical evolution, we calculate the orbits of model clusters using the outputs of the cosmological simulation of a Milky Way-sized galaxy. We find that at present the orbits are isotropic in the inner 50 kpc of the Galaxy and preferentially radial at larger distances. All clusters located outside 10 kpc from the center formed in the now-disrupted satellite galaxies. The spatial distribution of model clusters is spheroidal, with a power-law density profile consistent with observations. The combination of two-body scattering, tidal shocks, and stellar evolution results in the evolution of the cluster mass function from an initial power law to the observed log-normal distribution. However, not all initial conditions and not all evolution scenarios are consistent with the observed mass function.

  16. A fast quad-tree based two dimensional hierarchical clustering.

    PubMed

    Rajadurai, Priscilla; Sankaranarayanan, Swamynathan

    2012-01-01

    Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data.

  17. Multi-mode clustering model for hierarchical wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Hu, Xiangdong; Li, Yongfu; Xu, Huifen

    2017-03-01

    The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

  18. Globular cluster formation with multiple stellar populations from hierarchical star cluster complexes

    NASA Astrophysics Data System (ADS)

    Bekki, Kenji

    2017-01-01

    Most old globular clusters (GCs) in the Galaxy are observed to have internal chemical abundance spreads in light elements. We discuss a new GC formation scenario based on hierarchical star formation within fractal molecular clouds. In the new scenario, a cluster of bound and unbound star clusters (`star cluster complex', SCC) that have a power-law cluster mass function with a slope (β) of 2 is first formed from a massive gas clump developed in a dwarf galaxy. Such cluster complexes and β = 2 are observed and expected from hierarchical star formation. The most massive star cluster (`main cluster'), which is the progenitor of a GC, can accrete gas ejected from asymptotic giant branch (AGB) stars initially in the cluster and other low-mass clusters before the clusters are tidally stripped or destroyed to become field stars in the dwarf. The SCC is initially embedded in a giant gas hole created by numerous supernovae of the SCC so that cold gas outside the hole can be accreted onto the main cluster later. New stars formed from the accreted gas have chemical abundances that are different from those of the original SCC. Using hydrodynamical simulations of GC formation based on this scenario, we show that the main cluster with the initial mass as large as [2 - 5] × 105M⊙ can accrete more than 105M⊙ gas from AGB stars of the SCC. We suggest that merging of hierarchical star cluster complexes can play key roles in stellar halo formation around GCs and self-enrichment processes in the early phase of GC formation.

  19. 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.

  20. A data-driven approach to estimating the number of clusters in hierarchical clustering

    PubMed Central

    Zambelli, Antoine E.

    2016-01-01

    DNA microarray and gene expression problems often require a researcher to perform clustering on their data in a bid to better understand its structure. In cases where the number of clusters is not known, one can resort to hierarchical clustering methods. However, there currently exist very few automated algorithms for determining the true number of clusters in the data. We propose two new methods (mode and maximum difference) for estimating the number of clusters in a hierarchical clustering framework to create a fully automated process with no human intervention. These methods are compared to the established elbow and gap statistic algorithms using simulated datasets and the Biobase Gene ExpressionSet. We also explore a data mixing procedure inspired by cross validation techniques. We find that the overall performance of the maximum difference method is comparable or greater to that of the gap statistic in multi-cluster scenarios, and achieves that performance at a fraction of the computational cost. This method also responds well to our mixing procedure, which opens the door to future research. We conclude that both the mode and maximum difference methods warrant further study related to their mixing and cross-validation potential. We particularly recommend the use of the maximum difference method in multi-cluster scenarios given its accuracy and execution times, and present it as an alternative to existing algorithms.

  1. Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles.

    PubMed

    Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rudiger

    2017-01-01

    We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.

  2. Generating clustered journal maps: an automated system for hierarchical classification.

    PubMed

    Leydesdorff, Loet; Bornmann, Lutz; Wagner, Caroline S

    2017-01-01

    Journal maps and classifications for 11,359 journals listed in the combined Journal Citation Reports 2015 of the Science and Social Sciences Citation Indexes are provided at https://leydesdorff.github.io/journals/ and http://www.leydesdorff.net/jcr15. A routine using VOSviewer for integrating the journal mapping and their hierarchical clusterings is also made available. In this short communication, we provide background on the journal mapping/clustering and an explanation about and instructions for the routine. We compare journal maps for 2015 with those for 2014 and show the delineations among fields and subfields to be sensitive to fluctuations. Labels for fields and sub-fields are not provided by the routine, but an analyst can add them for pragmatic or intellectual reasons. The routine provides a means of testing one's assumptions against a baseline without claiming authority; clusters of related journals can be visualized to understand communities. The routine is generic and can be used for any 1-mode network.

  3. A combined multidimensional scaling and hierarchical clustering view for the exploratory analysis of multidimensional data

    NASA Astrophysics Data System (ADS)

    Craig, Paul; Roa-Seïler, Néna

    2013-01-01

    This paper describes a novel information visualization technique that combines multidimensional scaling and hierarchical clustering to support the exploratory analysis of multidimensional data. The technique displays the results of multidimensional scaling using a scatter plot where the proximity of any two items' representations is approximate to their similarity according to a Euclidean distance metric. The results of hierarchical clustering are overlaid onto this view by drawing smoothed outlines around each nested cluster. The difference in similarity between successive cluster combinations is used to colour code clusters and make stronger natural clusters more prominent in the display. When a cluster or group of items is selected, multidimensional scaling and hierarchical clustering are re-applied to a filtered subset of the data, and animation is used to smooth the transition between successive filtered views. As a case study we demonstrate the technique being used to analyse survey data relating to the appropriateness of different phrases to different emotionally charged situations.

  4. The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods.

    PubMed

    Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

    2007-01-01

    Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

  5. 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

  6. Hierarchical clusters in families with type 2 diabetes.

    PubMed

    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.

  7. 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."

  8. Structure of Hierarchic Clusterings: Implications for Information Retrieval and for Multivariate Data Analysis.

    ERIC Educational Resources Information Center

    Murtagh, F.

    1984-01-01

    Using examples of data from the areas of information retrieval and of multivariate data analysis, six hierarchic clustering algorithms (single link, median, centroid, group average, complete link, Wards's) are examined and evaluated by using three proposed coefficients of hierarchic structure. Nine references are cited. (EJS)

  9. 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.

  10. The identification of credit card encoders by hierarchical cluster analysis of the jitters of magnetic stripes.

    PubMed

    Leung, S C; Fung, W K; Wong, K H

    1999-01-01

    The relative bit density variation graphs of 207 specimen credit cards processed by 12 encoding machines were examined first visually, and then classified by means of hierarchical cluster analysis. Twenty-nine credit cards being treated as 'questioned' samples were tested by way of cluster analysis against 'controls' derived from known encoders. It was found that hierarchical cluster analysis provided a high accuracy of identification with all 29 'questioned' samples classified correctly. On the other hand, although visual comparison of jitter graphs was less discriminating, it was nevertheless capable of giving a reasonably accurate result.

  11. 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.…

  12. 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.

  13. 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

  14. Hierarchical Clustering for Development Equality Analysis: Indonesian Data of Educational Support (2011 – 2014)

    NASA Astrophysics Data System (ADS)

    Wijayanto, Feri

    2017-03-01

    Indonesia which contains more than 30 provinces with the decentralization system needs to identify its development equality. Because inequality in development performance will bring disparity among provinces. At present, the development monitoring is using the indicator’s values and comparing those values among provinces. There are no tools which could be used to identify the general development performance, moreover regarding the equality. This research wants to see the possibility of using hierarchical clustering to observe this equality, especially on educational support development. In result, the graph which is plotted using the dissimilarity values as a side result of hierarchical clustering could describe the trend of the equality.

  15. Hierarchical Clustering: A Bibliography. Technical Report No. 1.

    ERIC Educational Resources Information Center

    Farrell, William T.

    "Classification: Purposes, Principles, Progress, Prospects" by Robert R. Sokal is reprinted in this document. It summarizes the principles of classification and cluster analysis in a manner which is of specific value to the Marine Corps Office of Manpower Utilization. Following the article is a 184 item bibliography on cluster analysis…

  16. Heuristic Experiments of Threading and Equal Load Partitioning For Hierarchical Heterogeneous Cluster

    NASA Astrophysics Data System (ADS)

    Khalid, Noor Elaiza Abdul; Hashim, Rathiah; Noor, Noorhayati Mohamed; Helmi Rosli, Muhammad; Manaf, Mazani

    2016-11-01

    Presently, the issue of processing large data on a timely manner poses as a challenge to many ICT researchers. Most commodity computers are interconnected in a network forming a cluster computing resource simulating a super computer. This paper explores heuristically the performance of homogeneous, heterogeneous and multi-core clusters. This work consists of five experiments: Equal task partitioning according to the number of nodes in homogeneous cluster, number of nodes in heterogeneous cluster, number of nodes in heterogeneous cluster with multithreading, number of cores in heterogeneous cluster and number of cores in heterogeneous cluster with multithreading. The task is Sobel edge detection method tested with an array of images. The images are processed in three different sizes; 1K × 1K, 2K × 2K and 3K × 3K. The performance evaluations are based on processing speed. The results yield promising impact of equal partitioning and threading in parallel processing hierarchical heterogeneous cluster.

  17. Hierarchical clustering analysis of flexible GBR 12909 dialkyl piperazine and piperidine analogs

    NASA Astrophysics Data System (ADS)

    Gilbert, Kathleen M.; Venanzi, Carol A.

    2006-04-01

    Pharmacophore modeling of large, drug-like molecules, such as the dopamine reuptake inhibitor GBR 12909, is complicated by their flexibility. A comprehensive hierarchical clustering study of two GBR 12909 analogs was performed to identify representative conformers for input to three-dimensional quantitative structure-activity relationship studies of closely-related analogs. Two data sets of more than 700 conformers each produced by random search conformational analysis of a piperazine and a piperidine GBR 12909 analog were studied. Several clustering studies were carried out based on different feature sets that include the important pharmacophore elements. The distance maps, the plot of the effective number of clusters versus actual number of clusters, and the novel derived clustering statistic, percentage change in the effective number of clusters, were shown to be useful in determining the appropriate clustering level. Six clusters were chosen for each analog, each representing a different region of the torsional angle space that determines the relative orientation of the pharmacophore elements. Conformers of each cluster that are representative of these regions were identified and compared for each analog. This study illustrates the utility of using hierarchical clustering for the classification of conformers of highly flexible molecules in terms of the three-dimensional spatial orientation of key pharmacophore elements.

  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.

  20. 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.

  1. 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...

  2. Comparative Evaluation of Two Superior Stopping Rules for Hierarchical Cluster Analysis.

    ERIC Educational Resources Information Center

    Atlas, Robert S.; Overall, John E.

    1994-01-01

    A split-sample replication stopping rule for hierarchical cluster analysis is compared with the internal criterion previously found superior by Milligan and Cooper (1985) in their comparison of 30 different procedures. Situations under which the methods are equivalent or not equally useful are discussed. (SLD)

  3. 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.

  4. Hierarchical cluster analysis for exposure assessment of workers in the Semiconductor Health Study.

    PubMed

    Hines, C J; Selvin, S; Samuels, S J; Hammond, S K; Woskie, S R; Hallock, M F; Schenker, M B

    1995-12-01

    The fabrication of integrated circuits in the semiconductor industry involves worker exposures to multiple chemical and physical agents. The potential for a high degree of correlation among exposure variables was of concern in the Semiconductor Health Study. Hierarchical cluster analysis was used to identify groups or "clusters" of correlated variables. Several variations of hierarchical cluster analysis were performed on 14 chemical and physical agents, using exposure data on 882 subjects from the historical cohort of the epidemiological studies. Similarity between agent pairs was determined by calculating two metrics of dissimilarity, and hierarchical trees were constructed using three clustering methods. Among subjects exposed to ethylene-based glycol ethers (EGE), xylene, or n-butyl acetate (nBA), 83% were exposed to EGE and xylene, 86% to EGE and nBA, and 94% to xylene and nBA, suggesting that exposures to EGE, xylene, and nBA were highly correlated. A high correlation was also found for subjects exposed to boron and phosphorus (80%). The trees also revealed cluster groups containing agents associated with work-group exposure categories developed for the epidemiologic analyses.

  5. 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.

  6. 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

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

    NASA Astrophysics Data System (ADS)

    Grasha, Kathryn; Elmegreen, Bruce; Calzetti, Daniela

    2017-01-01

    We present an analysis of the positions and ages of star clusters in eight local galaxies and find a correlation between the age difference and separation of cluster pairs. We infer that cluster formation is correlated in time such that clusters that are close to each have similar ages. In addition, the age between cluster pairs increases with their separation to the 0.3 - 0.6 power, close to the expected slope of 0.5 that would arise in a turbulent-driven interstellar medium. This suggests that not only is star formation hierarchical both in space and in time, but that the duration of star formation depends on the region of interest: smaller regions will form stars over a shorter time frame whereas larger regions form stars over a longer time frame.

  8. 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

  9. 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

  10. Bayesian latent variable models for hierarchical clustered count outcomes with repeated measures in microbiome studies.

    PubMed

    Xu, Lizhen; Paterson, Andrew D; Xu, Wei

    2017-04-01

    Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational taxonomic units within a single taxonomic cluster. This novel method can incorporate both negative binomial and zero-inflated negative binomial responses, and can account for serial and familial correlations. We develop a Markov chain Monte Carlo algorithm that is built on a data augmentation scheme using Pólya-Gamma random variables. Hierarchical centering and parameter expansion techniques are also used to improve the convergence of the Markov chain. We evaluate the performance of our proposed method through extensive simulations. We also apply our method to a human microbiome study.

  11. 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.

  12. Star Cluster Mass Functions and Hierarchical Clustering: Learning from Koposov 1 and 2

    NASA Astrophysics Data System (ADS)

    Paust, Nathaniel; Wilson, Danielle; van Belle, Gerard

    2017-01-01

    We present photometry of two halo star clusters, Koposov 1 and 2. Found as over-densities in the Sloan Digital Sky Survey, these clusters were intially believed to be heavily stripped globular clusters, given the small number of stars per cluster. In this work, we have used isochrone fitting to determine the age, distance, and metallicity of the clusters. These results confirm tha tthe clusters are in the halo but also reveal surprisingly young ages and high metallicities. Investigation of the cluster mass functions reveals a steep negatively-sloped present day mass function in contrast to the flatish positively-sloped mass functions seen in heavily stripped Galactic globular clusters. The mass function slope, proximity to the Sagittarius stream, and common metallicity with M54, which is related to the Sagittarius dwarf, leads to a very interesting conclusion: Koposov 1 and 2 are open clusters removed from the Sagittarius dwarf through tidal stripping.

  13. 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

  14. 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.

  15. [Pyrolysis-gas chromatographic fingerprints with hierarchical cluster analysis for Dendrobium candidum Wall. ex Lindl].

    PubMed

    Wang, Lili; Wang, Cong; Pan, Zaifa; Sun, Fa

    2008-09-01

    The pyrogram fingerprints of Dendrobium candidum Wall. ex Lindl. from different regions were studied by pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) and compared with hierarchical cluster analysis. The effect of pyrolysis temperature on the fingerprint was examined by evolved gas analysis, and then 450 degrees C was selected as the optimized pyrolysis temperature. An amount of 0.4 mg of raw drug powder was pyrolysed in a vertical microfurnace pyrolyzer, and the products were directly introduced into a gas chromatograph equipped with a flame ionization detector (FID) and a fused-silica capillary column (30 m x 0.25 mm x 0.25 microm). The pyrogram fingerprints of 10 samples from different regions showed a high similarity and a good reproducibility with the relative standard deviations (RSDs) of the retention times less than 0.33% and the RSDs of the relative peak areas less than 4.8%. Therefore, each sample was characterized by the peak area of 31 peaks in each pyrogram and these peaks were employed for hierarchical cluster analysis. Furthermore, the discrimination of the sample from different regions was achieved by hierarchical cluster analysis via recognizing the 10 x 31 data matrix. Thus, the results proved it is a simple, rapid and accurate method suitable for the quality control of the traditional Chinese medicines.

  16. 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.

  17. COCO-CL: hierarchical clustering of homology relations based on evolutionary correlations

    PubMed Central

    Zotenko, Elena; Tasneem, Asba

    2006-01-01

    Motivation Determining orthology relations among genes across multiple genomes is an important problem in the post-genomic era. Identifying orthologous genes can not only help predict functional annotations for newly sequenced or poorly characterized genomes, but can also help predict new protein–protein interactions. Unfortunately, determining orthology relation through computational methods is not straightforward due to the presence of paralogs. Traditional approaches have relied on pairwise sequence comparisons to construct graphs, which were then partitioned into putative clusters of orthologous groups. These methods do not attempt to preserve the non-transitivity and hierarchic nature of the orthology relation. Results We propose a new method, COCO-CL, for hierarchical clustering of homology relations and identification of orthologous groups of genes. Unlike previous approaches, which are based on pairwise sequence comparisons, our method explores the correlation of evolutionary histories of individual genes in a more global context. COCO-CL can be used as a semi-independent method to delineate the orthology/paralogy relation for a refined set of homologous proteins obtained using a less-conservative clustering approach, or as a refiner that removes putative out-paralogs from clusters computed using a more inclusive approach. We analyze our clustering results manually, with support from literature and functional annotations. Since our orthology determination procedure does not employ a species tree to infer duplication events, it can be used in situations when the species tree is unknown or uncertain. PMID:16434444

  18. Permutation Tests of Hierarchical Cluster Analyses of Carrion Communities and Their Potential Use in Forensic Entomology.

    PubMed

    van der Ham, Joris L

    2016-05-19

    Forensic entomologists can use carrion communities' ecological succession data to estimate the postmortem interval (PMI). Permutation tests of hierarchical cluster analyses of these data provide a conceptual method to estimate part of the PMI, the post-colonization interval (post-CI). This multivariate approach produces a baseline of statistically distinct clusters that reflect changes in the carrion community composition during the decomposition process. Carrion community samples of unknown post-CIs are compared with these baseline clusters to estimate the post-CI. In this short communication, I use data from previously published studies to demonstrate the conceptual feasibility of this multivariate approach. Analyses of these data produce series of significantly distinct clusters, which represent carrion communities during 1- to 20-day periods of the decomposition process. For 33 carrion community samples, collected over an 11-day period, this approach correctly estimated the post-CI within an average range of 3.1 days.

  19. 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.

  20. Hierarchical cluster analysis of matrix effects on 110 pesticide residues in 28 tea matrixes.

    PubMed

    Li, Yan; Pang, Guo-Fang; Fan, Chun-Lin; Chen, Xi

    2013-01-01

    Matrix effects on 110 pesticides in 28 tea matrixes of different varieties and origins by LC/MS/MS were studied, and most of the pesticides exhibited soft and medium signal suppression. To better understand the influence of the tea varieties and the physicochemical characteristics of pesticides on the matrix effects, the multivariate analysis tool called hierarchical cluster analysis was applied. Tea matrixes were grouped into three clusters: unfermented, fermented, and post-fermented teas. Any type of tea can be chosen from each cluster as a corresponding representative matrix within that cluster to make matrix-matched solutions, which could simplify analysis while guaranteeing its accuracy. Matrix effects on most pesticides were similar despite the physicochemical diversities of the pesticides.

  1. Considerations on the thermal performances of ribbed channels by means of a novel dynamic method for hierarchical clustering

    NASA Astrophysics Data System (ADS)

    Niro, A.; Fustinoni, D.; Vignati, F.; Gramazio, P.; Ciminà, S.

    2016-09-01

    The investigation of ribbed surfaces for the enhancement of heat transfer in forced convection allowed to observe that different geometries may lead to comparable performances. Due to the lack of an underlying structure of the data, a novel method for data clustering is introduced here, to assess to what extent comparable performances can be achieved using different rib geometries. The clustering method is an agglomerative technique, based on the inclusion of each configuration in another ones bounding box, whose size depends dynamically on the Nusselt number and the pumping power. The method is applied to a large database experimentally obtained at ThermALab of Politecnico di Milano, in order to identify the Nusselt number and the friction factor for diverse-rib configurations in a large-aspect ratio channel with low-Reynolds flows. The clusters are determined, and the resulting families of configurations are used to assess the possible effects of the rib geometry on the thermal and fluid-dynamic performances. The clustering analysis results suggest interesting considerations.

  2. SHIPS: Spectral Hierarchical clustering for the Inference of Population Structure in genetic studies.

    PubMed

    Bouaziz, Matthieu; Paccard, Caroline; Guedj, Mickael; Ambroise, Christophe

    2012-01-01

    Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising

  3. Using Multi-Instance Hierarchical Clustering Learning System to Predict Yeast Gene Function

    PubMed Central

    Liao, Bo; Li, Yun; Jiang, Yan; Cai, Lijun

    2014-01-01

    Time-course gene expression datasets, which record continuous biological processes of genes, have recently been used to predict gene function. However, only few positive genes can be obtained from annotation databases, such as gene ontology (GO). To obtain more useful information and effectively predict gene function, gene annotations are clustered together to form a learnable and effective learning system. In this paper, we propose a novel multi-instance hierarchical clustering (MIHC) method to establish a learning system by clustering GO and compare this method with other learning system establishment methods. Multi-label support vector machine classifier and multi-label K-nearest neighbor classifier are used to verify these methods in four yeast time-course gene expression datasets. The MIHC method shows good performance, which serves as a guide to annotators or refines the annotation in detail. PMID:24621610

  4. 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.

  5. A novel approach to the problem of non-uniqueness of the solution in hierarchical clustering.

    PubMed

    Cattinelli, Isabella; Valentini, Giorgio; Paulesu, Eraldo; Borghese, Nunzio Alberto

    2013-07-01

    The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different cluster sets that, in turns, may lead to different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the definition of an equivalence relation over dendrograms that allows developing all and only the significantly different dendrograms for the same dataset, thus reducing the computational complexity to polynomial from the exponential obtained when all possible dendrograms are considered. Experimental results in the neuroimaging and bioinformatics domains show the effectiveness of the proposed method.

  6. Solvothermal synthesis and thermoelectric properties of indium telluride nanostring-cluster hierarchical structures

    PubMed Central

    2011-01-01

    A simple solvothermal approach has been developed to successfully synthesize n-type α-In2Te3 thermoelectric nanomaterials. The nanostring-cluster hierarchical structures were prepared using In(NO3)3 and Na2TeO3 as the reactants in a mixed solvent of ethylenediamine and ethylene glycol at 200°C for 24 h. A diffusion-limited reaction mechanism was proposed to explain the formation of the hierarchical structures. The Seebeck coefficient of the bulk pellet pressed by the obtained samples exhibits 43% enhancement over that of the corresponding thin film at room temperature. The electrical conductivity of the bulk pellet is one to four orders of magnitude higher than that of the corresponding thin film or p-type bulk sample. The synthetic route can be applied to obtain other low-dimensional semiconducting telluride nanostructures. PACS: 65.80.-g, 68.35.bg, 68.35.bt PMID:21711853

  7. Solvothermal synthesis and thermoelectric properties of indium telluride nanostring-cluster hierarchical structures

    NASA Astrophysics Data System (ADS)

    Tai, Guo'an; Miao, Chunyang; Wang, Yubo; Bai, Yunrui; Zhang, Haiqian; Guo, Wanlin

    2011-12-01

    A simple solvothermal approach has been developed to successfully synthesize n-type α-In2Te3 thermoelectric nanomaterials. The nanostring-cluster hierarchical structures were prepared using In(NO3)3 and Na2TeO3 as the reactants in a mixed solvent of ethylenediamine and ethylene glycol at 200°C for 24 h. A diffusion-limited reaction mechanism was proposed to explain the formation of the hierarchical structures. The Seebeck coefficient of the bulk pellet pressed by the obtained samples exhibits 43% enhancement over that of the corresponding thin film at room temperature. The electrical conductivity of the bulk pellet is one to four orders of magnitude higher than that of the corresponding thin film or p-type bulk sample. The synthetic route can be applied to obtain other low-dimensional semiconducting telluride nanostructures. PACS: 65.80.-g, 68.35.bg, 68.35.bt

  8. Solvothermal synthesis and thermoelectric properties of indium telluride nanostring-cluster hierarchical structures.

    PubMed

    Tai, Guo'an; Miao, Chunyang; Wang, Yubo; Bai, Yunrui; Zhang, Haiqian; Guo, Wanlin

    2011-04-13

    A simple solvothermal approach has been developed to successfully synthesize n-type α-In2Te3 thermoelectric nanomaterials. The nanostring-cluster hierarchical structures were prepared using In(NO3)3 and Na2TeO3 as the reactants in a mixed solvent of ethylenediamine and ethylene glycol at 200°C for 24 h. A diffusion-limited reaction mechanism was proposed to explain the formation of the hierarchical structures. The Seebeck coefficient of the bulk pellet pressed by the obtained samples exhibits 43% enhancement over that of the corresponding thin film at room temperature. The electrical conductivity of the bulk pellet is one to four orders of magnitude higher than that of the corresponding thin film or p-type bulk sample. The synthetic route can be applied to obtain other low-dimensional semiconducting telluride nanostructures.PACS: 65.80.-g, 68.35.bg, 68.35.bt.

  9. High- and low-level hierarchical classification algorithm based on source separation process

    NASA Astrophysics Data System (ADS)

    Loghmari, Mohamed Anis; Karray, Emna; Naceur, Mohamed Saber

    2016-10-01

    High-dimensional data applications have earned great attention in recent years. We focus on remote sensing data analysis on high-dimensional space like hyperspectral data. From a methodological viewpoint, remote sensing data analysis is not a trivial task. Its complexity is caused by many factors, such as large spectral or spatial variability as well as the curse of dimensionality. The latter describes the problem of data sparseness. In this particular ill-posed problem, a reliable classification approach requires appropriate modeling of the classification process. The proposed approach is based on a hierarchical clustering algorithm in order to deal with remote sensing data in high-dimensional space. Indeed, one obvious method to perform dimensionality reduction is to use the independent component analysis process as a preprocessing step. The first particularity of our method is the special structure of its cluster tree. Most of the hierarchical algorithms associate leaves to individual clusters, and start from a large number of individual classes equal to the number of pixels; however, in our approach, leaves are associated with the most relevant sources which are represented according to mutually independent axes to specifically represent some land covers associated with a limited number of clusters. These sources contribute to the refinement of the clustering by providing complementary rather than redundant information. The second particularity of our approach is that at each level of the cluster tree, we combine both a high-level divisive clustering and a low-level agglomerative clustering. This approach reduces the computational cost since the high-level divisive clustering is controlled by a simple Boolean operator, and optimizes the clustering results since the low-level agglomerative clustering is guided by the most relevant independent sources. Then at each new step we obtain a new finer partition that will participate in the clustering process to enhance

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

    PubMed Central

    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

  11. Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

    NASA Astrophysics Data System (ADS)

    Hadida, Jonathan; Desrosiers, Christian; Duong, Luc

    2011-03-01

    The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time. The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask. This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.

  12. 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.

  13. A structure-odour relationship study using EVA descriptors and hierarchical clustering.

    PubMed

    Takane, Shin-ya; Mitchell, John B O

    2004-11-21

    Structure-odour relationship analyses using hierarchical clustering were carried out on a diverse dataset of 47 molecules. These molecules were divided into seven odour categories: ambergris, bitter almond, camphoraceous, rose, jasmine, muguet, and musk. The alignment-independent descriptor EVA (EigenVAlue) was used as the molecular descriptor. The results were compared with those of another kind of descriptor, the UNITY 2D fingerprint. The dendrograms obtained with these descriptors were compared with the seven odour categories using the adjusted Rand index. The dendrograms produced by EVA consistently outperformed those from UNITY 2D in reproducing the experimental odour classifications of these 47 molecules.

  14. 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.

  15. Accurate lithography hotspot detection based on PCA-SVM classifier with hierarchical data clustering

    NASA Astrophysics Data System (ADS)

    Gao, Jhih-Rong; Yu, Bei; Pan, David Z.

    2014-03-01

    As technology nodes continues shrinking, layout patterns become more sensitive to lithography processes, resulting in lithography hotspots that need to be identified and eliminated during physical verification. In this paper, we propose an accurate hotspot detection approach based on PCA (principle component analysis)-SVM (sup- port vector machine) classifier. Several techniques, including hierarchical data clustering, data balancing, and multi-level training, are provided to enhance performance of the proposed approach. Our approach is accurate and more efficient than conventional time-consuming lithography simulation; in the meanwhile, provides high flexibility to adapt to new lithography processes and rules.

  16. Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R

    PubMed Central

    Murtagh, Fionn; Van Poucke, Sven; Lin, Su; Lan, Peng

    2017-01-01

    Big data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work. PMID:28275620

  17. 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.

  18. A new strategy of cooperativity of biclustering and hierarchical clustering: a case of analyzing yeast genomic microarray datasets.

    PubMed

    Mao, Daqing; Luo, Yi; Zhang, Jinghai; Zhu, Jun

    2005-05-01

    Hierarchical clustering is difficult to be deployed effectively in finding meaningful subtrees since genes rarely exhibit similar expression pattern across a wide range of conditions. It is also difficult to find a suitable level in cleaving a big hierarchy tree. Biclustering is a promising methodology in the field of the analysis of gene expression data of genechip. Generally it can be employed in identification of gene groups, which show a coherent expression profile across a subset of conditions. But in some cases of biclustering analysis of gene expressions, the genes in one bicluster are involved in more than one functional group, or all genes in one bicluster are involved in unknown functional groups (e.g. pattern VI and VIII in our studies). Then, how to predict the function of genes in these patterns? In the present research, we developed a new strategy of combining both of the clustering methods, hierarchical clustering and biclustering. The reserved conditions in datasets for hierarchical clustering were elicited according to the conditions in biclusters, and after hierarchical clustering, more detailed results in predicting unknown genes in certain patterns were obtained. This strategy of cooperating both of the methods during clustering procedure should be an effective guideline for functional predictions.

  19. Evaluation of a hierarchical ascendant clustering process implemented in a veterinary syndromic surveillance system.

    PubMed

    Behaeghel, Isabelle; Veldhuis, Anouk; Ren, Libo; Méroc, Estelle; Koenen, Frank; Kerkhofs, Pierre; Van der Stede, Yves; Barnouin, Jacques; Dispas, Marc

    2015-06-15

    Syndromic surveillance is considered as one of the surveillance components for early warning of health-related events, as it allows detection of aberrations in health indicators before laboratory confirmation. "MoSS-Emergences 2" (MoSS-E2), a tool for veterinary syndromic surveillance, aggregates groups of similar clinical observations by hierarchical ascendant classification (HAC). In the present study, this HAC clustering process was evaluated using a reference set of data that, for the purpose of this evaluation, was a priori divided and defined as Bluetongue (BTV) positive cases (PC) on the one hand and BTV negative cases (NC) on the other hand. By comparing the clustering result of MoSS-E2 with the expected outcome, the sensitivity (the ability to cluster PC together) and specificity (the ability to exclude NC from PC) of the clustering process were determined for this set of data. The stability of the classes obtained with the clustering algorithm was evaluated by comparing the MoSS-E2 generated dendrogram (applying complete linkage) with dendrograms of STATA® software applying average and single linkage methods. To assess the systems' robustness, the parameters of the distance measure were adjusted according to different scenarios and obtained outcomes were compared to the expected outcome based on the a priori known labels. Rand indexes were calculated to measure similarity between clustering outcomes. The clustering algorithm in its default settings successfully segregated the reference BTV cases from the non-BTV cases, resulting in a sensitivity of 100.0% (95% CI: 89.0-100.0) and a specificity of 100.0% (95% CI: 80.0-100.0) for this set of data. The different linkage methods showed similar clustering results indicating stability of the classes (Rand indexes of respectively 0.77 for average and 0.75 for single linkage). The system proved to be robust when changing the parameters as the BTV cases remained together in meaningful clusters (Rand indexes

  20. 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

  1. Cluster analysis of long time-series medical datasets

    NASA Astrophysics Data System (ADS)

    Hirano, Shoji; Tsumoto, Shusaku

    2004-04-01

    This paper presents a comparative study about the characteristics of clustering methods for inhomogeneous time-series medical datasets. Using various combinations of comparison methods and grouping methods, we performed clustering experiments of the hepatitis data set and evaluated validity of the results. The results suggested that (1) complete-linkage (CL) criterion in agglomerative hierarchical clustering (AHC) outperformed average-linkage (AL) criterion in terms of the interpretability of a dendrogram and clustering results, (2) combination of dynamic time warping (DTW) and CL-AHC constantly produced interpretable results, (3) combination of DTW and rough clustering (RC) would be used to find the core sequences of the clusters, (4) multiscale matching may suffer from the treatment of 'no-match' pairs, however, the problem may be eluded by using RC as a subsequent grouping method.

  2. Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

    PubMed

    Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

    2014-11-01

    Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.

  3. 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.

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

    PubMed

    Martin, T M

    2016-01-01

    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 binding. In vitro classification models yielded balanced accuracies ranging from 0.65 to 0.85 for the external prediction set. In vivo ER classification models yielded balanced accuracies ranging from 0.72 to 0.83. If used as additional biological descriptors for in vivo models, in vitro scores were found to increase the prediction accuracy of in vivo ER models. If in vitro activity was used directly as a surrogate for in vivo activity, the results were poor (balanced accuracy ranged from 0.49 to 0.72). Under-sampling negative compounds in the training set was found to increase the coverage (fraction of chemicals which can be predicted) and increase prediction sensitivity.

  5. 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.

  6. Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis.

    PubMed

    Bona, M T; Andrés, J M

    2007-06-15

    An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.

  7. 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.

  8. 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.

  9. Novel classification based on immunohistochemistry combined with hierarchical clustering analysis in non-functioning neuroendocrine tumor patients.

    PubMed

    Iida, Shinya; Miki, Yasuhiro; Ono, Katsuhiko; Akahira, Jun-ichi; Suzuki, Takashi; Ishida, Kazuyuki; Watanabe, Mika; Sasano, Hironobu

    2010-10-01

    Somatostatin analogues ameliorated many symptoms caused by neuroendocrine tumors (NET), but their antitumor activities are limited especially in non-functioning cases. An overactivation of signaling pathways under receptor tyrosine-kinase (RTK) has been recently demonstrated in some NET patients, but its details have remained largely unknown. Therefore, in this study, we immunolocalized therapeutic factors and evaluated the data to study the clinical significance of the molecules in non-functioning Japanese gastrointestinal NET. Fifty-two NET cases were available for examination in this study and expression of somatostatin receptor (sstr) 1, 2A, 2B, 3 and 5, activated form of mammalian target of rapamycin (mTOR), eukaryotic initiation factor 4-binding protein 1 (4EBP1), ribosomal protein s6 (S6), extracellular signal-regulated kinase (ERK) and insulin-like growth factor 1 receptor (IGF-1R) was evaluated using immunohistochemistry. We then studied the correlation among the immunohistochemical results of the individual cases using hierarchical clustering analysis. Results of clustering analysis demonstrated that NET cases were basically classified into Cluster I and II. Cluster I was associated with higher expression of sstr1, 2B and 3 and Cluster II was characterized by an activation of the PI3K/Akt pathway and IGF-1R and higher proliferative status. Cluster II was further classified into Cluster IIa and IIb. Cluster IIa was associated with higher expression of sstr1 and 5 and higher proliferative status and Cluster IIb was characterized by ERK activation. Hierarchical clustering analysis of immunoreactivity of the therapeutic factors can classify NET cases into three distinctive groups and the medical treatment may be determined according to this novel classification method for non-functioning NET patients.

  10. Using Hierarchical Clustering of Secreted Protein Families to Classify and Rank Candidate Effectors of Rust Fungi

    PubMed Central

    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

  11. BLACK HOLE MERGERS AND BLUE STRAGGLERS FROM HIERARCHICAL TRIPLES FORMED IN GLOBULAR CLUSTERS

    SciTech Connect

    Antonini, Fabio; Chatterjee, Sourav; Rodriguez, Carl L.; Morscher, Meagan; Pattabiraman, Bharath; Kalogera, Vicky; Rasio, Frederic A.

    2016-01-10

    Hierarchical triple-star systems are expected to form frequently via close binary–binary encounters in the dense cores of globular clusters (GCs). In a sufficiently inclined triple, gravitational interactions between the inner and outer binary can cause large-amplitude oscillations in the eccentricity of the inner orbit (“Lidov–Kozai (LK) cycles”), which can lead to a collision and merger of the two inner components. In this paper we use Monte Carlo models of dense star clusters to identify all triple systems formed dynamically and we compute their evolution using a highly accurate three-body integrator which incorporates relativistic and tidal effects. We find that a large fraction of these triples evolve through a non-secular dynamical phase which can drive the inner binary to higher eccentricities than predicted by the standard secular perturbation theory (even including octupole-order terms). We place constraints on the importance of LK-induced mergers for producing: (i) gravitational wave sources detectable by Advanced LIGO (aLIGO), for triples with an inner pair of stellar black holes (BHs); and (ii) blue straggler stars, for triples with main-sequence-star components. We find a realistic aLIGO detection rate of BH mergers due to the LK mechanism of ∼1 yr{sup −1}, with about 20% of these having a finite eccentricity when they first chirp into the aLIGO frequency band. While rare, these events are likely to dominate among eccentric compact object inspirals that are potentially detectable by aLIGO. For blue stragglers, we find that the LK mechanism can contribute up to ∼10% of their total numbers in GCs.

  12. 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.

  13. Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification

    PubMed Central

    Fong, Simon

    2012-01-01

    Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying speakers' gender, ethnicity and emotion states, and so forth. In this paper, a collection of computational algorithms are proposed to support voice classification; the algorithms are a combination of hierarchical clustering, dynamic time wrap transform, discrete wavelet transform, and decision tree. The proposed algorithms are relatively more transparent and interpretable than the existing ones, though many techniques such as Artificial Neural Networks, Support Vector Machine, and Hidden Markov Model (which inherently function like a black box) have been applied for voice verification and voice identification. Two datasets, one that is generated synthetically and the other one empirically collected from past voice recognition experiment, are used to verify and demonstrate the effectiveness of our proposed voice classification algorithm. PMID:22619492

  14. 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.

    2016-12-01

    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 GW 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.

  15. 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.

  16. 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

  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. Insights into environmental drivers of acoustic angular response using a self-organising map and hierarchical clustering

    NASA Astrophysics Data System (ADS)

    Daniell, James; Siwabessy, Justy; Nichol, Scott; Brooke, Brendan

    2015-10-01

    Acoustic backscatter from the seafloor is a complex function of signal frequency, seabed roughness, grain size distribution, benthos, bioturbation, volume reverberation, and other factors. Angular response is the variation in acoustic backscatter with incident angle and is considered be an intrinsic property of the seabed. An unsupervised classification technique combining a self-organising map (SOM) and hierarchical clustering was used to create an angular response facies map and explore the relationships between acoustic facies and ground truth data. Cluster validation routines indicated that a two cluster solution was optimal and separated sediment dominated environments from mixtures of sediment and hard ground. Low cluster separation limited cluster validation routines from identifying fine cluster structure visible with an AR density plot. Cluster validation, aided by a visual comparison with an AR density plot, indicated that a 14 cluster solution was also a suitable representation of the input dataset. Clusters that were a mixture of hard and unconsolidated substrates displayed an increase in backscatter with an increase in the occurrence of hard ground and highlighted the sensitivity of AR curves to the presence of even modest amounts of hard ground. Remapping video observations and sediment data onto the SOM matrix is innovative and depicts the relationship between ground truth data and cluster structure. Mapping environmental variables onto the SOM matrix can show broad trends and localised peaks and troughs and display the variability of ground truth data within designated clusters. These variables, when linked to AR curves via clusters, can indicate how environmental factors influence the shape of the curves. Once these links are established they can be incorporated into improved geoacoustic models that replicate field observations.

  19. Novel objective classification of reactive microglia following hypoglossal axotomy using hierarchical cluster analysis.

    PubMed

    Yamada, Jun; Jinno, Shozo

    2013-04-01

    A total of 136 microglia were intracellularly labeled and their morphological features were evaluated by 3D morphometric measurement. According to hierarchical cluster analysis, microglia were objectively categorized into four groups termed types I-IV. The validity of this classification was confirmed by principal component analysis and linear discriminant analysis. Type I microglia were found in sham-operated mice and in mice sacrificed 28 days (D28) after axotomy. The appearance of type I cells was similar to so-called ramified microglia in a resting state. Type II microglia were mainly seen in D14 mice, which exhibited small cell bodies with thin and short processes. Interestingly, none of the already-known morphological types of microglia seemed to be comparable to type II cells. We thus named type II microglia "small ramified" cells. Types III and IV microglia were mainly seen in D3 and D7 mice and their appearances were similar to hypertrophied and bushy cells, respectively. Proliferating cell nuclear antigen (PCNA), a mitosis marker, was almost exclusively expressed in D3 mice. On the other hand, voltage-dependent potassium channels (Kv1.3/1.5), neurotoxicity-related molecules, were most highly expressed in D14 mice. Increased expression of Kv1.3/1.5 in D14 mice was suppressed by minocycline treatment. These findings indicate that type II and III microglia may be involved in neurotoxicity and mitosis, respectively. Type IV microglial cells are assumed to be in the process of losing mitotic activity and gaining neurotoxicity. Our data also suggest that type II microglia can be a potential therapeutic target against neurodegenerative diseases.

  20. Data Clustering

    NASA Astrophysics Data System (ADS)

    Wagstaff, Kiri L.

    2012-03-01

    particular application involves considerations of the kind of data being analyzed, algorithm runtime efficiency, and how much prior knowledge is available about the problem domain, which can dictate the nature of clusters sought. Fundamentally, the clustering method and its representations of clusters carries with it a definition of what a cluster is, and it is important that this be aligned with the analysis goals for the problem at hand. In this chapter, I emphasize this point by identifying for each algorithm the cluster representation as a model, m_j , even for algorithms that are not typically thought of as creating a “model.” This chapter surveys a basic collection of clustering methods useful to any practitioner who is interested in applying clustering to a new data set. The algorithms include k-means (Section 25.2), EM (Section 25.3), agglomerative (Section 25.4), and spectral (Section 25.5) clustering, with side mentions of variants such as kernel k-means and divisive clustering. The chapter also discusses each algorithm’s strengths and limitations and provides pointers to additional in-depth reading for each subject. Section 25.6 discusses methods for incorporating domain knowledge into the clustering process. This chapter concludes with a brief survey of interesting applications of clustering methods to astronomy data (Section 25.7). The chapter begins with k-means because it is both generally accessible and so widely used that understanding it can be considered a necessary prerequisite for further work in the field. EM can be viewed as a more sophisticated version of k-means that uses a generative model for each cluster and probabilistic item assignments. Agglomerative clustering is the most basic form of hierarchical clustering and provides a basis for further exploration of algorithms in that vein. Spectral clustering permits a departure from feature-vector-based clustering and can operate on data sets instead represented as affinity, or similarity

  1. 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.

  2. A Bayesian Hierarchical Non-Linear Regression Model in Receiver Operating Characteristic Analysis of Clustered Continuous Diagnostic Data

    PubMed Central

    Zou, Kelly H.; O’Malley, A. James

    2005-01-01

    Receiver operating characteristic (ROC) analysis is a useful evaluative method of diagnostic accuracy. A Bayesian hierarchical nonlinear regression model for ROC analysis was developed. A validation analysis of diagnostic accuracy was conducted using prospective multi-center clinical trial prostate cancer biopsy data collected from three participating centers. The gold standard was based on radical prostatectomy to determine local and advanced disease. To evaluate the diagnostic performance of PSA level at fixed levels of Gleason score, a normality transformation was applied to the outcome data. A hierarchical regression analysis incorporating the effects of cluster (clinical center) and cancer risk (low, intermediate, and high) was performed, and the area under the ROC curve (AUC) was estimated. PMID:16161801

  3. [Identification of different Citrus sinensis (L.) Osbeck trees varieties using Fourier transform infrared spectroscopy and hierarchical cluster analysis].

    PubMed

    Yi, Shi-Lai; Deng, Lie; He, Shao-Lan; Shi, You-Ming; Zheng, Yong-Qiang; Lu, Qiang; Xie, Rang-Jin; Wei, Xian-Guoi; Li, Song-Wei; Jian, Shui-Xian

    2012-11-01

    Researched on diversity of the spring leaf samples of seven different Citrus sinensis (L.) Osbeck varieties by Fourier transform infrared (FTIR) spectroscopy technology, the results showed that the Fourier transform infrared spectra of seven varieties leaves was composited by the absorption band of cellulose and polysaccharide mainly, the wave number of characteristics absorption peaks were similar at their FTIR spectra. However, there were some differences in shape of peaks and relatively absorption intensity. The conspicuous difference was presented at the region between 1 500 and 700 cm(-1) by second derivative spectra. Through the hierarchical cluster analysis (HCA) of second derivative spectra between 1 500 and 700 cm(-1), the results showed that the clustering of the different varieties of Citrus sinensis (L.) Osbeck varieties was classification according to genetic relationship. The results showed that FTIR spectroscopy combined with hierarchical cluster analysis could be used to identify and classify of citrus varieties rapidly, it was an extension method to study on early leaves of varieties orange seedlings.

  4. Agglomerative Epigenetic Aberrations are a Common Event in Human Breast Cancer

    PubMed Central

    Petr, Novak; Taylor, Jensen; Oshiro Marc, M; Watts George, S; Kim Christina, J; Futscher Bernard, W

    2009-01-01

    Changes in DNA methylation patterns are a common characteristic of cancer cells. Recent studies suggest that DNA methylation affects not only discrete genes, but it can also affect large chromosomal regions, potentially leading to long range epigenetic silencing. It is unclear whether such long-range epigenetic events are relatively rare or frequent occurrences in cancer. Here we use a high-resolution promoter tiling array approach to analyze DNA methylation in breast cancer specimens and normal breast tissue to address this question. We identified 3506 cancer specific differentially methylated regions (DMR) in human breast cancer with 2033 being hypermethylation events and 1473 hypomethylation events. Most of these DMRs are recurrent in breast cancer; 90% of the identified DMRs occurred in at least 33% of the samples. Interestingly, we found a non-random spatial distribution of aberrantly methylated regions across the genome that showed a tendency to concentrate in relatively small genomic regions. Such agglomerates of hyper- and hypomethylated DMRs spanned up to several hundred kilobases and were frequently found at gene family clusters. The hypermethylation events usually occurred in the proximity of the transcription start site in CpG island promoters while hypomethylation events were frequently found in regions of segmental duplication. One example of a newly discovered agglomerate of hypermethylated DMRs associated with gene silencing in breast cancer that we examined in greater detail involved the protocadherin gene family clusters on chromosome 5 (PCDHA, PCDHB, and PCDHG). Taken together, our results suggest that agglomerative epigenetic aberrations are frequent events in human breast cancer. PMID:18922938

  5. Biomolecule-assisted hydrothermal synthesis and self-assembly of Bi2Te3 nanostring-cluster hierarchical structure.

    PubMed

    Mi, Jian-Li; Lock, Nina; Sun, Ting; Christensen, Mogens; Søndergaard, Martin; Hald, Peter; Hng, Huey H; Ma, Jan; Iversen, Bo B

    2010-05-25

    A simple biomolecule-assisted hydrothermal approach has been developed for the fabrication of Bi(2)Te(3) thermoelectric nanomaterials. The product has a nanostring-cluster hierarchical structure which is composed of ordered and aligned platelet-like crystals. The platelets are approximately 100 nm in diameter and only approximately 10 nm thick even though a high reaction temperature of 220 degrees C and a long reaction time of 24 h were applied to prepare the sample. The growth of the Bi(2)Te(3) hierarchical structure appears to be a self-assembly process. Initially, Te nanorods are formed using alginic acid as both reductant and template. Subsequently, Bi(2)Te(3) grows in a certain direction on the surface of the Te rods, resulting in the nanostring structure. The nanostrings further recombine side-by-side with each other to achieve the ordered nanostring clusters. The particle size and morphology can be controlled by adjusting the concentration of NaOH, which plays a crucial role on the formation mechanism of Bi(2)Te(3). An even smaller polycrystalline Bi(2)Te(3) superstructure composed of polycrystalline nanorods with some nanoplatelets attached to the nanorods is achieved at lower NaOH concentration. The room temperature thermoelectric properties have been evaluated with an average Seebeck coefficient of -172 microV K(-1), an electrical resistivity of 1.97 x 10(-3) Omegam, and a thermal conductivity of 0.29 W m(-1) K(-1).

  6. 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.

  7. The application of hierarchical cluster analysis and non-negative matrix factorization to European atmospheric monitoring site classification

    NASA Astrophysics Data System (ADS)

    Malley, Christopher S.; Braban, Christine F.; Heal, Mathew R.

    2014-03-01

    The effective classification of atmospheric monitoring sites within a network allows conclusions from measurements to be extrapolated beyond the confines of the site itself and applied to larger areas or populations. This is especially important for the European EMEP ‘supersites' because these are relatively few in number yet are subject to much investment in composition monitoring capability. Here, the representativeness of the two UK EMEP supersites, Auchencorth and Harwell, was evaluated using the hierarchical cluster analysis (HCA) of all available EMEP monitoring sites based on measured ozone concentration datasets for the period 1991-2010. A novel feature was to apply non-negative matrix factorization (NMF) to order the sites within the HCA dendrograms according to the relative anthropogenic influence on ozone. The ordered dendrograms enabled UK sites to be placed more precisely in a European context. For 2007-2010, all 19 UK EMEP sites were assigned to two of the site classification clusters, with 17 of the sites grouping closely with each other in each cluster. Auchencorth clustered with the sites characterised by less modification of hemispheric background ozone levels, whilst Harwell grouped with the sites showing a more polluted regime. A similar grouping of sites occurred between 1991 and 2010, with relatively closer clustering of Polluted UK sites compared with Remote UK sites due to the larger, transboundary spatial domain for which the Remote UK sites are representative. This tight clustering of the majority of the other UK ozone monitoring sites with either one of the supersites, shows that UK background ozone conditions are well represented by Auchencorth and Harwell, and gives confidence that more extensive chemical climatologies developed for the two supersites will have wider geographical relevance.

  8. Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model

    USGS Publications Warehouse

    Ellefsen, Karl J.; Smith, David

    2016-01-01

    Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples.

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

    PubMed

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

    2012-04-10

    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.

  11. Resolving misassembled cattle immune gene clusters with hierarchical, long read sequencing

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Animal health is a critical component of productivity; however, current genomic selection genotyping tools have a paucity of genetic markers within key immune gene clusters (IGC) involved in the cattle innate and adaptive immune systems. With diseases such as Bovine Tuberculosis and Johne’s disease ...

  12. 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.

  13. The impact of hierarchically constrained dynamics with a finite mean of cluster sizes on relaxation properties

    SciTech Connect

    Weron, Karina; Jurlewicz, Agnieszka; Patyk, Michał; Stanislavsky, Aleksander

    2013-05-15

    In this paper, a stochastic scenario of relaxation underlying the generalization (Kahlau et al., 2010) [15] of the Cole–Davidson (CD) and Kohlrausch–Williams–Watts (KWW) functions is proposed. As it has been shown (Kahlau et al., 2010) [15], the new three-parameter time-domain fitting function provides a very flexible description of the dielectric spectroscopy data for viscous glass-forming liquids. In relation to that result we discuss a hierarchically-constrained model yielding the proposed relaxation fitting function. Within the “exponentially decaying relaxation contributions” framework we show origins of the high-frequency (short-time, respectively) fractional power law, i.e., the characteristic feature of the new, as well as, of both CD and KWW response functions. We also bring into light a reason for which their common behavior in the opposite frequency limit is linear on external field frequency. Finally, we relate the new relaxation pattern (Kahlau et al., 2010) [15] with the Generalized Gamma (GG) survival probability of an imposed, non-equilibrium initial state in a relaxing system. -- Highlights: ► Combine the empirical Kohlrausch–Williams–Watts and Cole–Davidson laws of relaxation. ► Establish a microscopic stochastic scenario explaining the generalized law. ► Derive a frequency-domain relaxation function fitting the dielectric spectroscopy data. ► Find the low- and high-frequency properties for the relaxation pattern.

  14. A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS)

    PubMed Central

    Tiwari, Pallavi; Rosen, Mark; Madabhushi, Anant

    2009-01-01

    Magnetic resonance spectroscopy (MRS) has been shown to have great clinical potential as a supplement to magnetic resonance imaging in the detection of prostate cancer (CaP). MRS provides functional information in the form of changes in the relative concentration of specific metabolites including choline, creatine, and citrate which can be used to identify potential areas of CaP. With a view to assisting radiologists in interpretation and analysis of MRS data, some researchers have begun to develop computer-aided detection (CAD) schemes for CaP identification from spectroscopy. Most of these schemes have been centered on identifying and integrating the area under metabolite peaks which is then used to compute relative metabolite ratios. However, manual identification of metabolite peaks on the MR spectra, and especially via CAD, is a challenging problem due to low signal-to-noise ratio, baseline irregularity, peak overlap, and peak distortion. In this article the authors present a novel CAD scheme that integrates nonlinear dimensionality reduction (NLDR) with an unsupervised hierarchical clustering algorithm to automatically identify suspicious regions on the prostate using MRS and hence avoids the need to explicitly identify metabolite peaks. The methodology comprises two stages. In stage 1, a hierarchical spectral clustering algorithm is used to distinguish between extracapsular and prostatic spectra in order to localize the region of interest (ROI) corresponding to the prostate. Once the prostate ROI is localized, in stage 2, a NLDR scheme, in conjunction with a replicated clustering algorithm, is used to automatically discriminate between three classes of spectra (normal appearing, suspicious appearing, and indeterminate). The methodology was quantitatively and qualitatively evaluated on a total of 18 1.5 T in vivo prostate T2-weighted (w) and MRS studies obtained from the multisite, multi-institutional American College of Radiology (ACRIN) trial. In the

  15. Self-similar hierarchical energetics in the ICM of massive galaxy clusters

    NASA Astrophysics Data System (ADS)

    Miniati, Francesco; Beresnyak, Andrey

    Massive galaxy clusters (GC) are filled with a hot, turbulent and magnetised intra-cluster medium (ICM). They are still forming under the action of gravitational instability, which drives supersonic mass accretion flows. These partially dissipate into heat through a complex network of large scale shocks, and partly excite giant turbulent eddies and cascade. Turbulence dissipation not only contributes to heating of the ICM but also amplifies magnetic energy by way of dynamo action. The pattern of gravitational energy turning into kinetic, thermal, turbulent and magnetic is a fundamental feature of GC hydrodynamics but quantitative modelling has remained a challenge. In this contribution we present results from a recent high resolution, fully cosmological numerical simulation of a massive Coma-like galaxy cluster in which the time dependent turbulent motions of the ICM are resolved (Miniati 2014) and their statistical properties are quantified for the first time (Miniati 2015, Beresnyak & Miniati 2015). We combine these results with independent state-of-the art numerical simulations of MHD turbulence (Beresnyak 2012), which shows that in the nonlinear regime of turbulent dynamo (for magnetic Prandtl numbers>~ 1) the growth rate of the magnetic energy corresponds to a fraction CE ~= 4 - 5 × 10-2 of the turbulent dissipation rate. We thus determine without adjustable parameters the thermal, turbulent and magnetic history of giant GC (Miniati & Beresnyak 2015). We find that the energy components of the ICM are ordered according to a permanent hierarchy, in which the sonic Mach number at the turbulent injection scale is of order unity, the beta of the plasma of order forty and the ratio of turbulent injection scale to Alfvén scale is of order one hundred. These dimensionless numbers remain virtually unaltered throughout the cluster's history, despite evolution of each individual component and the drive towards equipartition of the turbulent dynamo, thus revealing a new

  16. 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

  17. 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.

  18. HIERARCHICAL FRAGMENTATION AND JET-LIKE OUTFLOWS IN IRDC G28.34+0.06: A GROWING MASSIVE PROTOSTAR CLUSTER

    SciTech Connect

    Wang Ke; Wu Yuefang; Zhang Huawei; Zhang Qizhou E-mail: qzhang@cfa.harvard.edu

    2011-07-01

    We present Submillimeter Array (SMA) {lambda} = 0.88 mm observations of an infrared dark cloud G28.34+0.06. Located in the quiescent southern part of the G28.34 cloud, the region of interest is a massive (>10{sup 3} M{sub sun}) molecular clump P1 with a luminosity of {approx}10{sup 3} L{sub sun}, where our previous SMA observations at 1.3 mm have revealed a string of five dust cores of 22-64 M{sub sun} along the 1 pc IR-dark filament. The cores are well aligned at a position angle (P.A.) of 48 deg. and regularly spaced at an average projected separation of 0.16 pc. The new high-resolution, high-sensitivity 0.88 mm image further resolves the five cores into 10 compact condensations of 1.4-10.6 M{sub sun}, with sizes of a few thousand AU. The spatial structure at clump ({approx}1 pc) and core ({approx}0.1 pc) scales indicates a hierarchical fragmentation. While the clump fragmentation is consistent with a cylindrical collapse, the observed fragment masses are much larger than the expected thermal Jeans masses. All the cores are driving CO (3-2) outflows up to 38 km s{sup -1}, the majority of which are bipolar, jet-like outflows. The moderate luminosity of the P1 clump sets a limit on the mass of protostars of 3-7 M{sub sun}. Because of the large reservoir of dense molecular gas in the immediate medium and ongoing accretion as evident by the jet-like outflows, we speculate that P1 will grow and eventually form a massive star cluster. This study provides a first glimpse of massive, clustered star formation that currently undergoes through an intermediate-mass stage.

  19. Hierarchical cluster analysis of environmental pollutants through P450 induction in cultured hepatic cells.

    PubMed

    Dubois, M; Plaisance, H; Thomé, J P; Kremers, P

    1996-08-01

    Environmental pollutants are classically associated with increased drug metabolism. Cultures of rat hepatocytes, quail hepatocytes, and human hepatoma (Hep G2) cells were used to study the effects of pesticides on drug-metabolizing enzymes. Membrane integrity and mitochondrial activity were evaluated and induction of ethoxycoumarin-O-deethylase and ethoxyresorufin-O-deethylase activities were measured. Induced P450s were identified by immunoblotting. Pentachlorophenol and lindane appeared as the strongest inducers. On the immunoblots, specific antibodies revealed induced CYP1A1 in fetal rat hepatocytes, CYP2B in quail hepatocytes, and CYP3A7 in Hep G2 cells. Pesticide effects on these different activities in each type of cultured cells were compared by cluster analysis. Results obtained under similar conditions with reference inducers phenobarbital (PB) and benzo[a]anthracene and other environmental pollutants (polychlorobiphenyls) were added to previous data prior to multivariate analysis. The tested products fell into four major groups: a first group with pentachlorophenol, identified as a CYP3A inducer; a second group containing the methylcholanthrene-type inducers that increase CYP1A-related activities; a third class represented by dieldrin, a PB-type inducer; a fourth group including inert compounds or weak inducers. Lindane shares the criteria of the second and third groups and seems to induce both CYP1A and CYP2B activities. The current study results highlight the advantage of using several types of cultured hepatocytes to evaluate the short-term toxicity of environmental pollutants in vitro and constitute a useful model for predicting the potential toxicity of pesticides in humans (Hep G2 cells) and wildlife (fetal quail hepatocytes).

  20. 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)

  1. 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

  2. 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

  3. Detecting hierarchical and overlapping network communities using locally optimal modularity changes

    NASA Astrophysics Data System (ADS)

    Barber, Michael J.

    2013-09-01

    Agglomerative clustering is a well established strategy for identifying communities in networks. Communities are successively merged into larger communities, coarsening a network of actors into a more manageable network of communities. The order in which merges should occur is not in general clear, necessitating heuristics for selecting pairs of communities to merge. We describe a hierarchical clustering algorithm based on a local optimality property. For each edge in the network, we associate the modularity change for merging the communities it links. For each community vertex, we call the preferred edge that edge for which the modularity change is maximal. When an edge is preferred by both vertices that it links, it appears to be the optimal choice from the local viewpoint. We use the locally optimal edges to define the algorithm: simultaneously merge all pairs of communities that are connected by locally optimal edges that would increase the modularity, redetermining the locally optimal edges after each step and continuing so long as the modularity can be further increased. We apply the algorithm to model and empirical networks, demonstrating that it can efficiently produce high-quality community solutions. We relate the performance and implementation details to the structure of the resulting community hierarchies. We additionally consider a complementary local clustering algorithm, describing how to identify overlapping communities based on the local optimality condition.

  4. Analysis of genetic diversity in banana cultivars (Musa cvs.) from the South of Oman using AFLP markers and classification by phylogenetic, hierarchical clustering and principal component analyses.

    PubMed

    Opara, Umezuruike Linus; Jacobson, Dan; Al-Saady, Nadiya Abubakar

    2010-05-01

    Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs. This study employed amplified fragment length polymorphism (AFLP) to investigate the genetic variation in local banana cultivars from the southern region of Oman. Using 12 primer combinations, a total of 1094 bands were scored, of which 1012 were polymorphic. Eighty-two unique markers were identified, which revealed the distinct separation of the seven cultivars. The results obtained show that AFLP can be used to differentiate the banana cultivars. Further classification by phylogenetic, hierarchical clustering and principal component analyses showed significant differences between the clusters found with molecular markers and those clusters created by previous studies using morphological analysis. Based on the analytical results, a consensus dendrogram of the banana cultivars is presented.

  5. Interactive Maximum Reliability Cluster Analysis.

    ERIC Educational Resources Information Center

    Mays, Robert

    1978-01-01

    A FORTRAN program for clustering variables using the alpha coefficient of reliability is described. For batch operation, a rule for stopping the agglomerative precedure is available. The conversational version of the program allows the user to intervene in the process in order to test the final solution for sensitivity to changes. (Author/JKS)

  6. Close relation of large cell carcinoma to adenocarcinoma by hierarchical cluster analysis: implications for histologic typing of lung cancer on biopsies.

    PubMed

    Hammer, Stephan H; Prall, Friedrich

    2015-09-01

    Determining histologic types of lung cancer on biopsies can be difficult. This study addresses the role of immunohistochemistry in histologic typing, using a tissue microarray (TMA) as "model biopsies," and presents a classification generated by an unsupervised hierarchical cluster analysis. A TMA was made from resection specimens of a consecutive series of 165 lung tumors. In a "tissue-spot review" with hematoxylin and eosin sections all the large cell carcinomas (N=22) were assigned to the noncommittal class of non-small cell lung cancer (NSCLC), as were an additional 37 tumors of defined histologic types. Adenocarcinomas and squamous cell carcinomas included with these NSCLC could be diagnosed by immunohistochemistry with antibodies against TTF-1, Napsin A, cytokeratin (CK)7, p40, p63, and CK5/6 with moderate to good sensitivities and specificities. Unsupervised hierarchical clustering was done with these data and additional high-molecular-weight cytokeratins, CD56, synaptophysin, and chromogranin immunohistochemistry. This delineated separate clusters for adenocarcinomas, large cell carcinomas, neuroendocrine tumors, and squamous cell carcinomas. Notably, adenocarcinoma and large cell carcinoma clusters were closely related and clearly set off from the squamous cell carcinoma cluster. As would be expected for a clinically well-staged series CDX2, GATA3, estrogen, and progesterone receptor immunohistochemistry remained negative in the vast majority of the tumors and, if positive, were restricted to very few cells. These results, the clustering data in particular, underpin the pragmatic recommendation canvassed with the IASLC/ATS/ERS classification of lung cancers that adenocarcinoma-type molecular studies should include NSCLC with a nonsquamous cell carcinoma immunophenotype.

  7. 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

  8. Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering

    NASA Astrophysics Data System (ADS)

    Yu, Bei; Gao, Jhih-Rong; Ding, Duo; Zeng, Xuan; Pan, David Z.

    2015-01-01

    As technology nodes continue to shrink, layout patterns become more sensitive to lithography processes, resulting in lithography hotspots that need to be identified and eliminated during physical verification. We propose an accurate hotspot detection approach based on principal component analysis-support vector machine classifier. Several techniques, including hierarchical data clustering, data balancing, and multilevel training, are provided to enhance the performance of the proposed approach. Our approach is accurate and more efficient than conventional time-consuming lithography simulation and provides a high flexibility for adapting to new lithography processes and rules.

  9. Detecting Esophageal Cancer Using Surface-Enhanced Raman Spectroscopy (SERS) of Serum Coupled with Hierarchical Cluster Analysis and Principal Component Analysis.

    PubMed

    Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Wang, Deli; Guan, Dagang

    2015-11-01

    Serum samples taken from healthy individuals and pre- and post-operative esophageal cancer patients were analyzed using surface-enhanced Raman spectroscopy (SERS) to explore the feasibility of diagnosing esophageal cancer using the technique. The serum spectrum data were collected using a He-Ne laser of wavelength 632.8 nm. Differences in peaks assigned to nucleic acids, lipids, and proteins were found to be statistically significant between groups, which implies that corresponding serum alterations occur with the development of esophageal diseases. For quantitative analysis, the chemometric methods of hierarchical clustering analysis and principal component analysis were utilized on the obtained SERS spectra for classification with good results.

  10. Separation of Cloud/No-Cloud Regions in Satellite Imagery Using a Variation of Hierarchical Clustering Analysis

    DTIC Science & Technology

    1991-12-01

    algorithms to use. Once the visible and infrared values for the cluster area have been placed into the array Parray , it is not necessary to have the...realtype; ZVis,ZIR,ZX,ZY realtype; Done .Boolean; Parray array’O. .149,0. 149,0.11 of byte; Cluster_-One_-IR,Cluster_-TwoIR :realtype; Cluster_-One_-Vis...arg); for m :=0 to 149 do (*Set all values in Parray to zero for n :=0 to 149 do begin Parrayfm,n1,0l 0; Parray [m,n,1] 0; end: (for n) for j :y s to

  11. Hierarchical rutile TiO2 flower cluster-based high efficiency dye-sensitized solar cells via direct hydrothermal growth on conducting substrates.

    PubMed

    Ye, Meidan; Liu, Hsiang-Yu; Lin, Changjian; Lin, Zhiqun

    2013-01-28

    Dye-sensitized solar cells (DSSCs) based on hierarchical rutile TiO(2) flower clusters prepared by a facile, one-pot hydrothermal process exhibit a high efficiency. Complex yet appealing rutile TiO(2) flower films are, for the first time, directly hydrothermally grown on a transparent conducting fluorine-doped tin oxide (FTO) substrate. The thickness and density of as-grown flower clusters can be readily tuned by tailoring growth parameters, such as growth time, the addition of cations of different valence and size, initial concentrations of precursor and cation, growth temperature, and acidity. Notably, the small lattice mismatch between the FTO substrate and rutile TiO(2) renders the epitaxial growth of a compact rutile TiO(2) layer on the FTO glass. Intriguingly, these TiO(2) flower clusters can then be exploited as photoanodes to produce DSSCs, yielding a power conversion efficiency of 2.94% despite their rutile nature, which is further increased to 4.07% upon the TiCl(4) treatment.

  12. 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

    2016-04-30

    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.

  13. 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.

  14. Cluster Analysis and Web-Based 3-D Visualization of Large-scale Geophysical Data

    NASA Astrophysics Data System (ADS)

    Kadlec, B. J.; Yuen, D. A.; Bollig, E. F.; Dzwinel, W.; da Silva, C. R.

    2004-05-01

    We present a problem-solving environment WEB-IS (Web-based Data Interrogative System), which we have developed for remote analysis and visualization of geophysical data [Garbow et. al., 2003]. WEB-IS employs agglomerative clustering methods intended for feature extraction and studying the predictions of large magnitude earthquake events. Data-mining is accomplished using a mutual nearest meighbor (MNN) algorithm for extracting event clusters of different density and shapes based on a hierarchical proximity measure. Clustering schemes used in molecular dynamics [Da Silva et. al., 2002] are also considered for increasing computational efficiency using a linked cell algorithm for creating a Verlet neighbor list (VNL) and extracting different cluster structures by applying a canonical backtracking search on the VNL. Space and time correlations between the events are visualized dynamically in 3-D through a filter by showing clusters at different timescales according to defined units of time ranging from days to years. This WEB-IS functionality was tested both on synthetic [Eneva and Ben-Zion, 1997] and actual earthquake catalogs of Japanese earthquakes and can be applied to the soft-computing data mining methods used in hydrology and geoinformatics. Da Silva, C.R.S., Justo, J.F., Fazzio, A., Phys Rev B, vol., 65, 2002. Eneva, M., Ben-Zion, Y.,J. Geophys. Res., 102, 17785-17795, 1997. Garbow, Z.A., Yuen, D.A., Erlebacher, G., Bollig, E.F., Kadlec, B.J., Vis. Geosci., 2003.

  15. 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

  16. 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

  17. ARC: automated resource classifier for agglomerative functional classification of prokaryotic proteins using annotation texts.

    PubMed

    Gnanamani, Muthiah; Kumar, Naveen; Ramachandran, Srinivasan

    2007-08-01

    Functional classification of proteins is central to comparative genomics. The need for algorithms tuned to enable integrative interpretation of analytical data is felt globally. The availability of a general,automated software with built-in flexibility will significantly aid this activity. We have prepared ARC (Automated Resource Classifier), which is an open source software meeting the user requirements of flexibility. The default classification scheme based on keyword match is agglomerative and directs entries into any of the 7 basic non-overlapping functional classes: Cell wall, Cell membrane and Transporters (C), Cell division (D), Information (I), Translocation (L), Metabolism (M), Stress(R), Signal and communication (S) and 2 ancillary classes: Others (O) and Hypothetical (H). The keyword library of ARC was built serially by first drawing keywords from Bacillus subtilis and Escherichia coli K12. In subsequent steps,this library was further enriched by collecting terms from archaeal representative Archaeoglobus fulgidus, Gene Ontology, and Gene Symbols. ARC is 94.04% successful on 6,75,663 annotated proteins from 348 prokaryotes. Three examples are provided to illuminate the current perspectives on mycobacterial physiology and costs of proteins in 333 prokaryotes. ARC is available at http://arc.igib.res.in.

  18. 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.

  19. HILIC-UPLC-MS/MS combined with hierarchical clustering analysis to rapidly analyze and evaluate nucleobases and nucleosides in Ginkgo biloba leaves.

    PubMed

    Yao, Xin; Zhou, Guisheng; Tang, Yuping; Guo, Sheng; Qian, Dawei; Duan, Jin-Ao

    2015-02-01

    Ginkgo biloba leaf extract has been widely used in dietary supplements and more recently in some foods and beverages. In addition to the well-known flavonol glycosides and terpene lactones, G. biloba leaves are also rich in nucleobases and nucleosides. To determine the content of nucleobases and nucleosides in G. biloba leaves at trace levels, a reliable method has been established by using hydrophilic interaction ultra performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (HILIC-UPLC-TQ-MS/MS) working in multiple reaction monitoring mode. Eleven nucleobases and nucleosides were simultaneously determined in seven min. The proposed method was fully validated in terms of linearity, sensitivity, and repeatability, as well as recovery. Furthermore, hierarchical clustering analysis (HCA) was performed to evaluate and classify the samples according to the contents of the eleven chemical constituents. The established approach could be helpful for evaluation of the potential values as dietary supplements and the quality control of G. biloba leaves, which might also be utilized for the investigation of other medicinal herbs containing nucleobases and nucleosides.

  20. Hierarchical cluster analysis and chemical characterisation of Myrtus communis L. essential oil from Yemen region and its antimicrobial, antioxidant and anti-colorectal adenocarcinoma properties.

    PubMed

    Anwar, Sirajudheen; Crouch, Rebecca A; Awadh Ali, Nasser A; Al-Fatimi, Mohamed A; Setzer, William N; Wessjohann, Ludger

    2017-01-09

    The hydrodistilled essential oil obtained from the dried leaves of Myrtus communis, collected in Yemen, was analysed by GC-MS. Forty-one compounds were identified, representing 96.3% of the total oil. The major constituents of essential oil were oxygenated monoterpenoids (87.1%), linalool (29.1%), 1,8-cineole (18.4%), α-terpineol (10.8%), geraniol (7.3%) and linalyl acetate (7.4%). The essential oil was assessed for its antimicrobial activity using a disc diffusion assay and resulted in moderate to potent antibacterial and antifungal activities targeting mainly Bacillus subtilis, Staphylococcus aureus and Candida albicans. The oil moderately reduced the diphenylpicrylhydrazyl radical (IC50 = 4.2 μL/mL or 4.1 mg/mL). In vitro cytotoxicity evaluation against HT29 (human colonic adenocarcinoma cells) showed that the essential oil exhibited a moderate antitumor effect with IC50 of 110 ± 4 μg/mL. Hierarchical cluster analysis of M. communis has been carried out based on the chemical compositions of 99 samples reported in the literature, including Yemeni sample.

  1. 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

    2013-01-21

    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.

  2. Chemical fingerprint and quantitative analysis for the quality evaluation of Vitex negundo seeds by reversed-phase high-performance liquid chromatography coupled with hierarchical clustering analysis.

    PubMed

    Shu, Zhiheng; Li, Xiuqing; Rahman, Khalid; Qin, Luping; Zheng, Chengjian

    2016-01-01

    A simple and efficient method was developed for the chemical fingerprint analysis and simultaneous determination of four phenylnaphthalene-type lignans in Vitex negundo seeds using high-performance liquid chromatography with diode array detection. For fingerprint analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer-aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical clustering analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds.

  3. 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.

  4. Comparative evaluation of chemical profiles of three representative 'snow lotus' herbs by UPLC-DAD-QTOF-MS combined with principal component and hierarchical cluster analyses.

    PubMed

    Chen, Qi-Lei; Zhu, Lin; Tang, Yi-Na; Kwan, Hiu-Yee; Zhao, Zhong-Zhen; Chen, Hu-Biao; Yi, Tao

    2016-10-20

    Herbal healthcare products are used worldwide as relatively safe and effective alternatives to allopathic drugs. Saussurea laniceps Hand.-Mazz. (SL), S. medusa Maxim. (SM) and S. involucrata (Kar. et Kir.) Sch.Bip. (SI) are three sources of the renowned 'snow lotus', Chinese materia medica for treating inflammatory diseases. The three species have different therapeutic effects, among which SL has been proved to be the most potent, but they are frequently confused on the market and in the academic community. An ultra-high performance liquid chromatography-diode array detector-quadrupole time of flight-mass spectrometry (UPLC-DAD-QTOF-MS) method was developed and used to analyze 49 herbal samples for species analysis and overall quality evaluation. With 25 simultaneously identified constituents, of which 12 were quantified, the three herbs showed different chemical profiles. Four-dimensional principle component analysis (4D-PCA) and orthogonal hierarchical cluster analysis (2D-HCA) results illustrated that SL should be grouped away from SM and SI, contradicting the botanical record in Flora of China. The present chemical determination and pattern recognition results directly explain the therapeutic potency of SL and distinguish the three confused snow lotus herbs. Furthermore, the findings suggest a possible extensive quality evaluation model for multi-origin medicinal plants and help monitor falsification of snow lotus herbal products on the market, contributing to a more regulated pharmaceutical industry.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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)

  10. 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

  11. Using cluster analysis of cytokines to identify patterns of inflammation in hospitalized patients with community-acquired pneumonia: a pilot study

    PubMed Central

    Wiemken, Timothy L; Kelley, Robert R; Fernandez-Botran, Rafael; Mattingly, William A.; Arnold, Forest W.; Furmanek, Stephen P; Restrepo, Marcos I; Chalmers, James D; Peyrani, Paula; Cavallazzi, Rodrigo; Bordon, Jose; Aliberti, Stefano; Ramirez, Julio A.

    2017-01-01

    Introduction Patients with severe community-acquired pneumonia (CAP) are believed to have an exaggerated inflammatory response to bacterial infection. Therapies aiming to modulate the inflammatory response have been largely unsuccessful, perhaps reflecting that CAP is a heterogeneous disorder that cannot be modulated by a single anti-inflammatory approach. We hypothesize that the host inflammatory response to pneumonia may be characterized by distinct cytokine patterns, which can be harnessed for personalized therapies. Methods Here, we use hierarchical cluster analysis of cytokines to examine if patterns of inflammatory response in 13 hospitalized patients with CAP can be defined. This was a secondary data analysis of the Community-Acquired Pneumonia Inflammatory Study Group (CAPISG) database. The following cytokines were measured in plasma and sputum on the day of admission: interleukin (IL)-1β, IL-1 receptor antagonist (IL-1ra), IL-6, CXCL8 (IL-8), IL-10, IL-12p40, IL-17, interferon (IFN)γ, tumor necrosis factor (TNF)α, and CXCL10 (IP-10). Hierarchical agglomerative clustering algorithms were used to evaluate clusters of patients within plasma and sputum cytokine determinations. Results A total of thirteen patients were included in this pilot study. Cluster analysis identified distinct inflammatory response patterns of cytokines in the plasma, sputum, and the ratio of plasma to sputum. Conclusions Inflammatory response patterns in plasma and sputum can be identified in hospitalized patients with CAP. Characterization of the local and systemic inflammatory response may help to better discriminate patients for enrollment into clinical trials of immunomodulatory therapies. PMID:28393141

  12. 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.

  13. 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

  14. 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

  15. High-performance supercapacitor and lithium-ion battery based on 3D hierarchical NH4F-induced nickel cobaltate nanosheet-nanowire cluster arrays as self-supported electrodes

    NASA Astrophysics Data System (ADS)

    Chen, Yuejiao; Qu, Baihua; Hu, Lingling; Xu, Zhi; Li, Qiuhong; Wang, Taihong

    2013-09-01

    A facile hydrothermal method is developed for large-scale production of three-dimensional (3D) hierarchical porous nickel cobaltate nanowire cluster arrays derived from nanosheet arrays with robust adhesion on Ni foam. Based on the morphology evolution upon reaction time, a possible formation process is proposed. The role of NH4F in formation of the structure has also been investigated based on different NH4F amounts. This unique structure significantly enhances the electroactive surface areas of the NiCo2O4 arrays, leading to better interfacial/chemical distributions at the nanoscale, fast ion and electron transfer and good strain accommodation. Thus, when it is used for supercapacitor testing, a specific capacitance of 1069 F g-1 at a very high current density of 100 A g-1 was obtained. Even after more than 10 000 cycles at various large current densities, a capacitance of 2000 F g-1 at 10 A g-1 with 93.8% retention can be achieved. It also exhibits a high-power density (26.1 kW kg-1) at a discharge current density of 80 A g-1. When used as an anode material for lithium-ion batteries (LIBs), it presents a high reversible capacity of 976 mA h g-1 at a rate of 200 mA g-1 with good cycling stability and rate capability. This array material is rarely used as an anode material. Our results show that this unique 3D hierarchical porous nickel cobaltite is promising for electrochemical energy applications.A facile hydrothermal method is developed for large-scale production of three-dimensional (3D) hierarchical porous nickel cobaltate nanowire cluster arrays derived from nanosheet arrays with robust adhesion on Ni foam. Based on the morphology evolution upon reaction time, a possible formation process is proposed. The role of NH4F in formation of the structure has also been investigated based on different NH4F amounts. This unique structure significantly enhances the electroactive surface areas of the NiCo2O4 arrays, leading to better interfacial/chemical distributions

  16. Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

    PubMed Central

    Lensink, Marc F.; Petta, Andrea; Serra, Luigi; Scarano, Vittorio; Cavallo, Luigi; Oliva, Romina

    2016-01-01

    Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers’ performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

  17. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with multidimensional scaling, binary hierarchical cluster tree and selected diagnostic masses improves species identification of Neolithic keratin sequences from furs of the Tyrolean Iceman Oetzi.

    PubMed

    Hollemeyer, Klaus; Altmeyer, Wolfgang; Heinzle, Elmar; Pitra, Christian

    2012-08-30

    The identification of fur origins from the 5300-year-old Tyrolean Iceman's accoutrement is not yet complete, although definite identification is essential for the socio-cultural context of his epoch. Neither have all potential samples been identified so far, nor there has a consensus been reached on the species identified using the classical methods. Archaeological hair often lacks analyzable hair scale patterns in microscopic analyses and polymer chain reaction (PCR)-based techniques are often inapplicable due to the lack of amplifiable ancient DNA. To overcome these drawbacks, a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) method was used exclusively based on hair keratins. Thirteen fur specimens from his accoutrement were analyzed after tryptic digest of native hair. Peptide mass fingerprints (pmfs) from ancient samples and from reference species mostly occurring in the Alpine surroundings at his lifetime were compared to each other using multidimensional scaling and binary hierarchical cluster tree analysis. Both statistical methods highly reflect spectral similarities among pmfs as close zoological relationships. While multidimensional scaling was useful to discriminate specimens on the zoological order level, binary hierarchical cluster tree reached the family or subfamily level. Additionally, the presence and/or absence of order, family and/or species-specific diagnostic masses in their pmfs allowed the identification of mammals mostly down to single species level. Red deer was found in his shoe vamp, goat in the leggings, cattle in his shoe sole and at his quiver's closing flap as well as sheep and chamois in his coat. Canid species, like grey wolf, domestic dog or European red fox, were discovered in his leggings for the first time, but could not be differentiated to species level. This is widening the spectrum of processed fur-bearing species to at least one member of the Canidae family. His fur cap was

  18. 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

  19. 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

  20. 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

  1. Johnson-Neyman Type Technique in Hierarchical Linear Model.

    ERIC Educational Resources Information Center

    Miyazaki, Yasuo

    One of the innovative approaches in the use of hierarchical linear models (HLM) is to use HLM for Slopes as Outcomes models. This implies that the researcher considers that the regression slopes vary from cluster to cluster randomly as well as systematically with certain covariates at the cluster level. Among the covariates, group indicator…

  2. Hierarchical Theme and Topic Modeling.

    PubMed

    Chien, Jen-Tzung

    2016-03-01

    Considering the hierarchical data groupings in text corpus, e.g., words, sentences, and documents, we conduct the structural learning and infer the latent themes and topics for sentences and words from a collection of documents, respectively. The relation between themes and topics under different data groupings is explored through an unsupervised procedure without limiting the number of clusters. A tree stick-breaking process is presented to draw theme proportions for different sentences. We build a hierarchical theme and topic model, which flexibly represents the heterogeneous documents using Bayesian nonparametrics. Thematic sentences and topical words are extracted. In the experiments, the proposed method is evaluated to be effective to build semantic tree structure for sentences and the corresponding words. The superiority of using tree model for selection of expressive sentences for document summarization is illustrated.

  3. Hierarchical Multiagent Reinforcement Learning

    DTIC Science & Technology

    2004-01-25

    In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multiagent tasks. We...introduce a hierarchical multiagent reinforcement learning (RL) framework and propose a hierarchical multiagent RL algorithm called Cooperative HRL. In

  4. 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…

  5. 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.

  6. Automated tetraploid genotype calling by hierarchical clustering

    Technology Transfer Automated Retrieval System (TEKTRAN)

    SNP arrays are transforming breeding and genetics research for autotetraploids. To fully utilize these arrays, however, the relationship between signal intensity and allele dosage must be inferred independently for each marker. We developed an improved computational method to automate this process, ...

  7. 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…

  8. 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.

  9. Waveforms clustering and single-station location of microearthquake multiplets recorded in the northern Sicilian offshore region

    NASA Astrophysics Data System (ADS)

    D'Alessandro, Antonino; Mangano, Giorgio; D'Anna, Giuseppe; Luzio, Dario

    2013-09-01

    In 2009 December, the OBSLab-INGV (Istituto Nazionale di Geofisica e Vulcanologia) deployed an Ocean Bottom Seismometer with Hydrophone (OBS/H) near the epicentral area of the main shock of the Palermo seismic sequence of 2002. The monitoring activity had a total duration of about 8 months. During this experiment, the OBS/H recorded 247 very local microearthquakes, whose local magnitude is between -0.5 and 2.5 and TS - TP delay time between 0.2 and 5 s, almost all of which were undetected by the Italian National Seismic Network. This local microseismicity has been analysed using an innovative clustering technique that exploits the similarity between the waveforms generated by different events. The clustering technique implemented, based on hierarchical agglomerative algorithms, nearest neighbour technique and dendrogram representation, allowed us to identify nine distinct multiplets characterized by a high degree of similarity between the waveforms. The microevents were located through an improved single-station location (SSL) technique based on the polarization analysis of the 3C signals and on the estimation of the TS - TP time. In the new SSL technique, an unbiased covariance matrix was defined and a ray tracer-based determination of the epicentral distance and hypocentral depth was proposed. All the multiplets were generated by events with hypocentres that were very close to each other. However, not all the identified clusters are also clustered in the time-magnitude domain. It was also observed that some multiplets have clouds of hypocentres overlapping each other. These clusters, indistinguishable without the application of a waveforms clustering technique, show differences in the waveforms that must be attributed to differences in the focal mechanisms which generated the waveforms. The local seismic events recorded are typical of a seismicity generated by a volume characterized by a highly complex fracturing pattern and by an important role in the dynamics

  10. 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

  11. Optimal wavelength band clustering for multispectral iris recognition.

    PubMed

    Gong, Yazhuo; Zhang, David; Shi, Pengfei; Yan, Jingqi

    2012-07-01

    This work explores the possibility of clustering spectral wavelengths based on the maximum dissimilarity of iris textures. The eventual goal is to determine how many bands of spectral wavelengths will be enough for iris multispectral fusion and to find these bands that will provide higher performance of iris multispectral recognition. A multispectral acquisition system was first designed for imaging the iris at narrow spectral bands in the range of 420 to 940 nm. Next, a set of 60 human iris images that correspond to the right and left eyes of 30 different subjects were acquired for an analysis. Finally, we determined that 3 clusters were enough to represent the 10 feature bands of spectral wavelengths using the agglomerative clustering based on two-dimensional principal component analysis. The experimental results suggest (1) the number, center, and composition of clusters of spectral wavelengths and (2) the higher performance of iris multispectral recognition based on a three wavelengths-bands fusion.

  12. 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.

  13. Hierarchical Approximate Bayesian Computation

    PubMed Central

    Turner, Brandon M.; Van Zandt, Trisha

    2013-01-01

    Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function. PMID:24297436

  14. 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

  15. 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

  16. Hierarchical auxetic mechanical metamaterials.

    PubMed

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

    2015-02-11

    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.

  17. 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.

  18. Adaptive hierarchical fuzzy controller

    SciTech Connect

    Raju, G.V.S.; Jun Zhou

    1993-07-01

    A methodology for designing adaptive hierarchical fuzzy controllers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzy controller to accommodate the variations of system parameters. The proposed algorithm was used in feedwater flow control to a steam generator. Simulation studies are presented that illustrate the effectiveness of the approach

  19. Hierarchical modeling of protein interactions.

    PubMed

    Kurcinski, Mateusz; Kolinski, Andrzej

    2007-07-01

    A novel approach to hierarchical peptide-protein and protein-protein docking is described and evaluated. Modeling procedure starts from a reduced space representation of proteins and peptides. Polypeptide chains are represented by strings of alpha-carbon beads restricted to a fine-mesh cubic lattice. Side chains are represented by up to two centers of interactions, corresponding to beta-carbons and the centers of mass of the remaining portions of the side groups, respectively. Additional pseudoatoms are located in the centers of the virtual bonds connecting consecutive alpha carbons. These pseudoatoms support a model of main-chain hydrogen bonds. Docking starts from a collection of random configurations of modeled molecules. Interacting molecules are flexible; however, higher accuracy models are obtained when the conformational freedom of one (the larger one) of the assembling molecules is limited by a set of weak distance restraints extracted from the experimental (or theoretically predicted) structures. Sampling is done by means of Replica Exchange Monte Carlo method. Afterwards, the set of obtained structures is subject to a hierarchical clustering. Then, the centroids of the resulting clusters are used as scaffolds for the reconstruction of the atomic details. Finally, the all-atom models are energy minimized and scored using classical tools of molecular mechanics. The method is tested on a set of macromolecular assemblies consisting of proteins and peptides. It is demonstrated that the proposed approach to the flexible docking could be successfully applied to prediction of protein-peptide and protein-protein interactions. The obtained models are almost always qualitatively correct, although usually of relatively low (or moderate) resolution. In spite of this limitation, the proposed method opens new possibilities of computational studies of macromolecular recognition and mechanisms of assembly of macromolecular complexes.

  20. 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.

  1. 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.

  2. Hierarchical organization unveiled by functional connectivity in complex brain networks.

    PubMed

    Zhou, Changsong; Zemanová, Lucia; Zamora, Gorka; Hilgetag, Claus C; Kurths, Jürgen

    2006-12-08

    How do diverse dynamical patterns arise from the topology of complex networks? We study synchronization dynamics in the cortical brain network of the cat, which displays a hierarchically clustered organization, by modeling each node (cortical area) with a subnetwork of interacting excitable neurons. We find that in the biologically plausible regime the dynamics exhibits a hierarchical modular organization, in particular, revealing functional clusters coinciding with the anatomical communities at different scales. Our results provide insights into the relationship between network topology and functional organization of complex brain networks.

  3. Exploring hierarchical visualization designs using phylogenetic trees

    NASA Astrophysics Data System (ADS)

    Li, Shaomeng; Crouser, R. Jordan; Griffin, Garth; Gramazio, Connor; Schulz, Hans-Jörg; Childs, Hank; Chang, Remco

    2015-01-01

    Ongoing research on information visualization has produced an ever-increasing number of visualization designs. Despite this activity, limited progress has been made in categorizing this large number of information visualizations. This makes understanding their common design features challenging, and obscures the yet unexplored areas of novel designs. With this work, we provide categorization from an evolutionary perspective, leveraging a computational model to represent evolutionary processes, the phylogenetic tree. The result - a phylogenetic tree of a design corpus of hierarchical visualizations - enables better understanding of the various design features of hierarchical information visualizations, and further illuminates the space in which the visualizations lie, through support for interactive clustering and novel design suggestions. We demonstrate these benefits with our software system, where a corpus of two-dimensional hierarchical visualization designs is constructed into a phylogenetic tree. This software system supports visual interactive clustering and suggesting for novel designs; the latter capacity is also demonstrated via collaboration with an artist who sketched new designs using our system.

  4. 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…

  5. Hierarchical Porous Structures

    SciTech Connect

    Grote, Christopher John

    2016-06-07

    Materials Design is often at the forefront of technological innovation. While there has always been a push to generate increasingly low density materials, such as aero or hydrogels, more recently the idea of bicontinuous structures has gone more into play. This review will cover some of the methods and applications for generating both porous, and hierarchically porous structures.

  6. 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.

  7. Contour detection and hierarchical image segmentation.

    PubMed

    Arbeláez, Pablo; Maire, Michael; Fowlkes, Charless; Malik, Jitendra

    2011-05-01

    This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

  8. Modeling the deformation behavior of nanocrystalline alloy with hierarchical microstructures

    NASA Astrophysics Data System (ADS)

    Liu, Hongxi; Zhou, Jianqiu; Zhao, Yonghao

    2016-02-01

    A mechanism-based plasticity model based on dislocation theory is developed to describe the mechanical behavior of the hierarchical nanocrystalline alloys. The stress-strain relationship is derived by invoking the impeding effect of the intra-granular solute clusters and the inter-granular nanostructures on the dislocation movements along the sliding path. We found that the interaction between dislocations and the hierarchical microstructures contributes to the strain hardening property and greatly influence the ductility of nanocrystalline metals. The analysis indicates that the proposed model can successfully describe the enhanced strength of the nanocrystalline hierarchical alloy. Moreover, the strain hardening rate is sensitive to the volume fraction of the hierarchical microstructures. The present model provides a new perspective to design the microstructures for optimizing the mechanical properties in nanostructural metals.

  9. Johnson-Neyman Type Technique in Hierarchical Linear Models

    ERIC Educational Resources Information Center

    Miyazaki, Yasuo; Maier, Kimberly S.

    2005-01-01

    In hierarchical linear models we often find that group indicator variables at the cluster level are significant predictors for the regression slopes. When this is the case, the average relationship between the outcome and a key independent variable are different from group to group. In these settings, a question such as "what range of the…

  10. Cluster Physics with Merging Galaxy Clusters

    NASA Astrophysics Data System (ADS)

    Molnar, Sandor

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

  11. Hierarchical manifold learning.

    PubMed

    Bhatia, Kanwal K; Rao, Anil; Price, Anthony N; Wolz, Robin; Hajnal, Jo; Rueckert, Daniel

    2012-01-01

    We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate its utility in two very different settings: (1) to learn the regional correlations in motion within a sequence of time-resolved images of the thoracic cavity; (2) to find discriminative regions of 3D brain images in the classification of neurodegenerative disease,

  12. Hierarchical Pattern Classifier

    NASA Technical Reports Server (NTRS)

    Yates, Gigi L.; Eberlein, Susan J.

    1992-01-01

    Hierarchical pattern classifier reduces number of comparisons between input and memory vectors without reducing detail of final classification by dividing classification process into coarse-to-fine hierarchy that comprises first "grouping" step and second classification step. Three-layer neural network reduces computation further by reducing number of vector dimensions in processing. Concept applicable to pattern-classification problems with need to reduce amount of computation necessary to classify, identify, or match patterns to desired degree of resolution.

  13. HDS: Hierarchical Data System

    NASA Astrophysics Data System (ADS)

    Pearce, Dave; Walter, Anton; Lupton, W. F.; Warren-Smith, Rodney F.; Lawden, Mike; McIlwrath, Brian; Peden, J. C. M.; Jenness, Tim; Draper, Peter W.

    2015-02-01

    The Hierarchical Data System (HDS) is a file-based hierarchical data system designed for the storage of a wide variety of information. It is particularly suited to the storage of large multi-dimensional arrays (with their ancillary data) where efficient access is needed. It is a key component of the Starlink software collection (ascl:1110.012) and is used by the Starlink N-Dimensional Data Format (NDF) library (ascl:1411.023). HDS organizes data into hierarchies, broadly similar to the directory structure of a hierarchical filing system, but contained within a single HDS container file. The structures stored in these files are self-describing and flexible; HDS supports modification and extension of structures previously created, as well as functions such as deletion, copying, and renaming. All information stored in HDS files is portable between the machines on which HDS is implemented. Thus, there are no format conversion problems when moving between machines. HDS can write files in a private binary format (version 4), or be layered on top of HDF5 (version 5).

  14. 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

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

    PubMed

    Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

    2012-01-01

    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. 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

  17. 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.

  18. 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.

  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. Nonmetric Grouping: Clusters and Cliques

    ERIC Educational Resources Information Center

    Peay, Edmund R.

    1975-01-01

    A class of closely related hierarchical grouping methods are discussed and a procedure which implements them in an integrated fashion is presented. These methods avoid some theoretical anomalies inherent in clustering and provide a framework for viewing partitioning and nonpartitioning grouping. Significant relationships between these methods and…

  1. How hierarchical is language use?

    PubMed

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

    2012-11-22

    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.

  2. 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

  3. Hierarchical partial order ranking.

    PubMed

    Carlsen, Lars

    2008-09-01

    Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritization of polluted sites is given.

  4. Adaptive color visualization for dichromats using a customized hierarchical palette

    NASA Astrophysics Data System (ADS)

    Rodríguez-Pardo, Carlos E.; Sharma, Gaurav

    2011-01-01

    We propose a user-centric methodology for displaying digital color documents, that optimizes color representations in an observer specific and adaptive fashion. We apply our framework to situations involving viewers with common dichromatic color vision deficiencies, who face challenges in perceiving information presented in color images and graphics designed for color normal individuals. For situations involving qualitative data visualization, we present a computationally efficient solution that combines a customized observer-specific hierarchical palette with "display time" selection of the number of colors to generate renderings with colors that are easily discriminated by the intended viewer. The palette design is accomplished via a clustering algorithm, that arranges colors in a hierarchical tree based on their perceived differences for the intended viewer. A desired number of highly discriminable colors are readily obtained from the hierarchical palette via a simple truncation. As an illustration, we demonstrate the application of the methodology to Ishihara style images.

  5. Associative Hierarchical Random Fields.

    PubMed

    Ladický, L'ubor; Russell, Chris; Kohli, Pushmeet; Torr, Philip H S

    2014-06-01

    This paper makes two contributions: the first is the proposal of a new model-The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic segmentation. Most methods for semantic segmentation are formulated as a labeling problem for variables that might correspond to either pixels or segments such as super-pixels. It is well known that the generation of super pixel segmentations is not unique. This has motivated many researchers to use multiple super pixel segmentations for problems such as semantic segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the segmentations form a segmentation tree. Our new hierarchical random field model allows information from all of the multiple segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or segments, and the resulting labelings can be viewed both as a detailed segmentation at the pixel level, or at the other extreme, as a segment selector that pieces together a solution like a jigsaw, selecting the best segments from different segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class segmentation, and show that this ability to perform inference using multiple overlapping segmentations leads to state-of-the-art results.

  6. Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map.

    PubMed

    Orsi, Rebecca

    2017-02-01

    Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research.

  7. 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

  8. 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.

  9. 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.

  10. Influence of expansion on hierarchical structure.

    PubMed

    Miller, Bruce N; Rouet, J L

    2002-05-01

    We study a one-dimensional model of gravitational instability in an Einstein-de Sitter universe. Scaling in both space and time results in an autonomous set of coupled Poisson-Vlasov equations for both the field and phase space density, and the N-body problem. Using dynamical simulation, we find direct evidence of hierarchical clustering. A multifractal analysis reveals a bifractal geometry similar to that observed in the distribution of galaxies. To demonstrate the role of scaling, we compare the system to other one-dimensional models recently employed to study structure formation. Finally we show that the model yields an estimate of the time of galaxy formation of the correct order.

  11. Cluster Headache

    MedlinePlus

    Cluster headache Overview By Mayo Clinic Staff Cluster headaches, which occur in cyclical patterns or clusters, are one of the most painful types of headache. A cluster headache commonly awakens you ...

  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. Chimera states in networks of Van der Pol oscillators with hierarchical connectivities

    NASA Astrophysics Data System (ADS)

    Ulonska, Stefan; Omelchenko, Iryna; Zakharova, Anna; Schöll, Eckehard

    2016-09-01

    Chimera states are complex spatio-temporal patterns that consist of coexisting domains of coherent and incoherent dynamics. We analyse chimera states in ring networks of Van der Pol oscillators with hierarchical coupling topology. We investigate the stepwise transition from a nonlocal to a hierarchical topology and propose the network clustering coefficient as a measure to establish a link between the existence of chimera states and the compactness of the initial base pattern of a hierarchical topology; we show that a large clustering coefficient promotes the occurrence of chimeras. Depending on the level of hierarchy and base pattern, we obtain chimera states with different numbers of incoherent domains. We investigate the chimera regimes as a function of coupling strength and nonlinearity parameter of the individual oscillators. The analysis of a network with larger base pattern resulting in larger clustering coefficient reveals two different types of chimera states and highlights the increasing role of amplitude dynamics.

  14. Hierarchical Star Formation Across Galactic Disks

    NASA Astrophysics Data System (ADS)

    Gouliermis, Dimitrios

    2016-09-01

    Most stars form in clusters. This fact has emerged from the finding that "embedded clusters account for the 70 - 90% fraction of all stars formed in Giant Molecular Clouds (GMCs)." While this is the case at scales of few 10 parsecs, typical for GMCs, a look at star-forming galaxies in the Local Group (LG) shows significant populations of enormous loose complexes of early-type stars extending at scales from few 100 to few 1000 parsecs. The fact that these stellar complexes host extremely large numbers of loosely distributed massive blue stars implies either that stars form also in an unbound fashion or they are immediately dislocated from their original compact birthplaces or both. The Legacy Extra-Galactic UV Survey (LEGUS) has produced remarkable collections of resolved early-type stars in 50 star-forming LG galaxies, suited for testing ideas about recent star formation. I will present results from our ongoing project on star formation across LEGUS disk galaxies. We characterize the global clustering behavior of the massive young stars in order to understand the morphology of star formation over galactic scales. This morphology appears to be self-similar with fractal dimensions comparable to those of the molecular interstellar medium, apparently driven by large-scale turbulence. Our clustering analysis reveals compact stellar systems nested in larger looser concentrations, which themselves are the dense parts of unbound complexes and super-structures, giving evidence of hierarchical star formation up to galactic scales. We investigate the structural and star formation parameters demographics of the star-forming complexes revealed at various levels of compactness. I will discuss the outcome of our correlation and regression analyses on these parameters in an attempt to understand the link between galactic disk dynamics and morphological structure in spiral and ring galaxies of the local universe.

  15. Segmenting Student Markets with a Student Satisfaction and Priorities Survey. AIR 1994 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Borden, Victor M. H.

    A market segmentation analysis was conducted on students at a large midwestern urban university using two forms of hierarchical cluster analysis on student characteristics: an agglomerative procedure using a matching-type association measure and a divisive chi-square based automatic interaction detection (CHAID) procedure. Data were extracted from…

  16. Clustering with shallow trees

    NASA Astrophysics Data System (ADS)

    Bailly-Bechet, M.; Bradde, S.; Braunstein, A.; Flaxman, A.; Foini, L.; Zecchina, R.

    2009-12-01

    We propose a new method for obtaining hierarchical clustering based on the optimization of a cost function over trees of limited depth, and we derive a message-passing method that allows one to use it efficiently. The method and the associated algorithm can be interpreted as a natural interpolation between two well-known approaches, namely that of single linkage and the recently presented affinity propagation. We analyse using this general scheme three biological/medical structured data sets (human population based on genetic information, proteins based on sequences and verbal autopsies) and show that the interpolation technique provides new insight.

  17. Splitting Methods for Convex Clustering

    PubMed Central

    Chi, Eric C.; Lange, Kenneth

    2016-01-01

    Clustering is a fundamental problem in many scientific applications. Standard methods such as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Recently introduced convex relaxations of k-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. In this work we present two splitting methods for solving the convex clustering problem. The first is an instance of the alternating direction method of multipliers (ADMM); the second is an instance of the alternating minimization algorithm (AMA). In contrast to previously considered algorithms, our ADMM and AMA formulations provide simple and unified frameworks for solving the convex clustering problem under the previously studied norms and open the door to potentially novel norms. We demonstrate the performance of our algorithm on both simulated and real data examples. While the differences between the two algorithms appear to be minor on the surface, complexity analysis and numerical experiments show AMA to be significantly more efficient. This article has supplemental materials available online. PMID:27087770

  18. 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…

  19. 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.

  20. 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.

  1. Synchronization patterns: from network motifs to hierarchical networks

    NASA Astrophysics Data System (ADS)

    Krishnagopal, Sanjukta; Lehnert, Judith; Poel, Winnie; Zakharova, Anna; Schöll, Eckehard

    2017-03-01

    We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart-Landau oscillators with fractal connectivities. The study of fractal or self-similar topology is motivated by the network of neurons in the brain. This fractal property is well represented in hierarchical networks, for which we present three different models. In addition, we introduce an analytical eigensolution method and provide a comprehensive picture of the interplay of network topology and the corresponding network dynamics, thus allowing us to predict the dynamics of arbitrarily large hierarchical networks simply by analysing small network motifs. We also show that oscillation death can be induced in these networks, even if the coupling is symmetric, contrary to previous understanding of oscillation death. Our results show that there is a direct correlation between topology and dynamics: hierarchical networks exhibit the corresponding hierarchical dynamics. This helps bridge the gap between mesoscale motifs and macroscopic networks. This article is part of the themed issue 'Horizons of cybernetical physics'.

  2. Synchronization patterns: from network motifs to hierarchical networks.

    PubMed

    Krishnagopal, Sanjukta; Lehnert, Judith; Poel, Winnie; Zakharova, Anna; Schöll, Eckehard

    2017-03-06

    We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart-Landau oscillators with fractal connectivities. The study of fractal or self-similar topology is motivated by the network of neurons in the brain. This fractal property is well represented in hierarchical networks, for which we present three different models. In addition, we introduce an analytical eigensolution method and provide a comprehensive picture of the interplay of network topology and the corresponding network dynamics, thus allowing us to predict the dynamics of arbitrarily large hierarchical networks simply by analysing small network motifs. We also show that oscillation death can be induced in these networks, even if the coupling is symmetric, contrary to previous understanding of oscillation death. Our results show that there is a direct correlation between topology and dynamics: hierarchical networks exhibit the corresponding hierarchical dynamics. This helps bridge the gap between mesoscale motifs and macroscopic networks.This article is part of the themed issue 'Horizons of cybernetical physics'.

  3. Parallel hierarchical global illumination

    SciTech Connect

    Snell, Quinn 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.

  4. 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.

  5. Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation

    PubMed Central

    Froyen, Vicky; Feldman, Jacob; Singh, Manish

    2015-01-01

    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

  6. Clusters of Galaxies

    NASA Astrophysics Data System (ADS)

    Huchtmeier, W. K.; Richter, O. G.; Materne, J.

    1981-09-01

    The large-scale structure of the universe is dominated by clustering. Most galaxies seem to be members of pairs, groups, clusters, and superclusters. To that degree we are able to recognize a hierarchical structure of the universe. Our local group of galaxies (LG) is centred on two large spiral galaxies: the Andromeda nebula and our own galaxy. Three sr:naller galaxies - like M 33 - and at least 23 dwarf galaxies (KraanKorteweg and Tammann, 1979, Astronomische Nachrichten, 300, 181) can be found in the evironment of these two large galaxies. Neighbouring groups have comparable sizes (about 1 Mpc in extent) and comparable numbers of bright members. Small dwarf galaxies cannot at present be observed at great distances.

  7. 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.

  8. Reasons for Hierarchical Linear Modeling: A Reminder.

    ERIC Educational Resources Information Center

    Wang, Jianjun

    1999-01-01

    Uses examples of hierarchical linear modeling (HLM) at local and national levels to illustrate proper applications of HLM and dummy variable regression. Raises cautions about the circumstances under which hierarchical data do not need HLM. (SLD)

  9. 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.

  10. 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…

  11. Sensory Hierarchical Organization and Reading.

    ERIC Educational Resources Information Center

    Skapof, Jerome

    The purpose of this study was to judge the viability of an operational approach aimed at assessing response styles in reading using the hypothesis of sensory hierarchical organization. A sample of 103 middle-class children from a New York City public school, between the ages of five and seven, took part in a three phase experiment. Phase one…

  12. Star Clusters in the Magellanic Clouds

    NASA Astrophysics Data System (ADS)

    Gallagher, J. S., III

    2014-09-01

    The Magellanic Clouds (MC) are prime locations for studies of star clusters covering a full range in age and mass. This contribution briefly reviews selected properties of Magellanic star clusters, by focusing first on young systems that show evidence for hierarchical star formation. The structures and chemical abundance patterns of older intermediate age star clusters in the Small Magellanic Cloud (SMC) are a second topic. These suggest a complex history has affected the chemical enrichment in the SMC and that low tidal stresses in the SMC foster star cluster survival.

  13. Complex earthquake networks: Hierarchical organization and assortative mixing

    NASA Astrophysics Data System (ADS)

    Abe, Sumiyoshi; Suzuki, Norikazu

    2006-08-01

    To characterize the dynamical features of seismicity as a complex phenomenon, the seismic data are mapped to a growing random graph, which is a small-world scale-free network. Here, hierarchical and mixing properties of such a network are studied. The clustering coefficient is found to exhibit asymptotic power-law decay with respect to connectivity, showing hierarchical organization. This structure is supported by not only main shocks but also small shocks, and may have its origin in the combined effect of vertex fitness and deactivation by stress release at faults. The nearest-neighbor average connectivity and the Pearson correlation coefficient are also calculated. It is found that the earthquake network has assortative mixing. This is a main difference of the earthquake network from the Internet with disassortative mixing. Physical implications of these results are discussed.

  14. 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

  15. Competitive cluster growth in complex networks

    NASA Astrophysics Data System (ADS)

    Moreira, André A.; Paula, Demétrius R.; Costa Filho, Raimundo N.; Andrade, José S., Jr.

    2006-06-01

    In this work we propose an idealized model for competitive cluster growth in complex networks. Each cluster can be thought of as a fraction of a community that shares some common opinion. Our results show that the cluster size distribution depends on the particular choice for the topology of the network of contacts among the agents. As an application, we show that the cluster size distributions obtained when the growth process is performed on hierarchical networks, e.g., the Apollonian network, have a scaling form similar to what has been observed for the distribution of a number of votes in an electoral process. We suggest that this similarity may be due to the fact that social networks involved in the electoral process may also possess an underlining hierarchical structure.

  16. 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.

  17. 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.

  18. The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

    NASA Astrophysics Data System (ADS)

    Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

    2016-09-01

    The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

  19. A framework for feature selection in clustering

    PubMed Central

    Witten, Daniela M.; Tibshirani, Robert

    2010-01-01

    We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to select the features. We use this framework to develop simple methods for sparse K-means and sparse hierarchical clustering. A single criterion governs both the selection of the features and the resulting clusters. These approaches are demonstrated on simulated data and on genomic data sets. PMID:20811510

  20. Cities and regions in Britain through hierarchical percolation

    NASA Astrophysics Data System (ADS)

    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.

  1. 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.

  2. 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

  3. Fortran IV computer program for rapid hierarchical classification of large data sets

    NASA Astrophysics Data System (ADS)

    Jambu, Michel

    1981-01-01

    A rapid hierarchical classification program enables the clustering of 5000 elements in only a few minutes of central processor time using an IBM 370/168 computer. The program algorithm, based on the reductibility axiom in graph theory, is related to the criterion of correspondence analysis. Its application to a set of hydrogeological data is described briefly.

  4. 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…

  5. Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors

    ERIC Educational Resources Information Center

    Chung, Yeojin; Rabe-Hesketh, Sophia; Gelman, Andrew; Dorie, Vincent; Liu, Jinchen

    2012-01-01

    Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as…

  6. 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…

  7. 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].

  8. 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

  9. 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.

  10. 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.

  11. Treatment Protocols as Hierarchical Structures

    PubMed Central

    Ben-Bassat, Moshe; Carlson, Richard W.; Puri, Vinod K.; Weil, Max Harry

    1978-01-01

    We view a treatment protocol as a hierarchical structure of therapeutic modules. The lowest level of this structure consists of individual therapeutic actions. Combinations of individual actions define higher level modules, which we call routines. Routines are designed to manage limited clinical problems, such as the routine for fluid loading to correct hypovolemia. Combinations of routines and additional actions, together with comments, questions, or precautions organized in a branching logic, in turn, define the treatment protocol for a given disorder. Adoption of this modular approach may facilitate the formulation of treatment protocols, since the physician is not required to prepare complex flowcharts. This hierarchical approach also allows protocols to be updated and modified in a flexible manner. By use of such a standard format, individual components may be fitted together to create protocols for multiple disorders. The technique is suited for computer implementation. We believe that this hierarchical approach may facilitate standarization of patient care as well as aid in clinical teaching. A protocol for acute pancreatitis is used to illustrate this technique.

  12. Personality Traits: Hierarchically Organized Systems.

    PubMed

    Fajkowska, Małgorzata

    2017-03-13

    Personality science has always been and is still ready for new theorizing on traits. Accordingly, this paper presents the recently proposed Traits as Hierarchical Systems (THS) model, where personality traits are not only the emergent properties of the three-level hierarchy of the personality system, but are also hierarchical per se. As hierarchical systems, they are organized into three levels: mechanisms and processes, structures, and behavioral markers. In this approach trait denotes the underlying, recurrent mechanisms that pattern its structure and account for the stability/variability of individual characteristics. Here, traits might be described as processes with a slow rate of change that can be substituted for structure. The main function of personality traits, within the personality system, is stimulation processing. Three dominant functions of stimulation processing in traits are proposed: reactive, regulative, and self-regulative. Some important questions regarding the concept of trait remain, e.g. concerning trait stability, determinacy, measurement, their relation to overt behaviors, personality type or state, differentiation between temperament traits and other-than-temperament personality traits. All of these topics are discussed in this paper, as well as the compatible and distinctive features of this approach in relation to selected, modern trait theories. This article is protected by copyright. All rights reserved.

  13. IDAS: a Windows based software package for cluster analysis

    NASA Astrophysics Data System (ADS)

    Bondarenko, Igor; Treiger, Boris; Van Grieken, René; Van Espen, Pierre

    1996-03-01

    This article is an electronic publication in Spectrochimica Acta Electronica (SAE), the electronic section of Spectrochimica Acta Part B (SAB). The hardcopy text, comprising the main article and one appendix, is accompanied by two installation diskettes with the software package and data files. The main article discusses the chemometric aspects of the package and explains its purpose. The IDAS software package combines three cluster analysis methods (hierarchical, non-hierarchical and fuzzy) and runs under MS Windows. Modified algorithms for non-hierarchical and fuzzy clusterings are described. The interpretation of the clustering results is facilitated by the extensive use of different types of graph. New approaches to the graphical representation of the results of fuzzy clustering are proposed. Two data sets, the Iris data by Fisher and a data set on the chemical composition of tea, are used to demonstrate the capabilities of the software.

  14. 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

  15. 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.

  16. The hierarchical brain network for face recognition.

    PubMed

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

  17. 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

  18. Cluster headache

    MedlinePlus

    Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... be related to the body's sudden release of histamine (chemical in the body released during an allergic ...

  19. 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.

  20. 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.

  1. 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.

  2. ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data.

    PubMed

    Seiler, Michael; Huang, C Chris; Szalma, Sandor; Bhanot, Gyan

    2010-02-01

    We have created a stand-alone software tool, ConsensusCluster, for the analysis of high-dimensional single nucleotide polymorphism (SNP) and gene expression microarray data. Our software implements the consensus clustering algorithm and principal component analysis to stratify the data into a given number of robust clusters. The robustness is achieved by combining clustering results from data and sample resampling as well as by averaging over various algorithms and parameter settings to achieve accurate, stable clustering results. We have implemented several different clustering algorithms in the software, including K-Means, Partition Around Medoids, Self-Organizing Map, and Hierarchical clustering methods. After clustering the data, ConsensusCluster generates a consensus matrix heatmap to give a useful visual representation of cluster membership, and automatically generates a log of selected features that distinguish each pair of clusters. ConsensusCluster gives more robust and more reliable clusters than common software packages and, therefore, is a powerful unsupervised learning tool that finds hidden patterns in data that might shed light on its biological interpretation. This software is free and available from http://code.google.com/p/consensus-cluster .

  3. Star clusters in the Magellanic Clouds - I. Parametrization and classification of 1072 clusters in the LMC

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

    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 III survey data. This study brings out 308 newly parametrized 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 catalogue with parameters, classification, and cleaned and isochrone fitted colour-magnitude diagrams 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.

  4. Hierarchical structure of the Sicilian goats revealed by Bayesian analyses of microsatellite information.

    PubMed

    Siwek, M; Finocchiaro, R; Curik, I; Portolano, B

    2011-02-01

    Genetic structure and relationship amongst the main goat populations in Sicily (Girgentana, Derivata di Siria, Maltese and Messinese) were analysed using information from 19 microsatellite markers genotyped on 173 individuals. A posterior Bayesian approach implemented in the program STRUCTURE revealed a hierarchical structure with two clusters at the first level (Girgentana vs. Messinese, Derivata di Siria and Maltese), explaining 4.8% of variation (amovaФ(ST) estimate). Seven clusters nested within these first two clusters (further differentiations of Girgentana, Derivata di Siria and Maltese), explaining 8.5% of variation (amovaФ(SC) estimate). The analyses and methods applied in this study indicate their power to detect subtle population structure.

  5. 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.

  6. Hierarchical star formation across the ring galaxy NGC 6503

    NASA Astrophysics Data System (ADS)

    Gouliermis, Dimitrios A.; Thilker, David; Elmegreen, Bruce G.; Elmegreen, Debra M.; Calzetti, Daniela; Lee, Janice C.; Adamo, Angela; Aloisi, Alessandra; Cignoni, Michele; Cook, David O.; Dale, Daniel A.; Gallagher, John S.; Grasha, Kathryn; Grebel, Eva K.; Davó, Artemio Herrero; Hunter, Deidre A.; Johnson, Kelsey E.; Kim, Hwihyun; Nair, Preethi; Nota, Antonella; Pellerin, Anne; Ryon, Jenna; Sabbi, Elena; Sacchi, Elena; Smith, Linda J.; Tosi, Monica; Ubeda, Leonardo; Whitmore, Brad

    2015-10-01

    We present a detailed clustering analysis of the young stellar population across the star-forming ring galaxy NGC 6503, based on the deep Hubble Space Telescope photometry obtained with the Legacy ExtraGalactic UV Survey. We apply a contour-based map analysis technique and identify in the stellar surface density map 244 distinct star-forming structures at various levels of significance. These stellar complexes are found to be organized in a hierarchical fashion with 95 per cent being members of three dominant super-structures located along the star-forming ring. The size distribution of the identified structures and the correlation between their radii and numbers of stellar members show power-law behaviours, as expected from scale-free processes. The self-similar distribution of young stars is further quantified from their autocorrelation function, with a fractal dimension of ˜1.7 for length-scales between ˜20 pc and 2.5 kpc. The young stellar radial distribution sets the extent of the star-forming ring at radial distances between 1 and 2.5 kpc. About 60 per cent of the young stars belong to the detected stellar structures, while the remaining stars are distributed among the complexes, still inside the ring of the galaxy. The analysis of the time-dependent clustering of young populations shows a significant change from a more clustered to a more distributed behaviour in a time-scale of ˜60 Myr. The observed hierarchy in stellar clustering is consistent with star formation being regulated by turbulence across the ring. The rotational velocity difference between the edges of the ring suggests shear as the driving mechanism for this process. Our findings reveal the interesting case of an inner ring forming stars in a hierarchical fashion.

  7. 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.

  8. 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.

  9. Hierarchical Inorganic Assemblies for Artificial Photosynthesis.

    PubMed

    Kim, Wooyul; Edri, Eran; Frei, Heinz

    2016-09-20

    Artificial photosynthesis is an attractive approach for renewable fuel generation because it offers the prospect of a technology suitable for deployment on highly abundant, non-arable land. Recent leaps forward in the development of efficient and durable light absorbers and catalysts for oxygen evolution and the growing attention to catalysts for carbon dioxide activation brings into focus the tasks of hierarchically integrating the components into assemblies for closing of the photosynthetic cycle. A particular challenge is the efficient coupling of the multi-electron processes of CO2 reduction and H2O oxidation. Among the most important requirements for a complete integrated system are catalytic rates that match the solar flux, efficient charge transport between the various components, and scalability of the photosynthetic assembly on the unprecedented scale of terawatts in order to have impact on fuel consumption. To address these challenges, we have developed a heterogeneous inorganic materials approach with molecularly precise control of light absorption and charge transport pathways. Oxo-bridged heterobinuclear units with metal-to-metal charge-transfer transitions absorbing deep in the visible act as single photon, single charge transfer pumps for driving multi-electron catalysts. A photodeposition method has been introduced for the spatially directed assembly of nanoparticle catalysts for selective coupling to the donor or acceptor metal of the light absorber. For CO2 reduction, a Cu oxide cluster is coupled to the Zr center of a ZrOCo light absorber, while coupling of an Ir nanoparticle catalyst for water oxidation to the Co donor affords closing of the photosynthetic cycle of CO2 conversion by H2O to CO and O2. Optical, vibrational, and X-ray spectroscopy provide detailed structural knowledge of the polynuclear assemblies. Time resolved visible and rapid-scan FT-IR studies reveal charge transfer mechanisms and transient surface intermediates under

  10. About the Clusters Program

    EPA Pesticide Factsheets

    The Environmental Technology Innovation Clusters Program advises cluster organizations, encourages collaboration between clusters, tracks U.S. environmental technology clusters, and connects EPA programs to cluster needs.

  11. On the unnecessary ubiquity of hierarchical linear modeling.

    PubMed

    McNeish, Daniel; Stapleton, Laura M; Silverman, Rebecca D

    2017-03-01

    In psychology and the behavioral sciences generally, the use of the hierarchical linear model (HLM) and its extensions for discrete outcomes are popular methods for modeling clustered data. HLM and its discrete outcome extensions, however, are certainly not the only methods available to model clustered data. Although other methods exist and are widely implemented in other disciplines, it seems that psychologists have yet to consider these methods in substantive studies. This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors. These alternative methods do not model random effects and thus make a smaller number of assumptions and are interpreted identically to single-level methods with the benefit that estimates are adjusted to reflect clustering of observations. Situations where these alternative methods may be advantageous are discussed including research questions where random effects are and are not required, when random effects can change the interpretation of regression coefficients, challenges of modeling with random effects with discrete outcomes, and examples of published psychology articles that use HLM that may have benefitted from using alternative methods. Illustrative examples are provided and discussed to demonstrate the advantages of the alternative methods and also when HLM would be the preferred method. (PsycINFO Database Record

  12. 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.…

  13. Genetic Network Inference Using Hierarchical Structure

    PubMed Central

    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

  14. 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.

  15. BioCluster: tool for identification and clustering of Enterobacteriaceae based on biochemical data.

    PubMed

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

    2015-06-01

    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/.

  16. 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.

  17. 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

  18. A neural signature of hierarchical reinforcement learning.

    PubMed

    Ribas-Fernandes, José J F; Solway, Alec; Diuk, Carlos; McGuire, Joseph T; Barto, Andrew G; Niv, Yael; Botvinick, Matthew M

    2011-07-28

    Human behavior displays hierarchical structure: simple actions cohere into subtask sequences, which work together to accomplish overall task goals. Although the neural substrates of such hierarchy have been the target of increasing research, they remain poorly understood. We propose that the computations supporting hierarchical behavior may relate to those in hierarchical reinforcement learning (HRL), a machine-learning framework that extends reinforcement-learning mechanisms into hierarchical domains. To test this, we leveraged a distinctive prediction arising from HRL. In ordinary reinforcement learning, reward prediction errors are computed when there is an unanticipated change in the prospects for accomplishing overall task goals. HRL entails that prediction errors should also occur in relation to task subgoals. In three neuroimaging studies we observed neural responses consistent with such subgoal-related reward prediction errors, within structures previously implicated in reinforcement learning. The results reported support the relevance of HRL to the neural processes underlying hierarchical behavior.

  19. 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.

  20. Quantum transport through hierarchical structures.

    PubMed

    Boettcher, S; Varghese, C; Novotny, M A

    2011-04-01

    The transport of quantum electrons through hierarchical lattices is of interest because such lattices have some properties of both regular lattices and random systems. We calculate the electron transmission as a function of energy in the tight-binding approximation for two related Hanoi networks. HN3 is a Hanoi network with every site having three bonds. HN5 has additional bonds added to HN3 to make the average number of bonds per site equal to five. We present a renormalization group approach to solve the matrix equation involved in this quantum transport calculation. We observe band gaps in HN3, while no such band gaps are observed in linear networks or in HN5. We provide a detailed scaling analysis near the edges of these band gaps.

  1. 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``).

  2. 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.

  3. 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.

  4. Hierarchical modeling for image classification

    NASA Technical Reports Server (NTRS)

    Likens, W.; Maw, K.

    1982-01-01

    As part of the California Integrated Remote Sensing System's (CIRSS) San Bernardino County Project, the use of data layers from a geographic information system (GIS) as an integral part of the Landsat image classification process was investigated. Through a hierarchical modeling technique, elevation, aspect, land use, vegetation, and growth management data from the project's data base were used to guide class labeling decisions in a 1976 Landsat MSS land cover classification. A similar model, incorporating 1976-1979 Landsat spectral change data in addition to other data base elements, was used in the classification of a 1979 Landsat image. The resultant Landsat products were integrated as additional layers into the data base for use in growth management, fire hazard, and hydrological modeling.

  5. Joint Hierarchical Category Structure Learning and Large-Scale Image Classification.

    PubMed

    Qu, Yanyun; Lin, Li; Shen, Fumin; Lu, Chang; Wu, Yang; Xie, Yuan; Tao, Dacheng

    2016-10-05

    We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical interclass structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. Experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification.

  6. 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.

  7. 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

  8. Clustering PPI data by combining FA and SHC method.

    PubMed

    Lei, Xiujuan; Ying, Chao; Wu, Fang-Xiang; Xu, Jin

    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.

  9. Cosmic clustering

    DOE PAGES

    Anninos, Dionysios; Denef, Frederik

    2016-06-30

    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. In conclusion, an equivalent mathematical structure organizes the state space of the Sherrington-Kirkpatrick model of a spin glass.

  10. 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.

  11. 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.

  12. 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

  13. A spatial analysis of hierarchical waste transport structures under growing demand.

    PubMed

    Tanguy, Audrey; Glaus, Mathias; Laforest, Valérie; Villot, Jonathan; Hausler, Robert

    2016-10-01

    The design of waste management systems rarely accounts for the spatio-temporal evolution of the demand. However, recent studies suggest that this evolution affects the planning of waste management activities like the choice and location of treatment facilities. As a result, the transport structure could also be affected by these changes. The objective of this paper is to study the influence of the spatio-temporal evolution of the demand on the strategic planning of a waste transport structure. More particularly this study aims at evaluating the effect of varying spatial parameters on the economic performance of hierarchical structures (with one transfer station). To this end, three consecutive generations of three different spatial distributions were tested for hierarchical and non-hierarchical transport structures based on costs minimization. Results showed that a hierarchical structure is economically viable for large and clustered spatial distributions. The distance parameter was decisive but the loading ratio of trucks and the formation of clusters of sources also impacted the attractiveness of the transfer station. Thus the territories' morphology should influence strategies as regards to the installation of transfer stations. The use of spatial-explicit tools such as the transport model presented in this work that take into account the territory's evolution are needed to help waste managers in the strategic planning of waste transport structures.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. A hierarchical artificial retina architecture

    NASA Astrophysics Data System (ADS)

    Parker, Alice C.; Azar, Adi N.

    2009-05-01

    Connectivity in the human retina is complex. Over one hundred million photoreceptors transduce light into electrical signals. These electrical signals are sent to the ganglion cells through amacrine and bipolar cells. Lateral connections involving horizontal and amacrine cells span throughout the outer plexiform layer and inner plexiform layer respectively. Horizontal cells are important for photoreceptor regulation by depolarizing them after an illumination occurs. Horizontal cells themselves form an electrical network that communicates by gap junctions, and these cells exhibit plasticity (change in behavior and structure) with respect to glycine receptors. The bipolar and amacrine cells transfer electrical signals from photoreceptors to the ganglion cells. Furthermore, amacrine cells are responsible for further processing the retinal image. Finally, the ganglion cells receive electrical signals from the bipolar and amacrine cells and will spike at a faster rate if there is a change in the overall intensity for a group of photoreceptors, sending a signal to the brain. Dramatic progress is being made with respect to retinal prostheses, raising hope for an entire synthetic retina in the future. We propose a bio-inspired 3D hierarchical pyramidal architecture for a synthetic retina that mimics the overall structure of the human retina. We chose to use a 3D architecture to facilitate connectivity among retinal cells, maintaining a hierarchical structure similar to that of the biological retina. The first layer of the architecture contains electronic circuits that model photoreceptors and horizontal cells. The second layer contains amacrine and bipolar electronic cells, and the third layer contains ganglion cells. Layer I has the highest number of cells, and layer III has the lowest number of cells, resulting in a pyramidal architecture. In our proposed architecture we intend to use photodetectors to transduce light into electrical signals. We propose to employ

  19. 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.

  20. 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.

  1. Spitzer Clusters

    NASA Astrophysics Data System (ADS)

    Krick, Kessica

    This proposal is a specific response to the strategic goal of NASA's research program to "discover how the universe works and explore how the universe evolved into its present form." Towards this goal, we propose to mine the Spitzer archive for all observations of galaxy groups and clusters for the purpose of studying galaxy evolution in clusters, contamination rates for Sunyaev Zeldovich cluster surveys, and to provide a database of Spitzer observed clusters to the broader community. Funding from this proposal will go towards two years of support for a Postdoc to do this work. After searching the Spitzer Heritage Archive, we have found 194 unique galaxy groups and clusters that have data from both the Infrared array camera (IRAC; Fazio et al. 2004) at 3.6 - 8 microns and the multiband imaging photometer for Spitzer (MIPS; Rieke et al. 2004) at 24microns. This large sample will add value beyond the individual datasets because it will be a larger sample of IR clusters than ever before and will have sufficient diversity in mass, redshift, and dynamical state to allow us to differentiate amongst the effects of these cluster properties. An infrared sample is important because it is unaffected by dust extinction while at the same time is an excellent measure of both stellar mass (IRAC wavelengths) and star formation rate (MIPS wavelengths). Additionally, IRAC can be used to differentiate star forming galaxies (SFG) from active galactic nuclei (AGN), due to their different spectral shapes in this wavelength regime. Specifically, we intend to identify SFG and AGN in galaxy groups and clusters. Groups and clusters differ from the field because the galaxy densities are higher, there is a large potential well due mainly to the mass of the dark matter, and there is hot X-ray gas (the intracluster medium; ICM). We will examine the impact of these differences in environment on galaxy formation by comparing cluster properties of AGN and SFG to those in the field. Also, we will

  2. Hierarchically nanostructured barium sulfate fibers.

    PubMed

    Romero-Ibarra, Issis C; Rodríguez-Gattorno, Geonel; García-Sánchez, Mario F; Sánchez-Solís, Antonio; Manero, Octavio

    2010-05-18

    BaSO(4) nanostructures with controlled morphologies were successfully produced via one-step process through precipitation of BaSO(4) in aqueous and organic media. The synthesis is carried out by mixing solutions of BaCl(2) and Na(2)SO(4) in presence of EDTA (disodium ethylenediaminetetraacetic acid) at room temperature. The influence of the reaction conditions such as initial reactants concentration, pH, EDTA/[Ba(2+)] ratio and aging on the BaSO(4) nanoparticles organization is studied. Using EDTA in aqueous media, spherical secondary particles of 500 nm diameter are obtained, which are formed by 4 nm size primary particles. With dimethyl sulfoxide and small amounts of water (5%) and EDTA, the aging process allows the production of long homogeneous fibers, related to hierarchical organization of BaSO(4) nanoparticles. Direct observation of self-assembling of primary particles by HRTEM allows proposing a mechanism for fiber formation, which is based on multipolar attractions that lead to a brick-by-brick organization along a preferential orientation. Results evidence the role of EDTA as controlling agent of the morphology and primary and secondary mean particle size.

  3. A serach for 'failed clusters' of galaxies

    NASA Technical Reports Server (NTRS)

    Tucker, W. H.; Tananbaum, H.; Remillard, R. A.

    1995-01-01

    We describe a search for a new type of object - large clouds of hot gas with no visible galaxies - which we call failed clusters of galaxies. We calculate the expected X-ray luminosity, temperature, and angular diameter of such objects as a function of total cloud mass and convert the results to expected X-ray fluxes from failed clusters at different redshifts. Using the Einstein Imaging Proportional Counter (IPC) database, we establish a strategy to search for candidate failed clusters. From this initial screening of 1435 IPC fields, 17 candidates are selected for more detailed analysis, which indicates that 10 of these are very probably extended X-ray sources. Optical follow-up on the 10 prime candidates finds eight clusters of galaxies (including six reproted for the first time in this paper), one stellar identification, and one without an obvious optical counterpart (the candidate with the weakest evidence for X-ray extent). Investigation of several candidates with less evidence for X-ray extent yields two additional new clusters of galaxies. A conservative comparison of our results with the Einstein Extended Medium Sensitivity Survey demonstrates that failed clusters are a relatively unimportant contributor to the mass density of the universe. Our inability to find failed clusters is consistent with the hierarchical clustering scenario for the formation of galaxies and clusters.

  4. Nursing home care quality: a cluster analysis.

    PubMed

    Grøndahl, Vigdis Abrahamsen; Fagerli, Liv Berit

    2017-02-13

    Purpose The purpose of this paper is to explore potential differences in how nursing home residents rate care quality and to explore cluster characteristics. Design/methodology/approach A cross-sectional design was used, with one questionnaire including questions from quality from patients' perspective and Big Five personality traits, together with questions related to socio-demographic aspects and health condition. Residents ( n=103) from four Norwegian nursing homes participated (74.1 per cent response rate). Hierarchical cluster analysis identified clusters with respect to care quality perceptions. χ(2) tests and one-way between-groups ANOVA were performed to characterise the clusters ( p<0.05). Findings Two clusters were identified; Cluster 1 residents (28.2 per cent) had the best care quality perceptions and Cluster 2 (67.0 per cent) had the worst perceptions. The clusters were statistically significant and characterised by personal-related conditions: gender, psychological well-being, preferences, admission, satisfaction with staying in the nursing home, emotional stability and agreeableness, and by external objective care conditions: healthcare personnel and registered nurses. Research limitations/implications Residents assessed as having no cognitive impairments were included, thus excluding the largest group. By choosing questionnaire design and structured interviews, the number able to participate may increase. Practical implications Findings may provide healthcare personnel and managers with increased knowledge on which to develop strategies to improve specific care quality perceptions. Originality/value Cluster analysis can be an effective tool for differentiating between nursing homes residents' care quality perceptions.

  5. 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

  6. Uncovering hierarchical data structure in single molecule transport

    NASA Astrophysics Data System (ADS)

    Wu, Ben H.; Ivie, Jeffrey A.; Johnson, Tyler K.; Monti, Oliver L. A.

    2017-03-01

    Interpretation of single molecule transport data is complicated by the fact that all such data are inherently highly stochastic in nature. Features are often broad, seemingly unstructured and distributed over more than an order of magnitude. However, the distribution contains information necessary for capturing the full variety of processes relevant in nanoscale transport, and a better understanding of its hierarchical structure is needed to gain deeper insight into the physics and chemistry of single molecule electronics. Here, we describe a novel data analysis approach based on hierarchical clustering to aid in the interpretation of single molecule conductance-displacement histograms. The primary purpose of statistically partitioning transport data is to provide avenues for unbiased hypothesis generation in single molecule break junction experiments by revealing otherwise potentially hidden aspects in the conductance data. Our approach is generalizable to the analysis of a wide variety of other single molecule experiments in molecular electronics, as well as in single molecule fluorescence spectroscopy, force microscopy, and ion-channel conductance measurements.

  7. 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.

  8. Zeolitic materials with hierarchical porous structures.

    PubMed

    Lopez-Orozco, Sofia; Inayat, Amer; Schwab, Andreas; Selvam, Thangaraj; Schwieger, Wilhelm

    2011-06-17

    During the past several years, different kinds of hierarchical structured zeolitic materials have been synthesized due to their highly attractive properties, such as superior mass/heat transfer characteristics, lower restriction of the diffusion of reactants in the mesopores, and low pressure drop. Our contribution provides general information regarding types and preparation methods of hierarchical zeolitic materials and their relative advantages and disadvantages. Thereafter, recent advances in the preparation and characterization of hierarchical zeolitic structures within the crystallites by post-synthetic treatment methods, such as dealumination or desilication; and structured devices by in situ and ex situ zeolite coatings on open-cellular ceramic foams as (non-reactive as well as reactive) supports are highlighted. Specific advantages of using hierarchical zeolitic catalysts/structures in selected catalytic reactions, such as benzene to phenol (BTOP) and methanol to olefins (MTO) are presented.

  9. Multi-scale, Hierarchically Nested Young Stellar Structures in LEGUS Galaxies

    NASA Astrophysics Data System (ADS)

    Thilker, David A.; LEGUS Team

    2017-01-01

    The study of star formation in galaxies has predominantly been limited to either young stellar clusters and HII regions, or much larger kpc-scale morphological features such as spiral arms. The HST Legacy ExtraGalactic UV Survey (LEGUS) provides a rare opportunity to link these scales in a diverse sample of nearby galaxies and obtain a more comprehensive understanding of their co-evolution for comparison against model predictions. We have utilized LEGUS stellar photometry to identify young, resolved stellar populations belonging to several age bins and then defined nested hierarchical structures as traced by these subsamples of stars. Analagous hierarchical structures were also defined using LEGUS catalogs of unresolved young stellar clusters. We will present our emerging results concerning the physical properties (e.g. area, star counts, stellar mass, star formation rate, ISM characteristics), occupancy statistics (e.g. clusters per substructure versus age and scale, parent/child demographics) and relation to overall galaxy morphology/mass for these building blocks of hierarchical star-forming structure.

  10. K-band Properties of Galaxy Clusters and Groups: Brightest Cluster Galaxies and Intracluster Light

    NASA Astrophysics Data System (ADS)

    Lin, Yen-Ting; Mohr, Joseph J.

    2004-12-01

    We investigate the near-infrared K-band properties of the brightest cluster galaxies (BCGs) in a sample of 93 X-ray galaxy clusters and groups, using data from the Two Micron All Sky Survey. Our cluster sample spans a factor of 70 in mass, making it sensitive to any cluster mass-related trends. We derive the cumulative radial distribution for the BCGs in the ensemble and find that 70% of the BCGs are centered in the cluster to within 5% of the virial radius r200; this quantifies earlier findings that BCG position coincides with the cluster center as defined by the X-ray emission peak. We study the correlations between the luminosity of the BCGs (Lb) and the mass and the luminosity of the host clusters, finding that BCGs in more massive clusters are more luminous than their counterparts in less massive systems and that the BCGs become less important in the overall cluster light (L200) as cluster mass increases. By examining a large sample of optically selected groups, we find that these correlations hold for galactic systems less massive than our clusters (<3×1013 Msolar). From the differences between luminosity functions in high- and low-mass clusters, we argue that BCGs grow in luminosity mainly by merging with other luminous galaxies as the host clusters grow hierarchically; the decreasing BCG luminosity fraction (Lb/L200) with cluster mass indicates that the rate of luminosity growth in BCGs is slow compared to the rate at which clusters acquire galaxy light from the field or other merging clusters. Utilizing the observed correlation between the cluster luminosity and mass and a merger tree model for cluster formation, we estimate that the amount of intracluster light (ICL) increases with cluster mass; our calculations suggest that in 1015 Msolar clusters more than 50% of total stellar mass is in ICL, making the role of ICL very important in the evolution and thermodynamic history of clusters. The cluster baryon fraction accounting for the ICL is in good

  11. Star clusters

    NASA Astrophysics Data System (ADS)

    Labhardt, Lukas; Binggeli, Bruno

    Star clusters are at the heart of astronomy, being key objects for our understanding of stellar evolution and galactic structure. Observations with the Hubble Space Telescope and other modern equipment have revealed fascinating new facts about these galactic building blocks. This book provides two comprehensive and up-to-date, pedagogically designed reviews on star clusters by two well-known experts in the field. Bruce Carney presents our current knowledge of the relative and absolute ages of globular clusters and the chemical history of our Galaxy. Bill Harris addresses globular clusters in external galaxies and their use as tracers of galaxy formation and cosmic distance indicators. The book is written for graduate students as well as professionals in astronomy and astrophysics.

  12. 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…

  13. 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.

  14. 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.

  15. 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.

  16. Hierarchical Models of the Nearshore Complex System

    DTIC Science & Technology

    2004-01-01

    unclassified unclassified /,andard Form 7 7Qien. -pii Prescrbed by ANS Sid 239-18 zgB -10z Hierarchical Models of the Nearshore Complex System: Final...TITLE AND SUBTITLE S. FUNDING NUMBERS Hierarchical Models of the Nearshore Complex System N00014-02-1-0358 6. AUTHOR(S) Brad Werner 7. PERFORMING...8217 ........... The long-term goal of this reasearch was to develop and test predictive models for nearshore processes. This grant was terminaton funding for the

  17. 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.

  18. Evolution of the BCG in Disturbed Galaxy Clusters

    NASA Astrophysics Data System (ADS)

    Ardila, Felipe; Strauss, Michael A.; Lauer, Tod R.; Postman, Marc

    2017-01-01

    The present paradigm in cosmology tells us that large-scale structures grow hierarchically. This suggests that galaxy clusters grow by accreting mass and merging with other clusters, a process which should be detectable by the presence of substructure within a cluster. Using the Dressler-Shectman (DS) three-dimensional test for dynamical substructure, we determined which clusters showed evidence for disturbance from a set of 227 Abell clusters from Lauer et al. (2014) with at least 50 member galaxies and spectroscopic redshifts, z < 0.08. Our results show that 155 (68.2%) of the clusters showed evidence for substructure at ≥ 95% confidence, while 72 did not. Kolmogorov-Smirnov tests suggest that the two populations of clusters (those with and without detected substructure) are significantly different in their distributions of BCG luminosities (Lm), but not in their BCG stellar velocity dispersions (σ), their BCG spatial offsets from the x-ray centers of the clusters, their BCG velocity offsets from the mean cluster velocity, the logarithmic slopes of their BCG photometric curves of growth (α), their cluster velocity dispersions, or their luminosity differences between the BCG and the second-ranked galaxy in the cluster (M2). Similarly, no significant difference was found in the fitting of the Lm-α-σ metric plane for BCGs of clusters with substructure compared those in which there is not substructure. This is surprising since our hierarchical growth models suggest that some of these BCG/cluster properties would be affected by a disturbance of the cluster, indicating that our understanding of how BCGs evolve with their clusters is incomplete and we should explore other ways to probe the level of disturbance.

  19. 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.

  20. 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

  1. Estimating Cosmological Parameters and Cluster Masses through Escape Velocity Measurements in Galaxy Clusters

    NASA Astrophysics Data System (ADS)

    Gifford, Daniel William

    2016-08-01

    Galaxy clusters are large virialized structures that exist at the intersection of filaments of matter that make up the cosmic web. Due to their hierarchical growth history, they are excellent probes of the cosmology that governs our universe. Here, we aim to use clusters to better constrain cosmological parameters by systematically studying the uncertainties on galaxy cluster mass estimation for use in a halo mass function analysis. We find that the caustic technique is capable on average of recovering unbiased cluster masses to within 30% for well sampled systems. We also quantify potential statistical and systematic biases due to observational challenges. To address statistical biases in the caustic technique, we developed a new stacking algorithm to measure the average cluster mass for a single stack of projected cluster phase-spaces. By varying the number of galaxies and number of clusters we stack, we find that the single limited value is the total number of galaxies in the stack opening up the possibility for self-calibrated mass estimates of low mass or poorly sampled clusters in large surveys. We then utilize the SDSS-C4 catalog of galaxy clusters to place some of the tightest galaxy cluster based constraints on the matter density and power spectrum normalization for matter in our universe.

  2. Large-scale clustering of CAGE tag expression data

    PubMed Central

    Shimokawa, Kazuro; Okamura-Oho, Yuko; Kurita, Takio; Frith, Martin C; Kawai, Jun; Carninci, Piero; Hayashizaki, Yoshihide

    2007-01-01

    Background Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed. Results We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70–100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups. Conclusion Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data. PMID:17517134

  3. 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…

  4. 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.

  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].

  6. 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

  7. Statistical label fusion with hierarchical performance models

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    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.

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. Towards a sustainable manufacture of hierarchical zeolites.

    PubMed

    Verboekend, Danny; Pérez-Ramírez, Javier

    2014-03-01

    Hierarchical zeolites have been established as a superior type of aluminosilicate catalysts compared to their conventional (purely microporous) counterparts. An impressive array of bottom-up and top-down approaches has been developed during the last decade to design and subsequently exploit these exciting materials catalytically. However, the sustainability of the developed synthetic methods has rarely been addressed. This paper highlights important criteria to ensure the ecological and economic viability of the manufacture of hierarchical zeolites. Moreover, by using base leaching as a promising case study, we verify a variety of approaches to increase reactor productivity, recycle waste streams, prevent the combustion of organic compounds, and minimize separation efforts. By reducing their synthetic footprint, hierarchical zeolites are positioned as an integral part of sustainable chemistry.

  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. Static and dynamic friction of hierarchical surfaces

    NASA Astrophysics Data System (ADS)

    Costagliola, Gianluca; Bosia, Federico; Pugno, Nicola M.

    2016-12-01

    Hierarchical structures are very common in nature, but only recently have they been systematically studied in materials science, in order to understand the specific effects they can have on the mechanical properties of various systems. Structural hierarchy provides a way to tune and optimize macroscopic mechanical properties starting from simple base constituents and new materials are nowadays designed exploiting this possibility. This can be true also in the field of tribology. In this paper we study the effect of hierarchical patterned surfaces on the static and dynamic friction coefficients of an elastic material. Our results are obtained by means of numerical simulations using a one-dimensional spring-block model, which has previously been used to investigate various aspects of friction. Despite the simplicity of the model, we highlight some possible mechanisms that explain how hierarchical structures can significantly modify the friction coefficients of a material, providing a means to achieve tunability.

  15. Intelligent controllers as hierarchical stochastic automata.

    PubMed

    Lima, P U; Saridis, G N

    1999-01-01

    This paper introduces a design methodology for intelligent controllers, based on a hierarchical linguistic model of command translation by tasks-primitive tasks-primitive actions, and on a two-stage hierarchical learning stochastic automaton that models the translation interfaces of a three-level hierarchical intelligent controller. The methodology relies on the designer's a priori knowledge on how to implement by primitive actions the different primitive tasks which define the intelligent controller. A cost function applicable to any primitive task is introduced and used to learn on-line the optimal choices from the corresponding predesigned sets of primitive actions. The same concept applies to the optimal tasks for each command, whose choice is based on conflict sets of stochastic grammar productions. Optional designs can be compared using this performance measure. A particular design evolves towards the command translation (by tasks-primitive tasks-primitive actions) that minimizes the cost function.

  16. Static and dynamic friction of hierarchical surfaces.

    PubMed

    Costagliola, Gianluca; Bosia, Federico; Pugno, Nicola M

    2016-12-01

    Hierarchical structures are very common in nature, but only recently have they been systematically studied in materials science, in order to understand the specific effects they can have on the mechanical properties of various systems. Structural hierarchy provides a way to tune and optimize macroscopic mechanical properties starting from simple base constituents and new materials are nowadays designed exploiting this possibility. This can be true also in the field of tribology. In this paper we study the effect of hierarchical patterned surfaces on the static and dynamic friction coefficients of an elastic material. Our results are obtained by means of numerical simulations using a one-dimensional spring-block model, which has previously been used to investigate various aspects of friction. Despite the simplicity of the model, we highlight some possible mechanisms that explain how hierarchical structures can significantly modify the friction coefficients of a material, providing a means to achieve tunability.

  17. 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.

  18. [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.

  19. Anisotropic wettability on imprinted hierarchical structures.

    PubMed

    Zhang, Fengxiang; Low, Hong Yee

    2007-07-03

    A series of two-level hierarchical structures on polystyrene (PS) and poly(methyl methacrylate) (PMMA) were fabricated using sequential nanoimprinting lithography (NIL). The hierarchical structures consist of micrometer and sub-micrometer scale grating imprinted with varying orientations. Through water contact angle measurements, these surface hierarchical structures showed a wide range of anisotropic wettabilities on PMMA and PS, with PMMA having an anisotropic wettability from 6 degrees to 54 degrees and PS having an anisotropic wettability from 8 degrees to 32 degrees. At the same time, the water contact angle of PMMA and PS can be tuned to nearly 120 degrees without modifying the surface chemistry. A tunable anisotropic wettability is beneficial for applications where controlling the direction of liquid flow is important, such as in microfluidic devices.

  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. Globular Cluster Systems in Brightest Cluster Galaxies. III: Beyond Bimodality

    NASA Astrophysics Data System (ADS)

    Harris, William E.; Ciccone, Stephanie M.; Eadie, Gwendolyn M.; Gnedin, Oleg Y.; Geisler, Douglas; Rothberg, Barry; Bailin, Jeremy

    2017-01-01

    We present new deep photometry of the rich globular cluster (GC) systems around the Brightest Cluster Galaxies UGC 9799 (Abell 2052) and UGC 10143 (Abell 2147), obtained with the Hubble Space Telescope (HST) ACS and WFC3 cameras. For comparison, we also present new reductions of similar HST/ACS data for the Coma supergiants NGC 4874 and 4889. All four of these galaxies have huge cluster populations (to the radial limits of our data, comprising from 12,000 to 23,000 clusters per galaxy). The metallicity distribution functions (MDFs) of the GCs can still be matched by a bimodal-Gaussian form where the metal-rich and metal-poor modes are separated by ≃ 0.8 dex, but the internal dispersions of each mode are so large that the total MDF becomes very broad and nearly continuous from [Fe/H] ≃ ‑2.4 to solar. There are, however, significant differences between galaxies in the relative numbers of metal-rich clusters, suggesting that they underwent significantly different histories of mergers with massive gas-rich halos. Last, the proportion of metal-poor GCs rises especially rapidly outside projected radii R≳ 4 {R}{eff}, suggesting the importance of accreted dwarf satellites in the outer halo. Comprehensive models for the formation of GCs as part of the hierarchical formation of their parent galaxies will be needed to trace the systematic change in structure of the MDF with galaxy mass, from the distinctly bimodal form in smaller galaxies up to the broad continuum that we see in the very largest systems.

  3. 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.

  4. 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.

  5. Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

    PubMed

    Chen, Ying-Chih; Wen, Chih-Yu

    2012-11-08

    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.

  6. Cluster bulleticity

    NASA Astrophysics Data System (ADS)

    Massey, Richard; Kitching, Thomas; Nagai, Daisuke

    2011-05-01

    The unique properties of dark matter are revealed during collisions between clusters of galaxies, such as the bullet cluster (1E 0657-56) and baby bullet (MACS J0025-12). These systems provide evidence for an additional, invisible mass in the separation between the distributions of their total mass, measured via gravitational lensing, and their ordinary 'baryonic' matter, measured via its X-ray emission. Unfortunately, the information available from these systems is limited by their rarity. Constraints on the properties of dark matter, such as its interaction cross-section, are therefore restricted by uncertainties in the individual systems' impact velocity, impact parameter and orientation with respect to the line of sight. Here we develop a complementary, statistical measurement in which every piece of substructure falling into every massive cluster is treated as a bullet. We define 'bulleticity' as the mean separation between dark matter and ordinary matter, and we measure the signal in hydrodynamical simulations. The phase space of substructure orbits also exhibits symmetries that provide an equivalent control test. Any detection of bulleticity in real data would indicate a difference in the interaction cross-sections of baryonic and dark matter that may rule out hypotheses of non-particulate dark matter that are otherwise able to model individual systems. A subsequent measurement of bulleticity could constrain the dark matter cross-section. Even with conservative estimates, the existing Hubble Space Telescope archive should yield an independent constraint tighter than that from the bullet cluster. This technique is then trivially extendable to and benefits enormously from larger, future surveys.

  7. Poisson approach to clustering analysis of regulatory sequences.

    PubMed

    Wang, Haiying; Zheng, Huiru; Hu, Jinglu

    2008-01-01

    The presence of similar patterns in regulatory sequences may aid users in identifying co-regulated genes or inferring regulatory modules. By modelling pattern occurrences in regulatory regions with Poisson statistics, this paper presents a log likelihood ratio statistics-based distance measure to calculate pair-wise similarities between regulatory sequences. We employed it within three clustering algorithms: hierarchical clustering, Self-Organising Map, and a self-adaptive neural network. The results indicate that, in comparison to traditional clustering algorithms, the incorporation of the log likelihood ratio statistics-based distance into the learning process may offer considerable improvements in the process of regulatory sequence-based classification of genes.

  8. X-ray luminosity functions of clusters of galaxies

    NASA Technical Reports Server (NTRS)

    Cavaliere, A.; Burg, R.; Giacconi, R.

    1991-01-01

    Clusters of galaxies must have a considerable intrinsic spread in their X-ray luminosities at given mass if they are formed bottom-up by direct gravitational instability. The distributions of luminosities at given mass take on the general form of a flat power law with a sharp upper cutoff, consistent with the recently obtained luminosity functions for Abell clusters of given richness classes. The quantitative features depend on the specific hierarchical cosmogony, with models including mass accretion after first collapse providing the best agreement. The same clustering mechanism, after integrating over mass, yields a steep overall luminosity function consistent with existing measurements.

  9. Clustering rainfall pattern in Malaysia using functional data analysis

    NASA Astrophysics Data System (ADS)

    Hamdan, Muhammad Fauzee; Suhaila, Jamaludin; Jemain, Abdul Aziz

    2015-02-01

    Understanding rainfall pattern is important for planning and prediction in hydrology, meteorology, water planning and agriculture. There are two important features of rainfall: the rainfall amount and the probability of rainfall occurrence. The discrete raw data of rainfall precipitation was reconstructed into rainfall amount curves by using functional data analysis method. Hierarchical clustering method with complete-linkage method was used to search for natural similar groupings of rainfall amount curves. The functional clustering illustrated the four dominant patterns for rainfall amount curves. In additional, adaptive Neyman test showed that each clusters are significantly different with from each others.

  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. 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…

  12. 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…

  13. 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…

  14. 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…

  15. The Lyman Alpha Forest in hierarchical cosmologies

    SciTech Connect

    Anninos, P; Bryan, G L; Machacek, M; Moiksin, A; Norman, M L; Zhang, Y

    1999-07-02

    The comparison of quasar absorption spectra with numerically simulated spectra from hierarchical cosmological models of structure formation promises to be a valuable tool to discriminate among these models. We present simulation results for the column density, Doppler b parameter, and optical depth probability distributions for five popular cosmological models.

  16. 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,…

  17. Parallel Temporal Dynamics in Hierarchical Cognitive Control

    PubMed Central

    Ranti, Carolyn; Chatham, Christopher H.; Badre, David

    2015-01-01

    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

  18. Metal oxide nanostructures with hierarchical morphology

    SciTech Connect

    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.

  19. Arbitrary Order Hierarchical Bases for Computational Electromagnetics

    SciTech Connect

    Rieben, R N; White, D; Rodrigue, G

    2002-12-20

    We present a clear and general method for constructing hierarchical vector bases of arbitrary polynomial degree for use in the finite element solution of Maxwell's equations. Hierarchical bases enable p-refinement methods, where elements in a mesh can have different degrees of approximation, to be easily implemented. This can prove to be quite useful as sections of a computational domain can be selectively refined in order to achieve a greater error tolerance without the cost of refining the entire domain. While there are hierarchical formulations of vector finite elements in publication (e.g. [1]), they are defined for tetrahedral elements only, and are not generalized for arbitrary polynomial degree. Recently, Hiptmair, motivated by the theory of exterior algebra and differential forms presented a unified mathematical framework for the construction of conforming finite element spaces [2]. In [2], both 1-form (also called H(curl)) and 2-form (also called H(div)) conforming finite element spaces and the definition of their degrees of freedom are presented. These degrees of freedom are weighted integrals where the weighting function determines the character of the bases, i.e. interpolatory, hierarchical, etc.

  20. The Hierarchical Structure of Formal Operational Tasks.

    ERIC Educational Resources Information Center

    Bart, William M.; Mertens, Donna M.

    1979-01-01

    The hierarchical structure of the formal operational period of Piaget's theory of cognitive development was explored through the application of ordering theoretical methods to a set of data that systematically utilized the various formal operational schemes. Results suggested a common structure underlying task performance. (Author/BH)

  1. 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…

  2. Hierarchical Context Modeling for Video Event Recognition.

    PubMed

    Wang, Xiaoyang; Ji, Qiang

    2016-10-11

    Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.

  3. Bone hierarchical structure in three dimensions.

    PubMed

    Reznikov, Natalie; Shahar, Ron; Weiner, Steve

    2014-09-01

    Bone is a complex hierarchically structured family of materials that includes a network of cells and their interconnected cell processes. New insights into the 3-D structure of various bone materials (mainly rat and human lamellar bone and minipig fibrolamellar bone) were obtained using a focused ion beam electron microscope and the serial surface view method. These studies revealed the presence of two different materials, the major material being the well-known ordered arrays of mineralized collagen fibrils and associated macromolecules, and the minor component being a relatively disordered material composed of individual collagen fibrils with no preferred orientation, with crystals inside and possibly between fibrils, and extensive ground mass. Significantly, the canaliculi and their cell processes are confined within the disordered material. Here we present a new hierarchical scheme for several bone tissue types that incorporates these two materials. The new scheme updates the hierarchical scheme presented by Weiner and Wagner (1998). We discuss the structures at different hierarchical levels with the aim of obtaining further insights into structure-function-related questions, as well as defining some remaining unanswered questions.

  4. Hierarchical Bayesian Models of Subtask Learning

    ERIC Educational Resources Information Center

    Anglim, Jeromy; Wynton, Sarah K. A.

    2015-01-01

    The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…

  5. The hierarchical rupture process of a fault: an experimental study

    NASA Astrophysics Data System (ADS)

    Lei, Xinglin; Kusunose, Kinichiro; Satoh, Takashi; Nishizawa, Osamu

    2003-05-01

    We describe the detailed faulting process of a naturally healed fault containing geometric and mechanical asperities in a granitic porphyry sample, based on data collected with a high-speed acoustic emission (AE) waveform recording system. Asperity failure is examined using the detailed spatio-temporal distribution of AE hypocenters. The initial phase of AE activity is also examined using high dynamic range waveforms. Our experimental results indicate that quasi-static nucleation of the heterogeneous fault is associated with the failure of asperities on the fault plane. The fracturing of an asperity is characterized by a dense spatial clustering of AE events and a changing b-value ( b, hereinafter), which is manifest in three typical stages of failure as follows: (1) foreshocks exhibiting a decrease in b, (2) a period of mainshocks corresponding to a minimum in b, and (3) aftershocks of increasing b. The progressive fracture of several coupled asperities results in short-term precursory fluctuations in both b and AE rate. Furthermore, some AE events possess similar dynamic rupture features to those of earthquakes, having an initial phase associated with the transition from quasi-dynamic to dynamic rupture. We conclude based on these experimental observations that fault rupture has hierarchical characteristics. Quasi-static nucleation of fault rupture represents dynamic fracture of the asperities on the fault plane; likewise, a quasi-static nucleation process characterized by dynamic microfracturing precedes the fracture of an asperity. Since dynamic motions are easier to detect remotely than static deformations, understanding the hierarchical processes underlying fault rupture may thus be helpful for elucidating quasi-static nucleation at larger scales in terms of the dynamic rupture of the asperities at smaller scales. Careful studies of asperity failure in the lab may guide future seismic studies of large asperities on natural faults, potentially making it

  6. Generic hierarchical engine for mask data preparation

    NASA Astrophysics Data System (ADS)

    Kalus, Christian K.; Roessl, Wolfgang; Schnitker, Uwe; Simecek, Michal

    2002-07-01

    Electronic layouts are usually flattened on their path from the hierarchical source downstream to the wafer. Mask data preparation has certainly been identified as a severe bottleneck since long. Data volumes are not only doubling every year along the ITRS roadmap. With the advent of optical proximity correction and phase-shifting masks data volumes are escalating up to non-manageable heights. Hierarchical treatment is one of the most powerful means to keep memory and CPU consumption in reasonable ranges. Only recently, however, has this technique acquired more public attention. Mask data preparation is the most critical area calling for a sound infrastructure to reduce the handling problem. Gaining more and more attention though, are other applications such as large area simulation and manufacturing rule checking (MRC). They all would profit from a generic engine capable to efficiently treat hierarchical data. In this paper we will present a generic engine for hierarchical treatment which solves the major problem, steady transitions along cell borders. Several alternatives exist how to walk through the hierarchy tree. They have, to date, not been thoroughly investigated. One is a bottom-up attempt to treat cells starting with the most elementary cells. The other one is a top-down approach which lends itself to creating a new hierarchy tree. In addition, since the variety, degree of hierarchy and quality of layouts extends over a wide range a generic engine has to take intelligent decisions when exploding the hierarchy tree. Several applications will be shown, in particular how far the limits can be pushed with the current hierarchical engine.

  7. Hierarchical organisation in perception of orientation.

    PubMed

    Spinelli, D; Antonucci, G; Daini, R; Martelli, M L; Zoccolotti, P

    1999-01-01

    According to Rock [1990, in The Legacy of Solomon Asch (Hillsdale, NJ: Lawrence Erlbaum Associates)], hierarchical organisation of perception describes cases in which the orientation of an object is affected by the immediately surrounding elements in the visual field. Various experiments were performed to study the hierarchical organisation of orientation perception. In most of them the rod-and-frame-illusion (RFI: change of the apparent vertical measured on a central rod surrounded by a tilted frame) was measured in the presence/absence of a second inner frame. The first three experiments showed that, when the inner frame is vertical, the direction and size of the illusion are consistent with expectancies based on the hierarchical organisation hypothesis. An analysis of published and unpublished data collected on a large number of subjects showed that orientational hierarchical effects are independent from the absolute size of the RFI. In experiments 4 to 7 we examined the perceptual conditions of the inner stimulus (enclosure, orientation, and presence of luminance borders) critical for obtaining a hierarchical organisation effect. Although an inner vertical square was effective in reducing the illusion (experiment 3), an inner circle enclosing the rod was ineffective (experiment 4). This indicates that definite orientation is necessary to modulate the illusion. However, orientational information provided by a vertical or horizontal rectangle presented near the rod, but not enclosing it, did not modulate the RFI (experiment 5). This suggests that the presence of a figure with oriented contours enclosing the rod is critical. In experiments 6 and 7 we studied whether the presence of luminance borders is important or whether the inner upright square might be effective also if made of subjective contours. When the subjective contour figure was salient and the observers perceived it clearly, its effectiveness in modulating the RFI was comparable to that observed with

  8. 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.

  9. A Study of Hierarchical Classification in Concrete and Formal Thought.

    ERIC Educational Resources Information Center

    Lowell, Walter E.

    This researcher investigated the relationship of hierarchical classification processes in subjects categorized as to developmental level as defined by Piaget's theory, and explored the validity of the hierarchical model and test used in the study. A hierarchical classification test and a battery of four Piaget-type tasks were administered…

  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. 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)

  12. 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.

  13. Automated iterative reclustering framework for determining hierarchical functional networks in resting state fMRI.

    PubMed

    Shams, Seyed-Mohammad; Afshin-Pour, Babak; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali; Strother, Stephen C

    2015-09-01

    To spatially cluster resting state-functional magnetic resonance imaging (rs-fMRI) data into potential networks, there are only a few general approaches that determine the number of networks/clusters, despite a wide variety of techniques proposed for clustering. For individual subjects, extraction of a large number of spatially disjoint clusters results in multiple small networks that are spatio-temporally homogeneous but irreproducible across subjects. Alternatively, extraction of a small number of clusters creates spatially large networks that are temporally heterogeneous but spatially reproducible across subjects. We propose a fully automatic, iterative reclustering framework in which a small number of spatially large, heterogeneous networks are initially extracted to maximize spatial reproducibility. Subsequently, the large networks are iteratively subdivided to create spatially reproducible subnetworks until the overall within-network homogeneity does not increase substantially. The proposed approach discovers a rich network hierarchy in the brain while simultaneously optimizing spatial reproducibility of networks across subjects and individual network homogeneity. We also propose a novel metric to measure the connectivity of brain regions, and in a simulation study show that our connectivity metric and framework perform well in the face of low signal to noise and initial segmentation errors. Experimental results generated using real fMRI data show that the proposed metric improves stability of network clusters across subjects, and generates a meaningful pattern for spatially hierarchical structure of the brain.

  14. Early dynamical evolution of young substructured clusters

    NASA Astrophysics Data System (ADS)

    Dorval, Julien; Boily, Christian

    2017-03-01

    Stellar clusters form with a high level of substructure, inherited from the molecular cloud and the star formation process. Evidence from observations and simulations also indicate the stars in such young clusters form a subvirial system. The subsequent dynamical evolution can cause important mass loss, ejecting a large part of the birth population in the field. It can also imprint the stellar population and still be inferred from observations of evolved clusters. Nbody simulations allow a better understanding of these early twists and turns, given realistic initial conditions. Nowadays, substructured, clumpy young clusters are usually obtained through pseudo-fractal growth and velocity inheritance. We introduce a new way to create clumpy initial conditions through a ''Hubble expansion'' which naturally produces self consistent clumps, velocity-wise. In depth analysis of the resulting clumps shows consistency with hydrodynamical simulations of young star clusters. We use these initial conditions to investigate the dynamical evolution of young subvirial clusters. We find the collapse to be soft, with hierarchical merging leading to a high level of mass segregation. The subsequent evolution is less pronounced than the equilibrium achieved from a cold collapse formation scenario.

  15. Hierarchical organization in the temporal structure of infant-direct speech and song.

    PubMed

    Falk, Simone; Kello, Christopher T

    2017-06-01

    Caregivers alter the temporal structure of their utterances when talking and singing to infants compared with adult communication. The present study tested whether temporal variability in infant-directed registers serves to emphasize the hierarchical temporal structure of speech. Fifteen German-speaking mothers sang a play song and told a story to their 6-months-old infants, or to an adult. Recordings were analyzed using a recently developed method that determines the degree of nested clustering of temporal events in speech. Events were defined as peaks in the amplitude envelope, and clusters of various sizes related to periods of acoustic speech energy at varying timescales. Infant-directed speech and song clearly showed greater event clustering compared with adult-directed registers, at multiple timescales of hundreds of milliseconds to tens of seconds. We discuss the relation of this newly discovered acoustic property to temporal variability in linguistic units and its potential implications for parent-infant communication and infants learning the hierarchical structures of speech and language.

  16. The relationships between electricity consumption and GDP in Asian countries, using hierarchical structure methods

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Keskin, Mustafa

    2013-11-01

    This study uses hierarchical structure methods (minimal spanning tree (MST) and hierarchical tree (HT)) to examine the relationship between energy consumption and economic growth in a sample of 30 Asian countries covering the period 1971-2008. These countries are categorized into four panels based on the World Bank income classification, namely high, upper middle, lower middle, and low income. In particular, we use the data of electricity consumption and real gross domestic product (GDP) per capita to detect the topological properties of the countries. We show a relationship between electricity consumption and economic growth by using the MST and HT. We also use the bootstrap technique 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. 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 relationship between energy consumption and economic growth for all income groups considered in this study and (iii) the results are in good agreement with the causal relationship between electricity consumption and economic growth.

  17. Colloidal Assembly of Hierarchically Structured Porous Supraparticles from Flower-Shaped Protein-Inorganic Hybrid Nanoparticles.

    PubMed

    Park, Won Min; Champion, Julie A

    2016-09-27

    Mimicry of biomineralization is an attractive strategy to fabricate nanostructured hybrid materials. While biomineralization involves processes that organize hybrid clusters into complex structures with hierarchy, arrangement of artificial components in biomimetic approaches has been challenging. Here, we demonstrate self-assembly of hierarchically structured porous supraparticles from protein-inorganic hybrid flower-shaped (FS) nanoparticle building blocks. In our strategy, the FS nanoparticles self-assemble via high valency interactions in combination with interfacial adsorption and compression. The flower-like shape directed robust assembly of the FS nanoparticles into chain-like clusters in solution, which were further assembled into spherical supraparticles during rotation of FS nanoparticle solution. Continuously expanding and contracting the air-water interface during rotation catalyzed assembly of FS nanoparticle clusters, indicating that adsorption and compression of the building blocks at the interface were critical. The resulting supraparticles contain hierarchical pores which are translated from the structural characteristics of individual FS nanoparticle building blocks. The protein-inorganic supraparticles are protein-compatible, have large surface area, and provide specific affinity recognition for robust protein immobilization. A variety of functional proteins could be immobilized to the porous supraparticles, making it a general platform that could provide benefits for many applications.

  18. On the geostatistical characterization of hierarchical media

    NASA Astrophysics Data System (ADS)

    Neuman, Shlomo P.; Riva, Monica; Guadagnini, Alberto

    2008-02-01

    The subsurface consists of porous and fractured materials exhibiting a hierarchical geologic structure, which gives rise to systematic and random spatial and directional variations in hydraulic and transport properties on a multiplicity of scales. Traditional geostatistical moment analysis allows one to infer the spatial covariance structure of such hierarchical, multiscale geologic materials on the basis of numerous measurements on a given support scale across a domain or "window" of a given length scale. The resultant sample variogram often appears to fit a stationary variogram model with constant variance (sill) and integral (spatial correlation) scale. In fact, some authors, who recognize that hierarchical sedimentary architecture and associated log hydraulic conductivity fields tend to be nonstationary, nevertheless associate them with stationary "exponential-like" transition probabilities and variograms, respectively, the latter being a consequence of the former. We propose that (1) the apparent ability of stationary spatial statistics to characterize the covariance structure of nonstationary hierarchical media is an artifact stemming from the finite size of the windows within which geologic and hydrologic variables are ubiquitously sampled, and (2) the artifact is eliminated upon characterizing the covariance structure of such media with the aid of truncated power variograms, which represent stationary random fields obtained upon sampling a nonstationary fractal over finite windows. To support our opinion, we note that truncated power variograms arise formally when a hierarchical medium is sampled jointly across all geologic categories and scales within a window; cite direct evidence that geostatistical parameters (variance and integral scale) inferred on the basis of traditional variograms vary systematically with support and window scales; demonstrate the ability of truncated power models to capture these variations in terms of a few scaling parameters

  19. 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.

  20. A hierarchical spatial modelling approach to investigate MRSA transmission in a tertiary hospital

    PubMed Central

    2013-01-01

    Background Most hospitals have a hierarchical design with beds positioned within cubicles and cubicles positioned within wards. Transmission of MRSA may be facilitated by patient proximity and thus the spatial arrangements of beds, cubicles and wards could be important in understanding MRSA transmission risk. Identifying high-risk areas of transmission may be useful in the design of more effective, targeted MRSA interventions. Methods Retrospective data on numbers of multi-resistant and non-multiresistant MRSA acquisitions were collected for 52 weeks in 2007 in a tertiary hospital in Brisbane, Australia. A hierarchical Bayesian spatio-temporal modelling approach was used to investigate spatial correlation in the hierarchically arranged datasets. The spatial component of the model decomposes cubicle-level variation into a spatially structured component and a spatially unstructured component, thereby encapsulating the influence of unmeasured predictor variables that themselves are spatially clustered and/or random. A fixed effect for the presence of another patient with the same type of MRSA in the cubicles two weeks prior was included. Results The best-fitting model for non-multiresistant MRSA had an unstructured random effect but no spatially structured random effect. The best-fitting model for multiresistant MRSA incorporated both spatially structured and unstructured random effects. While between-cubicle variability in risk of MRSA acquisition within the hospital was significant, there was only weak evidence to suggest that MRSA is spatially clustered. Presence of another patient with the same type of MRSA in the cubicles two weeks prior was a significant predictor of both types of MRSA in all models. Conclusions We found weak evidence of clustering of MRSA acquisition within the hospital. The presence of an infected patient in the same cubicle two weeks prior may support the importance of environmental contamination as a source of MRSA transmission. PMID

  1. 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

  2. Design of Hierarchical Structures for Synchronized Deformations

    PubMed Central

    Seifi, Hamed; Javan, Anooshe Rezaee; Ghaedizadeh, Arash; Shen, Jianhu; Xu, Shanqing; Xie, Yi Min

    2017-01-01

    In this paper we propose a general method for creating a new type of hierarchical structures at any level in both 2D and 3D. A simple rule based on a rotate-and-mirror procedure is introduced to achieve multi-level hierarchies. These new hierarchical structures have remarkably few degrees of freedom compared to existing designs by other methods. More importantly, these structures exhibit synchronized motions during opening or closure, resulting in uniform and easily-controllable deformations. Furthermore, a simple analytical formula is found which can be used to avoid collision of units of the structure during the closing process. The novel design concept is verified by mathematical analyses, computational simulations and physical experiments. PMID:28117427

  3. Hierarchical model of vulnerabilities for emotional disorders.

    PubMed

    Norton, Peter J; Mehta, Paras D

    2007-01-01

    Clark and Watson's (1991) tripartite model of anxiety and depression has had a dramatic impact on our understanding of the dispositional variables underlying emotional disorders. More recently, calls have been made to examine not simply the influence of negative affectivity (NA) but also mediating factors that might better explain how NA influences anxious and depressive syndromes (e.g. Taylor, 1998; Watson, 2005). Extending preliminary projects, this study evaluated two hierarchical models of NA, mediating factors of anxiety sensitivity and intolerance of uncertainty, and specific emotional manifestations. Data provided a very good fit to a model elaborated from preliminary studies, lending further support to hierarchical models of emotional vulnerabilities. Implications for classification and diagnosis are discussed.

  4. 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.

  5. Design of Hierarchical Structures for Synchronized Deformations

    NASA Astrophysics Data System (ADS)

    Seifi, Hamed; Javan, Anooshe Rezaee; Ghaedizadeh, Arash; Shen, Jianhu; Xu, Shanqing; Xie, Yi Min

    2017-01-01

    In this paper we propose a general method for creating a new type of hierarchical structures at any level in both 2D and 3D. A simple rule based on a rotate-and-mirror procedure is introduced to achieve multi-level hierarchies. These new hierarchical structures have remarkably few degrees of freedom compared to existing designs by other methods. More importantly, these structures exhibit synchronized motions during opening or closure, resulting in uniform and easily-controllable deformations. Furthermore, a simple analytical formula is found which can be used to avoid collision of units of the structure during the closing process. The novel design concept is verified by mathematical analyses, computational simulations and physical experiments.

  6. 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.

  7. 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

  8. Object tracking with hierarchical multiview learning

    NASA Astrophysics Data System (ADS)

    Yang, Jun; Zhang, Shunli; Zhang, Li

    2016-09-01

    Building a robust appearance model is useful to improve tracking performance. We propose a hierarchical multiview learning framework to construct the appearance model, which has two layers for tracking. On the top layer, two different views of features, grayscale value and histogram of oriented gradients, are adopted for representation under the cotraining framework. On the bottom layer, for each view of each feature, three different random subspaces are generated to represent the appearance from multiple views. For each random view submodel, the least squares support vector machine is employed to improve the discriminability for concrete and efficient realization. These two layers are combined to construct the final appearance model for tracking. The proposed hierarchical model assembles two types of multiview learning strategies, in which the appearance can be described more accurately and robustly. Experimental results in the benchmark dataset demonstrate that the proposed method can achieve better performance than many existing state-of-the-art algorithms.

  9. Hierarchical networks, power laws, and neuronal avalanches.

    PubMed

    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.

  10. 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

  11. Hierarchical abstract semantic model for image classification

    NASA Astrophysics Data System (ADS)

    Ye, Zhipeng; Liu, Peng; Zhao, Wei; Tang, Xianglong

    2015-09-01

    Semantic gap limits the performance of bag-of-visual-words. To deal with this problem, a hierarchical abstract semantics method that builds abstract semantic layers, generates semantic visual vocabularies, measures semantic gap, and constructs classifiers using the Adaboost strategy is proposed. First, abstract semantic layers are proposed to narrow the semantic gap between visual features and their interpretation. Then semantic visual words are extracted as features to train semantic classifiers. One popular form of measurement is used to quantify the semantic gap. The Adaboost training strategy is used to combine weak classifiers into strong ones to further improve performance. For a testing image, the category is estimated layer-by-layer. Corresponding abstract hierarchical structures for popular datasets, including Caltech-101 and MSRC, are proposed for evaluation. The experimental results show that the proposed method is capable of narrowing semantic gaps effectively and performs better than other categorization methods.

  12. 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.

  13. 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

    2013-08-02

    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.

  14. Neural decoding with hierarchical generative models.

    PubMed

    van Gerven, Marcel A J; de Lange, Floris P; Heskes, Tom

    2010-12-01

    Recent research has shown that reconstruction of perceived images based on hemodynamic response as measured with functional magnetic resonance imaging (fMRI) is starting to become feasible. In this letter, we explore reconstruction based on a learned hierarchy of features by employing a hierarchical generative model that consists of conditional restricted Boltzmann machines. In an unsupervised phase, we learn a hierarchy of features from data, and in a supervised phase, we learn how brain activity predicts the states of those features. Reconstruction is achieved by sampling from the model, conditioned on brain activity. We show that by using the hierarchical generative model, we can obtain good-quality reconstructions of visual images of handwritten digits presented during an fMRI scanning session.

  15. Text Clustering Based on the User Search Intention

    NASA Astrophysics Data System (ADS)

    Liu, Wenjing; Zhou, Yanquan; Ren, Fuji

    This paper presents a novel algorithm of Text Clustering. With the popularity of the Internet, text information on the web shows explosive growth trend. Text Clustering technology as a method of unsupervised machine learning, which does not need the training process and pre-manual tagging, so Text Clustering is an effective way for dealing with massive text messages. The traditional Text Clustering is based on the content of the article, and they think that the articles which belong to the same class have the greater similarity. In this paper, we extracted label word from the summary information returned by search engine. Then did hierarchical clustering based on the text feature of the label word. Experiment shows that the algorithm is feasible.

  16. 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.

  17. Globular cluster origin of X-ray bursters

    NASA Technical Reports Server (NTRS)

    Grindlay, J. E.

    1984-01-01

    X-ray bursters and galactic bulge X-ray sources, or the most luminous X-ray sources in the Galaxy, are reasonably well constrained in their basic nature but not in their origin. It is suggested they may all have been produced by tidal capture in high density cores of globular clusters, which have now largely been disrupted by tidal stripping and shocking in the galactic plane. General arguments are presented for cluster disruption by the possible ring of giant molecular clouds in the Galaxy. Tests of the cluster disruption hypothesis are in progress and preliminary results are summarized here. The G-K star 'companions' previously noted for at least four bursters have spectra (in the two cases observed) consistent with metal-rich cluster giants. Several possibilities are discussed, including the formation of hierarchical triples in the dissolving cluster or in the galactic plane.

  18. 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)

  19. Hierarchical optimization for neutron scattering problems

    DOE PAGES

    Bao, Feng; Archibald, Rick; Bansal, Dipanshu; ...

    2016-03-14

    In this study, 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.

  20. Hierarchical Bayesian Approach to Locating Seismic Events

    SciTech Connect

    Johannesson, G; Myers, S C; Hanley, W G

    2005-11-09

    We propose a hierarchical Bayesian model for conducting inference on the location of multiple seismic events (earthquakes) given data on the arrival of various seismic phases to sensor locations. The model explicitly accounts for the uncertainty associated with a theoretical seismic-wave travel-time model used along with the uncertainty of the arrival data. Posterior inferences is carried out using Markov chain Monte Carlo (MCMC).

  1. Megavariate analysis of hierarchical QSAR data

    NASA Astrophysics Data System (ADS)

    Eriksson, Lennart; Johansson, Erik; Lindgren, Fredrik; Sjöström, Michael; Wold, Svante

    2002-10-01

    Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and makes the modelling efforts less reliable. Model interpretation may be misleading and predictive power may deteriorate. A better alternative is usually to partition the variables into blocks of logically related variables and apply hierarchical data analysis. Such blocked data may be analyzed by PCA and PLS. This modelling forms the base-level of the hierarchical modelling set-up. On the base-level in-depth information is extracted for the different blocks. The score vectors formed on the base-level, here called `super variables', may be linked together in new matrices on the top-level. On the top-level superficial relationships between the X- and the Y-data are investigated. In this paper the basic principles of hierarchical modelling by means of PCA and PLS are reviewed. One objective of the paper is to disseminate this concept to a broader QSAR audience. The hierarchical methods are used to analyze a set of 10 haloalkanes for which K = 30 chemical descriptors and M = 255 biological responses have been gathered. Due to the complexity of the biological data, they are sub-divided in four blocks. All the modelling steps on the base-level and the top-level are reported and the final QSAR model is interpreted thoroughly.

  2. Angelic Hierarchical Planning: Optimal and Online Algorithms

    DTIC Science & Technology

    2008-12-06

    describe an alternative “satisficing” algorithm, AHSS . 4.1 Abstract Lookahead Trees Our ALT data structures support our search algorithms by efficiently...Angelic Hierarchical Satisficing Search ( AHSS ), which at- tempts to find a plan that reaches the goal with at most some pre-specified cost α. AHSS can be...much more efficient than AHA*, since it can commit to a plan without first proving its optimality. At each step, AHSS (see Algorithm 3) begins by

  3. Hierarchical optimization for neutron scattering problems

    SciTech Connect

    Bao, Feng; Archibald, Rick; Bansal, Dipanshu; Delaire, Olivier

    2016-06-15

    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.

  4. 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.

  5. Hierarchical self-assembly of colloidal magnetic particles into reconfigurable spherical structures.

    PubMed

    Morphew, Daniel; Chakrabarti, Dwaipayan

    2015-05-14

    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.

  6. 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.

  7. Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

    PubMed Central

    Wen, Chih-Yu; Chen, Ying-Chih

    2009-01-01

    This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks. PMID:22412343

  8. The polarimetric entropy classification of SAR based on the clustering and signal noise ration

    NASA Astrophysics Data System (ADS)

    Shi, Lei; Yang, Jie; Lang, Fengkai

    2009-10-01

    Usually, Wishart H/α/A classification is an effective unsupervised classification method. However, the anisotropy parameter (A) is an unstable factor in the low signal noise ration (SNR) areas; at the same time, many clusters are useless to manually recognize. In order to avoid too many clusters to affect the manual recognition and the convergence of iteration and aiming at the drawback of the Wishart classification, in this paper, an enhancive unsupervised Wishart classification scheme for POLSAR data sets is introduced. The anisotropy parameter A is used to subdivide the target after H/α classification, this parameter has the ability to subdivide the homogeneity area in high SNR condition which can not be classified by using H/α. It is very useful to enhance the adaptability in difficult areas. Yet, the target polarimetric decomposition is affected by SNR before the classification; thus, the local homogeneity area's SNR evaluation is necessary. After using the direction of the edge detection template to examine the direction of POL-SAR images, the results can be processed to estimate SNR. The SNR could turn to a powerful tool to guide H/α/A classification. This scheme is able to correct the mistake judging of using A parameter such as eliminating much insignificant spot on the road and urban aggregation, even having a good performance in the complex forest. To convenience the manual recognition, an agglomerative clustering algorithm basing on the method of deviation-class is used to consolidate some clusters which are similar in 3by3 polarimetric coherency matrix. This classification scheme is applied to full polarimetric L band SAR image of Foulum area, Denmark.

  9. 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.

  10. A Hierarchical Approach to Buckling Load Calculations

    NASA Technical Reports Server (NTRS)

    Arbocz, Johann; Starnes, James H.; Nemeth, Michael P.

    1999-01-01

    The advantages of using a hierarchical analysis approach to calculate the buckling load of an axially compressed composite cylindrical shell is demonstrated using an example taken from a recent experimental program. The Delft Interactive Shell DEsign COde (DISDECO) shell design code is used for this hierarchical analysis approach to provide an accurate prediction of the critical buckling load of the actual shell structure. DISDECO includes the influence of the boundary conditions, initial geometric imperfections, the effects of stiffener and load eccentricities, and the effects of prebuckling deformations caused by edge constraints in the analysis. It is shown that the use of DISDECO makes it relatively simple to proceed step by step from simple to more complex models and solution procedures. As a final step in the hierarchical analysis approach, the critical buckling load and the estimated imperfection sensitivity of the shell are verified by conducting an analysis of a large finite element model with one of the current generation two-dimensional shell analysis codes with advanced capabilities needed to represent both geometric and material nonlinearities.

  11. 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.

  12. Anisotropic and Hierarchical Porosity in Multifunctional Ceramics

    NASA Astrophysics Data System (ADS)

    Lichtner, Aaron Zev

    The performance of multifunctional porous ceramics is often hindered by the seemingly contradictory effects of porosity on both mechanical and non-structural properties and yet a sufficient body of knowledge linking microstructure to these properties does not exist. Using a combination of tailored anisotropic and hierarchical materials, these disparate effects may be reconciled. In this project, a systematic investigation of the processing, characterization and properties of anisotropic and isotropic hierarchically porous ceramics was conducted. The system chosen was a composite ceramic intended as the cathode for a solid oxide fuel cell (SOFC). Comprehensive processing investigations led to the development of approaches to make hierarchical, anisotropic porous microstructures using directional freeze-casting of well dispersed slurries. The effect of all the important processing parameters was investigated. This resulted in an ability to tailor and control the important microstructural features including the scale of the microstructure, the macropore size and total porosity. Comparable isotropic porous ceramics were also processed using fugitive pore formers. A suite of characterization techniques including x-ray tomography and 3-D sectional scanning electron micrographs (FIB-SEM) was used to characterize and quantify the green and partially sintered microstructures. The effect of sintering temperature on the microstructure was quantified and discrete element simulations (DEM) were used to explain the experimental observations. Finally, the comprehensive mechanical properties, at room temperature, were investigated, experimentally and using DEM, for the different microstructures.

  13. 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.

  14. 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

  15. Hierarchical image classification in the bioscience literature.

    PubMed

    Kim, Daehyun; Yu, Hong

    2009-11-14

    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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. An Experiment in Automatic Hierarchical Document Classification.

    ERIC Educational Resources Information Center

    Garland, Kathleen

    1983-01-01

    Describes method of automatic document classification in which documents classed as QA by Library of Congress classification system were clustered at six thresholds by keyword using single link technique. Automatically generated clusters were compared to Library of Congress subclasses, and partial classified hierarchy was formed. Twelve references…

  1. Sun Protection Belief Clusters: Analysis of Amazon Mechanical Turk Data.

    PubMed

    Santiago-Rivas, Marimer; Schnur, Julie B; Jandorf, Lina

    2016-12-01

    This study aimed (i) to determine whether people could be differentiated on the basis of their sun protection belief profiles and individual characteristics and (ii) explore the use of a crowdsourcing web service for the assessment of sun protection beliefs. A sample of 500 adults completed an online survey of sun protection belief items using Amazon Mechanical Turk. A two-phased cluster analysis (i.e., hierarchical and non-hierarchical K-means) was utilized to determine clusters of sun protection barriers and facilitators. Results yielded three distinct clusters of sun protection barriers and three distinct clusters of sun protection facilitators. Significant associations between gender, age, sun sensitivity, and cluster membership were identified. Results also showed an association between barrier and facilitator cluster membership. The results of this study provided a potential alternative approach to developing future sun protection promotion initiatives in the population. Findings add to our knowledge regarding individuals who support, oppose, or are ambivalent toward sun protection and inform intervention research by identifying distinct subtypes that may best benefit from (or have a higher need for) skin cancer prevention efforts.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. Formation of bimetallic nanoalloys by Au coating of size-selected Cu clusters

    NASA Astrophysics Data System (ADS)

    Yin, Feng; Wang, Zhi Wei; Palmer, Richard E.

    2012-10-01

    Bimetallic clusters display new characteristics that could not be obtained by varying either the size of pure metallic systems or the composition of bulk bimetals alone. Coating of pre-deposited clusters by vapour deposition is a typical synthesis process of bimetallic clusters. Here, we have demonstrated that hierarchical, gold cluster-decorated copper clusters as well as both heterogeneous and homogeneous Cu-Au bimetallic clusters (4.6 to 10.7 nm) can be prepared by coating pre-deposited, size-selected Cu5000 (4.6 ± 0.2 nm) with Au evaporation at various temperatures. These bimetallic clusters were analyzed by aberration-corrected scanning transmission electron microscopy and associated electron energy loss spectroscopy. The results indicate that the growth of bimetallic clusters is controlled by a competition between nucleation and diffusion of the coating Au atoms.

  8. 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).

  9. Cluster headache

    PubMed Central

    Leroux, Elizabeth; Ducros, Anne

    2008-01-01

    Cluster headache (CH) is a primary headache disease characterized by recurrent short-lasting attacks (15 to 180 minutes) of excruciating unilateral periorbital pain accompanied by ipsilateral autonomic signs (lacrimation, nasal congestion, ptosis, miosis, lid edema, redness of the eye). It affects young adults, predominantly males. Prevalence is estimated at 0.5–1.0/1,000. CH has a circannual and circadian periodicity, attacks being clustered (hence the name) in bouts that can occur during specific months of the year. Alcohol is the only dietary trigger of CH, strong odors (mainly solvents and cigarette smoke) and napping may also trigger CH attacks. During bouts, attacks may happen at precise hours, especially during the night. During the attacks, patients tend to be restless. CH may be episodic or chronic, depending on the presence of remission periods. CH is associated with trigeminovascular activation and neuroendocrine and vegetative disturbances, however, the precise cautive mechanisms remain unknown. Involvement of the hypothalamus (a structure regulating endocrine function and sleep-wake rhythms) has been confirmed, explaining, at least in part, the cyclic aspects of CH. The disease is familial in about 10% of cases. Genetic factors play a role in CH susceptibility, and a causative role has been suggested for the hypocretin receptor gene. Diagnosis is clinical. Differential diagnoses include other primary headache diseases such as migraine, paroxysmal hemicrania and SUNCT syndrome. At present, there is no curative treatment. There are efficient treatments to shorten the painful attacks (acute treatments) and to reduce the number of daily attacks (prophylactic treatments). Acute treatment is based on subcutaneous administration of sumatriptan and high-flow oxygen. Verapamil, lithium, methysergide, prednisone, greater occipital nerve blocks and topiramate may be used for prophylaxis. In refractory cases, deep-brain stimulation of the hypothalamus and

  10. Formation of Cluster Complexes by Cluster-Cluster-Collisions

    NASA Astrophysics Data System (ADS)

    Ichihashi, Masahiko; Odaka, Hideho

    2015-03-01

    Multi-element clusters are interested in their chemical and physical properties, and it is expected that they are utilized as catalysts, for example. Their properties critically depend on the size, composition and atomic ordering, and it should be important to adjust the above parameters for their functionality. One of the ways to form a multi-element cluster is to employ a low-energy collision between clusters. Here, we show characteristic results obtained in the collision between a neutral Ar cluster and a size-selected Co cluster ion. Low-energy collision experiment was accomplished by using a newly developed merging-beam apparatus. Cobalt cluster ions were produced by laser ablation, and mass-selected. On the other hand, argon clusters were prepared by the supersonic expansion of Ar gas. Both cluster beams were merged together in an ion guide, and ionic cluster complexes were mass-analyzed. In the collision of Co2+ and ArN, Co2Arn+ (n = 1 - 30) were observed, and the total intensity of Co2Arn+ (n >= 1) is inversely proportional to the relative velocity between Co2+ and ArN. This suggests that the charge-induced dipole interaction between Co2+ and a neutral Ar cluster is dominant in the formation of the cluster complex, Co2+Arn.

  11. Visualization of large-scale aqueous solubility data using a novel hierarchical data visualization technique.

    PubMed

    Yamashita, Fumiyoshi; Itoh, Takayuki; Hara, Hideto; Hashida, Mitsuru

    2006-01-01

    It is a difficult task to recognize the trends in molecular physical properties relevant to a specific chemical class and find a way to optimize potential compounds. We present here a novel hierarchical data visualization technique, named "HeiankyoView", to visualize large-scale multidimensional chemical information. HeiankyoView represents hierarchically organized data objects by mapping leaf nodes as colored square icons and nonleaf nodes as rectangular borders. In this way, data objects can be expressed as equishaped icons without overlapping one another in the two-dimensional display space. HeiankyoView has been applied to visualize aqueous solubility data for 908 compounds collected from the published literature. When the results of a recursive partitioning analysis and hierarchical clustering analysis were visualized, the trends hidden in the solubility data could be effectively displayed as intuitively understandable visual images. Most interestingly, the data visualization technique, without any statistical computations, was able to assist us in extracting from such large-scale data meaningful information establishing that ClogP and the molecular weight are critical factors in determining aqueous solubility. Thus, HeiankyoView is a powerful tool to help us understand structure-activity relationships intuitively from a large-scale data set.

  12. Cluster and constraint analysis in tetrahedron packings.

    PubMed

    Jin, Weiwei; Lu, Peng; Liu, Lufeng; Li, Shuixiang

    2015-04-01

    The disordered packings of tetrahedra often show no obvious macroscopic orientational or positional order for a wide range of packing densities, and it has been found that the local order in particle clusters is the main order form of tetrahedron packings. Therefore, a cluster analysis is carried out to investigate the local structures and properties of tetrahedron packings in this work. We obtain a cluster distribution of differently sized clusters, and peaks are observed at two special clusters, i.e., dimer and wagon wheel. We then calculate the amounts of dimers and wagon wheels, which are observed to have linear or approximate linear correlations with packing density. Following our previous work, the amount of particles participating in dimers is used as an order metric to evaluate the order degree of the hierarchical packing structure of tetrahedra, and an order map is consequently depicted. Furthermore, a constraint analysis is performed to determine the isostatic or hyperstatic region in the order map. We employ a Monte Carlo algorithm to test jamming and then suggest a new maximally random jammed packing of hard tetrahedra from the order map with a packing density of 0.6337.

  13. Shock Features in Merging Galaxy Clusters

    NASA Astrophysics Data System (ADS)

    Dasadia, Sarthak; Sun, Ming; Morandi, Andrea

    2017-01-01

    Clusters of galaxies are the largest and the most massive gravitationally collapsed objects in the universe. In the hierarchical scenarios of the large-scale structure formation of the universe, they form by subcluster mergers and infall. Major mergers inject tremendous amounts of energy (˜1064 erg) into the intracluster medium (ICM), triggering shocks and generating. These hydro-dynamical activities may amplify magnetic fields in the cluster and accelerate relativistic particles. These non-thermal phenomena have been revealed by the detection of Mpc-scale diffuse radio emission. Current studies hint at a correlation between X-ray and Radio morphologies.To further address this issue, shock properties of 15 galaxy clusters were studied. The sample was divided into two categories: with and without diffuse radio emission. In my dissertation, my goal is to address questions: Do more luminous clusters have stronger shocks?, How continuous gas stripping affect cool cores?, Why some clusters exhibit a stronger correlation between X-ray shocks and radio relic?

  14. 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

  15. N-body simulations of star clusters

    NASA Astrophysics Data System (ADS)

    Engle, Kimberly Anne

    1999-10-01

    We investigate the structure and evolution of underfilling (i.e. non-Roche-lobe-filling) King model globular star clusters using N-body simulations. We model clusters with various underfilling factors and mass distributions to determine their evolutionary tracks and lifetimes. These models include a self-consistent galactic tidal field, mass loss due to stellar evolution, ejection, and evaporation, and binary evolution. We find that a star cluster that initially does not fill its Roche lobe can live many times longer than one that does initially fill its Roche lobe. After a few relaxation times, the cluster expands to fill its Roche lobe. We also find that the choice of initial mass function significantly affects the lifetime of the cluster. These simulations were performed on the GRAPE-4 (GRAvity PipE) special-purpose hardware with the stellar dynamics package ``Starlab.'' The GRAPE-4 system is a massively-parallel computer designed to calculate the force (and its first time derivative) due to N particles. Starlab's integrator ``kira'' employs a 4th- order Hermite scheme with hierarchical (block) time steps to evolve the stellar system. We discuss, in some detail, the design of the GRAPE-4 system and the manner in which the Hermite integration scheme with block time steps is implemented in the hardware.

  16. 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

  17. A map of the protein space--an automatic hierarchical classification of all protein sequences.

    PubMed

    Yona, G; Linial, N; Tishby, N; Linial, M

    1998-01-01

    We investigate the space of all protein sequences. We combine the standard measures of similarity (SW, FASTA, BLAST), to associate with each sequence an exhaustive list of neighboring sequences. These lists induce a (weighted directed) graph whose vertices are the sequences. The weight of an edge connecting two sequences represents their degree of similarity. This graph encodes much of the fundamental properties of the sequence space. We look for clusters of related proteins in this graph. These clusters correspond to strongly connected sets of vertices. Two main ideas underlie our work: i) Interesting homologies among proteins can be deduced by transitivity. ii) Transitivity should be applied restrictively in order to prevent unrelated proteins from clustering together. Our analysis starts from a very conservative classification, based on very significant similarities, that has many classes. Subsequently, classes are merged to include less significant similarities. Merging is performed via a novel two phase algorithm. First, the algorithm identifies groups of possibly related clusters (based on transitivity and strong connectivity) using local considerations, and merges them. Then, a global test is applied to identify nuclei of strong relationships within these groups of clusters, and the classification is refined accordingly. This process takes place at varying thresholds of statistical significance, where at each step the algorithm is applied on the classes of the previous classification, to obtain the next one, at the more permissive threshold. Consequently, a hierarchical organization of all proteins is obtained. The resulting classification splits the space of all protein sequences into well defined groups of proteins. The results show that the automatically induced sets of proteins are closely correlated with natural biological families and super families. The hierarchical organization reveals finer sub-families that make up known families of proteins as

  18. 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

  19. Wind farms model aggregation using probabilistic clustering

    NASA Astrophysics Data System (ADS)

    Fernandes, Paula Odete; Ferreira, Ángela Paula

    2013-10-01

    The main objective of this research is the identification of homogeneous groups within wind farms of a major operator playing in the energy sector in Portugal, based on two multivariate analyses: Hierarchical Cluster Analysis and Discriminant Analysis, by using two independent variables: annual liquid hours and net production. From the produced outputs there were identified three homogenous groups of wind farms: (1) medium Installed Capacity and Induction Generator based Technology, (2) high Installed Capacity and Synchronous Generator based Technology and (3) medium Installed Capacity and Synchronous Generator based Technology, which includes the wind farms with the higher annual liquid hours. It has been found that the results obtained by cluster analysis are well classified, with a total percentage of correct classification of 97,1%, which can be considered excellent.

  20. 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.

  1. Hierarchical Naive Bayes for genetic association studies

    PubMed Central

    2012-01-01

    Background Genome Wide Association Studies represent powerful approaches that aim at disentangling the genetic and molecular mechanisms underlying complex traits. The usual "one-SNP-at-the-time" testing strategy cannot capture the multi-factorial nature of this kind of disorders. We propose a Hierarchical Naïve Bayes classification model for taking into account associations in SNPs data characterized by Linkage Disequilibrium. Validation shows that our model reaches classification performances superior to those obtained by the standard Naïve Bayes classifier for simulated and real datasets. Methods In the Hierarchical Naïve Bayes implemented, the SNPs mapping to the same region of Linkage Disequilibrium are considered as "details" or "replicates" of the locus, each contributing to the overall effect of the region on the phenotype. A latent variable for each block, which models the "population" of correlated SNPs, can be then used to summarize the available information. The classification is thus performed relying on the latent variables conditional probability distributions and on the SNPs data available. Results The developed methodology has been tested on simulated datasets, each composed by 300 cases, 300 controls and a variable number of SNPs. Our approach has been also applied to two real datasets on the genetic bases of Type 1 Diabetes and Type 2 Diabetes generated by the Wellcome Trust Case Control Consortium. Conclusions The approach proposed in this paper, called Hierarchical Naïve Bayes, allows dealing with classification of examples for which genetic information of structurally correlated SNPs are available. It improves the Naïve Bayes performances by properly handling the within-loci variability. PMID:23095471

  2. A new clustering algorithm for scanning electron microscope images

    NASA Astrophysics Data System (ADS)

    Yousef, Amr; Duraisamy, Prakash; Karim, Mohammad

    2016-04-01

    A scanning electron microscope (SEM) is a type of electron microscope that produces images of a sample by scanning it with a focused beam of electrons. The electrons interact with the sample atoms, producing various signals that are collected by detectors. The gathered signals contain information about the sample's surface topography and composition. The electron beam is generally scanned in a raster scan pattern, and the beam's position is combined with the detected signal to produce an image. The most common configuration for an SEM produces a single value per pixel, with the results usually rendered as grayscale images. The captured images may be produced with insufficient brightness, anomalous contrast, jagged edges, and poor quality due to low signal-to-noise ratio, grained topography and poor surface details. The segmentation of the SEM images is a tackling problems in the presence of the previously mentioned distortions. In this paper, we are stressing on the clustering of these type of images. In that sense, we evaluate the performance of the well-known unsupervised clustering and classification techniques such as connectivity based clustering (hierarchical clustering), centroid-based clustering, distribution-based clustering and density-based clustering. Furthermore, we propose a new spatial fuzzy clustering technique that works efficiently on this type of images and compare its results against these regular techniques in terms of clustering validation metrics.

  3. Characterization of population exposure to organochlorines: a cluster analysis application.

    PubMed

    Guimarães, Raphael Mendonça; Asmus, Carmen Ildes Rodrigues Fróes; Burdorf, Alex

    2013-06-01

    This study aimed to show the results from a cluster analysis application in the characterization of population exposure to organochlorines through variables related to time and exposure dose. Characteristics of 354 subjects in a population exposed to organochlorine pesticides residues related to time and exposure dose were subjected to cluster analysis to separate them into subgroups. We performed hierarchical cluster analysis. To evaluate the classification accuracy, compared to intra-group and inter-group variability by ANOVA for each dimension. The aggregation strategy was accomplished by the method of Ward. It was, for the creation of clusters, variables associated with exposure and routes of contamination. The information on the estimated intake doses of compound were used to weight the values of exposure time at each of the routes, so as to obtain values proxy exposure intensity. The results showed three clusters: cluster 1 (n = 45), characteristics of greatest exposure, the cluster 2 (n = 103), intermediate exposure, and cluster 3 (n = 206), less exposure. The bivariate analyzes performed with groups that are groups showed a statistically significant difference. This study demonstrated the applicability of cluster analysis to categorize populations exposed to organochlorines and also points to the relevance of typological studies that may contribute to a better classification of subjects exposed to chemical agents, which is typical of environmental epidemiology studies to a wider understanding of etiological, preventive and therapeutic contamination.

  4. Extensions to Real-time Hierarchical Mine Detection Algorithm

    DTIC Science & Technology

    2002-09-01

    Extensions to Real-Time Hierarchical Mine Detection Algorithm System Number: Patron Number: Requester: Notes: DSIS Use only: Deliver to: DK...Recherche et developpement pour Ia defense Canada Extensions to Real-Time Hierarchical Mine Detection Algorithm Final Report Sinh Duong and Mabo R. Ito...EXTENSIONS TO REAL-TIME HIERARCHICAL MINE DETECTION ALGORITHM FINAL REPORT by Smh Duong and Mabo R Ito The Univer~ity of Bntl~h Columbia Vancouver

  5. 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

  6. Hierarchical multisensor analysis for robotic exploration

    NASA Technical Reports Server (NTRS)

    Eberlein, Susan; Yates, Gigi; Majani, Eric

    1991-01-01

    Robotic vehicles for lunar and Mars exploration will carry an array of complex instruments requiring real-time data interpretation and fusion. The system described here uses hierarchical multiresolution analysis of visible and multispectral images to extract information on mineral composition, texture and object shape. This information is used to characterize the site geology and choose interesting samples for acquisition. Neural networks are employed for many data analysis steps. A decision tree progressively integrates information from multiple instruments and performs goal-driven decision making. The system is designed to incorporate more instruments and data types as they become available.

  7. Hierarchical multisensor analysis for robotic exploration

    NASA Astrophysics Data System (ADS)

    Eberlein, Susan; Yates, Gigi; Majani, Eric

    1991-03-01

    Robotic vehicles for lunar and Mars exploration will carry an array of complex instruments requiring real-time data interpretation and fusion. The system described here uses hierarchical multiresolution analysis of visible and multispectral images to extract information on mineral composition, texture and object shape. This information is used to characterize the site geology and choose interesting samples for acquisition. Neural networks are employed for many data analysis steps. A decision tree progressively integrates information from multiple instruments and performs goal-driven decision making. The system is designed to incorporate more instruments and data types as they become available.

  8. Hierarchical structure description of spatiotemporal chaos.

    PubMed

    Liu, Jian; She, Zhen-Su; Guo, Hongyu; Li, Liang; Ouyang, Qi

    2004-09-01

    We develop a hierarchical structure (HS) analysis for quantitative description of statistical states of spatially extended systems. Examples discussed here include an experimental reaction-diffusion system with Belousov-Zhabotinsky kinetics, the two-dimensional complex Ginzburg-Landau equation, and the modified FitzHugh-Nagumon equation, which all show complex dynamics of spirals and defects. We demonstrate that the spatial-temporal fluctuation fields in the above-mentioned systems all display the HS similarity property originally proposed for the study of fully developed turbulence [Phys. Rev. Lett. 72, 336 (1994)

  9. Hierarchical nucleus segmentation in digital pathology images

    NASA Astrophysics Data System (ADS)

    Gao, Yi; Ratner, Vadim; Zhu, Liangjia; Diprima, Tammy; Kurc, Tahsin; Tannenbaum, Allen; Saltz, Joel

    2016-03-01

    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.

  10. 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

  11. Additive Manufacturing of Hierarchical Porous Structures

    SciTech Connect

    Grote, Christopher John

    2016-08-30

    Additive manufacturing has become a tool of choice for the development of customizable components. Developments in this technology have led to a powerful array of printers that t serve a variety of needs. However, resin development plays a crucial role in leading the technology forward. This paper addresses the development and application of printing hierarchical porous structures. Beginning with the development of a porous scaffold, which can be functionalized with a variety of materials, and concluding with customized resins for metal, ceramic, and carbon structures.

  12. 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.

  13. An infinite square lattice of super-supertetrahedral T(6)-like tin oxyselenide clusters.

    PubMed

    Lin, Qipu; Bu, Xianhui; Feng, Pingyun

    2014-04-21

    A new super-supertetrahedral cluster, resembling a coreless supertetrahedral T6 cluster, was made as a tin oxyselenide by integrating hard and soft Lewis bases (O(2-) and Se(2-)) into the tetravalent system. Its hierarchical architecture, built from supertetrahedral T3-[Sn10O4Se20] and Sn2Se6 clusters, represents a new level of complexity in the cluster-based construction. Compared to pure tin selenides, the new tin oxyselenide material in this work shows much enhanced stability and size-dependent band energy level.

  14. An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins

    PubMed Central

    Babbitt, Patricia C.; Ferrin, Thomas E.

    2017-01-01

    Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially—MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method’s novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated

  15. Predicting failure using conditioning on damage history: Demonstration on percolation and hierarchical fiber bundles

    SciTech Connect

    Andersen, J.V.; Sornette, D.

    2005-11-01

    We formulate the problem of probabilistic predictions of global failure in the simplest possible model based on site percolation and on one of the simplest models of time-dependent rupture, a hierarchical fiber bundle model. We show that conditioning the predictions on the knowledge of the current degree of damage (occupancy density p or number and size of cracks) and on some information on the largest cluster improves significantly the prediction accuracy, in particular by allowing one to identify those realizations which have anomalously low or large clusters (cracks). We quantify the prediction gains using two measures, the relative specific information gain (which is the variation of entropy obtained by adding new information) and the root mean square of the prediction errors over a large ensemble of realizations. The bulk of our simulations have been obtained with the two-dimensional site percolation model on a lattice of size LxL=20x20 and hold true for other lattice sizes. For the hierarchical fiber bundle model, conditioning the measures of damage on the information of the location and size of the largest crack extends significantly the critical region and the prediction skills. These examples illustrate how ongoing damage can be used as a revelation of both the realization-dependent preexisting heterogeneity and the damage scenario undertaken by each specific sample.

  16. [Non-parametric Bootstrap estimation on the intraclass correlation coefficient generated from quantitative hierarchical data].

    PubMed

    Liang, Rong; Zhou, Shu-dong; Li, Li-xia; Zhang, Jun-guo; Gao, Yan-hui

    2013-09-01

    This paper aims to achieve Bootstraping in hierarchical data and to provide a method for the estimation on confidence interval(CI) of intraclass correlation coefficient(ICC).First, we utilize the mixed-effects model to estimate data from ICC of repeated measurement and from the two-stage sampling. Then, we use Bootstrap method to estimate CI from related ICCs. Finally, the influences of different Bootstraping strategies to ICC's CIs are compared. The repeated measurement instance show that the CI of cluster Bootsraping containing the true ICC value. However, when ignoring the hierarchy characteristics of data, the random Bootsraping method shows that it has the invalid CI. Result from the two-stage instance shows that bias observed between cluster Bootstraping's ICC means while the ICC of the original sample is the smallest, but with wide CI. It is necessary to consider the structure of data as important, when hierarchical data is being resampled. Bootstrapping seems to be better on the higher than that on lower levels.

  17. 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.

  18. 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.

  19. 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.

  20. Clustering of Mueller matrix images for skeletonized structure detection

    NASA Astrophysics Data System (ADS)

    Collet, Christophe; Zallat, Jihad; Takakura, Yoshitate

    2004-04-01

    This paper extends and refines previous work on clustering of polarization-encoded images. The polarization-encoded images used in this work are considered as multidimensional parametric images where a clustering scheme based on Markovian Bayesian inference is applied. Hidden Markov Chains Model (HMCM) and Hidden Hierarchical Markovian Model (HHMM) show to handle effectively Mueller images and give very good results for biological tissues (vegetal leaves). Pretreatments attempting to reduce the image dimensionality based on the Principal Component Analysis (PCA) turns out to be useless for Mueller matrix images.

  1. Mutual synchronization and clustering in randomly coupled chaotic dynamical networks.

    PubMed

    Manrubia, S C; Mikhailov, A S

    1999-08-01

    We introduce and study systems of randomly coupled maps where the relevant parameter is the degree of connectivity in the system. Global (almost-) synchronized states are found (equivalent to the synchronization observed in globally coupled maps) until a certain critical threshold for the connectivity is reached. We further show that not only the average connectivity, but also the architecture of the couplings is responsible for the cluster structure observed. We analyze the different phases of the system and use various correlation measures in order to detect ordered nonsynchronized states. Finally, it is shown that the system displays a dynamical hierarchical clustering which allows the definition of emerging graphs.

  2. Early dynamical evolution of substructured stellar clusters

    NASA Astrophysics Data System (ADS)

    Dorval, Julien; Boily, Christian

    2015-08-01

    It is now widely accepted that stellar clusters form with a high level of substructure (Kuhn et al. 2014, Bate 2009), inherited from the molecular cloud and the star formation process. Evidence from observations and simulations also indicate the stars in such young clusters form a subvirial system (Kirk et al. 2007, Maschberger et al. 2010). The subsequent dynamical evolution can cause important mass loss, ejecting a large part of the birth population in the field. It can also imprint the stellar population and still be inferred from observations of evolved clusters. Nbody simulations allow a better understanding of these early twists and turns, given realistic initial conditions. Nowadays, substructured, clumpy young clusters are usually obtained through pseudo-fractal growth (Goodwin et al. 2004) and velocity inheritance. Such models are visually realistics and are very useful, they are however somewhat artificial in their velocity distribution. I introduce a new way to create clumpy initial conditions through a "Hubble expansion" which naturally produces self consistent clumps, velocity-wise. A velocity distribution analysis shows the new method produces realistic models, consistent with the dynamical state of the newly created cores in hydrodynamic simulation of cluster formation (Klessen & Burkert 2000). I use these initial conditions to investigate the dynamical evolution of young subvirial clusters, up to 80000 stars. I find an overall soft evolution, with hierarchical merging leading to a high level of mass segregation. I investigate the influence of the mass function on the fate of the cluster, specifically on the amount of mass loss induced by the early violent relaxation. Using a new binary detection algorithm, I also find a strong processing of the native binary population.

  3. Efficient Record Linkage Algorithms Using Complete Linkage Clustering

    PubMed Central

    Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar

    2016-01-01

    Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times. PMID:27124604

  4. Efficient Record Linkage Algorithms Using Complete Linkage Clustering.

    PubMed

    Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar

    2016-01-01

    Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times.

  5. Obstructive sleep apnea detection using clustering classification of nonlinear features from nocturnal oximetry.

    PubMed

    Alvarez, Daniel; Hornero, Roberto; Marcos, J Víctor; del Campo, Félix; López, Miguel

    2007-01-01

    This study is focused on the classification of patients suspected of suffering from obstructive sleep apnea (OSA) by means of cluster analysis. We assessed the diagnostic ability of three clustering algorithms: k-means, hierarchical and fuzzy c-means (FCM). Nonlinear features of blood oxygen saturation (SaO2) from nocturnal oximetry were used as inputs to the clustering methods. Three nonlinear methods were used: approximate entropy (ApEn), central tendency measure (CTM) and Lempel-Ziv (LZ) complexity. A population of 74 subjects (44 OSA positive and 30 OSA negative) was studied. 90.5%, 87.8% and 86.5% accuracies were reached with k-means, hierarchical and FCM algorithms, respectively. The diagnostic accuracy values improved those obtained with each nonlinear method individually. Our results suggest that nonlinear analysis and clustering classification could provide useful information to help in the diagnosis of OSA syndrome.

  6. [FTIR study of the influence of leaf senescence on magnoliaceae cluster analysis].

    PubMed

    Li, Lun; Liu, Gang; Ou, Quan-hong; Zhang, Li; Liu, Jian-hong; Sun, Shi-zhong

    2013-09-01

    Fourier transform infrared (FTIR) spectroscopy combined with hierarchical cluster analysis was used to study the influence of leaf senescence on magnoliaceae cluster. FTIR spectra of young, mature and old yellow leaves were obtained from 14 species trees belonging to the three magnoliaceae subtribes. Results showed that the infrared spectra of the three subtribes plant leaves were similar, only with minor differences in the absorption intensity of several peaks. Hierarchical cluster analysis was performed on the second derivative infrared spectra in the range 1800-700 cm(-1). The HCA results showed that the cluster based on mature leaves is better than that based on young and old yellow leaves. Our study suggests that it should be cautious to select leaf sample while using leaf spectra for classification.

  7. 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.

  8. A Hierarchical Bayes Ensemble Kalman Filter

    NASA Astrophysics Data System (ADS)

    Tsyrulnikov, Michael; Rakitko, Alexander

    2017-01-01

    A new ensemble filter that allows for the uncertainty in the prior distribution is proposed and tested. The filter relies on the conditional Gaussian distribution of the state given the model-error and predictability-error covariance matrices. The latter are treated as random matrices and updated in a hierarchical Bayes scheme along with the state. The (hyper)prior distribution of the covariance matrices is assumed to be inverse Wishart. The new Hierarchical Bayes Ensemble Filter (HBEF) assimilates ensemble members as generalized observations and allows ordinary observations to influence the covariances. The actual probability distribution of the ensemble members is allowed to be different from the true one. An approximation that leads to a practicable analysis algorithm is proposed. The new filter is studied in numerical experiments with a doubly stochastic one-variable model of "truth". The model permits the assessment of the variance of the truth and the true filtering error variance at each time instance. The HBEF is shown to outperform the EnKF and the HEnKF by Myrseth and Omre (2010) in a wide range of filtering regimes in terms of performance of its primary and secondary filters.

  9. Hierarchically structured activated carbon for ultracapacitors

    PubMed Central

    Kim, Mok-Hwa; Kim, Kwang-Bum; Park, Sun-Min; Roh, Kwang Chul

    2016-01-01

    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. PMID:26878820

  10. A hierarchical neuronal network for planning behavior.

    PubMed

    Dehaene, S; Changeux, J P

    1997-11-25

    Planning a goal-directed sequence of behavior is a higher function of the human brain that relies on the integrity of prefrontal cortical areas. In the Tower of London test, a puzzle in which beads sliding on pegs must be moved to match a designated goal configuration, patients with lesioned prefrontal cortex show deficits in planning a goal-directed sequence of moves. We propose a neuronal network model of sequence planning that passes this test and, when lesioned, fails in a way that mimics prefrontal patients' behavior. Our model comprises a descending planning system with hierarchically organized plan, operation, and gesture levels, and an ascending evaluative system that analyzes the problem and computes internal reward signals that index the correct/erroneous status of the plan. Multiple parallel pathways connecting the evaluative and planning systems amend the plan and adapt it to the current problem. The model illustrates how specialized hierarchically organized neuronal assemblies may collectively emulate central executive or supervisory functions of the human brain.

  11. 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.

  12. 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.

  13. Retrieving information from a hierarchical plan.

    PubMed

    Schneider, Darryl W; Logan, Gordon D

    2007-11-01

    Plans give structure to behavior by specifying whether and when different tasks must be performed. However, the structure of behavior need not mirror the structure of the plan. To investigate this idea, the authors studied how plan information is retrieved in the context of a novel sequence-position cuing procedure, wherein subjects memorize two task sequences, then perform trials on which they are randomly cued to perform a task at one of the serial positions in a sequence. Several empirical effects were consistent with retrieval from a hierarchically structured representation (but not a non-hierarchical representation), including large sequence-repetition benefits, position-repetition benefits only for sequence repetitions, and a lack of robust task-repetition benefits. The data were successfully modeled by assuming that retrieval was time-consuming, susceptible to priming, cue-dependent, structurally constrained, and token-specific. In tandem, the empirical data and modeling work provide deeper insight into the representation of and access to information in memory that comprises a plan for guiding behavior.

  14. Hierarchical feature selection for erythema severity estimation

    NASA Astrophysics Data System (ADS)

    Wang, Li; Shi, Chenbo; Shu, Chang

    2014-10-01

    At present PASI system of scoring is used for evaluating erythema severity, which can help doctors to diagnose psoriasis [1-3]. The system relies on the subjective judge of doctors, where the accuracy and stability cannot be guaranteed [4]. This paper proposes a stable and precise algorithm for erythema severity estimation. Our contributions are twofold. On one hand, in order to extract the multi-scale redness of erythema, we design the hierarchical feature. Different from traditional methods, we not only utilize the color statistical features, but also divide the detect window into small window and extract hierarchical features. Further, a feature re-ranking step is introduced, which can guarantee that extracted features are irrelevant to each other. On the other hand, an adaptive boosting classifier is applied for further feature selection. During the step of training, the classifier will seek out the most valuable feature for evaluating erythema severity, due to its strong learning ability. Experimental results demonstrate the high precision and robustness of our algorithm. The accuracy is 80.1% on the dataset which comprise 116 patients' images with various kinds of erythema. Now our system has been applied for erythema medical efficacy evaluation in Union Hosp, China.

  15. 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

  16. A hierarchical exact accelerated stochastic simulation algorithm

    NASA Astrophysics Data System (ADS)

    Orendorff, David; Mjolsness, Eric

    2012-12-01

    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.

  17. Learning deep hierarchical visual feature coding.

    PubMed

    Goh, Hanlin; Thome, Nicolas; Cord, Matthieu; Lim, Joo-Hwee

    2014-12-01

    In this paper, we propose a hybrid architecture that combines the image modeling strengths of the bag of words framework with the representational power and adaptability of learning deep architectures. Local gradient-based descriptors, such as SIFT, are encoded via a hierarchical coding scheme composed of spatial aggregating restricted Boltzmann machines (RBM). For each coding layer, we regularize the RBM by encouraging representations to fit both sparse and selective distributions. Supervised fine-tuning is used to enhance the quality of the visual representation for the categorization task. We performed a thorough experimental evaluation using three image categorization data sets. The hierarchical coding scheme achieved competitive categorization accuracies of 79.7% and 86.4% on the Caltech-101 and 15-Scenes data sets, respectively. The visual representations learned are compact and the model's inference is fast, as compared with sparse coding methods. The low-level representations of descriptors that were learned using this method result in generic features that we empirically found to be transferrable between different image data sets. Further analysis reveal the significance of supervised fine-tuning when the architecture has two layers of representations as opposed to a single layer.

  18. The physics and modes of star cluster formation: simulations.

    PubMed

    Clarke, Cathie

    2010-02-28

    We review progress in numerical simulations of star cluster formation. These simulations involve the bottom-up assembly of clusters through hierarchical mergers, which produces a fractal stellar distribution at young (approx. 0.5 Myr) ages. The resulting clusters are predicted to be mildly aspherical and highly mass-segregated, except in the immediate aftermath of mergers. The upper initial mass function within individual clusters is generally somewhat flatter than for the aggregate population. Recent work has begun to clarify the factors that control the mean stellar mass in a star-forming cloud and also the efficiency of star formation. The former is sensitive to the thermal properties of the gas while the latter depends both on the magnetic field and the initial degree of gravitational boundedness of the natal cloud. Unmagnetized clouds that are initially bound undergo rapid collapse, which is difficult to reverse by ionization feedback or stellar winds.

  19. The spatial structure of young stellar clusters

    NASA Astrophysics Data System (ADS)

    Kuhn, Michael A.

    Star formation is an extremely active area of astronomical research, and young stellar clusters in our Galaxy offer a useful laboratory where star-formation processes can be studied. Young stars form from the the gravitational collapse of molecular clouds that have a hierarchical spatial structure. This leads to stars forming in clustered environments, often with thousands of other young stars in environments that are strongly affected by feedback from massive O-type stars. The environments in these massive star-forming regions (MSFR) can affect how stars form and whether the young stellar clusters remain bound after star formation ends, both of which are questions that have received considerable attention from researchers. Studies of stellar populations in Galactic MSFRs are made difficult due to large numbers of fields stars in the Galactic Plane, large areas of the sky that must be surveyed, high optical extinction from dust, and nebulosity in the the optical and infrared. The Massive Young Star-Forming Complex Study in Infrared and X-ray (MYStIX) uses multiwavelength observations to overcome some of these difficulties, providing some of the most complete, clean membership lists for 20 MSFRs within 3.6 kpc of the Sun. I described X-ray catalogs and mid-infrared catalogs that were used in this survey. The spatial distribution of young stars in 17 MYStIX regions are used to probe the origin and dynamics of the young stellar clusters. Intrinsic stellar surface-density maps are made for each region, which reveal complex structures with dense subclusters. I examine in detail one of the nearest MYStIX young stellar clusters, W 40 (d = 500 pc), which has properties similar to many of the subclusters in more massive and more distant star-forming regions. The cluster in W 40 has a simple structure with mass segregation, indicating that it has undergone dynamical evolution, even though its young age (~0.8 Myr) is insufficient for relaxation from two-body interactions

  20. Trajectory Clustering: a Non-Parametric Method for Grouping Gene Expression Time Courses, with Applications to Mammary Development

    PubMed Central

    Phang, T.L.; Neville, M.C.; Rudolph, M.; Hunter, L.

    2008-01-01

    Trajectory clustering is a novel and statistically well-founded method for clustering time series data from gene expression arrays. Trajectory clustering uses non-parametric statistics and is hence not sensitive to the particular distributions underlying gene expression data. Each cluster is clearly defined in terms of direction of change of expression for successive time points (its ‘trajectory’), and therefore has easily appreciated biological meaning. Applying the method to a dataset from mouse mammary gland development, we demonstrate that it produces different clusters than Hierarchical, K-means, and Jackknife clustering methods, even when those methods are applied to differences between successive time points. Compared to all of the other methods, trajectory clustering was better able to match a manual clustering by a domain expert, and was better able to cluster groups of genes with known related functions. PMID:12603041