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Sample records for agglomerative hierarchical cluster

  1. Hesitant fuzzy agglomerative hierarchical clustering algorithms

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaolu; Xu, Zeshui

    2015-02-01

    Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering algorithm for HFSs. The algorithm considers each of the given HFSs as a unique cluster in the first stage, and then compares each pair of the HFSs by utilising the weighted Hamming distance or the weighted Euclidean distance. The two clusters with smaller distance are jointed. The procedure is then repeated time and again until the desirable number of clusters is achieved. Moreover, we extend the algorithm to cluster the interval-valued hesitant fuzzy sets, and finally illustrate the effectiveness of our clustering algorithms by experimental results.

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

    PubMed Central

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  5. 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. PMID:23674241

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

    NASA Astrophysics Data System (ADS)

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

    2013-07-01

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

  7. 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

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

    PubMed Central

    Huh, Yong; Yu, Kiyun; Park, Woojin

    2016-01-01

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

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

    PubMed

    Huh, Yong; Yu, Kiyun; Park, Woojin

    2016-01-01

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

  10. Recent Trends in Hierarchic Document Clustering: A Critical Review.

    ERIC Educational Resources Information Center

    Willett, Peter

    1988-01-01

    Reviews recent research into the use of hierarchic agglomerative clustering methods for document retrieval. The topics discussed include the calculation of interdocument similarities, algorithms used to implement clustering methods on large databases, validity testing of document hierarchies, appropriate search strategies, and other applications…

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

    PubMed

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

    2005-03-01

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

  12. 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.

  13. Detection of Significant Groups in Hierarchical Clustering by Resampling

    PubMed Central

    Sebastiani, Paola; Perls, Thomas T.

    2016-01-01

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

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

    PubMed

    Sebastiani, Paola; Perls, Thomas T

    2016-01-01

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

  15. Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

    PubMed Central

    Varshavsky, Roy; Horn, David; Linial, Michal

    2008-01-01

    Background A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied. Methodology/Principal Findings We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available. Conclusions Although currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide

  16. Magnetochirality in hierarchical magnetoplasmonic clusters

    NASA Astrophysics Data System (ADS)

    Yannopapas, Vassilios

    2015-09-01

    We show theoretically that finite hierarchical assemblies of clusters consisting magnetic (magnetite) and plasmonic (gold) nanoparticles show dramatically increased values of the magnetochiral dichroism compared to those measured in conventional materials exhibiting this phenomenon (liquid molecular systems, anisotropic crystals and chiral ferromagnets). These values are attributed to the strong interaction of the magnetite nanoparticles within the clusters as well as by the excitation of surface-plasmons in the gold nanoparticles. Along with the magnetochiral dichroism, in the studied hierarchical magnetoplasmonic designs, magneto-optical phenomena such as magnetic dichroism and Faraday rotation are also enhanced relative to the case of purely magnetic nanostructures.

  17. 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.

  18. 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

  19. 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…

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. Spatiotemporal antibiotic resistance pattern monitoring using geographical information system based hierarchical cluster analysis.

    PubMed

    Hewapathirana, Roshan; Wijayarathna, Gamini

    2010-01-01

    Bacterial antimicrobial resistance in both the medical and agricultural fields has become a serious problem worldwide. Antibiotic resistant strains of bacteria are an increasing threat to human health, with resistance mechanisms having been described to all known antimicrobials currently available for clinical use. Monitoring the geotemporal variations of antibiotic resistance pattern is crucial factor in planning a successful therapeutic guidelines preventing further emergence of antibiotic resistance. This study is based on the retrospective spatiotemporal analysis of laboratory results of Antibiotic Sensitivity Tests, time stamped with the date and time of the microbiological specimen dispatched to the laboratory. Geographic location of the isolated bacterial colony is specified with the latitude and the longitude of the patient's location. Agglomerative Hierarchical Clustering was performed on antimicrobial resistance findings based on the geographic locations generating series of Heatmaps to visualize the extent of the resistance pattern. Sequential Hierarchical cluster analysis was proven to be effective in visualization of antibiotic resistance using Heatmaps demonstrating the temporal variations of the antibiotic resistance patterns.

  5. Hierarchical Image Segmentation Using Correlation Clustering.

    PubMed

    Alush, Amir; Goldberger, Jacob

    2016-06-01

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

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

    PubMed Central

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

    2013-01-01

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

  7. 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...

  8. 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.

  9. 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...

  10. 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...

  11. 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.

  12. Hierarchical Clustering and the Concept of Space Distortion.

    ERIC Educational Resources Information Center

    Hubert, Lawrence; Schultz, James

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

  13. 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).

  14. Early Hemostatic Responses to Trauma Identified Using Hierarchical Clustering Analysis

    PubMed Central

    White, N.J.; Contaifer, D.; Martin, E.J.; Newton, J.C.; Mohammed, B.M.; Bostic, J.L.; Brophy, G.M.; Spiess, B.D.; Pusateri, A.E.; Ward, K.R.; Brophy, D.F.

    2015-01-01

    Background Trauma-induced coagulopathy is a complex multifactorial hemostatic response that is poorly understood. Objectives Identify distinct hemostatic responses to trauma and identify key components of the hemostatic system that vary between responses. Patients/Methods Cross-sectional observational study of adult trauma patients at an urban Level I trauma center Emergency Department. Hierarchical clustering analysis was used to identify distinct clusters of similar subjects using vital signs, injury/shock severity, and by comprehensive assessment of coagulation, clot formation, platelet function, and thrombin generation. Results Of 84 total trauma patients included in the model, three distinct trauma clusters were identified. Cluster 1 (N=57) displayed platelet activation, preserved peak thrombin generation, plasma coagulation dysfunction, moderately decreased fibrinogen concentration, and normal clot formation relative to healthy controls. Cluster 2 (N=18) displayed platelet activation, preserved peak thrombin generation, and preserved fibrinogen concentration with normal clot formation. Cluster 3 (N=9) was the most severely injured and shocked and displayed a strong inflammatory and bleeding phenotype. Platelet dysfunction, thrombin inhibition, plasma coagulation dysfunction, and decreased fibrinogen concentration were present in this cluster. Fibrinolytic activation was present in all clusters, but increased more so in Cluster 3. Trauma clusters were different most noticeably in their relative fibrinogen concentration, peak thrombin generation, and platelet-induced clot contraction. Conclusions Hierarchical clustering analysis identified 3 distinct hemostatic responses to trauma. Further insight into the underlying hemostatic mechanisms responsible for these responses is needed. PMID:25816845

  15. Assessment of hierarchical clustering methodologies for proteomic data mining.

    PubMed

    Meunier, Bruno; Dumas, Emilie; Piec, Isabelle; Béchet, Daniel; Hébraud, Michel; Hocquette, Jean-François

    2007-01-01

    Hierarchical clustering methodology is a powerful data mining approach for a first exploration of proteomic data. It enables samples or proteins to be grouped blindly according to their expression profiles. Nevertheless, the clustering results depend on parameters such as data preprocessing, between-profile similarity measurement, and the dendrogram construction procedure. We assessed several clustering strategies by calculating the F-measure, a widely used quality metric. The combination, on logged matrix, of Pearson correlation and Ward's methods for data aggregation is among the best clustering strategies, at least with the data sets we studied. This study was carried out using PermutMatrix, a freely available software derived from transcriptomics.

  16. Optimized leaf ordering with class labels for hierarchical clustering.

    PubMed

    Novoselova, Natalia; Wang, Junxi; Klawonn, Frank

    2015-08-01

    Hierarchical clustering is extensively used in the bioinformatics community to analyze biomedical data. These data are often tagged with class labels, as e.g. disease subtypes or gene ontology (GO) terms. Heatmaps in connection with dendrograms are the common standard to visualize results of hierarchical clustering. The heatmap can be enriched by an additional color bar at the side, indicating for each instance in the data set to which class it belongs. In the ideal case, when the clustering matches perfectly with the classes, one would expect that instances from the same class cluster together and the color bar consists of well-separated color blocks without frequent alteration of colors (classes). But even in the case when instances from the same class cluster perfectly together, the dendrogram might not reflect this important aspect due to the fact that its representation is not unique. In this paper, we propose a leaf ordering algorithm for the dendrogram that preserving the hierarchical clustering result tries to group instances from the same class together. It is based on the concept of dynamic programming which can efficiently compute the optimal or nearly optimal order, consistent with the structure of the tree.

  17. 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

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

    PubMed

    Boehlen, Anne; Heinemann, Uwe; Henneberger, Christian

    2016-04-21

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

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

    PubMed

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

    2007-11-01

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

  20. Unsupervised active learning based on hierarchical graph-theoretic clustering.

    PubMed

    Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

    2009-10-01

    Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples. PMID:19336318

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

    PubMed

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

    2016-06-01

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

  2. Hierarchical clustering in chameleon f(R) gravity

    NASA Astrophysics Data System (ADS)

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

    2013-11-01

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

  3. 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.

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

    PubMed

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

    2015-11-01

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

  5. The Formation of Hierarchical Systems in Star Clusters

    NASA Astrophysics Data System (ADS)

    Aarseth, S. J.

    Results of star cluster simulations on HARP show that hierarchical systems play an important role for the overall dynamics. Models with 8000 single stars and 2000 primordial binaries reveal a gradual build-up and more than 20 such systems may exist during the later stages of evolution. We concentrate on the formation of hierarchies and their stability. This analysis is facilitated by the use of chain regularization which provides a natural tool for investigating the formation mechanism. Although hierarchies can be considered as newly formed binaries, their mode of formation often leads directly to hard binding energies. Most of these systems are formed by close two-body encounters between binaries, whereas standard binaries form by the classical three-body process and their appearance is therefore coinsiderably less pronounced. Finally, we discuss the implications of persistent higher-order systems for direct N-body simulations of globular clusters.

  6. Hierarchical cluster analysis as an approach for systematic grouping of diet constituents on basis of fatty acid, energy and cholesterol content: application on consumable lamb products.

    PubMed

    Akbay, A; Elhan, A; Ozcan, C; Demirtaş, S

    2000-08-01

    The role of dietary fat in the etiology of chronic diseases is both a qualitative and a quantitative issue. The dietary fat intake is largely influenced by behavioral and social influences on food choice. Ongoing scientific research has led to dietary recommendations with main concerns being the percentage of saturated, essential fatty acids and cholesterol with respect to total energy intake. However, the compositional complexity of food choice constituting the diet is a critical concept complicating the interpretation of epidemiologic, clinical and laboratory evidence to define the role of dietary fat in the etiology of diseases. This study was conducted on the observation of the need to better systematically classify consumable food based on complex composition and lamb meat is randomly selected as a non-specific subset for application of hierarchical cluster analysis method to obtain the dendogram using average linkage. Data on fat composition of consumable lamb prepared by different methods was obtained from USDA Nutrient Database for Standart Reference. Using agglomerative hierarchical cluster analysis lamb meat was grouped into two main clusters among which one divided into two families of which each was subdivided into two subfamilies based on fatty acids, cholesterol and energy composition. Present work may be considered as a leading study to systematically classify larger food sets. As high fat foods are rich in flavor and overall palatability, the outcome of this study may lead to behaviorally more acceptable but healthier dietary replacements. Besides future use of the results obtained may reveal the effect of complex compositional dietary influences on health and disease and may have superiority to studies questioning individual dietary items. Furthermore, hieararchial cluster analysis may be used to cluster food including other compositional data in food items like amino acids, vitamins, carbohydrates, as well.

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

    PubMed Central

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

    2011-01-01

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

  8. 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

  9. 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.

  10. 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."

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

    SciTech Connect

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

    2012-11-01

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

  12. 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.

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

    PubMed

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

    2013-05-01

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

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

    PubMed

    Cao, Jia

    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.

  15. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

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

    PubMed Central

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

    2016-01-01

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

  18. 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.

  19. 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...

  20. 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.

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

    PubMed Central

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

    2004-01-01

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

  2. Exact hierarchical clustering in one dimension. [in universe

    NASA Technical Reports Server (NTRS)

    Williams, B. G.; Heavens, A. F.; Peacock, J. A.; Shandarin, S. F.

    1991-01-01

    The present adhesion model-based one-dimensional simulations of gravitational clustering have yielded bound-object catalogs applicable in tests of analytical approaches to cosmological structure formation. Attention is given to Press-Schechter (1974) type functions, as well as to their density peak-theory modifications and the two-point correlation function estimated from peak theory. The extent to which individual collapsed-object locations can be predicted by linear theory is significant only for objects of near-characteristic nonlinear mass.

  3. 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.

  4. Climate Regionalization through Hierarchical Clustering: Options and Recommendations for Africa

    NASA Astrophysics Data System (ADS)

    Badr, H. S.; Zaitchik, B. F.; Dezfuli, A. K.

    2014-12-01

    Climate regionalization is an important but often under-emphasized step in studies of climate variability and predictions. While most investigations of regional climate or statistical/dynamical predictions do make at least an implicit attempt to focus on a study region or sub-regions that are climatically coherent in some respect, rigorous climate regionalization--in which the study area is divided on the basis of the most relevant climate metrics and at a resolution most appropriate to the data and the scientific question--has the potential to enhance the precision and explanatory power of climate studies in many cases. This is particularly true for climatically complex regions such as the Greater Horn of Africa (GHA) and Equatorial West Africa. Here we present an improved clustering method and a flexible, open-source software tool (R package "HiClimR") designed specifically for climate regionalization. As a demonstration, we apply HiClimR to regionalize the GHA on the basis of interannual precipitation variability in each calendar month and for three-month running seasons. Different clustering methods are tested to show the behavior of each method and provide recommendations for specific problems. This would underscore the applicability of our work to a wide range of climate issues, and enable researchers to easily and quickly learn how to apply our tools to their own problems. Both the proposed methodology and the R package can be easily used for a broad range of climate applications.

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

    PubMed

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

    2014-01-01

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

  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. 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

  8. 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

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

    NASA Astrophysics Data System (ADS)

    Khan, Jesmin; Bhuiyan, Sharif; Adhami, Reza

    2014-04-01

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

  10. 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.

  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. Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements

    NASA Astrophysics Data System (ADS)

    Kaneko, Kunihiko

    1990-03-01

    A Network of chaotic elements is investigated with the use of globally coupled maps. A simple coding of many attractors with clustering is shown. Through the coding, the attractors are organized so that their change exhibits bifurcation-like phenomena. A precision-dependent tree is constructed which leads to the similarity of our attractor with those of spin-glasses. Hierarchical dynamics is constructed on the tree, which leads to the dynamical change of trees and the temporal change of effective degrees of freedom. By a simple input on a site, we can switch among attractors and tune the strength of chaos. A threshold on a cluster size is found, beyond which a peculiar “posi-nega” switch occurs. Possible application to biological information processing is discussed with the emphasis on the fuzzy switch (chaotic search) and hierarchical code (categorization).

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

    PubMed

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

    2015-01-01

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

  14. 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

  15. 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.

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

    ERIC Educational Resources Information Center

    Nimon, Kim

    2012-01-01

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

  17. 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.

  18. 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

  19. Estimating the number of endmembers in hyperspectral imagery using hierarchical agglomerate clustering

    NASA Astrophysics Data System (ADS)

    Wu, Jee-Cheng; Wu, Heng-Yang; Tsuei, Gwo-Chyang

    2013-10-01

    A classical spectral un-mixing of hyperspectral image involves identifying the unique signatures of the endmembers (i.e. pure materials) and estimating the proportions of endmembers for each pixel by inversion. The key to successful spectral un-mixing is indicating the number of endmembers and their corresponding spectral signatures. Currently, eigenvaluebased estimation of the number of endmembers in hyperspectral image is widely used. However, the eigenvalue-based methods are difficult to separate signal sources such as anomalies. In this paper, a two-stage process is proposed to estimate the endmember numbers. At the preprocessing stage, the spectral dimensions are reduced using principal component analysis and the spatial dimensions are reduced using convex hull computation based on reduced-spectral bands. At the hierarchical agglomerate clustering stage, a pixel vector is found by applying orthogonal subspace projection (OSP) and cluster pixel vectors using the spectral angle mapper (SAM), hierarchically. If the number of pixel vectors in a cluster is greater than the predefined number, the found pixel vector is set as the endmember. Otherwise, anomalous vectors are found. The proposed method was carried with both synthetic and real images for estimating the number of endmembers. The results demonstrate that the proposed method can be used to estimate more reasonable and precise number of endmembers than the eigenvalue-based methods.

  20. 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.

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

    PubMed

    Lamberti, F; Ciancio, A

    1993-09-01

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

  2. 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.

  3. Hierarchical clustering method for improved prostate cancer imaging in diffuse optical tomography

    NASA Astrophysics Data System (ADS)

    Kavuri, Venkaiah C.; Liu, Hanli

    2013-03-01

    We investigate the feasibility of trans-rectal near infrared (NIR) based diffuse optical tomography (DOT) for early detection of prostate cancer using a transrectal ultrasound (TRUS) compatible imaging probe. For this purpose, we designed a TRUS-compatible, NIR-based image system (780nm), in which the photo diodes were placed on the trans-rectal probe. DC signals were recorded and used for estimating the absorption coefficient. We validated the system using laboratory phantoms. For further improvement, we also developed a hierarchical clustering method (HCM) to improve the accuracy of image reconstruction with limited prior information. We demonstrated the method using computer simulations laboratory phantom experiments.

  4. 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.

  5. Compound hierarchical correlated beta mixture with an application to cluster mouse transcription factor DNA binding data.

    PubMed

    Dai, Hongying; Charnigo, Richard

    2015-10-01

    Modeling correlation structures is a challenge in bioinformatics, especially when dealing with high throughput genomic data. A compound hierarchical correlated beta mixture (CBM) with an exchangeable correlation structure is proposed to cluster genetic vectors into mixture components. The correlation coefficient, [Formula: see text], is homogenous within a mixture component and heterogeneous between mixture components. A random CBM with [Formula: see text] brings more flexibility in explaining correlation variations among genetic variables. Expectation-Maximization (EM) algorithm and Stochastic Expectation-Maximization (SEM) algorithm are used to estimate parameters of CBM. The number of mixture components can be determined using model selection criteria such as AIC, BIC and ICL-BIC. Extensive simulation studies were conducted to compare EM, SEM and model selection criteria. Simulation results suggest that CBM outperforms the traditional beta mixture model with lower estimation bias and higher classification accuracy. The proposed method is applied to cluster transcription factor-DNA binding probability in mouse genome data generated by Lahdesmaki and others (2008, Probabilistic inference of transcription factor binding from multiple data sources. PLoS One, 3: , e1820). The results reveal distinct clusters of transcription factors when binding to promoter regions of genes in JAK-STAT, MAPK and other two pathways.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

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

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

    PubMed Central

    Troche, Joshua; Crutch, Sebastian; Reilly, Jamie

    2014-01-01

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

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

    PubMed Central

    Lamberti, Franco; Ciancio, Aurelio

    1993-01-01

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

  10. Graph-theoretic quantum system modelling for neuronal microtubules as hierarchical clustered quantum Hopfield networks

    NASA Astrophysics Data System (ADS)

    Srivastava, D. P.; Sahni, V.; Satsangi, P. S.

    2014-08-01

    Graph-theoretic quantum system modelling (GTQSM) is facilitated by considering the fundamental unit of quantum computation and information, viz. a quantum bit or qubit as a basic building block. Unit directional vectors "ket 0" and "ket 1" constitute two distinct fundamental quantum across variable orthonormal basis vectors, for the Hilbert space, specifying the direction of propagation of information, or computation data, while complementary fundamental quantum through, or flow rate, variables specify probability parameters, or amplitudes, as surrogates for scalar quantum information measure (von Neumann entropy). This paper applies GTQSM in continuum of protein heterodimer tubulin molecules of self-assembling polymers, viz. microtubules in the brain as a holistic system of interacting components representing hierarchical clustered quantum Hopfield network, hQHN, of networks. The quantum input/output ports of the constituent elemental interaction components, or processes, of tunnelling interactions and Coulombic bidirectional interactions are in cascade and parallel interconnections with each other, while the classical output ports of all elemental components are interconnected in parallel to accumulate micro-energy functions generated in the system as Hamiltonian, or Lyapunov, energy function. The paper presents an insight, otherwise difficult to gain, for the complex system of systems represented by clustered quantum Hopfield network, hQHN, through the application of GTQSM construct.

  11. 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.

  12. Hierarchical Spectral Consensus Clustering for Group Analysis of Functional Brain Networks.

    PubMed

    Ozdemir, Alp; Bolaños, Marcos; Bernat, Edward; Aviyente, Selin

    2015-09-01

    A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the organization of functional connectivity networks, it is important to determine the community structure underlying these complex networks. Moreover, the study of brain functional networks is confounded by the fact that most neurophysiological studies consists of data collected from multiple subjects; thus, it is important to identify communities representative of all subjects. Typically, this problem is addressed by averaging the data across subjects which omits the variability across subjects or using voting methods, which requires a priori knowledge of cluster labels. In this paper, we propose a hierarchical consensus spectral clustering approach to address these problems. Furthermore, new information-theoretic criteria are introduced for selecting the optimal community structure. The proposed framework is applied to electroencephalogram data collected during a study of error-related negativity to better understand the community structure of functional networks involved in the cognitive control.

  13. Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

    NASA Astrophysics Data System (ADS)

    Farsadnia, Farhad; Ghahreman, Bijan

    2016-04-01

    Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

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

    PubMed Central

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

    2015-01-01

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

  15. Applying Robust Directional Similarity based Clustering approach RDSC to classification of gene expression data.

    PubMed

    Li, H X; Wang, Shitong; Xiu, Yu

    2006-06-01

    Despite the fact that the classification of gene expression data from a cDNA microarrays has been extensively studied, nowadays a robust clustering method, which can estimate an appropriate number of clusters and be insensitive to its initialization has not yet been developed. In this work, a novel Robust Clustering approach, RDSC, based on the new Directional Similarity measure is presented. This new approach RDSC, which integrates the Directional Similarity based Clustering Algorithm, DSC, with the Agglomerative Hierarchical Clustering Algorithm, AHC, exhibits its robustness to initialization and its capability to determine the appropriate number of clusters reasonably. RDSC has been successfully employed to both artificial and benchmarking gene expression datasets. Our experimental results demonstrate its distinctive superiority over the conventional method Kmeans and the two typical directional clustering algorithms SPKmeans and moVMF.

  16. 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.

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

    PubMed

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

    2012-01-01

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

  18. 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

  19. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

  1. 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'.

  2. 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.

  3. MtHc: a motif-based hierarchical method for clustering massive 16S rRNA sequences into OTUs.

    PubMed

    Wei, Ze-Gang; Zhang, Shao-Wu

    2015-07-01

    The recent sequencing revolution driven by high-throughput technologies has led to rapid accumulation of 16S rRNA sequences for microbial communities. Clustering short sequences into operational taxonomic units (OTUs) is an initial crucial process in analyzing metagenomic data. Although many methods have been proposed for OTU inferences, a major challenge is the balance between inference accuracy and computational efficiency. To address these challenges, we present a novel motif-based hierarchical method (namely MtHc) for clustering massive 16S rRNA sequences into OTUs with high clustering accuracy and low memory usage. Suppose all the 16S rRNA sequences can be used to construct a complete weighted network, where sequences are viewed as nodes, each pair of sequences is connected by an imaginary edge, and the distance of a pair of sequences represents the weight of the edge. MtHc consists of three main phrases. First, heuristically search the motif that is defined as n-node sub-graph (in the present study, n = 3, 4, 5), in which the distance between any two nodes is less than a threshold. Second, use the motif as a seed to form candidate clusters by computing the distances of other sequences with the motif. Finally, hierarchically merge the candidate clusters to generate the OTUs by only calculating the distances of motifs between two clusters. Compared with the existing methods on several simulated and real-life metagenomic datasets, we demonstrate that MtHc has higher clustering performance, less memory usage and robustness for setting parameters, and that it is more effective to handle the large-scale metagenomic datasets. The MtHC software can be freely download from for academic users.

  4. MtHc: a motif-based hierarchical method for clustering massive 16S rRNA sequences into OTUs.

    PubMed

    Wei, Ze-Gang; Zhang, Shao-Wu

    2015-07-01

    The recent sequencing revolution driven by high-throughput technologies has led to rapid accumulation of 16S rRNA sequences for microbial communities. Clustering short sequences into operational taxonomic units (OTUs) is an initial crucial process in analyzing metagenomic data. Although many methods have been proposed for OTU inferences, a major challenge is the balance between inference accuracy and computational efficiency. To address these challenges, we present a novel motif-based hierarchical method (namely MtHc) for clustering massive 16S rRNA sequences into OTUs with high clustering accuracy and low memory usage. Suppose all the 16S rRNA sequences can be used to construct a complete weighted network, where sequences are viewed as nodes, each pair of sequences is connected by an imaginary edge, and the distance of a pair of sequences represents the weight of the edge. MtHc consists of three main phrases. First, heuristically search the motif that is defined as n-node sub-graph (in the present study, n = 3, 4, 5), in which the distance between any two nodes is less than a threshold. Second, use the motif as a seed to form candidate clusters by computing the distances of other sequences with the motif. Finally, hierarchically merge the candidate clusters to generate the OTUs by only calculating the distances of motifs between two clusters. Compared with the existing methods on several simulated and real-life metagenomic datasets, we demonstrate that MtHc has higher clustering performance, less memory usage and robustness for setting parameters, and that it is more effective to handle the large-scale metagenomic datasets. The MtHC software can be freely download from for academic users. PMID:25912934

  5. 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

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

    DOEpatents

    Green, Norman W.

    1979-01-23

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

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

    NASA Astrophysics Data System (ADS)

    Azad, Puneet; Sharma, Vidushi

    2015-07-01

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

  8. 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.

  9. [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.

  10. bcl::Cluster : A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System

    PubMed Central

    Alexander, Nathan; Woetzel, Nils; Meiler, Jens

    2016-01-01

    Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.

  11. 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).

  12. 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.

  13. 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.

  14. 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.

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

    PubMed

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

    2016-03-01

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

  16. 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 ...

  17. 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.

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

    PubMed

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

    2015-01-01

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

  19. 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.

  20. Application of a Hierarchical Model Incorporating Intra-family Correlation and Cluster Effects

    PubMed Central

    Cheng, An-Lin; Kelly, Patricia J.

    2011-01-01

    Background Research interventions at the family level often include individual- and group-level data that can present an analytic challenge. The study that motivated this paper was an intervention study conducted with elementary school children and their parents. Randomization occurred at the school level, with families nested within schools. Repeated measurements collected from children and parents at different time points presented modeling challenges, including how to specify the covariance structure correctly among all measurements. Objectives To introduce a mixed model with random effects to model the correlations among family members, repeated measures, and the grouping effect. Method A hierarchical random-effect model was used that included both fixed and random effects; time and intervention-by-time variables were included as fixed effects, the school-specific variable was included as random effect, and the intrafamily correlation was modeled through a spatial autoregression covariance matrix. Comparisons were made between the performance of the proposed modeling method with other parsimony models using Akaike’s Information Criterion (AIC). Results The proposed modeling method produced a 3% and 9% reduction of AIC values, respectively, compared to the two other models. The likelihood ratio test further confirmed that the full model is better than the other two models (p < .0001 for both models). Discussion The data suggest that using the proposed mixed model technique will produce a significantly better model fit for intrafamily correlation with a nested study design. PMID:21317823

  1. 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

  2. The Absorbers Toward CSO 118: Hierarchical Clustering at z ~3, or an Intrinsic Absorption Complex?

    NASA Astrophysics Data System (ADS)

    Ganguly, R.; Charlton, J. C.

    2000-12-01

    We present a high signal-to-noise spectrum of the {z em=2.97} radio-quiet quasar CSO 118 observed by the Hobby-Eberly Telescope with the Marcario Low Resolution Spectrograph. The R ~1300 spectrum covers the wavelength range {4300Å-7300Å} which includes the C IV, Si IV, N V, and Ly α emission lines and well as the Ly α forest down to z≈2.5. We detect a complex of six C IV--selected absorbers in the range {2.65hierarchically collapsing as suggested by Kirkman & Tytler (1999); or (2) this is a multiple absorption complex due to gas intrinsic to the QSO. The line-of-sight velocity dispersion of the complex is { ~1.5*E4 km s-1}, much larger than the velocity dispersion of typical groups of galaxies ({σ v ~200 km s-1}). On the other hand, if these absorbers constitute ejected material, the ejection velocities range from { ~7300 km s-1} to { ~2.3*E4 km s-1}. Intrinsic narrow velocity dispersion absorbers have been reported with ejection velocities as high as { ~6*E4 km s-1}. It is also not unusual to have multiple absorption systems intrinsic to a QSO. Broad absorption lines, whose troughs extend from the QSO emission redshift to { ~0.1c}, are typically accompanied by their narrow kin. In addition, we will present evidence that the three pairs of systems may be line-locked. If verified, this would enforce the intrinsic origin of these systems. We gratefully acknowledge the support of two grants: NSF grant AST96-17185 and NASA grant NAG5-6399.

  3. 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.

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

    PubMed Central

    2012-01-01

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

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

    PubMed Central

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

    2012-01-01

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

  6. 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)

  7. 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

  8. FORMATION OF AN O-STAR CLUSTER BY HIERARCHICAL ACCRETION IN G20.08-0.14 N

    SciTech Connect

    Galvan-Madrid, Roberto; Keto, Eric; Zhang Qizhou; Ho, Paul T. P.; Kurtz, Stan; RodrIguez, Luis F.

    2009-12-01

    Spectral line and continuum observations of the ionized and molecular gas in G20.08-0.14 N explore the dynamics of accretion over a range of spatial scales in this massive star-forming region. Very Large Array (VLA) observations of NH{sub 3} at 4'' angular resolution show a large-scale (0.5 pc) molecular accretion flow around and into a star cluster with three small, bright H II regions. Higher resolution (0.''4) observations with the Submillimeter Array in hot core molecules (CH{sub 3}CN, OCS, and SO{sub 2}) and the VLA in NH{sub 3} show that the two brightest and smallest H II regions are themselves surrounded by smaller scale (0.05 pc) accretion flows. The axes of rotation of the large- and small-scale flows are aligned, and the timescale for the contraction of the cloud is short enough, 0.1 Myr, for the large-scale accretion flow to deliver significant mass to the smaller scales within the star formation timescale. The flow structure appears to be continuous and hierarchical from larger to smaller scales. Millimeter radio recombination line (RRL) observations at 0.''4 angular resolution indicate rotation and outflow of the ionized gas within the brightest H II region (A). The broad recombination lines and a continuum spectral energy distribution (SED) that rises continuously from cm to mm wavelengths, are both characteristic of the class of H II regions known as 'broad recombination line objects'. The SED indicates a density gradient inside this H II region, and the RRLs suggest supersonic flows. These observations are consistent with photoevaporation of the inner part of the rotationally flattened molecular accretion flow. We also report the serendipitous detection of a new NH{sub 3} (3,3) maser.

  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. 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 Central

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

    2010-01-01

    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. PMID:20443211

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  14. FragClust and TestClust, two informatics tools for chemical structure hierarchical clustering analysis applied to lipidomics. The example of Alzheimer's disease.

    PubMed

    Di Gaudio, Francesca; Indelicato, Sergio; Monastero, Roberto; Altieri, Grazia Ida; Fayer, Francesca; Palesano, Ornella; Fontana, Manuela; Cefalù, Angelo B; Greco, Massimiliano; Bongiorno, David; Indelicato, Serena; Aronica, Angela; Noto, Davide; Averna, Maurizio R

    2016-03-01

    Lipidomic analysis is able to measure simultaneously thousands of compounds belonging to a few lipid classes. In each lipid class, compounds differ only by the acyl radical, ranging between C10:0 (capric acid) and C24:0 (lignoceric acid). Although some metabolites have a peculiar pathological role, more often compounds belonging to a single lipid class exert the same biological effect. Here, we present a lipidomics workflow that extracts the tandem mass spectrometry data from individual files and uses them to group compounds into structurally homogeneous clusters by chemical structure hierarchical clustering analysis (CHCA). The case-to-control peak area ratios of the metabolites are then analyzed within clusters. We created two freely available applications to assist the workflow: FragClust to generate the tables to be subjected to CHCA, and TestClust to perform statistical analysis on clustered data. We used the lipidomics data from the plasma of Alzheimer's disease (AD) patients in comparison with healthy controls to test the workflow. To date, the search for plasma biomarkers in AD has not provided reliable results. This article shows that the workflow is helpful to understand the behavior of whole lipid classes in plasma of AD patients. PMID:26753967

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed Central

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

    2007-01-01

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

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

    PubMed

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

    2016-03-01

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

  18. 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.

  19. 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

  20. Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

    PubMed Central

    2011-01-01

    Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC

  1. 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.

  2. Use of hierarchical cluster analysis to assess the representativeness of a baseline groundwater quality monitoring network: comparison of New Zealand's national and regional groundwater monitoring programs

    NASA Astrophysics Data System (ADS)

    Daughney, Christopher J.; Raiber, Matthias; Moreau-Fournier, Magali; Morgenstern, Uwe; van der Raaij, Rob

    2012-02-01

    Baseline monitoring of groundwater quality aims to characterize the ambient condition of the resource and identify spatial or temporal trends. Sites comprising any baseline monitoring network must be selected to provide a representative perspective of groundwater quality across the aquifer(s) of interest. Hierarchical cluster analysis (HCA) has been used as a means of assessing the representativeness of a groundwater quality monitoring network, using example datasets from New Zealand. HCA allows New Zealand's national and regional monitoring networks to be compared in terms of the number of water-quality categories identified in each network, the hydrochemistry at the centroids of these water-quality categories, the proportions of monitoring sites assigned to each water-quality category, and the range of concentrations for each analyte within each water-quality category. Through the HCA approach, the National Groundwater Monitoring Programme (117 sites) is shown to provide a highly representative perspective of groundwater quality across New Zealand, relative to the amalgamated regional monitoring networks operated by 15 different regional authorities (680 sites have sufficient data for inclusion in HCA). This methodology can be applied to evaluate the representativeness of any subset of monitoring sites taken from a larger network.

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

    PubMed Central

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

    2014-01-01

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

  4. 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.

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

    PubMed

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

    2015-06-01

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

  6. 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.

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

    PubMed

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

    2015-06-01

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

  8. 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.

  9. Using Hierarchical Cluster Models to Systematically Identify Groups of Jobs With Similar Occupational Questionnaire Response Patterns to Assist Rule-Based Expert Exposure Assessment in Population-Based Studies

    PubMed Central

    Friesen, Melissa C.; Shortreed, Susan M.; Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Pronk, Anjoeka; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Armenti, Karla R.; Silverman, Debra T.; Yu, Kai

    2015-01-01

    Objectives: Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Methods: Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m−3 respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters’ homogeneity (defined as >75% with the same estimate

  10. Interest rates hierarchical structure

    NASA Astrophysics Data System (ADS)

    Di Matteo, T.; Aste, T.; Hyde, S. T.; Ramsden, S.

    2005-09-01

    We propose a general method to study the hierarchical organization of financial data by embedding the structure of their correlations in metric graphs in multi-dimensional spaces. An application to two different sets of interest rates is discussed by constructing triangular embeddings on the sphere. Three-dimensional representations of these embeddings with the correct metric geometry are constructed and visualized. The resulting graphs contain the minimum spanning tree as a sub-graph and they preserve its hierarchical structure. This produces a clear cluster differentiation and allows us to compute new local and global topological quantities.

  11. 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.

  12. 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

  13. Deterministic hierarchical networks

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    PubMed Central

    2011-01-01

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

  15. 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

  16. Hierarchical photocatalysts.

    PubMed

    Li, Xin; Yu, Jiaguo; Jaroniec, Mietek

    2016-05-01

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

  17. Hierarchical video summarization

    NASA Astrophysics Data System (ADS)

    Ratakonda, Krishna; Sezan, M. Ibrahim; Crinon, Regis J.

    1998-12-01

    We address the problem of key-frame summarization of vide in the absence of any a priori information about its content. This is a common problem that is encountered in home videos. We propose a hierarchical key-frame summarization algorithm where a coarse-to-fine key-frame summary is generated. A hierarchical key-frame summary facilitates multi-level browsing where the user can quickly discover the content of the video by accessing its coarsest but most compact summary and then view a desired segment of the video with increasingly more detail. At the finest level, the summary is generated on the basis of color features of video frames, using an extension of a recently proposed key-frame extraction algorithm. The finest level key-frames are recursively clustered using a novel pairwise K-means clustering approach with temporal consecutiveness constraint. We also address summarization of MPEG-2 compressed video without fully decoding the bitstream. We also propose efficient mechanisms that facilitate decoding the video when the hierarchical summary is utilized in browsing and playback of video segments starting at selected key-frames.

  18. 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)

  19. 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

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

    PubMed

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

    2016-01-01

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

  1. 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

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

    NASA Astrophysics Data System (ADS)

    Bizhani, Golnoosh; Grassberger, Peter; Paczuski, Maya

    2011-12-01

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

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

    PubMed

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

    2013-10-21

    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.

  4. QSAR studies of the pyrethroid insecticides. Part 3. A putative pharmacophore derived using methodology based on molecular dynamics and hierarchical cluster analysis.

    PubMed

    Ford, Martyn G; Hoare, Neil E; Hudson, Brian D; Nevell, Thomas G; Banting, Lee

    2002-08-01

    Previous studies of the conformational behaviour of a group of synthetic pyrethroid insecticides have been extended to a more structurally diverse set. This includes compounds with different backbones and differing stereochemistry, with both Types I and II biological activity. These compounds also encompass a large range of biological activities. A parameterisation of the CHARMM force field for these compounds has been performed and the extra parameters are reported. Conformational sampling, using molecular dynamics (MD), has been performed for each of the 41 active structures. The accessible conformations of each have been characterised by the values of the common torsion angles using hierarchichal cluster analysis (HCA). A further CA, based on the centroids derived from the conformational sampling, identified a conformation common to at least 39 of the 41 structures. The critical torsion angles of this conformation lie at the centre of the molecule about the ester linkage and are defining an extended conformation, which differs from the minimum energy conformation of deltamethrin used previously. This may represent a putative pharmacophore for kill. The methods used here improve significantly on those used previously. The CHARMM force field was parameterised for the compounds and an improved method of conformational sampling, based on centroid clustering, has also been used.

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

    PubMed Central

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

    2016-01-01

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

  6. 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

  7. 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

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

    PubMed

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

    2015-01-01

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

  9. Selection of key ambient particulate variables for epidemiological studies - applying cluster and heatmap analyses as tools for data reduction.

    PubMed

    Gu, Jianwei; Pitz, Mike; Breitner, Susanne; Birmili, Wolfram; von Klot, Stephanie; Schneider, Alexandra; Soentgen, Jens; Reller, Armin; Peters, Annette; Cyrys, Josef

    2012-10-01

    The success of epidemiological studies depends on the use of appropriate exposure variables. The purpose of this study is to extract a relatively small selection of variables characterizing ambient particulate matter from a large measurement data set. The original data set comprised a total of 96 particulate matter variables that have been continuously measured since 2004 at an urban background aerosol monitoring site in the city of Augsburg, Germany. Many of the original variables were derived from measured particle size distribution (PSD) across the particle diameter range 3 nm to 10 μm, including size-segregated particle number concentration, particle length concentration, particle surface concentration and particle mass concentration. The data set was complemented by integral aerosol variables. These variables were measured by independent instruments, including black carbon, sulfate, particle active surface concentration and particle length concentration. It is obvious that such a large number of measured variables cannot be used in health effect analyses simultaneously. The aim of this study is a pre-screening and a selection of the key variables that will be used as input in forthcoming epidemiological studies. In this study, we present two methods of parameter selection and apply them to data from a two-year period from 2007 to 2008. We used the agglomerative hierarchical cluster method to find groups of similar variables. In total, we selected 15 key variables from 9 clusters which are recommended for epidemiological analyses. We also applied a two-dimensional visualization technique called "heatmap" analysis to the Spearman correlation matrix. 12 key variables were selected using this method. Moreover, the positive matrix factorization (PMF) method was applied to the PSD data to characterize the possible particle sources. Correlations between the variables and PMF factors were used to interpret the meaning of the cluster and the heatmap analyses.

  10. Topological properties of hierarchical networks

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  11. Efficient clustering aggregation based on data fragments.

    PubMed

    Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

    2012-06-01

    Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy. PMID:22334025

  12. The hierarchical algorithms--theory and applications

    NASA Astrophysics Data System (ADS)

    Su, Zheng-Yao

    Monte Carlo simulations are one of the most important numerical techniques for investigating statistical physical systems. Among these systems, spin models are a typical example which also play an essential role in constructing the abstract mechanism for various complex systems. Unfortunately, traditional Monte Carlo algorithms are afflicted with "critical slowing down" near continuous phase transitions and the efficiency of the Monte Carlo simulation goes to zero as the size of the lattice is increased. To combat critical slowing down, a very different type of collective-mode algorithm, in contrast to the traditional single-spin-flipmode, was proposed by Swendsen and Wang in 1987 for Potts spin models. Since then, there has been an explosion of work attempting to understand, improve, or generalize it. In these so-called "cluster" algorithms, clusters of spin are regarded as one template and are updated at each step of the Monte Carlo procedure. In implementing these algorithms the cluster labeling is a major time-consuming bottleneck and is also isomorphic to the problem of computing connected components of an undirected graph seen in other application areas, such as pattern recognition.A number of cluster labeling algorithms for sequential computers have long existed. However, the dynamic irregular nature of clusters complicates the task of finding good parallel algorithms and this is particularly true on SIMD (single-instruction-multiple-data machines. Our design of the Hierarchical Cluster Labeling Algorithm aims at alleviating this problem by building a hierarchical structure on the problem domain and by incorporating local and nonlocal communication schemes. We present an estimate for the computational complexity of cluster labeling and prove the key features of this algorithm (such as lower computational complexity, data locality, and easy implementation) compared with the methods formerly known. In particular, this algorithm can be viewed as a generalized

  13. 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.

  14. 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…

  15. The Supervised Hierarchical Dirichlet Process.

    PubMed

    Dai, Andrew M; Storkey, Amos J

    2015-02-01

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

  16. 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.

  17. Statistical properties of convex clustering

    PubMed Central

    Tan, Kean Ming; Witten, Daniela

    2016-01-01

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

  18. 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

  19. [Cluster analysis in biomedical researches].

    PubMed

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

    2013-01-01

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

  20. 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

  1. 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

  2. Dynamic Organization of Hierarchical Memories.

    PubMed

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2016-01-01

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

  3. Dynamic Organization of Hierarchical Memories

    PubMed Central

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2016-01-01

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

  4. Spam Detection Based on a Hierarchical Self-Organizing Map

    NASA Astrophysics Data System (ADS)

    Palomo, Esteban José; Domínguez, Enrique; Luque, Rafael Marcos; Muñoz, José

    The GHSOM is an artificial neural network that has been widely used for data clustering. The hierarchical architecture of the GHSOM is more flexible than a single SOM since it is adapted to input data, mirroring inherent hierarchical relations among them. The adaptation process of the GHSOM architecture is controlled by two parameters. However, these parameters have to be established in advance and this task is not always easy. In this paper, a new hierarchical self-organizing model that has just one parameter is proposed. The performance of this model has been evaluated by building a spam detector. Experimental results confirm the goodness of this approach.

  5. 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.

  6. Hierarchical features of large-scale cortical connectivity

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Graymer, R. W.; Simpson, R.

    2012-12-01

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

  8. 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.

  9. Hierarchical Organization Unveiled by Functional Connectivity in Complex Brain Networks

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

    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.

  10. Systolic architecture for heirarchical clustering

    SciTech Connect

    Ku, L.C.

    1984-01-01

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

  11. An alternative hierarchical organization of the mental disorders of the DSM-IV.

    PubMed

    Flanagan, Elizabeth H; Keeley, Jared; Blashfield, Roger K

    2008-08-01

    With the approaching publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM), alternative organizations of the DSM (4th ed.; DSM-IV; American Psychiatric Association, 1994) categories have been proposed. This article compares several published alternative organizations to clinicians' organization of the DSM-IV categories. As demonstrations of their organization of DSM-IV categories, psychologists and psychiatrists sorted 66 DSM-IV diagnostic categories into groups of similar diagnoses and then made progressively larger and smaller groups of diagnoses or placed similar groups next to each other on a table. Hierarchical agglomerative data analysis of clinicians' individual sortings showed that clinicians retained many lower level DSM-IV categories (e.g., anxiety disorders, mood disorders), but not the higher level DSM-IV categories (e.g., Axis I vs. Axis II). Instead, at the highest hierarchical level, clinicians' categories resembled the structure of the first edition of the DSM (American Psychiatric Association, 1952), which followed clinicians' diagnostic decision-making scheme, dividing mental disorders into organic versus nonorganic and then psychotic versus neurotic disorders. At minimum, these data suggest a DSM organization that makes sense to clinicians.

  12. Microparticles with hierarchical porosity

    SciTech Connect

    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.

  13. 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…

  14. Statistics of voids in hierarchical universes

    NASA Technical Reports Server (NTRS)

    Fry, J. N.

    1986-01-01

    As one alternative to the N-point galaxy correlation function statistics, the distribution of holes or the probability that a volume of given size and shape be empty of galaxies can be considered. The probability of voids resulting from a variety of hierarchical patterns of clustering is considered, and these are compared with the results of numerical simulations and with observations. A scaling relation required by the hierarchical pattern of higher order correlation functions is seen to be obeyed in the simulations, and the numerical results show a clear difference between neutrino models and cold-particle models; voids are more likely in neutrino universes. Observational data do not yet distinguish but are close to being able to distinguish between models.

  15. Tucker2 Hierarchical Classes Analysis

    ERIC Educational Resources Information Center

    Ceulemans, Eva; Van Mechelen, Iven

    2004-01-01

    This paper presents a new hierarchical classes model, called Tucker2-HICLAS, for binary three-way three-mode data. As any three-way hierarchical classes model, the Tucker2-HICLAS model includes a representation of the association relation among the three modes and a hierarchical classification of the elements of each mode. A distinctive feature of…

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

    PubMed

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

    2016-05-18

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

  17. Classifying hospitals as mortality outliers: logistic versus hierarchical logistic models.

    PubMed

    Alexandrescu, Roxana; Bottle, Alex; Jarman, Brian; Aylin, Paul

    2014-05-01

    The use of hierarchical logistic regression for provider profiling has been recommended due to the clustering of patients within hospitals, but has some associated difficulties. We assess changes in hospital outlier status based on standard logistic versus hierarchical logistic modelling of mortality. The study population consisted of all patients admitted to acute, non-specialist hospitals in England between 2007 and 2011 with a primary diagnosis of acute myocardial infarction, acute cerebrovascular disease or fracture of neck of femur or a primary procedure of coronary artery bypass graft or repair of abdominal aortic aneurysm. We compared standardised mortality ratios (SMRs) from non-hierarchical models with SMRs from hierarchical models, without and with shrinkage estimates of the predicted probabilities (Model 1 and Model 2). The SMRs from standard logistic and hierarchical models were highly statistically significantly correlated (r > 0.91, p = 0.01). More outliers were recorded in the standard logistic regression than hierarchical modelling only when using shrinkage estimates (Model 2): 21 hospitals (out of a cumulative number of 565 pairs of hospitals under study) changed from a low outlier and 8 hospitals changed from a high outlier based on the logistic regression to a not-an-outlier based on shrinkage estimates. Both standard logistic and hierarchical modelling have identified nearly the same hospitals as mortality outliers. The choice of methodological approach should, however, also consider whether the modelling aim is judgment or improvement, as shrinkage may be more appropriate for the former than the latter. PMID:24711175

  18. 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…

  19. Aspects of hierarchical galaxy formation involving gas dynamics

    SciTech Connect

    Katz, N. )

    1992-09-01

    The viability of hierarchical structure formation theories (cold dark matter) through numerical simulations that include gasdynamics, are investigated. Gasdynamics is essential to obtaining realistic results to many problems in galaxy formation. Applications over the range from individual galaxy features (polar rings) to elliptical and spiral galaxies to quasars to clusters are considered. 42 refs.

  20. A Computer Program for Clustering Large Matrices

    ERIC Educational Resources Information Center

    Koch, Valerie L.

    1976-01-01

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

  1. Nested Hierarchical Dirichlet Processes.

    PubMed

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

    2015-02-01

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

  2. 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).

  3. 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

  4. 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

  5. A complete hierarchical key management scheme for heterogeneous wireless sensor networks.

    PubMed

    Chen, Chien-Ming; Zheng, Xinying; Wu, Tsu-Yang

    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.

  6. 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

  7. Hierarchical image enhancement

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  8. An exactly solvable model of hierarchical self-assembly.

    PubMed

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

    2009-06-14

    Many living and nonliving structures in the natural world form by hierarchical organization, but physical theories that describe this type of organization are scarce. To address this problem, a model of equilibrium self-assembly is formulated in which dynamically associating species organize into hierarchical structures that preserve their shape at each stage of assembly. In particular, we consider symmetric m-gons that associate at their vertices into Sierpinski gasket structures involving the hierarchical association of triangles, squares, hexagons, etc., at their corner vertices, thereby leading to fractal structures after many generations of assembly. This rather idealized model of hierarchical assembly yields an infinite sequence of self-assembly transitions as the morphology progressively organizes to higher levels of the hierarchy, and these structures coexists at dynamic equilibrium, as found in real hierarchically self-assembling systems such as amyloid fiber forming proteins. Moreover, the transition sharpness progressively grows with increasing m, corresponding to larger and larger loops in the assembled structures. Calculations are provided for several basic thermodynamic properties (including the order parameters for assembly for each stage of the hierarchy, average mass of clusters, specific heat, transition sharpness, etc.) that are required for characterizing the interaction parameters governing this type of self-assembly and for elucidating other basic qualitative aspects of these systems. Our idealized model of hierarchical assembly gives many insights into this ubiquitous type of self-organization process. PMID:19530788

  9. An exactly solvable model of hierarchical self-assembly

    NASA Astrophysics Data System (ADS)

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

    2009-06-01

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

  10. Hierarchical Star Formation in Nearby LEGUS Galaxies

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  11. 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.

  12. Reverse hierarchical PIV processing

    NASA Astrophysics Data System (ADS)

    Rohály, J.; Frigerio, F.; Hart, D. P.

    2002-07-01

    A novel hierarchical processing scheme is proposed to efficiently increase the spatial resolution and dynamic range of detecting particle image displacements in PIV images. The technique takes full advantage of the multi-resolution characteristic of the discrete correlation function by starting the processing at the smallest scale and, if necessary, gradually building correlation planes into larger interrogation areas based on the result of inter-level correlation correction and validation. It is shown that the algorithm can be implemented in both direct and FFT based correlation algorithms with greatly reduced computational complexity. The technique opens new perspectives for locally adaptive super-resolution processing taking flow field, seeding, and imaging anomalies into account. Processing at the lowest scale (e.g. pixel or particle image size) allows the combination of correlation planes on any shape. Hence the proposed reverse hierarchical processing represents interrogation area optimization both in size and shape in order to maximize the correlation plane signal-to-noise ratio. The method is successfully demonstrated on experimentally obtained images.

  13. 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…

  14. A Rasch Hierarchical Measurement Model.

    ERIC Educational Resources Information Center

    Maier, Kimberly S.

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

  15. Re-shaping colloidal clusters

    NASA Astrophysics Data System (ADS)

    Kraft, Daniela

    2015-03-01

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

  16. 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

  17. Optimisation by hierarchical search

    NASA Astrophysics Data System (ADS)

    Zintchenko, Ilia; Hastings, Matthew; Troyer, Matthias

    2015-03-01

    Finding optimal values for a set of variables relative to a cost function gives rise to some of the hardest problems in physics, computer science and applied mathematics. Although often very simple in their formulation, these problems have a complex cost function landscape which prevents currently known algorithms from efficiently finding the global optimum. Countless techniques have been proposed to partially circumvent this problem, but an efficient method is yet to be found. We present a heuristic, general purpose approach to potentially improve the performance of conventional algorithms or special purpose hardware devices by optimising groups of variables in a hierarchical way. We apply this approach to problems in combinatorial optimisation, machine learning and other fields.

  18. Modelling hierarchical and modular complex networks: division and independence

    NASA Astrophysics Data System (ADS)

    Kim, D.-H.; Rodgers, G. J.; Kahng, B.; Kim, D.

    2005-06-01

    We introduce a growing network model which generates both modular and hierarchical structure in a self-organized way. To this end, we modify the Barabási-Albert model into the one evolving under the principles of division and independence as well as growth and preferential attachment (PA). A newly added vertex chooses one of the modules composed of existing vertices, and attaches edges to vertices belonging to that module following the PA rule. When the module size reaches a proper size, the module is divided into two, and a new module is created. The karate club network studied by Zachary is a simple version of the current model. We find that the model can reproduce both modular and hierarchical properties, characterized by the hierarchical clustering function of a vertex with degree k, C(k), being in good agreement with empirical measurements for real-world networks.

  19. Hierarchical number estimation.

    PubMed

    Friedenberg, Jay; Limratana, William

    2005-01-01

    We investigated number estimation using dot patterns grouped by proximity into larger clusters. Participants estimated the number of dots and clusters in separate trials. Estimation was most accurate when the numbers of elements on both scales were the same. When the number of elements on the unattended scale was higher, overestimation occurred. Conversely, when the number of elements on the unattended scale was lower, underestimation occurred. In Experiment 2, response cues were blocked to reduce any tendency toward attending the irrelevant level. The results were essentially unchanged, indicating response confusion alone cannot account for the effect. The data support the existence of an opposite scale effect in which the number of elements at the unattended level influence the processing of number.

  20. Hierarchical multifunctional nanocomposites

    NASA Astrophysics Data System (ADS)

    Ghasemi-Nejhad, Mehrdad N.

    2014-03-01

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

  1. Topology of the correlation networks among major currencies using hierarchical structure methods

    NASA Astrophysics Data System (ADS)

    Keskin, Mustafa; Deviren, Bayram; Kocakaplan, Yusuf

    2011-02-01

    We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 period. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.

  2. Hierarchical Fast Multipole Simulation of Magnetic Colloids

    NASA Astrophysics Data System (ADS)

    Günal, Yüksel; Visscher, Pieter

    1997-03-01

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

  3. 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

  4. Hierarchical Linked Views

    SciTech Connect

    Erbacher, Robert; Frincke, Deb

    2007-07-02

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

  5. Self-organized Criticality in Hierarchical Brain Network

    NASA Astrophysics Data System (ADS)

    Yang, Qiu-Ying; Zhang, Ying-Yue; Chen, Tian-Lun

    2008-11-01

    It is shown that the cortical brain network of the macaque displays a hierarchically clustered organization and the neuron network shows small-world properties. Now the two factors will be considered in our model and the dynamical behavior of the model will be studied. We study the characters of the model and find that the distribution of avalanche size of the model follows power-law behavior.

  6. Hierarchical classification as relational framing.

    PubMed

    Slattery, Brian; Stewart, Ian

    2014-01-01

    The purpose of this study was to model hierarchical classification as contextually controlled, generalized relational responding or relational framing. In Experiment 1, a training procedure involving nonarbitrarily related multidimensional stimuli was used to establish two arbitrary shapes as contextual cues for 'member of' and 'includes' relational responding, respectively. Subsequently those cues were used to establish a network of arbitrary stimuli in particular hierarchical relations with each other, and then test for derivation of further untrained hierarchical relations as well as for transformation of functions. Resultant patterns of relational framing showed properties of transitive class containment, asymmetrical class containment, and unilateral property induction, consistent with conceptions of hierarchical classification as described within the cognitive developmental literature. Experiment 2 extended the basic model by using "fuzzy category" stimuli and providing a better controlled test of transformation of functions. Limitations and future research directions are discussed. PMID:24310480

  7. 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.

  8. Hierarchical video summarization for medical data

    NASA Astrophysics Data System (ADS)

    Zhu, Xingquan; Fan, Jianping; Elmagarmid, Ahmed K.; Aref, Walid G.

    2001-12-01

    To provide users with an overview of medical video content at various levels of abstraction which can be used for more efficient database browsing and access, a hierarchical video summarization strategy has been developed and is presented in this paper. To generate an overview, the key frames of a video are preprocessed to extract special frames (black frames, slides, clip art, sketch drawings) and special regions (faces, skin or blood-red areas). A shot grouping method is then applied to merge the spatially or temporally related shots into groups. The visual features and knowledge from the video shots are integrated to assign the groups into predefined semantic categories. Based on the video groups and their semantic categories, video summaries for different levels are constructed by group merging, hierarchical group clustering and semantic category selection. Based on this strategy, a user can select the layer of the summary to access. The higher the layer, the more concise the video summary; the lower the layer, the greater the detail contained in the summary.

  9. 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.

  10. 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.

  11. Color segmentation using MDL clustering

    NASA Astrophysics Data System (ADS)

    Wallace, Richard S.; Suenaga, Yasuhito

    1991-02-01

    This paper describes a procedure for segmentation of color face images. A cluster analysis algorithm uses a subsample of the input image color pixels to detect clusters in color space. The clustering program consists of two parts. The first part searches for a hierarchical clustering using the NIHC algorithm. The second part searches the resultant cluster tree for a level clustering having minimum description length (MDL). One of the primary advantages of the MDL paradigm is that it enables writing robust vision algorithms that do not depend on user-specified threshold parameters or other " magic numbers. " This technical note describes an application of minimal length encoding in the analysis of digitized human face images at the NTT Human Interface Laboratories. We use MDL clustering to segment color images of human faces. For color segmentation we search for clusters in color space. Using only a subsample of points from the original face image our clustering program detects color clusters corresponding to the hair skin and background regions in the image. Then a maximum likelyhood classifier assigns the remaining pixels to each class. The clustering program tends to group small facial features such as the nostrils mouth and eyes together but they can be separated from the larger classes through connected components analysis.

  12. Symbolic clustering

    SciTech Connect

    Reinke, R.E.

    1991-01-01

    Clustering is the problem of finding a good organization for data. Because there are many kinds of clustering problems, and because there are many possible clusterings for any data set, clustering programs use knowledge and assumptions about individual problems to make clustering tractable. Cluster-analysis techniques allow knowledge to be expressed in the choice of a pairwise distance measure and in the choice of clustering algorithm. Conceptual clustering adds knowledge and preferences about cluster descriptions. In this study the author describes symbolic clustering, which adds representation choice to the set of ways a data analyst can use problem-specific knowledge. He develops an informal model for symbolic clustering, and uses it to suggest where and how knowledge can be expressed in clustering. A language for creating symbolic clusters, based on the model, was developed and tested on three real clustering problems. The study concludes with a discussion of the implications of the model and the results for clustering in general.

  13. Parallel hierarchical global illumination

    SciTech Connect

    Snell, Q.O.

    1997-10-08

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

  14. 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.

  15. 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…

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

    PubMed

    Froyen, Vicky; Feldman, Jacob; Singh, Manish

    2015-10-01

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

  17. 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

  18. Supervised clustering of genes

    PubMed Central

    Dettling, Marcel; Bühlmann, Peter

    2002-01-01

    Background We focus on microarray data where experiments monitor gene expression in different tissues and where each experiment is equipped with an additional response variable such as a cancer type. Although the number of measured genes is in the thousands, it is assumed that only a few marker components of gene subsets determine the type of a tissue. Here we present a new method for finding such groups of genes by directly incorporating the response variables into the grouping process, yielding a supervised clustering algorithm for genes. Results An empirical study on eight publicly available microarray datasets shows that our algorithm identifies gene clusters with excellent predictive potential, often superior to classification with state-of-the-art methods based on single genes. Permutation tests and bootstrapping provide evidence that the output is reasonably stable and more than a noise artifact. Conclusions In contrast to other methods such as hierarchical clustering, our algorithm identifies several gene clusters whose expression levels clearly distinguish the different tissue types. The identification of such gene clusters is potentially useful for medical diagnostics and may at the same time reveal insights into functional genomics. PMID:12537558

  19. 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…

  20. Memory Stacking in Hierarchical Networks.

    PubMed

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

    2016-02-01

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

  1. Hierarchical storage management system evaluation

    NASA Technical Reports Server (NTRS)

    Woodrow, Thomas S.

    1993-01-01

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

  2. Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering.

    PubMed

    Knowles, David A; Ghahramani, Zoubin

    2015-02-01

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

  3. Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering.

    PubMed

    Knowles, David A; Ghahramani, Zoubin

    2015-02-01

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

  4. 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.

  5. Complex earthquake networks: Hierarchical organization and assortative mixing

    SciTech Connect

    Abe, Sumiyoshi; Suzuki, Norikazu

    2006-08-15

    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.

  6. 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.

  7. Hierarchical and non-hierarchical mineralisation of collagen

    PubMed Central

    Liu, Yan; Kim, Young-Kyung; Dai, Lin; Li, Nan; Khan, Sara; Pashley, David H.; Tay, Franklin R.

    2010-01-01

    Biomineralisation of collagen involves functional motifs incorporated in extracellular matrix protein molecules to accomplish the objectives of stabilising amorphous calcium phosphate into nanoprecursors and directing the nucleation and growth of apatite within collagen fibrils. Here we report the use of small inorganic polyphosphate molecules to template hierarchical intrafibrillar apatite assembly in reconstituted collagen in the presence of polyacrylic acid to sequester calcium and phosphate into transient amorphous nanophases. The use of polyphosphate without a sequestration analogue resulted only in randomly-oriented extrafibrillar precipitations along the fibrillar surface. Conversely, the use of polyacrylic acid without a templating analogue resulted only in non-hierarchical intrafibrillar mineralisation with continuous apatite strands instead of discrete crystallites. The ability of using simple non-protein molecules to recapitulate different levels of structural hierarchy in mineralised collagen signifies the ultimate simplicity in Nature’s biomineralisation design principles and challenges the need for using more complex recombinant matrix proteins in bioengineering applications. PMID:21040969

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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…

  15. 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.

  16. 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.

  17. 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

  18. Network clustering coefficient without degree-correlation biases.

    PubMed

    Soffer, Sara Nadiv; Vázquez, Alexei

    2005-05-01

    The clustering coefficient quantifies how well connected are the neighbors of a vertex in a graph. In real networks it decreases with the vertex degree, which has been taken as a signature of the network hierarchical structure. Here we show that this signature of hierarchical structure is a consequence of degree-correlation biases in the clustering coefficient definition. We introduce a definition in which the degree-correlation biases are filtered out, and provide evidence that in real networks the clustering coefficient is constant or decays logarithmically with vertex degree.

  19. 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.

  20. 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.

  1. Hierarchical Star Formation in LEGUS Galaxies

    NASA Astrophysics Data System (ADS)

    Elmegreen, Debra M.; Elmegreen, Bruce

    2014-06-01

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

  2. Evolution of brightest cluster galaxies in X-ray clusters

    NASA Astrophysics Data System (ADS)

    Brough, S.; Collins, C. A.; Burke, D. J.; Mann, R. G.; Lynam, P. D.

    2002-01-01

    A recent paper presents the analysis of the K-band Hubble diagram of 76 brightest cluster galaxies (BCGs) in X-ray clusters and shows that the properties of BCGs depend on the X-ray luminosity (LX) of their host clusters. Unfortunately, the low numbers of nearby clusters in this sample makes it difficult to constrain evolutionary trends. In this letter we extend the Hubble diagram of Burke, Collins & Mann to a total of 155 clusters using new data on 79 BCGs at z<=0.1 from the 2MASS extended source catalogue. We show that the major division between BCGs in high- and low-LX clusters disappears at z<=0.1, with BCGs having similar absolute magnitudes independent of the X-ray luminosity of their host clusters. At larger redshifts, the K-band light of BCGs in high-LX systems is consistent with little or no merging back to z~0.8, whereas BCGs in the low-LX systems have a different evolutionary history, with many increasing their mass by a factor >=4 since z~=1. This provides direct evidence of hierarchical merging in a galaxy population.

  3. 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

  4. 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.

  5. Entropy Bounds for Hierarchical Molecular Networks

    PubMed Central

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

    2008-01-01

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

  6. 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.

  7. 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.

  8. Clustering of Variables for Mixed Data

    NASA Astrophysics Data System (ADS)

    Saracco, J.; Chavent, M.

    2016-05-01

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

  9. 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 ...

  10. 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.

  11. 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

  12. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

  14. 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.

  15. 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.

  16. Abell Clusters

    NASA Astrophysics Data System (ADS)

    Katgert, P.; Murdin, P.

    2000-11-01

    Abell clusters are the most conspicuous groupings of galaxies identified by George Abell on the plates of the first photographic survey made with the SCHMIDT TELESCOPE at Mount Palomar in the 1950s. Sometimes, the term Abell clusters is used as a synonym of nearby, optically selected galaxy clusters....

  17. Medical record linkage in health information systems by approximate string matching and clustering

    PubMed Central

    Sauleau, Erik A; Paumier, Jean-Philippe; Buemi, Antoine

    2005-01-01

    Background Multiplication of data sources within heterogeneous healthcare information systems always results in redundant information, split among multiple databases. Our objective is to detect exact and approximate duplicates within identity records, in order to attain a better quality of information and to permit cross-linkage among stand-alone and clustered databases. Furthermore, we need to assist human decision making, by computing a value reflecting identity proximity. Methods The proposed method is in three steps. The first step is to standardise and to index elementary identity fields, using blocking variables, in order to speed up information analysis. The second is to match similar pair records, relying on a global similarity value taken from the Porter-Jaro-Winkler algorithm. And the third is to create clusters of coherent related records, using graph drawing, agglomerative clustering methods and partitioning methods. Results The batch analysis of 300,000 "supposedly" distinct identities isolates 240,000 true unique records, 24,000 duplicates (clusters composed of 2 records) and 3,000 clusters whose size is greater than or equal to 3 records. Conclusion Duplicate-free databases, used in conjunction with relevant indexes and similarity values, allow immediate (i.e.: real-time) proximity detection when inserting a new identity. PMID:16219102

  18. DEDICATED FILTER FOR DEFECTS CLUSTERING IN RADIOGRAPHIC IMAGE

    SciTech Connect

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

    2009-03-03

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

  19. 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.…

  20. Application and Interpretation of Hierarchical Multiple Regression.

    PubMed

    Jeong, Younhee; Jung, Mi Jung

    2016-01-01

    The authors reported the association between motivation and self-management behavior of individuals with chronic low back pain after adjusting control variables using hierarchical multiple regression (). This article describes details of the hierarchical regression applying the actual data used in the article by , including how to test assumptions, run the statistical tests, and report the results. PMID:27648796

  1. 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

  2. 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.

  3. Processing of hierarchical syntactic structure in music

    PubMed Central

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

    2013-01-01

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

  4. 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

  5. Processing of hierarchical syntactic structure in music.

    PubMed

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

    2013-09-17

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

  6. Hierarchical condensation near phase equilibrium

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

  7. Multicollinearity in hierarchical linear models.

    PubMed

    Yu, Han; Jiang, Shanhe; Land, Kenneth C

    2015-09-01

    This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model.

  8. 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.

  9. 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``).

  10. 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.

  11. 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/.

  12. 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.

  13. Hierarchical drivers of reef-fish metacommunity structure.

    PubMed

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

    2009-01-01

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

  14. 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

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

    PubMed Central

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

    2014-01-01

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

  16. 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

  17. 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.

  18. 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.

  19. Exploring the hierarchical structure in road network

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  20. 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.

  1. 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.

  2. 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.

  3. 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

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

    PubMed Central

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Deviren, Bayram; Keskin, Mustafa

    2014-11-01

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

  6. Jamming transition in hierarchical networks

    NASA Astrophysics Data System (ADS)

    Cheng, Xiang; Boettcher, Stefan

    2014-03-01

    Jamming transitions arise in disordered granular materials where the systems fall out of equilibrium due to an increase in the packing density. A kinetically constrained lattice gas model due to Biroli and Mezard (BM) has connected the jamming transition to an equilibrium phase transition. In this description, before this equilibrium transition can be reached, any experiment or simulation would fall out of equilibrium at a Kauzmann transition. However, this analysis is based on a mean-field calculation which, for disordered systems, may have limited relevance in finite dimensions. We study the BM-model on a lattice-like network, which mixes geometric and mean-field features, to reproduce such a phase transition. Computationally, we use the Wang-Landau algorithm which should be less affected by the jamming near the phase transition. The algorithm produces the density of states and, hence, the entropy directly, in addition to many critical properties, such as packing fraction, compressibility, etc. Also, lattice-like hierarchical networks conveniently allow exact or approximate renormalization group treatments, extending analytical results to the thermodynamic limit. Supported through NSF grant DMR-1207431.

  7. Hierarchical model for distributed seismicity

    SciTech Connect

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

    2010-07-15

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

  8. HIERARCHICAL STELLAR STRUCTURES IN THE LOCAL GROUP DWARF GALAXY NGC 6822

    SciTech Connect

    Gouliermis, Dimitrios A.; Walter, Fabian; Schmeja, Stefan; Klessen, Ralf S.; De Blok, W. J. G. E-mail: walter@mpia-hd.mpg.d E-mail: rklessen@ita.uni-heidelberg.d

    2010-12-20

    We present a comprehensive study of the star cluster population and the hierarchical structure in the clustering of blue stars with ages {approx}<500 Myr in the Local Group dwarf irregular galaxy NGC 6822. Our observational material comprises the most complete optical stellar catalog of the galaxy from imaging with the Suprime-Cam at the 8.2 m Subaru Telescope. We identify 47 distinct star clusters with the application of the nearest-neighbor density method to this catalog for a detection threshold of 3{sigma} above the average stellar density. The size distribution of the detected clusters can be very well approximated by a Gaussian with a peak at {approx}68 pc. The total stellar masses of the clusters are estimated by extrapolating the cumulative observed stellar mass function of all clusters to be in the range 10{sup 3}-10{sup 4} M{sub sun}. Their number distribution is fitted very well by a power law with index {alpha} {approx} 1.5 {+-} 0.7, which is consistent with the cluster mass functions of other Local Group galaxies and the cluster initial mass function. In addition to the detected star clusters of the galaxy, the application of the nearest-neighbor density method for various density thresholds, other than 3{sigma}, enabled the identification of stellar concentrations in various lengthscales. The stellar density maps constructed with this technique provide a direct proof of hierarchically structured stellar concentrations in NGC 6822, in the sense that smaller dense stellar concentrations are located inside larger and looser ones. We illustrate this hierarchy by the so-called dendrogram, or structure tree of the detected stellar structures, which demonstrates that most of the detected structures split up into several substructures over at least three levels. We quantify the hierarchy of these structures with the use of the minimum spanning tree method. We find that structures detected at 1, 2, and 3{sigma} density thresholds are hierarchically constructed

  9. 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.

  10. Hierarchical Nanowires Synthesized by Supramolecular Stepwise Polymerization.

    PubMed

    Zhuang, Zeliang; Jiang, Tao; Lin, Jiaping; Gao, Liang; Yang, Chaoying; Wang, Liquan; Cai, Chunhua

    2016-09-26

    The self-organization of pre-assembled aggregates is an efficient stepwise strategy for fabricating nanostructures with a second level of hierarchy. Herein, we report that anisotropic spindle-like micelles, self-assembled from polypeptide graft copolymers with rigid backbones, can serve as ideal pre-assembled subunits for constructing one-dimensional materials with hierarchical structures. By adding organic solvents and dialyzing against water, reactive points can be generated at the ends of the spindle-like micelles, which subsequently drive the anisotropic micelles to grow as rods in a chain and eventually self-assemble into hierarchical nanowires in a stepwise manner. The second self-assembly step is a hierarchical process that resembles step polymerization. Hierarchical structures can be precisely synthesized by this new type of polymerization. These nanostructures can be tailored by the activity of the reactive points, which depends on the nature of the solvent and the molecular architecture. PMID:27604499

  11. Hierarchically Structured Nanomaterials for Electrochemical Energy Conversion.

    PubMed

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

    2016-01-01

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

  12. 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.

  13. Efficient hierarchical interconnection for multiprocessor systems

    NASA Technical Reports Server (NTRS)

    Wei, Sizheng; Levy, Saul

    1992-01-01

    The authors present a novel approach to the design of a class of hierarchical interconnection networks for multiprocessor systems. This approach, based on an architecture providing separate networks for each level, gives a general and flexible way to construct efficient hierarchical networks. The performance and cost-effectiveness of the resulting networks are analyzed and compared in detail, using both unbuffered and buffered network models. It is shown that, if the design parameters are determined based on the degree of locality, the cost-effectiveness of a hierarchical network can be significantly improved. In addition, the authors investigate how to construct a cost-effectiveness hierarchical network by determining appropriate design parameters. Two associated algorithms are developed for this purpose.

  14. 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.

  15. 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.

  16. A fuzzy ontological knowledge document clustering methodology.

    PubMed

    Trappey, Amy J C; Trappey, Charles V; Hsu, Fu-Chiang; Hsiao, David W

    2009-06-01

    This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s) for given patents based on their derived ontological semantic webs. Finally, three case studies are used to test the approach. The first test case analyzed and clustered 100 patents for chemical and mechanical polishing retrieved from the World Intellectual Property Organization (WIPO). The second test case analyzed and clustered 100 patent news articles retrieved from online Web sites. The third case analyzed and clustered 100 patents for radio-frequency identification retrieved from WIPO. The results show that the fuzzy ontology-based document clustering approach outperforms the K-means approach in precision, recall, F-measure, and Shannon's entropy.

  17. 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.

  18. 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.

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

    NASA Astrophysics Data System (ADS)

    Kantar, Ersin; Keskin, Mustafa; Deviren, Bayram

    2012-04-01

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

  1. 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

  2. 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…

  3. 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.

  4. Visualization of mappings between the gene ontology and cluster trees

    NASA Astrophysics Data System (ADS)

    Jusufi, Ilir; Kerren, Andreas; Aleksakhin, Vladyslav; Schreiber, Falk

    2012-01-01

    Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This paper proposes a new visualization method for this task.

  5. Automatic style clustering of printed characters in form images

    NASA Astrophysics Data System (ADS)

    Liu, Changsong; Ding, Xiaoqing

    2005-01-01

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

  6. Automatic style clustering of printed characters in form images

    NASA Astrophysics Data System (ADS)

    Liu, Changsong; Ding, Xiaoqing

    2004-12-01

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

  7. 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

  8. Hierarchical Structures in Granular Matter

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  9. 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].

  10. 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

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

    ERIC Educational Resources Information Center

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

    2008-01-01

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

  12. 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.

  13. 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…

  14. Statistical label fusion with hierarchical performance models

    PubMed Central

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

    2014-01-01

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

  15. 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.

  16. 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

  17. Hierarchical regression for analyses of multiple outcomes.

    PubMed

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

    2015-09-01

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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Superoleophobic Surfaces Obtained via Hierarchical Metallic Meshes.

    PubMed

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

    2016-05-01

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

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

    PubMed Central

    Rosvall, Martin; Bergstrom, Carl T.

    2011-01-01

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

  4. Hierarchical bioinspired adhesive surfaces-a review.

    PubMed

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

    2016-01-01

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

  5. 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.

  6. Hierarchical assembly of diphenylalanine into dendritic nanoarchitectures.

    PubMed

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

    2010-09-01

    Highly ordered, multi-dimensional dendritic nanoarchitectures were created via self-assembly of diphenylalanine from an acidic buffer solution. The self-similarity of dendritic structures was characterized by examining their fractal dimensions with the box-counting method. The fractal dimension was determined to be 1.7, which demonstrates the fractal dimension of structures generated by diffusion limited aggregation on a two-dimensional substrate surface. By confining the dendritic assembly of diphenylalanine within PDMS microchannels, the self-similar dendritic growth could be hierarchically directed to create linearly assembled nanoarchitectures. Our approach offers a novel pathway for creating and directing hierarchical nanoarchitecture from biomolecular assembly. PMID:20605423

  7. 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.

  8. Inference of ancestry: constructing hierarchical reference populations and assigning unknown individuals

    PubMed Central

    2006-01-01

    The ability to infer personal genetic ancestry is being increasingly utilised in certain medical and forensic situations. Herein, the unsupervised Bayesian clustering algorithms structure, is employed to analyse 377 autosomal short tandem repeats typed on 1,056 individuals from the Centre d'Etude du Polymorphisme Humain Human Diversity Panel. Individuals of known geographical origin were hierarchically classified into a framework of increasingly homogeneous clusters to serve as reference populations into which individuals of unknown ancestry can be assigned. The groupings were characterised by the geographical affinities of cluster members and the accuracy of these procedures was verified using several genetic indices. Fine-scale substructure was detectable beyond the broad population level classifications that previously have been explored in this dataset. Metrics indicated that within certain lines, the strongest structuring signals were detected at the leaves of the hierarchy where lineage-specific groupings were identified. The accuracy of unknown assignment was assessed at each level of the hierarchy using a 'leave one out' strategy in which each individual was stripped of cluster membership and then re-assigned using the supervised Bayesian clustering algorithm implemented in GeneClass2. Although most clusters at all levels of resolution experienced highly accurate assignment, a decline was observed in the finer levels due to the mixed membership characteristics of some individuals. The parameters defined by this study allowed for assignment of unknown individuals to genetically defined clusters with measured likelihood. Shared ancestry data can then be inferred for the unknown individual. PMID:16460647

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

  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. iHAT: interactive hierarchical aggregation table for genetic association data.

    PubMed

    Heinrich, Julian; Vehlow, Corinna; Battke, Florian; Jäger, Günter; Weiskopf, Daniel; Nieselt, Kay

    2012-01-01

    In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT), facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data.

  13. 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.

  14. The Origin of the Brightest Cluster Galaxies

    NASA Astrophysics Data System (ADS)

    Dubinski, John

    1998-07-01

    Most clusters and groups of galaxies contain a giant elliptical galaxy in their centers that far outshines and outweighs normal ellipticals. The origin of these brightest cluster galaxies is intimately related to the collapse and formation of the cluster. Using an N-body simulation of a cluster of galaxies in a hierarchical cosmological model, we show that galaxy merging naturally produces a massive central galaxy with surface brightness and velocity dispersion profiles similar to those of observed BCGs. To enhance the resolution of the simulation, 100 dark halos at z = 2 are replaced with self-consistent disk + bulge + halo galaxy models following a Tully-Fisher relation using 100,000 particles for the 20 largest galaxies and 10,000 particles for the remaining ones. This technique allows us to analyze the stellar and dark-matter components independently. The central galaxy forms through the merger of several massive galaxies along a filament early in the cluster's history. Galactic cannibalism of smaller galaxies through dynamical friction over a Hubble time only accounts for a small fraction of the accreted mass. The galaxy is a flattened, triaxial object whose long axis aligns with the primordial filament and the long axis of the cluster galaxy distribution, agreeing with observed trends for galaxy cluster alignment.

  15. Some Basic Elements in Clustering and Classification

    NASA Astrophysics Data System (ADS)

    Grégoire, G.

    2016-05-01

    This chapter deals with basic tools useful in clustering and classification and present some commonly used approaches for these two problems. Since several chapters in these proceedings are devoted to approaches to deal with classification, we give more attention in this chapter to clustering issues. We are first concerned with notions of distances or dissimilarities between objects we are to group in clusters. Then based on these inter-objects distances we define distances between sets of objects, such as single linkage, complete linkage or Ward distance. Three clustering algorithms are presented with some details and compared: Kmeans, Ascendant Hierarchical and DBSCAN algorithms. The comparison between partitions and the issue of choosing the correct number of clusters are investigated and the proposed procedures are tested on two data sets. We emphasize the fact that the results provided by the numerous indices available in the literature for selecting the number of clusters is largely depending upon the shape and the dispersion we are assuming for these clusters. Finally the last section is devoted to classification. Some basic notions such as training sets, test sets and cross-validation are discussed. Two particular approaches are detailed, the K-nearest neighbors method and the logistic regression, and comparisons with LDA (Linear Discriminant Analysis) and QDA (Quadratic Discriminant Analysis) are analyzed.

  16. 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.

  17. Decentralized Cooperative TOA/AOA Target Tracking for Hierarchical Wireless Sensor Networks

    PubMed Central

    Chen, Ying-Chih; Wen, Chih-Yu

    2012-01-01

    This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation. PMID:23202212

  18. Progressive Image Coding by Hierarchical Linear Approximation.

    ERIC Educational Resources Information Center

    Wu, Xiaolin; Fang, Yonggang

    1994-01-01

    Proposes a scheme of hierarchical piecewise linear approximation as an adaptive image pyramid. A progressive image coder comes naturally from the proposed image pyramid. The new pyramid is semantically more powerful than regular tessellation but syntactically simpler than free segmentation. This compromise between adaptability and complexity…

  19. 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…

  20. 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…

  1. Hierarchical Forms Processing in Adults and Children

    ERIC Educational Resources Information Center

    Harrison, Tamara B.; Stiles, Joan

    2009-01-01

    Two experiments examined child and adult processing of hierarchical stimuli composed of geometric forms. Adults (ages 18-23 years) and children (ages 7-10 years) performed a forced-choice task gauging similarity between visual stimuli consisting of large geometric objects (global level) composed of small geometric objects (local level). The…

  2. Metal oxide nanostructures with hierarchical morphology

    DOEpatents

    Ren, Zhifeng; Lao, Jing Yu; Banerjee, Debasish

    2007-11-13

    The present invention relates generally to metal oxide materials with varied symmetrical nanostructure morphologies. In particular, the present invention provides metal oxide materials comprising one or more metallic oxides with three-dimensionally ordered nanostructural morphologies, including hierarchical morphologies. The present invention also provides methods for producing such metal oxide materials.

  3. Performance Measurement Framework for Hierarchical Text Classification.

    ERIC Educational Resources Information Center

    Sun, Aixin; Lim, Ee-Peng; Ng, Wee-Keong

    2003-01-01

    Discusses hierarchical text classification for electronic information retrieval and the measures used to evaluate performance. Proposes new performance measures that consist of category similarity measures and distance-based measures that consider the contributions of misclassified documents, and explains a blocking measure that identifies…

  4. 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.

  5. 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…

  6. 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…

  7. 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…

  8. Hybrid Hierarchical Classifiers for Categorization of Medical Documents.

    ERIC Educational Resources Information Center

    Ruiz, Miguel E.; Stinivasan, Padmini

    2003-01-01

    Explores the use of linear models and a combination of neural networks and linear classifiers to create a hybrid hierarchical mixture of experts (HME) model. Results confirm that using the hierarchical structure of the classification vocabulary improves categorization performance. (AEF)

  9. 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.

  10. Identification of chronic rhinosinusitis phenotypes using cluster analysis

    PubMed Central

    Soler, Zachary M.; Hyer, J. Madison; Ramakrishnan, Viswanathan; Smith, Timothy L.; Mace, Jess; Rudmik, Luke; Schlosser, Rodney J.

    2015-01-01

    Introduction Current clinical classifications of chronic rhinosinusitis (CRS) have been largely defined based upon preconceived notions of factors thought to be important, such as polyp or eosinophil status. Unfortunately, these classification systems have little correlation with symptom severity or treatment outcomes. Unsupervised clustering can be used to identify phenotypic subgroups of CRS patients, describe clinical differences in these clusters and define simple algorithms for classification. Methods A multi-institutional, prospective study of 382 patients with CRS who had failed initial medical therapy completed the SinoNasal Outcome Test (SNOT-22), Rhinosinusitis Disability Index (RSDI), Short Form-12 (SF-12), Pittsburgh Sleep Quality Index (PSQI), and Patient Health Questionnaire (PHQ-2). Objective measures of CRS severity included Brief Smell Identification Test (B-SIT), CT and endoscopy scoring. All variables were reduced and unsupervised hierarchical clustering was performed. After clusters were defined, variations in medication usage were analyzed. Discriminant analysis was performed to develop a simplified, clinically useful algorithm for clustering. Results Clustering was largely determined by age, severity of patient reported outcome measures, depression and fibromyalgia. CT and endoscopy varied somewhat among clusters. Traditional clinical measures including polyp/atopic status, prior surgery, B-SIT and asthma did not vary among clusters. A simplified algorithm based upon productivity loss, SNOT-22 score and age predicted clustering with 89% accuracy. Medication usage among clusters did vary significantly. Discussion A simplified algorithm based upon hierarchical clustering is able to classify CRS patients and predict medication usage. Further studies are warranted to determine if such clustering predicts treatment outcomes. PMID:25694390

  11. Novel Approach for Clustering Zeolite Crystal Structures.

    PubMed

    Lach-Hab, M; Yang, S; Vaisman, I I; Blaisten-Barojas, E

    2010-04-12

    Informatics approaches play an increasingly important role in the design of new materials. In this work we apply unsupervised statistical learning for identifying four framework-type attractors of zeolite crystals in which several of the zeolite framework types are grouped together. Zeolites belonging to these super-classes manifest important topological, chemical and physical similarities. The zeolites form clusters located around four core framework types: LTA, FAU, MFI and the combination of EDI, HEU, LTL and LAU. Clustering is performed in a 9-dimensional space of attributes that reflect topological, chemical and physical properties for each individual zeolite crystalline structure. The implemented machine learning approach relies on hierarchical top-down clustering approach and the expectation maximization method. The model is trained and tested on ten partially independent data sets from the FIZ/NIST Inorganic Crystal Structure Database.

  12. 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.

  13. Nanotribological and wetting performance of hierarchical patterns.

    PubMed

    Grewal, H S; Piao, Shuxue; Cho, Il-Joo; Jhang, Kyung-Young; Yoon, Eui-Sung

    2016-01-21

    Surface modification is a promising method to solve the tribological problems in microsystems. To modify the surface, we fabricated hierarchical patterns with different pitches of nano-scale features and different surface chemistries. Micro- and nano-patterns with similar geometrical configurations were also fabricated for comparison. The nano-tribological behavior of the patterns was investigated using an atomic force microscope at different relative humidity levels (5% to 80%) and applied normal loads (40 nN to 120 nN) under a constant sliding velocity. The results showed significant enhancement in the de-wetting and tribological performance of the hierarchical patterns compared with those of flat and micro- and nano-patterned surfaces. The PTFE-coated hierarchical patterns showed similar dynamic contact angles (advancing and receding) to those of the real lotus leaf. The influence of relative humidity on adhesion and friction behavior was found to be significant for all the tested surfaces. The tribological performance was improved as the pitch of the nano-scale geometry of the hierarchical pattern increased, even though the wetting property was not influenced significantly. A model was proposed based on the role of intermolecular force to explain the effect of the pitch of the hierarchical patterns on the adhesion and friction behavior. According to the model based on the molecular force, the contact between a ball and the patterned surface was a multi-asperity contact, contrary to the single-asperity contact predicted by the Johnson-Kendall-Roberts (JKR) and Maugis-Dugdale (MD) models. The strong intermolecular forces, which are activated in the confined spaces between the adjacent nano-pillars and the ball, contributed to the contact area and hence the adhesion and friction forces. PMID:26549103

  14. 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

  15. 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.

  16. 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)

  17. 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.

  18. 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…

  19. 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.

  20. Clustering vertical ground reaction force curves produced during countermovement jumps.

    PubMed

    Richter, Chris; O'Connor, Noel E; Marshall, Brendan; Moran, Kieran

    2014-07-18

    The aim of this study is to assess and compare the performance of commonly used hierarchical, partitional (k-means) and Gaussian model-based (Expectation-Maximization algorithm) clustering techniques to appropriately identify subgroup patterns within vertical ground reaction force data, using a continuous waveform analysis. In addition, we also compared the performance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred and twenty two vertical jumps) were analyzed. The performance of each cluster technique was measured by assessing the ability to explain variances in jump height using a stepwise regression analysis. Only k-means (normalized scores; 82%) and hierarchical clustering (normalized scores; 85%) were able to extend the ability to describe variances in jump height beyond that achieved using the group analysis (i.e. one cluster; 78%). Further, our findings strongly indicate the need to normalize the input data (similarity measure) when clustering. In contrast to the group analysis, the subgroup analysis was able to identify cluster specific phases of variance, which improved the ability to explain variances in jump height, due to the identification of cluster specific predictor variables. Our findings therefore highlight the benefit of performing a subgroup analysis and may explain, at least in part, the contrasting findings between previous studies that used a single group level of analysis.

  1. The Effect of Mergers on Galaxy Cluster Mass Estimates

    NASA Astrophysics Data System (ADS)

    Johnson, Ryan E.; Zuhone, John A.; Thorsen, Tessa; Hinds, Andre

    2015-08-01

    At vertices within the filamentary structure that describes the universal matter distribution, clusters of galaxies grow hierarchically through merging with other clusters. As such, the most massive galaxy clusters should have experienced many such mergers in their histories. Though we cannot see them evolve over time, these mergers leave lasting, measurable effects in the cluster galaxies' phase space. By simulating several different galaxy cluster mergers here, we examine how the cluster galaxies kinematics are altered as a result of these mergers. Further, we also examine the effect of our line of sight viewing angle with respect to the merger axis. In projecting the 6-dimensional galaxy phase space onto a 3-dimensional plane, we are able to simulate how these clusters might actually appear to optical redshift surveys. We find that for those optical cluster statistics which are most often used as a proxy for the cluster mass (variants of σv), the uncertainty due to an inprecise or unknown line of sight may alter the derived cluster masses moreso than the kinematic disturbance of the merger itself. Finally, by examining these, and several other clustering statistics, we find that significant events (such as pericentric crossings) are identifiable over a range of merger initial conditions and from many different lines of sight.

  2. Cluster headache

    PubMed Central

    2010-01-01

    Introduction The revised International Headache Society (IHS) criteria for cluster headache are: attacks of severe or very severe, strictly unilateral pain, which is orbital, supraorbital, or temporal pain, lasting 15 to 180 minutes and occurring from once every other day to eight times daily. Methods and outcomes We conducted a systematic review and aimed to answer the following clinical questions: What are the effects of interventions to abort cluster headache? What are the effects of interventions to prevent cluster headache? We searched: Medline, Embase, The Cochrane Library, and other important databases up to June 2009 (Clinical Evidence reviews are updated periodically; please check our website for the most up-to-date version of this review). We included harms alerts from relevant organisations, such as the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA). Results We found 23 systematic reviews, RCTs, or observational studies that met our inclusion criteria. We performed a GRADE evaluation of the quality of evidence for interventions. Conclusions In this systematic review, we present information relating to the effectiveness and safety of the following interventions: baclofen (oral); botulinum toxin (intramuscular); capsaicin (intranasal); chlorpromazine; civamide (intranasal); clonidine (transdermal); corticosteroids; ergotamine and dihydroergotamine (oral or intranasal); gabapentin (oral); greater occipital nerve injections (betamethasone plus xylocaine); high-dose and high-flow-rate oxygen; hyperbaric oxygen; leuprolide; lidocaine (intranasal); lithium (oral); melatonin; methysergide (oral); octreotide (subcutaneous); pizotifen (oral); sodium valproate (oral); sumatriptan (oral, subcutaneous, and intranasal); topiramate (oral); tricyclic antidepressants (TCAs); verapamil; and zolmitriptan (oral and intranasal). PMID:21718584

  3. 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.

  4. Self-healing of hierarchical materials.

    PubMed

    Bosia, Federico; Abdalrahman, Tamer; Pugno, Nicola M

    2014-02-01

    We present a theoretical and numerical analysis of the mechanical behavior of self-healing materials using an analytical model and numerical calculations both based on a Hierarchical Fiber Bundle Model, and applying them to graphene- or carbon-nanotube-based materials. The self-healing process can be described essentially through a single parameter, that is, the healing rate, but numerical simulations also highlight the influence of the location of the healing process on the overall strengthening and toughening of the material. The role of hierarchy is discussed, showing that full-scale hierarchical structures can in fact acquire more favorable properties than smaller, nonhierarchical ones through interaction with the self-healing process, thus inverting the common notion in fracture mechanics that specimen strength increases with decreasing size. Further, the study demonstrates that the developed analytical and numerical tools can be useful to develop strategies for the optimization of strength and toughness of synthetic bioinspired materials. PMID:24364755

  5. Hierarchical structure of the logical Internet graph

    NASA Astrophysics Data System (ADS)

    Ge, Zihui; Figueiredo, Daniel R.; Jaiswal, Sharad; Gao, Lixin

    2001-07-01

    The study of the Internet topology has recently received much attention from the research community. In particular, the observation that the network graph has interesting properties, such as power laws, that might be explored in a myriad of ways. Most of the work in characterizing the Internet graph is based on the physical network graph, i.e., the connectivity graph. In this paper we investigate how logical relationships between nodes of the AS graph can be used to gain insight to its structure. We characterize the logical graph using various metrics and identify the presence of power laws in the number of customers that a provider has. Using these logical relationships we define a structural model of the AS graph. The model highlights the hierarchical nature of logical relationships and the preferential connection to larger providers. We also investigate the consistency of this model over time and observe interesting properties of the hierarchical structure.

  6. First-passage phenomena in hierarchical networks.

    PubMed

    Tavani, Flavia; Agliari, Elena

    2016-02-01

    In this paper we study Markov processes and related first-passage problems on a class of weighted, modular graphs which generalize the Dyson hierarchical model. In these networks, the coupling strength between two nodes depends on their distance and is modulated by a parameter σ. We find that, in the thermodynamic limit, ergodicity is lost and the "distant" nodes cannot be reached. Moreover, for finite-sized systems, there exists a threshold value for σ such that, when σ is relatively large, the inhomogeneity of the coupling pattern prevails and "distant" nodes are hardly reached. The same analysis is carried on also for generic hierarchical graphs, where interactions are meant to involve p-plets (p>2) of nodes, finding that ergodicity is still broken in the thermodynamic limit, but no threshold value for σ is evidenced, ultimately due to a slow growth of the network diameter with the size. PMID:26986314

  7. First-passage phenomena in hierarchical networks

    NASA Astrophysics Data System (ADS)

    Tavani, Flavia; Agliari, Elena

    2016-02-01

    In this paper we study Markov processes and related first-passage problems on a class of weighted, modular graphs which generalize the Dyson hierarchical model. In these networks, the coupling strength between two nodes depends on their distance and is modulated by a parameter σ . We find that, in the thermodynamic limit, ergodicity is lost and the "distant" nodes cannot be reached. Moreover, for finite-sized systems, there exists a threshold value for σ such that, when σ is relatively large, the inhomogeneity of the coupling pattern prevails and "distant" nodes are hardly reached. The same analysis is carried on also for generic hierarchical graphs, where interactions are meant to involve p -plets (p >2 ) of nodes, finding that ergodicity is still broken in the thermodynamic limit, but no threshold value for σ is evidenced, ultimately due to a slow growth of the network diameter with the size.

  8. 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

  9. Non-Homogeneous Fractal Hierarchical Weighted Networks

    PubMed Central

    Dong, Yujuan; Dai, Meifeng; Ye, Dandan

    2015-01-01

    A model of fractal hierarchical structures that share the property of non-homogeneous weighted networks is introduced. These networks can be completely and analytically characterized in terms of the involved parameters, i.e., the size of the original graph Nk and the non-homogeneous weight scaling factors r1, r2, · · · rM. We also study the average weighted shortest path (AWSP), the average degree and the average node strength, taking place on the non-homogeneous hierarchical weighted networks. Moreover the AWSP is scrupulously calculated. We show that the AWSP depends on the number of copies and the sum of all non-homogeneous weight scaling factors in the infinite network order limit. PMID:25849619

  10. Hierarchical curiosity loops and active sensing.

    PubMed

    Gordon, Goren; Ahissar, Ehud

    2012-08-01

    A curious agent acts so as to optimize its learning about itself and its environment, without external supervision. We present a model of hierarchical curiosity loops for such an autonomous active learning agent, whereby each loop selects the optimal action that maximizes the agent's learning of sensory-motor correlations. The model is based on rewarding the learner's prediction errors in an actor-critic reinforcement learning (RL) paradigm. Hierarchy is achieved by utilizing previously learned motor-sensory mapping, which enables the learning of other mappings, thus increasing the extent and diversity of knowledge and skills. We demonstrate the relevance of this architecture to active sensing using the well-studied vibrissae (whiskers) system, where rodents acquire sensory information by virtue of repeated whisker movements. We show that hierarchical curiosity loops starting from optimally learning the internal models of whisker motion and then extending to object localization result in free-air whisking and object palpation, respectively. PMID:22386787

  11. 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.

  12. Hierarchical networks, power laws, and neuronal avalanches

    NASA Astrophysics Data System (ADS)

    Friedman, Eric J.; Landsberg, Adam S.

    2013-03-01

    We show that in networks with a hierarchical architecture, critical dynamical behaviors can emerge even when the underlying dynamical processes are not critical. This finding provides explicit insight into current studies of the brain's neuronal network showing power-law avalanches in neural recordings, and provides a theoretical justification of recent numerical findings. Our analysis shows how the hierarchical organization of a network can itself lead to power-law distributions of avalanche sizes and durations, scaling laws between anomalous exponents, and universal functions—even in the absence of self-organized criticality or critical points. This hierarchy-induced phenomenon is independent of, though can potentially operate in conjunction with, standard dynamical mechanisms for generating power laws.

  13. 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.

  14. 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.

  15. 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

  16. Biomimetic silicification of demineralized hierarchical collagenous tissues.

    PubMed

    Niu, Li-na; Jiao, Kai; 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

    2013-05-13

    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 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.

  17. Non-homogeneous fractal hierarchical weighted networks.

    PubMed

    Dong, Yujuan; Dai, Meifeng; Ye, Dandan

    2015-01-01

    A model of fractal hierarchical structures that share the property of non-homogeneous weighted networks is introduced. These networks can be completely and analytically characterized in terms of the involved parameters, i.e., the size of the original graph Nk and the non-homogeneous weight scaling factors r1, r2, · · · rM. We also study the average weighted shortest path (AWSP), the average degree and the average node strength, taking place on the non-homogeneous hierarchical weighted networks. Moreover the AWSP is scrupulously calculated. We show that the AWSP depends on the number of copies and the sum of all non-homogeneous weight scaling factors in the infinite network order limit.

  18. Hierarchical optimization for neutron scattering problems

    DOE PAGES

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

    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.

  19. 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.

  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. Comparison of 2D fingerprint types and hierarchy level selection methods for structural grouping using Ward's clustering

    PubMed

    Wild; Blankley

    2000-01-01

    Four different two-dimensional fingerprint types (MACCS, Unity, BCI, and Daylight) and nine methods of selecting optimal cluster levels from the output of a hierarchical clustering algorithm were evaluated for their ability to select clusters that represent chemical series present in some typical examples of chemical compound data sets. The methods were evaluated using a Ward's clustering algorithm on subsets of the publicly available National Cancer Institute HIV data set, as well as with compounds from our corporate data set. We make a number of observations and recommendations about the choice of fingerprint type and cluster level selection methods for use in this type of clustering

  2. 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.

  3. Hierarchically Structured Materials for Lithium Batteries

    SciTech Connect

    Xiao, Jie; Zheng, Jianming; Li, Xiaolin; Shao, Yuyan; Zhang, Jiguang

    2013-09-25

    Lithium-ion battery (LIB) is one of the most promising power sources to be deployed in electric vehicles (EV), including solely battery powered vehicles, plug-in hybrid electric vehicles, and hybrid electrical vehicles. With the increasing demand on devices of high energy densities (>500 Wh/kg) , new energy storage systems, such as lithium-oxygen (Li-O2) batteries and other emerging systems beyond the conventional LIB also attracted worldwide interest for both transportation and grid energy storage applications in recent years. It is well known that the electrochemical performances of these energy storage systems depend not only on the composition of the materials, but also on the structure of electrode materials used in the batteries. Although the desired performances characteristics of batteries often have conflict requirements on the micro/nano-structure of electrodes, hierarchically designed electrodes can be tailored to satisfy these conflict requirements. This work will review hierarchically structured materials that have been successfully used in LIB and Li-O2 batteries. Our goal is to elucidate 1) how to realize the full potential of energy materials through the manipulation of morphologies, and 2) how the hierarchical structure benefits the charge transport, promotes the interfacial properties, prolongs the electrode stability and battery lifetime.

  4. Hierarchical patch dynamics and animal movement pattern.

    PubMed

    Fauchald, Per; Tveraa, Torkild

    2006-09-01

    In hierarchical patch systems, small-scale patches of high density are nested within large-scale patches of low density. The organization of multiple-scale hierarchical systems makes non-random strategies for dispersal and movement particularly important. Here, we apply a new method based on first-passage time on the pathway of a foraging seabird, the Antarctic petrel (Thalassoica antarctica), to quantify its foraging pattern and the spatial dynamics of its foraging areas. Our results suggest that Antarctic petrels used a nested search strategy to track a highly dynamic hierarchical patch system where small-scale patches were congregated within patches at larger scales. The birds searched for large-scale patches by traveling fast and over long distances. Once within a large-scale patch, the birds concentrated their search to find smaller scale patches. By comparing the pathway of different birds we were able to quantify the spatial scale and turnover of their foraging areas. On the largest scale we found foraging areas with a characteristic scale of about 400 km. Nested within these areas we found foraging areas with a characteristic scale of about 100 km. The large-scale areas disappeared or moved within a time frame of weeks while the nested small-scale areas disappeared or moved within days. Antarctic krill (Euphausia superba) is the dominant food item of Antarctic petrels and we suggest that our findings reflect the spatial dynamics of krill in the area. PMID:16794832

  5. Hierarchically structured materials for lithium batteries.

    PubMed

    Xiao, Jie; Zheng, Jianming; Li, Xiaolin; Shao, Yuyan; Zhang, Ji-Guang

    2013-10-25

    The lithium-ion battery (LIB) is one of the most promising power sources to be deployed in electric vehicles, including solely battery powered vehicles, plug-in hybrid electric vehicles, and hybrid electric vehicles. With the increasing demand for devices of high-energy densities (>500 Wh kg(-1)), new energy storage systems, such as lithium-oxygen (Li-O2) batteries and other emerging systems beyond the conventional LIB, have attracted worldwide interest for both transportation and grid energy storage applications in recent years. It is well known that the electrochemical performance of these energy storage systems depends not only on the composition of the materials, but also on the structure of the electrode materials used in the batteries. Although the desired performance characteristics of batteries often have conflicting requirements with the micro/nano-structure of electrodes, hierarchically designed electrodes can be tailored to satisfy these conflicting requirements. This work will review hierarchically structured materials that have been successfully used in LIB and Li-O2 batteries. Our goal is to elucidate (1) how to realize the full potential of energy materials through the manipulation of morphologies, and (2) how the hierarchical structure benefits the charge transport, promotes the interfacial properties and prolongs the electrode stability and battery lifetime.

  6. Hierarchical nature of the quantum Hall effects.

    PubMed

    Bonderson, Parsa

    2012-02-10

    I demonstrate that the wave function for a ν=n+ν[over ˜] quantum Hall state with Landau levels 0,1,…,n-1 filled and a filling fraction ν[over ˜] quantum Hall state with 0<ν[over ˜]≤1 in the nth Landau level can be obtained hierarchically from the ν=n state by introducing quasielectrons which are then projected into the (conjugate of the) ν[over ˜] state. In particular, the ν[over ˜]=1 case produces the filled Landau level wave functions hierarchically, thus establishing the hierarchical nature of the integer quantum Hall states. It follows that the composite fermion description of fractional quantum Hall states fits within the hierarchy theory of the fractional quantum Hall effect. I also demonstrate this directly by generating the composite fermion ground-state wave functions via application of the hierarchy construction to fractional quantum Hall states, starting from the ν=1/m Laughlin states.

  7. 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

  8. 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

  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. PMID:26330605

  10. Image Search Reranking With Hierarchical Topic Awareness.

    PubMed

    Tian, Xinmei; Yang, Linjun; Lu, Yijuan; Tian, Qi; Tao, Dacheng

    2015-10-01

    With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

  11. 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.

  12. 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)

  13. 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.

  14. A systematic comparison of genome-scale clustering algorithms

    PubMed Central

    2012-01-01

    Background A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each cluster's agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further

  15. Complex networks with scale-free nature and hierarchical modularity

    NASA Astrophysics Data System (ADS)

    Shekatkar, Snehal M.; Ambika, G.

    2015-09-01

    Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

  16. Hierarchical structure and physicochemical properties of plasticized chitosan.

    PubMed

    Meng, Qingkai; Heuzey, Marie-Claude; Carreau, Pierre J

    2014-04-14

    Plasticized chitosan with hierarchical structure, including multiple length scale structural units, was prepared by a "melt"-based method, that is, thermomechanical mixing, as opposed to the usual casting-evaporation procedure. Chitosan was successfully plasticized by thermomechanical mixing in the presence of concentrated lactic acid and glycerol using a batch mixer. Different plasticization formulations were compared in this study, in which concentrated lactic acid was used as protonation agent as well as plasticizer. The microstructure of thermomechanically plasticized chitosan was investigated by X-ray diffraction, scanning electron microscopy, and optical microscopy. With increasing amount of additional plasticizers (glycerol or water), the crystallinity of the plasticized chitosan decreased from 63.7% for the original chitosan powder to almost zero for the sample plasticized with additional water. Salt linkage between lactic acid molecules and amino side chains of chitosan was confirmed by FTIR spectroscopy: the lactic acid molecules expanded the space between the chitosan molecules of the crystalline phase. In the presence of other plasticizers (glycerol and water), various levels of structural units including an amorphous phase, nanofibrils, nanofibril clusters, and microfibers were produced under mechanical shear and thermal energy and identified for the first time. The thermal and thermomechanical properties of the plasticized chitosan were measured by thermogravimetric analysis, differential scanning calorimetric, and DMA. These properties were correlated with the different levels of microstructure, including multiple structural units. PMID:24564751

  17. 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.

  18. Cheetah: A Framework for Scalable Hierarchical Collective Operations

    SciTech Connect

    Graham, Richard L; Gorentla Venkata, Manjunath; Ladd, Joshua S; Shamis, Pavel; Rabinovitz, Ishai; Filipov, Vasily; Shainer, Gilad

    2011-01-01

    Collective communication operations, used by many scientific applications, tend to limit overall parallel application performance and scalability. Computer systems are becoming more heterogeneous with increasing node and core-per-node counts. Also, a growing number of data-access mechanisms, of varying characteristics, are supported within a single computer system. We describe a new hierarchical collective communication framework that takes advantage of hardware-specific data-access mechanisms. It is flexible, with run-time hierarchy specification, and sharing of collective communication primitives between collective algorithms. Data buffers are shared between levels in the hierarchy reducing collective communication management overhead. We have implemented several versions of the Message Passing Interface (MPI) collective operations, MPI Barrier() and MPI Bcast(), and run experiments using up to 49, 152 processes on a Cray XT5, and a small InfiniBand based cluster. At 49, 152 processes our barrier implementation outperforms the optimized native implementation by 75%. 32 Byte and one Mega-Byte broadcasts outperform it by 62% and 11%, respectively, with better scalability characteristics. Improvements relative to the default Open MPI implementation are much larger.

  19. 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. Fuzzy clustering of physicochemical and biochemical properties of amino acids.

    PubMed

    Saha, Indrajit; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra; Plewczynski, Dariusz

    2012-08-01

    In this article, we categorize presently available experimental and theoretical knowledge of various physicochemical and biochemical features of amino acids, as collected in the AAindex database of known 544 amino acid (AA) indices. Previously reported 402 indices were categorized into six groups using hierarchical clustering technique and 142 were left unclustered. However, due to the increasing diversity of the database these indices are overlapping, therefore crisp clustering method may not provide optimal results. Moreover, in various large-scale bioinformatics analyses of whole proteomes, the proper selection of amino acid indices representing their biological significance is crucial for efficient and error-prone encoding of the short functional sequence motifs. In most cases, researchers perform exhaustive manual selection of the most informative indices. These two facts motivated us to analyse the widely used AA indices. The main goal of this article is twofold. First, we present a novel method of partitioning the bioinformatics data using consensus fuzzy clustering, where the recently proposed fuzzy clustering techniques are exploited. Second, we prepare three high quality subsets of all available indices. Superiority of the consensus fuzzy clustering method is demonstrated quantitatively, visually and statistically by comparing it with the previously proposed hierarchical clustered results. The processed AAindex1 database, supplementary material and the software are available at http://sysbio.icm.edu.pl/aaindex/ .

  1. Statistical discovery of site inter-dependencies in sub-molecular hierarchical protein structuring

    PubMed Central

    2012-01-01

    Background Much progress has been made in understanding the 3D structure of proteins using methods such as NMR and X-ray crystallography. The resulting 3D structures are extremely informative, but do not always reveal which sites and residues within the structure are of special importance. Recently, there are indications that multiple-residue, sub-domain structural relationships within the larger 3D consensus structure of a protein can be inferred from the analysis of the multiple sequence alignment data of a protein family. These intra-dependent clusters of associated sites are used to indicate hierarchical inter-residue relationships within the 3D structure. To reveal the patterns of associations among individual amino acids or sub-domain components within the structure, we apply a k-modes attribute (aligned site) clustering algorithm to the ubiquitin and transthyretin families in order to discover associations among groups of sites within the multiple sequence alignment. We then observe what these associations imply within the 3D structure of these two protein families. Results The k-modes site clustering algorithm we developed maximizes the intra-group interdependencies based on a normalized mutual information measure. The clusters formed correspond to sub-structural components or binding and interface locations. Applying this data-directed method to the ubiquitin and transthyretin protein family multiple sequence alignments as a test bed, we located numerous interesting associations of interdependent sites. These clusters were then arranged into cluster tree diagrams which revealed four structural sub-domains within the single domain structure of ubiquitin and a single large sub-domain within transthyretin associated with the interface among transthyretin monomers. In addition, several clusters of mutually interdependent sites were discovered for each protein family, each of which appear to play an important role in the molecular structure and/or function

  2. 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

  3. 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).

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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

  9. 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.

  10. PHAT Star Clusters in M31: Insight on Environmental Dependence of Star & Cluster Formation

    NASA Astrophysics Data System (ADS)

    Johnson, Lent C.; Dalcanton, Julianne; Seth, Anil; Beerman, Lori; Lewis, Alexia; Fouesneau, Morgan; Weisz, Daniel R.; Andromeda Project Team, PHAT Team

    2015-01-01

    Theoretical studies of star cluster formation suggest that the star formation efficiency (SFE) of a cluster's progenitor cloud dictates whether or not a gravitationally bound grouping will emerge from an embedded region after gas expulsion. I measure the fraction of stars formed in long-lived clusters relative to unbound field stars on a spatial resolved basis in the Andromeda galaxy. These observations test theoretical predictions that star clusters are formed within a hierarchical interstellar medium at peaks in the gas density where local SFEs are enhanced and regions become stellar dominated. Using data from the Panchromatic Hubble Andromeda Treasury (PHAT) survey and ancillary observations of M31's gas phase, I investigate how cluster formation correlates with galactic environment and galaxy-scale properties of the star formation. We construct a sample of >2700 star clusters through a crowd-sourced visual search of the high spatial resolution HST imaging data. Our catalog uses ~2 million image classifications collected by the Andromeda Project citizen science website to provide an unparalleled census of clusters that spans ~4 orders of magnitude in mass (50% completeness at ~500 M⊙ at <100 Myr) and increases the number of known clusters within the PHAT survey footprint by a factor of ~6. Cluster ages and masses are obtained by fitting to color-magnitude diagrams (CMDs) of individually resolved stars within each cluster. Furthermore, we insure our ability to accurately interpret cluster age and mass distributions through careful catalog completeness characterization, made possible by thousands of synthetic cluster tests included during catalog construction work. We combine our high quality cluster sample with spatially resolved star formation histories, derived from CMD fitting of PHAT's photometry of ~117 million resolved field stars. We derived the fraction of stars formed in long-lived clusters and show that only a few percent of coeval stars are found in

  11. Structural analysis of hierarchically organized zeolites

    NASA Astrophysics Data System (ADS)

    Mitchell, Sharon; Pinar, Ana B.; Kenvin, Jeffrey; Crivelli, Paolo; Kärger, Jörg; Pérez-Ramírez, Javier

    2015-10-01

    Advances in materials synthesis bring about many opportunities for technological applications, but are often accompanied by unprecedented complexity. This is clearly illustrated by the case of hierarchically organized zeolite catalysts, a class of crystalline microporous solids that has been revolutionized by the engineering of multilevel pore architectures, which combine unique chemical functionality with efficient molecular transport. Three key attributes, the crystal, the pore and the active site structure, can be expected to dominate the design process. This review examines the adequacy of the palette of techniques applied to characterize these distinguishing features and their catalytic impact.

  12. 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.

  13. Hierarchical modelling of mobile, seeing robots

    NASA Technical Reports Server (NTRS)

    Luh, Cheng-Jye; Zeigler, Bernard P.

    1990-01-01

    This paper describes the implementation of a hierarchical robot simulation which supports the design of robots with vision and mobility. A seeing robot applies a classification expert system for visual identification of laboratory objects. The visual data acquisition algorithm used by the robot vision system has been developed to exploit multiple viewing distances and perspectives. Several different simulations have been run testing the visual logic in a laboratory environment. Much work remains to integrate the vision system with the rest of the robot system.

  14. Scalable hierarchical video summary and search

    NASA Astrophysics Data System (ADS)

    Sull, Sanghoon; Kim, Jung-Rim; Kim, Yunam; Chang, Hyun S.; Lee, Sang U.

    2000-12-01

    Recently, a huge amount of the video data available in the digital form has given users to allow more ubiquitous access to visual information than ever. To efficiently manage such huge amount of video data, we need such tools as video summarization and search. In this paper, we propose a novel scheme allowing for both scalable hierarchical video summary and efficient retrieval by introducing a notion of fidelity. The notion of fidelity in the tree-structured key frame hierarchy describes how well the key frames at one level are represented by the parent key frame, relative to the other children of the parent. The experimental results demonstrate the feasibility of our scheme.

  15. Scalable hierarchical video summary and search

    NASA Astrophysics Data System (ADS)

    Sull, Sanghoon; Kim, Jung-Rim; Kim, Yunam; Chang, Hyun S.; Lee, Sang U.

    2001-01-01

    Recently, a huge amount of the video data available in the digital form has given users to allow more ubiquitous access to visual information than ever. To efficiently manage such huge amount of video data, we need such tools as video summarization and search. In this paper, we propose a novel scheme allowing for both scalable hierarchical video summary and efficient retrieval by introducing a notion of fidelity. The notion of fidelity in the tree-structured key frame hierarchy describes how well the key frames at one level are represented by the parent key frame, relative to the other children of the parent. The experimental results demonstrate the feasibility of our scheme.

  16. 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

  17. 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.

  18. Universality: Accurate Checks in Dyson's Hierarchical Model

    NASA Astrophysics Data System (ADS)

    Godina, J. J.; Meurice, Y.; Oktay, M. B.

    2003-06-01

    In this talk we present high-accuracy calculations of the susceptibility near βc for Dyson's hierarchical model in D = 3. Using linear fitting, we estimate the leading (γ) and subleading (Δ) exponents. Independent estimates are obtained by calculating the first two eigenvalues of the linearized renormalization group transformation. We found γ = 1.29914073 ± 10 -8 and, Δ = 0.4259469 ± 10-7 independently of the choice of local integration measure (Ising or Landau-Ginzburg). After a suitable rescaling, the approximate fixed points for a large class of local measure coincide accurately with a fixed point constructed by Koch and Wittwer.

  19. 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

  20. Evaluating δ(15)N-body size relationships across taxonomic levels using hierarchical models.

    PubMed

    Reum, Jonathan C P; Marshall, Kristin N

    2013-12-01

    Ecologists routinely set out to estimate the trophic position of individuals, populations, and species composing food webs, and nitrogen stable isotopes (δ(15)N) are a widely used proxy for trophic position. Although δ(15)N values are often sampled at the level of individuals, estimates and confidence intervals are frequently sought for aggregations of individuals. If individual δ(15)N values are correlated as an artifact of sampling design (e.g., clustering of samples in space or time) or due to intrinsic groupings (e.g., life history stages, social groups, taxonomy), such estimates may be biased and exhibit overly optimistic confidence intervals. However, these issues can be accommodated using hierarchical modeling methods. Here, we demonstrate how hierarchical models offer an additional quantitative tool for investigating δ(15)N variability and we explicitly evaluate how δ(15)N varies with body size at successively higher levels of taxonomic aggregation in a diverse fish assemblage. The models take advantage of all available data, better account for uncertainty in parameters estimates, may improve inferences on coefficients corresponding to groups with small to moderate sample sizes, and partition variation across model levels, which provides convenient summaries of the 'importance' of each level in terms of unexplained heterogeneity in the data. These methods can easily be applied to diet-based studies of trophic position. Although hierarchical models are well-understood and established tools, their benefits have yet to be fully reaped by stable isotope and food web ecologists. We suggest that hierarchical models can provide a robust framework for conceptualizing and statistically modeling trophic position at multiple levels of aggregation. PMID:23812110

  1. Is a hierarchical dynamics the best route to the self-assembly of a hierarchical structure?

    NASA Astrophysics Data System (ADS)

    Haxton, Thomas; Whitelam, Stephen

    2013-03-01

    Mimicking nature's ability to assemble functional hierarchical materials will require understanding how to promote the self-assembly of structure on multiple lengthscales while avoiding kinetic traps. We use computer simulation to study the self-assembly of a simple hierarchical structure, a square lattice whose repeat unit is a tetramer. Although the target material is organized hierarchically, it self-assembles most reliably when its assembly pathway consists of the sequential addition of monomers to a single structure. Hierarchical assembly pathways via dimer and tetramer intermediates result in lower yield, because these intermediates tend to associate in ways incompatible with the target structure. In addition, assembly via tetramers results in the formation of incomplete building blocks (trimers) that cannot combine to form the target crystal. We use analytic theory to relate assembly pathways to the underlying thermodynamics, identifying two principles for optimal assembly: 1) make the free energy gap between the target phase and the most stable fluid phase comparable to the thermal energy, and 2) ensure that no other dense phases (liquids or close-packed solids of monomers or oligomers) or fluids of incomplete building blocks fall within this gap.

  2. Fractal image perception provides novel insights into hierarchical cognition.

    PubMed

    Martins, M J; Fischmeister, F P; Puig-Waldmüller, E; Oh, J; Geissler, A; Robinson, S; Fitch, W T; Beisteiner, R

    2014-08-01

    Hierarchical structures play a central role in many aspects of human cognition, prominently including both language and music. In this study we addressed hierarchy in the visual domain, using a novel paradigm based on fractal images. Fractals are self-similar patterns generated by repeating the same simple rule at multiple hierarchical levels. Our hypothesis was that the brain uses different resources for processing hierarchies depending on whether it applies a "fractal" or a "non-fractal" cognitive strategy. We analyzed the neural circuits activated by these complex hierarchical patterns in an event-related fMRI study of 40 healthy subjects. Brain activation was compared across three different tasks: a similarity task, and two hierarchical tasks in which subjects were asked to recognize the repetition of a rule operating transformations either within an existing hierarchical level, or generating new hierarchical levels. Similar hierarchical images were generated by both rules and target images were identical. We found that when processing visual hierarchies, engagement in both hierarchical tasks activated the visual dorsal stream (occipito-parietal cortex, intraparietal sulcus and dorsolateral prefrontal cortex). In addition, the level-generating task specifically activated circuits related to the integration of spatial and categorical information, and with the integration of items in contexts (posterior cingulate cortex, retrosplenial cortex, and medial, ventral and anterior regions of temporal cortex). These findings provide interesting new clues about the cognitive mechanisms involved in the generation of new hierarchical levels as required for fractals.

  3. Hierarchical modeling of genome-wide Short Tandem Repeat (STR) markers infers native American prehistory.

    PubMed

    Lewis, Cecil M

    2010-02-01

    This study examines a genome-wide dataset of 678 Short Tandem Repeat loci characterized in 444 individuals representing 29 Native American populations as well as the Tundra Netsi and Yakut populations from Siberia. Using these data, the study tests four current hypotheses regarding the hierarchical distribution of neutral genetic variation in native South American populations: (1) the western region of South America harbors more variation than the eastern region of South America, (2) Central American and western South American populations cluster exclusively, (3) populations speaking the Chibchan-Paezan and Equatorial-Tucanoan language stock emerge as a group within an otherwise South American clade, (4) Chibchan-Paezan populations in Central America emerge together at the tips of the Chibchan-Paezan cluster. This study finds that hierarchical models with the best fit place Central American populations, and populations speaking the Chibchan-Paezan language stock, at a basal position or separated from the South American group, which is more consistent with a serial founder effect into South America than that previously described. Western (Andean) South America is found to harbor similar levels of variation as eastern (Equatorial-Tucanoan and Ge-Pano-Carib) South America, which is inconsistent with an initial west coast migration into South America. Moreover, in all relevant models, the estimates of genetic diversity within geographic regions suggest a major bottleneck or founder effect occurring within the North American subcontinent, before the peopling of Central and South America.

  4. 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.

  5. 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.

  6. 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.

  7. A hierarchical exact accelerated stochastic simulation algorithm

    PubMed Central

    Orendorff, David; Mjolsness, Eric

    2012-01-01

    A new algorithm, “HiER-leap” (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled “blocks” and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms. PMID:23231214

  8. Oracle Database DBFS Hierarchical Storage Overview

    SciTech Connect

    Rivenes, A

    2011-07-25

    The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory creates large numbers of images during each shot cycle for the analysis of optics, target inspection and target diagnostics. These images must be readily accessible once they are created and available for the 30 year lifetime of the facility. The Livermore Computing Center (LC) runs a High Performance Storage System (HPSS) that is capable of storing NIF's estimated 1 petabyte of diagnostic images at a fraction of what it would cost NIF to operate its own automated tape library. With Oracle 11g Release 2 database, it is now possible to create an application transparent, hierarchical storage system using the LC's HPSS. Using the Oracle DBMS-LOB and DBMS-DBFS-HS packages a SecureFile LOB can now be archived to storage outside of the database and accessed seamlessly through a DBFS 'link'. NIF has chosen to use this technology to implement a hierarchical store for its image based SecureFile LOBs. Using a modified external store and DBFS links, files are written to and read from a disk 'staging area' using Oracle's backup utility. Database external procedure calls invoke OS based scripts to manage a staging area and the transfer of the backup files between the staging area and the Lab's HPSS.

  9. 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.

  10. 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.

  11. CP violation in hierarchical Yukawa models

    SciTech Connect

    Peccei, R. D.

    2011-04-01

    Because 4-dimensional CP is a good symmetry of many higher-dimensional theories, this suggests the possible existence of an universal CP-violating phase originating from the process of compactification. Such a phase, if it existed, would not be easy to uncover since the phases in Yukawa matrices are not simply related to the observed Cabibbo-Kobayashi-Maskawa phase {delta}. Hierarchical Yukawa models, of the type arising in F-theory GUT models provide an interesting exception. Recently, Heckman and Vafa studied a particular F-theory GUT model with hierarchical Yukawa matrices with complex phases of O(1) and showed, by examining the Jarlskog invariant, that this model leads to sin{delta}{approx}O(1). A more detailed examination of the model, although confirming their results, is seen to be also compatible with having a phase {delta}{sub o}={pi}/3 imprinted on the Hermitian Yukawa matrices, leading to sin{delta}{approx_equal}sin{delta}{sub o}.

  12. Standardization of a Hierarchical Transactive Control System

    SciTech Connect

    Hammerstrom, Donald J.; Oliver, Terry V.; Melton, Ronald B.; Ambrosio, Ron

    2010-12-03

    The authors describe work they have conducted toward the generalization and standardization of the transactive control approach that was first demonstrated in the Olympic Peninsula Project for the management of a transmission constraint. The newly generalized approach addresses several potential shortfalls of the prior approach: First, the authors have formalized a hierarchical node structure which defines the nodes and the functional signal pathways between these nodes. Second, by fully generalizing the inputs, outputs, and functional responsibilities of each node, the authors make the approach available to a much wider set of responsive assets and operational objectives. Third, the new, generalized approach defines transactive signals that include the predicted day-ahead future. This predictive feature allows the market-like bids and offers to become resolved iteratively over time, thus allowing the behaviors of responsive assets to be called upon both for the present and as future dispatch decisions are being made. The hierarchical transactive control approach is a key feature of a proposed Pacific Northwest smart grid demonstration.

  13. Hierarchical majorana neutrinos from democratic mass matrices

    NASA Astrophysics Data System (ADS)

    Yang, Masaki J. S.

    2016-09-01

    In this paper, we obtain the light neutrino masses and mixings consistent with the experiments, in the democratic texture approach. The essential ansatz is that νRi are assumed to transform as "right-handed fields" 2R +1R under the S3L ×S3R symmetry. The symmetry breaking terms are assumed to be diagonal and hierarchical. This setup only allows the normal hierarchy of the neutrino mass, and excludes both of inverted hierarchical and degenerated neutrinos. Although the neutrino sector has nine free parameters, several predictions are obtained at the leading order. When we neglect the smallest parameters ζν and ζR, all components of the mixing matrix UPMNS are expressed by the masses of light neutrinos and charged leptons. From the consistency between predicted and observed UPMNS, we obtain the lightest neutrino masses m1 = (1.1 → 1.4) meV, and the effective mass for the double beta decay ≃ 4.5 meV.

  14. 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

  15. Geophysical Inversion through Hierarchical Genetic Algorithm Scheme

    NASA Astrophysics Data System (ADS)

    Furman, Alex; Huisman, Johan A.

    2010-05-01

    Geophysical investigation is a powerful tool that allows non-invasive and non-destructive mapping of subsurface states and properties. However, non-uniqueness associated with the inversion process halts these methods from becoming of more quantitative use. One major direction researchers are going is constraining the inverse problem by hydrological observations and models. An alternative to the commonly used direct inversion methods are global optimization schemes (such as genetic algorithms and Monte Carlo Markov Chain methods). However, the major limitation here is the desired high resolution of the tomographic image, which leads to a large number of parameters and an unreasonably high computational effort when using global optimization schemes. One way to overcome these problems is to combine the advantages of both direct and global inversion methods through hierarchical inversion. That is, starting the inversion with relatively coarse resolution of parameters, achieving good inversion using one of the two inversion schemes (global or direct), and then refining the resolution and applying a combination of global and direct inversion schemes for the whole domain or locally. In this work we explore through synthetic case studies the option of using a global optimization scheme for inversion of electrical resistivity tomography data through hierarchical refinement of the model resolution.

  16. A Hierarchical Bayesian Model for Crowd Emotions.

    PubMed

    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

  17. 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.

  18. A Hierarchical Bayesian Model for Crowd Emotions.

    PubMed

    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.

  19. 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.

  20. 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

  1. Nuclear quantum effects on the stability of cationic neon clusters

    NASA Astrophysics Data System (ADS)

    Calvo, F.; Naumkin, F. Y.; Wales, D. J.

    2012-11-01

    The stable structures of cationic neon clusters containing up to 57 atoms have been located using a diatomic-in-molecules potential energy surface and basin-hopping hierarchical optimization. The effects of vibrational delocalization were included either in the harmonic approximation, or by performing Langevin molecular dynamics simulations coupled to a quantum thermal bath at T=0. For most clusters, zero-point motion is sufficiently high to blur the picture of a single well-defined structure. However, structural diversity of the ground state wavefunction is found to be lower at sizes 14, 21, and 56, which correspond to special stabilities in experimental mass spectra.

  2. 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.

  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. 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.

  5. Topological cluster analysis reveals the systemic organization of the Caenorhabditis elegans connectome.

    PubMed

    Sohn, Yunkyu; Choi, Myung-Kyu; Ahn, Yong-Yeol; Lee, Junho; Jeong, Jaeseung

    2011-05-01

    The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors. PMID:21625578

  6. 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.

  7. 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

  8. Hierarchical population structure and habitat differences in a highly mobile marine species: the Atlantic spotted dolphin.

    PubMed

    Viricel, Amélia; Rosel, Patricia E

    2014-10-01

    Recent molecular studies have shown that highly mobile species with continuous distributions can exhibit fine-scale population structure. In this context, we assessed genetic structure within a marine species with high dispersal potential, the Atlantic spotted dolphin (Stenella frontalis). Using 19 microsatellite loci and mitochondrial control region sequences, population structure was investigated in the western North Atlantic, the Gulf of Mexico and the Azores Islands. Analyses of the microsatellite data identified four distinct genetic clusters, which were supported by the control region sequences. The highest level of divergence was seen between two clusters corresponding to previously described morphotypes that inhabit oceanic and shelf waters. The combined morphological and genetic evidence suggests these two lineages are on distinct evolutionary trajectories and could be considered distinct subspecies despite their parapatry. Further analysis of the continental shelf cluster resulted in three groups: animals inhabiting shelf waters in the western North Atlantic, the eastern Gulf of Mexico and the western Gulf of Mexico. Analyses of environmental data indicate the four genetic clusters inhabit distinct habitats in terms of depth and sea surface temperature. Contemporary dispersal rate estimates suggest all of these populations should be considered as distinct management units. Conversely, no significant genetic differentiation was observed between S. frontalis from offshore waters of the western North Atlantic and the Azores, which are separated by approximately 4500 km. Overall, the hierarchical structure observed within the Atlantic spotted dolphin shows that the biogeography of the species is complex because it is not shaped solely by geographic distance. PMID:25256360

  9. Hierarchical population structure and habitat differences in a highly mobile marine species: the Atlantic spotted dolphin.

    PubMed

    Viricel, Amélia; Rosel, Patricia E

    2014-10-01

    Recent molecular studies have shown that highly mobile species with continuous distributions can exhibit fine-scale population structure. In this context, we assessed genetic structure within a marine species with high dispersal potential, the Atlantic spotted dolphin (Stenella frontalis). Using 19 microsatellite loci and mitochondrial control region sequences, population structure was investigated in the western North Atlantic, the Gulf of Mexico and the Azores Islands. Analyses of the microsatellite data identified four distinct genetic clusters, which were supported by the control region sequences. The highest level of divergence was seen between two clusters corresponding to previously described morphotypes that inhabit oceanic and shelf waters. The combined morphological and genetic evidence suggests these two lineages are on distinct evolutionary trajectories and could be considered distinct subspecies despite their parapatry. Further analysis of the continental shelf cluster resulted in three groups: animals inhabiting shelf waters in the western North Atlantic, the eastern Gulf of Mexico and the western Gulf of Mexico. Analyses of environmental data indicate the four genetic clusters inhabit distinct habitats in terms of depth and sea surface temperature. Contemporary dispersal rate estimates suggest all of these populations should be considered as distinct management units. Conversely, no significant genetic differentiation was observed between S. frontalis from offshore waters of the western North Atlantic and the Azores, which are separated by approximately 4500 km. Overall, the hierarchical structure observed within the Atlantic spotted dolphin shows that the biogeography of the species is complex because it is not shaped solely by geographic distance.

  10. A CLUSTER IN THE MAKING: ALMA REVEALS THE INITIAL CONDITIONS FOR HIGH-MASS CLUSTER FORMATION

    SciTech Connect

    Rathborne, J. M.; Contreras, Y.; Longmore, S. N.; Bastian, N.; Jackson, J. M.; Alves, J. F.; Bally, J.; Foster, J. B.; Garay, G.; Kruijssen, J. M. D.; Testi, L.; Walsh, A. J.

    2015-04-01

    G0.253+0.016 is a molecular clump that appears to be on the verge of forming a high-mass cluster: its extremely low dust temperature, high mass, and high density, combined with its lack of prevalent star formation, make it an excellent candidate for an Arches-like cluster in a very early stage of formation. Here we present new Atacama Large Millimeter/Sub-millimeter Array observations of its small-scale (∼0.07 pc) 3 mm dust continuum and molecular line emission from 17 different species that probe a range of distinct physical and chemical conditions. The data reveal a complex network of emission features with a complicated velocity structure: there is emission on all spatial scales, the morphology of which ranges from small, compact regions to extended, filamentary structures that are seen in both emission and absorption. The dust column density is well traced by molecules with higher excitation energies and critical densities, consistent with a clump that has a denser interior. A statistical analysis supports the idea that turbulence shapes the observed gas structure within G0.253+0.016. We find a clear break in the turbulent power spectrum derived from the optically thin dust continuum emission at a spatial scale of ∼0.1 pc, which may correspond to the spatial scale at which gravity has overcome the thermal pressure. We suggest that G0.253+0.016 is on the verge of forming a cluster from hierarchical, filamentary structures that arise from a highly turbulent medium. Although the stellar distribution within high-mass Arches-like clusters is compact, centrally condensed, and smooth, the observed gas distribution within G0.253+0.016 is extended, with no high-mass central concentration, and has a complex, hierarchical structure. If this clump gives rise to a high-mass cluster and its stars are formed from this initially hierarchical gas structure, then the resulting cluster must evolve into a centrally condensed structure via a dynamical process.

  11. Acoustic performance of reiterated hierarchical honeycomb structures

    NASA Astrophysics Data System (ADS)

    Nainar, Naveen

    Sandwich panels constructed from honeycomb structures have been found to reduce sound transmission and improve vibration isolation. In this work, reiterated hierarchical honeycomb structures have been modeled for the core in sandwich panels and studied for sound transmission properties using finite element analysis. Several honeycomb unit cell geometries are considered, including, regular hexagonal, auxetic with properties of negative Poisson's ratio, and different reiterated hierarchical structures. Previous studies have shown that auxetic honeycomb structures exhibit improved sound transmission loss compared to regular honeycomb sandwich panels. Two different orientations of the honeycomb unit cell geometry have been studied, namely, the zigzag and armchair configurations, which are, rotated 90 degrees. Both regular and auxetic honeycombs have been used in both these configurations. The finite element model of the panels are used to extract natural frequencies and mode shapes and to perform steady state frequency response dynamic analysis up to 1000 Hz. The transmitted sound pressure levels on the surface of each structure is extracted and compared to study the influence of the reiterated hierarchy on sound transmission characteristics. The influence of corner reinforcement constructed by subtracting interior high-level hierarchical structure except at the vertices of the underlying lower-level honeycomb unit cell was also studied. Furthermore, a study was conducted to quantify the effect of changing the ratio of cell-wall thickness between various levels of hierarchy. Special focus on the limiting case of level-1 hierarchy with zero level-0 thickness is also studied. In all cases, the total mass was kept constant in order to isolate only stiffness and mass distribution effects. The results show that introduction of reiterated hierarchy in level-1 structures reduced the sound transmission of honeycomb sandwich panels compared to parent level-0 geometry. Results

  12. Hierarchical Strategy for Rapid Analysis Environment

    NASA Technical Reports Server (NTRS)

    Whitcomb, John

    2003-01-01

    A new philosophy is developed wherein the hierarchical definition of data is made use of in creating a better environment to conduct analyses of practical problems. This system can be adapted to conduct virtually any type of analysis, since this philosophy is not bound to any specific kind of analysis. It provides a framework to manage different models and its results and more importantly, the interaction between the different models. Thus, it is ideal for many types of finite element analyses like globalAoca1 analysis and those that involve multiple scales and fields. The system developed during the course of this work is just a demonstrator of the basic concepts. A complete implementation of this strategy could potentially make a major impact on the way analyses are conducted. It could considerably reduce the time frame required to conduct the analysis of real-life problems by efficient management of the data involved and reducing the human effort involved. It also helps in better decision making because of more ways to interpret the results. The strategy has been currently implemented for structural analysis, but with more work it could be extended to other fields of science when the finite element method is used to solve the differential equations numerically. This report details the work that has been done during the course of this project and its achievements and results. The following section discusses the meaning of the word hierarchical and the different references to the term in the literature. It talks about the development of the finite element method, its different versions and how hierarchy has been used to improve the methodology. The next section describes the hierarchical philosophy in detail and explains the different concepts and terms associated with it. It goes on to describe the implementation and the features of the demonstrator. A couple of problems are analyzed using the demonstrator program to show the working of the system. The two

  13. Concepts and Misconceptions in Comprehension of Hierarchical Graphs

    ERIC Educational Resources Information Center

    Korner, Christof

    2005-01-01

    Hierarchical graphs represent relationships between objects (like computer file systems, family trees etc.). Graph nodes represent the objects and interconnecting lines represent the relationships. In two experiments we investigated what concepts are necessary for understanding hierarchical graphs, what misconceptions evolve when some of the…

  14. Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences

    ERIC Educational Resources Information Center

    Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

    2015-01-01

    This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…

  15. Hierarchical Data Structures, Institutional Research, and Multilevel Modeling

    ERIC Educational Resources Information Center

    O'Connell, Ann A.; Reed, Sandra J.

    2012-01-01

    Multilevel modeling (MLM), also referred to as hierarchical linear modeling (HLM) or mixed models, provides a powerful analytical framework through which to study colleges and universities and their impact on students. Due to the natural hierarchical structure of data obtained from students or faculty in colleges and universities, MLM offers many…

  16. English Pyramids: Using Hierarchical Diagrams for Communication Activities.

    ERIC Educational Resources Information Center

    Johnson, Tia; Sheetz-Brunetti, Judy

    The pyramid, or hierarchical diagram, is used in teaching writing English as a second language (ESL) as a visual representation of the way English speakers and writers organize ideas, for comparison with discourse organization in other cultures. A common problem of ESL students is an inability to organize ideas hierarchically. One class activity…

  17. Higher-Order Item Response Models for Hierarchical Latent Traits

    ERIC Educational Resources Information Center

    Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming

    2013-01-01

    Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…

  18. Using Hierarchical Folders and Tags for File Management

    ERIC Educational Resources Information Center

    Ma, Shanshan

    2010-01-01

    Hierarchical folders have been widely used for managing digital files. A well constructed hierarchical structure can keep files organized. A parent folder can have several subfolders and one subfolder can only reside in one parent folder. Files are stored in folders or subfolders. Files can be found by traversing a given path, going through…

  19. Signaling Hierarchical and Sequential Organization in Expository Text

    ERIC Educational Resources Information Center

    Lorch, Robert; Lemarie, Julie; Grant, Russell

    2011-01-01

    Four experiments tested a hypothesized function of signaling devices, namely, to communicate information about text organization. Experiments 1 and 2 compared headings that communicated the hierarchical organization of text topics with headings that did not communicate the hierarchical organization. Signaling organization led to more complete and…

  20. Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

    ERIC Educational Resources Information Center

    Klauer, Karl Christoph

    2010-01-01

    Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…

  1. Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia

    PubMed Central

    Bassett, Danielle S.; Bullmore, Edward; Verchinski, Beth A.; Mattay, Venkata S.; Weinberger, Daniel R.; Meyer-Lindenberg, Andreas

    2009-01-01

    The complex organization of connectivity in the human brain is incompletely understood. Recently, topological measures based on graph theory have provided a new approach to quantify large-scale cortical networks. These methods have been applied to anatomical connectivity data on non-human species and cortical networks have been shown to have small-world topology, associated with high local and global efficiency of information transfer. Anatomical networks derived from cortical thickness measurements have shown the same organizational properties of the healthy human brain, consistent with similar results reported in functional networks derived from resting state functional MRI and MEG data. Here we show, using anatomical networks derived from analysis of inter-regional covariation of gray matter volume in magnetic resonance imaging (MRI) data on 259 healthy volunteers, that classical divisions of cortex (multimodal, unimodal and transmodal) have some distinct topological attributes. While all cortical divisions shared non-random properties of small-worldness and efficient wiring (short mean Euclidean distance between connected regions), the multimodal network had a hierarchical organization, dominated by frontal hubs with low clustering, whereas the transmodal network was assortative. Moreover, in a sample of 203 people with schizophrenia, multimodal network organization was abnormal, as indicated by reduced hierarchy, the loss of frontal and the emergence of non-frontal hubs, and increased connection distance. We propose that the topological differences between divisions of normal cortex may represent the outcome of different growth processes for multimodal and transmodal networks; and that neurodevelopmental abnormalities in schizophrenia specifically impact multimodal cortical organization. PMID:18784304

  2. Improved Adhesion and Compliancy of Hierarchical Fibrillar Adhesives.

    PubMed

    Li, Yasong; Gates, Byron D; Menon, Carlo

    2015-08-01

    The gecko relies on van der Waals forces to cling onto surfaces with a variety of topography and composition. The hierarchical fibrillar structures on their climbing feet, ranging from mesoscale to nanoscale, are hypothesized to be key elements for the animal to conquer both smooth and rough surfaces. An epoxy-based artificial hierarchical fibrillar adhesive was prepared to study the influence of the hierarchical structures on the properties of a dry adhesive. The presented experiments highlight the advantages of a hierarchical structure despite a reduction of overall density and aspect ratio of nanofibrils. In contrast to an adhesive containing only nanometer-size fibrils, the hierarchical fibrillar adhesives exhibited a higher adhesion force and better compliancy when tested on an identical substrate.

  3. Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

    PubMed Central

    Grillet, Yves; Richard, Philippe; Stach, Bruno; Vivodtzev, Isabelle; Timsit, Jean-Francois; Lévy, Patrick; Tamisier, Renaud; Pépin, Jean-Louis

    2016-01-01

    Background The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. Methods An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. Conclusions Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies. PMID:27314230

  4. Bioinspired design of a hierarchically structured adhesive.

    PubMed

    Arul, Edward Peter; Ghatak, Animangsu

    2009-01-01

    The mechanism by which many creatures such as geckos can run at ease on a vertical wall and yet remain strongly adhered has been linked to hierarchically patterned microstructures: flexible pads, hairs, and subsurface fluidic vessels at their feet. Despite many advances, how these features of different length scales and the associated physical phenomena couple to engender this "smart" adhesive is yet to be understood and mimicked. In this context, we have designed elastomeric films of poly(dimethylsiloxane) embedded with stacks of planar microchannels, curved and straight, and channels with microscopically patterned walls. We have altered also chemically the adhesive surface including that of the microchannel walls by creating dangling chains. During indentation experiments, deformation and self-adhesion of these structures enhance the effective area of adhesion with a consequent increase in adhesion hysteresis over orders of magnitude. In addition, suitable orientation of these buried channels allows the generation of load dependent hysteresis and its spatial modulation. PMID:19063623

  5. Hierarchical Bayesian models of cognitive development.

    PubMed

    Glassen, Thomas; Nitsch, Verena

    2016-06-01

    This article provides an introductory overview of the state of research on Hierarchical Bayesian Modeling in cognitive development. First, a brief historical summary and a definition of hierarchies in Bayesian modeling are given. Subsequently, some model structures are described based on four examples in the literature. These are models for the development of the shape bias, for learning ontological kinds and causal schemata as well as for the categorization of objects. The Bayesian modeling approach is then compared with the connectionist and nativist modeling paradigms and considered in view of Marr's (1982) three description levels of information-processing mechanisms. In this context, psychologically plausible algorithms and ideas of their neural implementation are presented. In addition to criticism and limitations of the approach, research needs are identified. PMID:27222110

  6. Hierarchical decomposition model for reconfigurable architecture

    NASA Astrophysics Data System (ADS)

    Erdogan, Simsek; Wahab, Abdul

    1996-10-01

    This paper introduces a systematic approach for abstract modeling of VLSI digital systems using a hierarchical decomposition process and HDL. In particular, the modeling of the back propagation neural network on a massively parallel reconfigurable hardware is used to illustrate the design process rather than toy examples. Based on the design specification of the algorithm, a functional model is developed through successive refinement and decomposition for execution on the reconfiguration machine. First, a top- level block diagram of the system is derived. Then, a schematic sheet of the corresponding structural model is developed to show the interconnections of the main functional building blocks. Next, the functional blocks are decomposed iteratively as required. Finally, the blocks are modeled using HDL and verified against the block specifications.

  7. A continuum model for hierarchical fibril assembly

    NASA Astrophysics Data System (ADS)

    van Lith, B. S.; Muntean, A.; Storm, C.

    2014-06-01

    Most of the biological polymers that make up our cells and tissues are hierarchically structured. For biopolymers ranging from collagen, to actin, to fibrin and amyloid fibrils this hierarchy provides vitally important versatility. The structural hierarchy must be encoded in the self-assembly process, from the earliest stages onward, in order to produce the appropriate substructures. In this letter, we explore the kinetics of multistage self-assembly processes in a model system which allows comparison to bulk probes such as light scattering. We apply our model to recent turbidimetry data on the self-assembly of collagen fibrils. Our analysis suggests a connection between diffusion-limited aggregation kinetics and fibril growth, supported by slow, power-law growth at very long time scales.

  8. Hierarchical image segmentation for learning object priors

    SciTech Connect

    Prasad, Lakshman; Yang, Xingwei; Latecki, Longin J; Li, Nan

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  9. Image Segmentation Using Hierarchical Merge Tree

    NASA Astrophysics Data System (ADS)

    Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-10-01

    This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with over-segmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other very recent methods on six public data sets demonstrate that our approach achieves the state-of-the-art region accuracy and is very competitive in image segmentation without semantic priors.

  10. A Hierarchical Approach to Fracture Mechanics

    NASA Technical Reports Server (NTRS)

    Saether, Erik; Taasan, Shlomo

    2004-01-01

    Recent research conducted under NASA LaRC's Creativity and Innovation Program has led to the development of an initial approach for a hierarchical fracture mechanics. This methodology unites failure mechanisms occurring at different length scales and provides a framework for a physics-based theory of fracture. At the nanoscale, parametric molecular dynamic simulations are used to compute the energy associated with atomic level failure mechanisms. This information is used in a mesoscale percolation model of defect coalescence to obtain statistics of fracture paths and energies through Monte Carlo simulations. The mathematical structure of predicted crack paths is described using concepts of fractal geometry. The non-integer fractal dimension relates geometric and energy measures between meso- and macroscales. For illustration, a fractal-based continuum strain energy release rate is derived for inter- and transgranular fracture in polycrystalline metals.

  11. Deep Learning with Hierarchical Convolutional Factor Analysis

    PubMed Central

    Chen, Bo; Polatkan, Gungor; Sapiro, Guillermo; Blei, David; Dunson, David; Carin, Lawrence

    2013-01-01

    Unsupervised multi-layered (“deep”) models are considered for general data, with a particular focus on imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis, that explicitly exploit the convolutional nature of the expansion. In order to address large-scale and streaming data, an online version of VB is also developed. The number of basis functions or dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature. PMID:23787342

  12. Crack propagation in bamboo's hierarchical cellular structure.

    PubMed

    Habibi, Meisam K; Lu, Yang

    2014-07-07

    Bamboo, as a natural hierarchical cellular material, exhibits remarkable mechanical properties including excellent flexibility and fracture toughness. As far as bamboo as a functionally graded bio-composite is concerned, the interactions of different constituents (bamboo fibers; parenchyma cells; and vessels.) alongside their corresponding interfacial areas with a developed crack should be of high significance. Here, by using multi-scale mechanical characterizations coupled with advanced environmental electron microscopy (ESEM), we unambiguously show that fibers' interfacial areas along with parenchyma cells' boundaries were preferred routes for crack growth in both radial and longitudinal directions. Irrespective of the honeycomb structure of fibers along with cellular configuration of parenchyma ground, the hollow vessels within bamboo culm affected the crack propagation too, by crack deflection or crack-tip energy dissipation. It is expected that the tortuous crack propagation mode exhibited in the present study could be applicable to other cellular natural materials as well.

  13. Epidemic Control in a Hierarchical Social Network

    NASA Astrophysics Data System (ADS)

    Grabowski, Andrzej; Kosiński, Robert A.

    The phenomenon of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The SIR model with incubation time is used. In our model the localization of individuals in different social groups, the effectiveness of different interpersonal interactions and the mobility of a contemporary community are taken into account. The influence of different control methods on the spreading process is investigated as a function of different initial conditions. The cost-effectiveness of mass preventive random vaccinations, target vaccinations and sick leaves are compared. A critical range of vaccinations, sufficient for suppressing of an epidemic is calculated. The results of numerical calculations are similar to the solutions of the master equation for the spreading process.

  14. Epidemic spreading in a hierarchical social network

    NASA Astrophysics Data System (ADS)

    Grabowski, A.; Kosiński, R. A.

    2004-09-01

    A model of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The structure of interpersonal connections is based on a scale-free network. Spatial localization of individuals belonging to different social groups, and the mobility of a contemporary community, as well as the effectiveness of different interpersonal interactions, are taken into account. Typical relations characterizing the spreading process, like a range of epidemic and epidemic curves, are discussed. The influence of preventive vaccinations on the spreading process is investigated. The critical value of preventively vaccinated individuals that is sufficient for the suppression of an epidemic is calculated. Our results are compared with solutions of the master equation for the spreading process and good agreement of the character of this process is found.

  15. Evolution of groups with a hierarchical structure

    NASA Astrophysics Data System (ADS)

    Ohnishi, Teruaki

    2012-12-01

    The universal occurrence of a hierarchical structure and its dynamic behavior in various types of group, living or abstract, are discussed. Here the word “group” refers not only to tangible aggregation but also to invisible aggregation of social psychological and of geopolitical meaning. The evolution of these groups is simulated using a model of agents distributed on the lattices of cellular grids. It is assumed that agents, fearing isolation, interact asymmetrically with each other with regard to exchange of “power”. As an indicator of hierarchy, the Gini coefficient is introduced. Example calculations are made for the aggregation, fusion and fission of animal groups, and for the appearance of a powerful empire and the rise and fall of supremacy. It is shown that such abstract objects evolve with time in accordance with the universal rules of groups common to birds and fish.

  16. Hierarchical loop detection for mobile outdoor robots

    NASA Astrophysics Data System (ADS)

    Lang, Dagmar; Winkens, Christian; Häselich, Marcel; Paulus, Dietrich

    2012-01-01

    Loop closing is a fundamental part of 3D simultaneous localization and mapping (SLAM) that can greatly enhance the quality of long-term mapping. It is essential for the creation of globally consistent maps. Conceptually, loop closing is divided into detection and optimization. Recent approaches depend on a single sensor to recognize previously visited places in the loop detection stage. In this study, we combine data of multiple sensors such as GPS, vision, and laser range data to enhance detection results in repetitively changing environments that are not sufficiently explained by a single sensor. We present a fast and robust hierarchical loop detection algorithm for outdoor robots to achieve a reliable environment representation even if one or more sensors fail.

  17. Hierarchical, 4-connected Small-World Graph

    NASA Astrophysics Data System (ADS)

    Goncalves, Bruno; Boettcher, Stefan

    2008-03-01

    A new sequences of graphs are introduced that mimic small-world properties. The graphs are recursively constructed but retain a fixed, regular degree. They consist of a one-dimensional lattice backbone overlayed by a hierarchical sequence of long-distance links in a pattern reminiscent of the tower-of-hanoi sequence. These 4-regular graphs are non-planar, have a diameter growing as 2^√2N^2 (or as [2N]^α with α˜√2N^2/22N^2), and a nontrivial phase transition Tc>0, for the Ising ferromagnet. These results suggest that these graphs are similar to small-world graphs with mean-field-like properties.

  18. Fluorocarbon Adsorption in Hierarchical Porous Frameworks

    SciTech Connect

    Motkuri, Radha K.; Annapureddy, Harsha V.; Vijayakumar, M.; Schaef, Herbert T.; Martin, P F.; McGrail, B. Peter; Dang, Liem X.; Krishna, Rajamani; Thallapally, Praveen K.

    2014-07-09

    The adsorption behavior of a series of fluorocarbon derivatives was examined on a set of microporous metal organic framework (MOF) sorbents and another set of hierarchical mesoporous MOFs. The microporous M-DOBDC (M = Ni, Co) showed a saturation uptake capacity for R12 of over 4 mmol/g at a very low relative saturation pressure (P/Po) of 0.02. In contrast, the mesoporous MOF MIL-101 showed an exceptionally high uptake capacity reaching over 14 mmol/g at P/Po of 0.4. Adsorption affinity in terms of mass loading and isosteric heats of adsorption were found to generally correlate with the polarizability of the refrigerant with R12 > R22 > R13 > R14 > methane. These results suggest the possibility of exploiting MOFs for separation of azeotropic mixtures of fluorocarbons and use in eco-friendly fluorocarbon-based adsorption cooling and refrigeration applications.

  19. Hierarchical motion organization in random dot configurations

    NASA Technical Reports Server (NTRS)

    Bertamini, M.; Proffitt, D. R.; Kaiser, M. K. (Principal Investigator)

    2000-01-01

    Motion organization has 2 aspects: the extraction of a (moving) frame of reference and the hierarchical organization of moving elements within the reference frame. Using a discrimination of relative motions task, the authors found large differences between different types of motion (translation, divergence, and rotation) in the degree to which each can serve as a moving frame of reference. Translation and divergence are superior to rotation. There are, however, situations in which rotation can serve as a reference frame. This is due to the presence of a second factor, structural invariants (SIs). SIs are spatial relationships persisting among the elements within a configuration such as a collinearity among points or one point coinciding with the center of rotation for another (invariant radius). The combined effect of these 2 factors--motion type and SIs-influences perceptual motion organization.

  20. HLM in Cluster-Randomised Trials--Measuring Efficacy across Diverse Populations of Learners

    ERIC Educational Resources Information Center

    Hegedus, Stephen; Tapper, John; Dalton, Sara; Sloane, Finbarr

    2013-01-01

    We describe the application of Hierarchical Linear Modelling (HLM) in a cluster-randomised study to examine learning algebraic concepts and procedures in an innovative, technology-rich environment in the US. HLM is applied to measure the impact of such treatment on learning and on contextual variables. We provide a detailed description of such…